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- .gitattributes +37 -0
- .gitignore +6 -0
- Framework.png +3 -0
- README.md +187 -0
- metadata/CVD_2014_metadata.csv +235 -0
- metadata/KONVID_1K_metadata.csv +1201 -0
- metadata/LIVE_QUALCOMM_metadata.csv +209 -0
- metadata/LIVE_VQC_metadata.csv +586 -0
- metadata/LSVQ_TEST_1080P_metadata.csv +0 -0
- metadata/LSVQ_TEST_metadata.csv +0 -0
- metadata/LSVQ_TRAIN_metadata.csv +0 -0
- metadata/YOUTUBE_UGC_1080P_metadata.csv +295 -0
- metadata/YOUTUBE_UGC_2160P_metadata.csv +111 -0
- metadata/YOUTUBE_UGC_360P_metadata.csv +225 -0
- metadata/YOUTUBE_UGC_480P_metadata.csv +266 -0
- metadata/YOUTUBE_UGC_720P_metadata.csv +257 -0
- metadata/YOUTUBE_UGC_metadata.csv +0 -0
- metadata/greyscale_report/LSVQ_TEST_1080P_greyscale_metadata.csv +11 -0
- metadata/greyscale_report/LSVQ_TEST_greyscale_metadata.csv +65 -0
- metadata/greyscale_report/LSVQ_TRAIN_greyscale_metadata.csv +210 -0
- metadata/greyscale_report/YOUTUBE_UGC_greyscale_metadata.csv +47 -0
- metadata/mos_files/cvd_2014_MOS_test.csv +48 -0
- metadata/mos_files/cvd_2014_MOS_train.csv +188 -0
- metadata/mos_files/konvid_1k_MOS_test.csv +241 -0
- metadata/mos_files/konvid_1k_MOS_train.csv +961 -0
- metadata/mos_files/live_vqc_MOS_test.csv +118 -0
- metadata/mos_files/live_vqc_MOS_train.csv +469 -0
- metadata/mos_files/lsvq_train_MOS_test.csv +0 -0
- metadata/mos_files/lsvq_train_MOS_train.csv +0 -0
- metadata/mos_files/youtube_ugc_MOS_test.csv +222 -0
- metadata/mos_files/youtube_ugc_MOS_train.csv +883 -0
- metadata/test_videos.csv +2 -0
- reported_result.ipynb +1954 -0
- requirements.txt +132 -0
- src/data_processing/__init__.py +2 -0
- src/data_processing/check_greyscale.py +102 -0
- src/data_processing/extract_metadata_NR.py +194 -0
- src/data_processing/recover_median_train_test.py +76 -0
- src/data_processing/split_train_test.py +180 -0
- src/demo_test_gpu.py +247 -0
- src/extractor/__init__.py +2 -0
- src/extractor/vf_extract.py +72 -0
- src/extractor/visualise_resnet.py +187 -0
- src/extractor/visualise_resnet_layer.py +194 -0
- src/extractor/visualise_vgg.py +159 -0
- src/extractor/visualise_vgg_layer.py +211 -0
- src/extractor/visualise_vit_layer.py +516 -0
- src/feature_fragment_layerstack.py +318 -0
- src/feature_fragment_pool.py +333 -0
- src/feature_layerstack.py +239 -0
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metadata/.DS_Store
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model/.DS_Store
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model/
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src/.DS_Store
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utils/.DS_Store
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Framework.png
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README.md
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# ReLaX-VQA
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[](https://github.com/xinyiW915/ReLaX-VQA)
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[](https://arxiv.org/abs/2407.11496)
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Official Code for the following paper:
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**X. Wang, A. Katsenou, and D. Bull**. [ReLaX-VQA: Residual Fragment and Layer Stack Extraction for Enhancing Video Quality Assessment](https://arxiv.org/abs/2407.11496)
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### ☑️ TODO
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- [ ] **Update** `reported_result.ipynb` for performance comparisons
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- [ ] More updates coming soon... 😊
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---
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[//]: # (## Abstract)
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[//]: # (With the rapid growth of User-Generated Content (UGC) exchanged between users and sharing platforms, the need for video quality assessment in the wild has emerged. UGC is mostly acquired using consumer devices and undergoes multiple rounds of compression or transcoding before reaching the end user. Therefore, traditional quality metrics that require the original content as a reference cannot be used. In this paper, we propose ReLaX-VQA, a novel No-Reference Video Quality Assessment (NR-VQA) model that aims to address the challenges of evaluating the diversity of video content and the assessment of its quality without reference videos. ReLaX-VQA uses fragments of residual frames and optical flow, along with different expressions of spatial features of the sampled frames, to enhance motion and spatial perception. Furthermore, the model enhances abstraction by employing layer-stacking techniques in deep neural network features (from Residual Networks and Vision Transformers). Extensive testing on four UGC datasets confirms that ReLaX-VQA outperforms existing NR-VQA methods with an average SRCC value of 0.8658 and PLCC value of 0.8872. We will open source the code and trained models to facilitate further research and applications of NR-VQA.)
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## Performance
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[](https://paperswithcode.com/sota/video-quality-assessment-on-live-vqc?p=relax-vqa-residual-fragment-and-layer-stack)
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[](https://paperswithcode.com/sota/video-quality-assessment-on-youtube-ugc?p=relax-vqa-residual-fragment-and-layer-stack)
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[](https://paperswithcode.com/sota/video-quality-assessment-on-konvid-1k?p=relax-vqa-residual-fragment-and-layer-stack)
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We evaluate the performance of ReLaX-VQA on four datasets. ReLaX-VQA has three different versions based on the training and testing strategies:
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- **ReLaX-VQA**: Trained and tested on each dataset with an **80%-20% random split**.
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- **ReLaX-VQA (w/o FT)**: Trained on **[LSVQ](https://github.com/baidut/PatchVQ)**, and the frozen model was tested on other datasets.
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- **ReLaX-VQA (w/ FT)**: Trained on **[LSVQ](https://github.com/baidut/PatchVQ)**, and the frozen model was **fine-tuned** on other datasets.
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#### **Spearman’s Rank Correlation Coefficient (SRCC)**
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| Model | CVD2014 | KoNViD-1k | LIVE-VQC | YouTube-UGC |
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|-----------------------|--------|--------|--------|--------|
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| ReLaX-VQA | 0.8643 | 0.8535 | 0.7655 | 0.8014 |
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| ReLaX-VQA (w/o FT) | 0.7845 | 0.8312 | 0.7664 | 0.8104 |
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| **ReLaX-VQA (w/ FT)** | **0.8974** | **0.8720** | **0.8468** | **0.8469** |
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#### **Pearson’s Linear Correlation Coefficient (PLCC)**
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| Model | CVD2014 | KoNViD-1k | LIVE-VQC | YouTube-UGC |
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|-----------------------|------------|----------|----------|-------------|
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| ReLaX-VQA | 0.8895 | 0.8473 | 0.8079 | 0.8204 |
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| ReLaX-VQA (w/o FT) | 0.8336 | 0.8427 | 0.8242 | 0.8354 |
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| **ReLaX-VQA (w/ FT)** | **0.9294** | **0.8668** | **0.8876** | **0.8652** |
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More results can be found in **[reported_result.ipynb](https://github.com/xinyiW915/ReLaX-VQA/blob/main/reported_result.ipynb)**.
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## Proposed Model
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The figure shows the overview of the proposed ReLaX-VQA framework. The architectures of ResNet-50 Stack (I) and ResNet-50 Pool (II) are provided in Fig.2 in the paper.
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<img src="./Framework.png" alt="proposed_ReLaX-VQA_framework" width="800"/>
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## Usage
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### 📌 Install Requirement
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The repository is built with **Python 3.10.14** and can be installed via the following commands:
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```shell
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git clone https://github.com/xinyiW915/ReLaX-VQA.git
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cd ReLaX-VQA
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conda create -n relaxvqa python=3.10.14 -y
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conda activate relaxvqa
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pip install -r requirements.txt
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```
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### 📥 Download UGC Datasets
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The corresponding raw video datasets can be downloaded from the following sources:
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[LSVQ](https://github.com/baidut/PatchVQ), [KoNViD-1k](https://database.mmsp-kn.de/konvid-1k-database.html), [LIVE-VQC](https://live.ece.utexas.edu/research/LIVEVQC/), [YouTube-UGC](https://media.withyoutube.com/), [CVD2014](https://qualinet.github.io/databases/video/cvd2014_video_database/).
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The metadata for the experimented UGC dataset is available under [`./metadata`](./metadata).
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Once downloaded, place the datasets in [`./ugc_original_videos`](./ugc_original_videos) or any other storage location of your choice.
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Ensure that the `video_path` in the [`get_video_paths`](./src/main_relaxvqa_feats.py) function inside `main_relaxvqa_feats.py` is updated accordingly.
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### 🎬 Test Demo
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Run the pre-trained models to evaluate the quality of a single video.
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The model weights provided in [`./model`](./model) contain the best-performing saved weights from training.
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To evaluate the quality of a specific video, run the following command:
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```shell
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python demo_test_gpu.py
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-device <DEVICE>
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-train_data_name <TRAIN_DATA_NAME>
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-is_finetune <True/False>
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-save_path <MODEL_PATH>
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-video_type <DATASET_NAME>
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-video_name <VIDEO_NAME>
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-framerate <FRAMERATE>
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```
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Or simply try our demo video by running:
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```shell
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python demo_test_gpu.py
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```
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## Training
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Steps to train ReLaX-VQA from scratch on different datasets.
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### Extract Features
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Run the following command to extract features from videos:
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```shell
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python main_relaxvqa_feats.py -device gpu -video_type youtube_ugc
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```
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### Train Model
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Train our model using extracted features:
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```shell
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python model_regression_simple.py -data_name youtube_ugc -feature_path ../features/ -save_path ../model/
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```
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For **LSVQ**, train the model using:
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```shell
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python model_regression.py -data_name lsvq_train -feature_path ../features/ -save_path ../model/
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```
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### Fine-Tuning
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To fine-tune the pre-trained model on a new dataset, modify [`train_data_name`](./src/model_finetune.py)
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to match the dataset used for training, and [`test_data_name`](./src/model_finetune.py) to specify the dataset for fine-tuning.
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```shell
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python model_finetune.py
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```
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## Ablation Study
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A detailed analysis of different components in ReLaX-VQA.
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### Spatio-Temporal Fragmentation & DNN Layer Stacking
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Key techniques used in ReLaX-VQA:
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- **Fragmentation with DNN layer stacking:**
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```shell
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python feature_fragment_layerstack.py
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```
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- **Fragmentation with DNN layer pooling:**
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```shell
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python feature_fragment_pool.py
|
| 141 |
+
```
|
| 142 |
+
|
| 143 |
+
- **Frame with DNN layer stacking:**
|
| 144 |
+
```shell
|
| 145 |
+
python feature_layerstack.py
|
| 146 |
+
```
|
| 147 |
+
|
| 148 |
+
- **Frame with DNN layer pooling:**
|
| 149 |
+
```shell
|
| 150 |
+
python feature_pool.py
|
| 151 |
+
```
|
| 152 |
+
|
| 153 |
+
### Other Utilities
|
| 154 |
+
#### Excluding Greyscale Videos
|
| 155 |
+
We exclude greyscale videos in our experiments. You can use [`check_greyscale.py`](./src/data_processing/check_greyscale.py)to filter out greyscale videos from the VQA dataset you want to use.
|
| 156 |
+
```shell
|
| 157 |
+
python check_greyscale.py
|
| 158 |
+
```
|
| 159 |
+
|
| 160 |
+
|
| 161 |
+
#### Metadata Extraction
|
| 162 |
+
For easy extraction of metadata from your VQA dataset, use:
|
| 163 |
+
```shell
|
| 164 |
+
python extract_metadata_NR.py
|
| 165 |
+
```
|
| 166 |
+
|
| 167 |
+
|
| 168 |
+
## Acknowledgment
|
| 169 |
+
This work was funded by the UKRI MyWorld Strength in Places Programme (SIPF00006/1) as part of my PhD study.
|
| 170 |
+
|
| 171 |
+
## Citation
|
| 172 |
+
If you find this paper and the repo useful, please cite our paper 😊:
|
| 173 |
+
|
| 174 |
+
```bibtex
|
| 175 |
+
@article{wang2024relax,
|
| 176 |
+
title={ReLaX-VQA: Residual Fragment and Layer Stack Extraction for Enhancing Video Quality Assessment},
|
| 177 |
+
author={Wang, Xinyi and Katsenou, Angeliki and Bull, David},
|
| 178 |
+
year={2024},
|
| 179 |
+
eprint={2407.11496},
|
| 180 |
+
archivePrefix={arXiv},
|
| 181 |
+
primaryClass={eess.IV},
|
| 182 |
+
url={https://arxiv.org/abs/2407.11496},
|
| 183 |
+
}
|
| 184 |
+
```
|
| 185 |
+
|
| 186 |
+
## Contact:
|
| 187 |
+
Xinyi WANG, ```xinyi.wang@bristol.ac.uk```
|
metadata/CVD_2014_metadata.csv
ADDED
|
@@ -0,0 +1,235 @@
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|
|
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|
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|
|
|
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|
|
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|
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|
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|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
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|
|
|
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|
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|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
vid,mos,width,height,pixfmt,framerate,nb_frames,bitdepth,bitrate
|
| 2 |
+
Test1/City/Test01_City_D01,67.29,640,480,uyvy422,30.00030000300003,364,N/A,147863694
|
| 3 |
+
Test1/City/Test01_City_D02,24.86,640,480,uyvy422,15.151515151515152,173,N/A,74905708
|
| 4 |
+
Test1/City/Test01_City_D03,17.96,640,480,uyvy422,14.460897732531235,170,N/A,71498786
|
| 5 |
+
Test1/City/Test01_City_D04,30.23,640,480,uyvy422,30.003540417769297,345,N/A,147902104
|
| 6 |
+
Test1/City/Test01_City_D05,25.08,640,480,uyvy422,14.689766814641585,172,N/A,72625382
|
| 7 |
+
Test1/City/Test01_City_D06,43.78,640,480,uyvy422,28.600764784450337,325,N/A,141012363
|
| 8 |
+
Test1/City/Test01_City_D07,51.67,640,480,uyvy422,28.474695961433873,358,N/A,140350867
|
| 9 |
+
Test1/City/Test01_City_D08,63.68,640,480,uyvy422,25.0,295,N/A,123297959
|
| 10 |
+
Test1/City/Test01_City_D09,64.95,640,480,uyvy422,25.0,308,N/A,123280261
|
| 11 |
+
Test1/Talking_head/Test01_Talking_Head_D01,69.37,640,480,uyvy422,30.00030000300003,485,N/A,147762139
|
| 12 |
+
Test1/Talking_head/Test01_Talking_Head_D02,31.65,640,480,uyvy422,15.151515151515152,258,N/A,74762504
|
| 13 |
+
Test1/Talking_head/Test01_Talking_Head_D03,31.98,640,480,uyvy422,14.715663941325705,254,N/A,72616322
|
| 14 |
+
Test1/Talking_head/Test01_Talking_Head_D04,30.87,640,480,uyvy422,30.06334346468008,509,N/A,148058226
|
| 15 |
+
Test1/Talking_head/Test01_Talking_Head_D05,35.1,640,480,uyvy422,14.753875843184005,253,N/A,72806021
|
| 16 |
+
Test1/Talking_head/Test01_Talking_Head_D06,38.22,640,480,uyvy422,27.52697643690817,458,N/A,135596657
|
| 17 |
+
Test1/Talking_head/Test01_Talking_Head_D07,35.99,640,480,uyvy422,29.52657096120799,497,N/A,145421600
|
| 18 |
+
Test1/Talking_head/Test01_Talking_Head_D08,53.69,640,480,uyvy422,25.0,402,N/A,123186434
|
| 19 |
+
Test1/Talking_head/Test01_Talking_Head_D09,64.59,640,480,uyvy422,25.0,403,N/A,123185672
|
| 20 |
+
Test1/Traffic/Test01_Traffic_D01,67.74,640,480,uyvy422,30.00030000300003,695,N/A,147669949
|
| 21 |
+
Test1/Traffic/Test01_Traffic_D02,29.57,640,480,uyvy422,15.151515151515152,334,N/A,74696369
|
| 22 |
+
Test1/Traffic/Test01_Traffic_D03,27.76,640,480,uyvy422,14.848603637313946,391,N/A,73170995
|
| 23 |
+
Test1/Traffic/Test01_Traffic_D04,25.72,640,480,uyvy422,29.98689572656749,636,N/A,147623703
|
| 24 |
+
Test1/Traffic/Test01_Traffic_D05,30.21,640,480,uyvy422,14.86484879475806,360,N/A,73267225
|
| 25 |
+
Test1/Traffic/Test01_Traffic_D06,43.36,640,480,uyvy422,28.173934602663,617,N/A,138705329
|
| 26 |
+
Test1/Traffic/Test01_Traffic_D07,65.53,640,480,uyvy422,29.453953162323682,650,N/A,144995140
|
| 27 |
+
Test1/Traffic/Test01_Traffic_D08,63.4,640,480,uyvy422,25.0,603,N/A,123084120
|
| 28 |
+
Test1/Traffic/Test01_Traffic_D09,60.7,640,480,uyvy422,25.0,620,N/A,123078514
|
| 29 |
+
Test2/City/Test02_City_D01,53.35,640,480,uyvy422,30.00030000300003,460,N/A,147778733
|
| 30 |
+
Test2/City/Test02_City_D02,9.99,640,480,uyvy422,29.95599464386816,477,N/A,147549032
|
| 31 |
+
Test2/City/Test02_City_D03,18.83,640,480,uyvy422,14.96284725027756,240,N/A,73853108
|
| 32 |
+
Test2/City/Test02_City_D04,9.25,640,480,uyvy422,14.848735927110525,245,N/A,73283624
|
| 33 |
+
Test2/City/Test02_City_D05,45.91,640,480,uyvy422,29.06833092550659,458,N/A,143189301
|
| 34 |
+
Test2/City/Test02_City_D06,38.99,640,480,uyvy422,24.949041582567606,364,N/A,122967352
|
| 35 |
+
Test2/City/Test02_City_D07,62.54,640,480,uyvy422,29.526832509042592,467,N/A,145441726
|
| 36 |
+
Test2/City/Test02_City_D08,36.88,640,480,uyvy422,29.73986539736921,471,N/A,146488402
|
| 37 |
+
Test2/City/Test02_City_D09,56.28,640,480,uyvy422,25.0,402,N/A,123186434
|
| 38 |
+
Test2/City/Test02_City_D10,0.21,640,480,uyvy422,29.415225320625957,484,N/A,144881057
|
| 39 |
+
Test2/Talking_head/Test02_Talking_Head_D01,64.68,640,480,uyvy422,30.00030000300003,509,N/A,147747745
|
| 40 |
+
Test2/Talking_head/Test02_Talking_Head_D02,22.64,640,480,uyvy422,30.00444065721727,497,N/A,147775161
|
| 41 |
+
Test2/Talking_head/Test02_Talking_Head_D03,22.11,640,480,uyvy422,14.949627231044994,263,N/A,73760867
|
| 42 |
+
Test2/Talking_head/Test02_Talking_Head_D04,16.69,640,480,uyvy422,14.746652509880256,256,N/A,72766992
|
| 43 |
+
Test2/Talking_head/Test02_Talking_Head_D05,46.3,640,480,uyvy422,28.728862739239606,498,N/A,141492227
|
| 44 |
+
Test2/Talking_head/Test02_Talking_Head_D06,24.35,640,480,uyvy422,23.151149223047433,390,N/A,114085054
|
| 45 |
+
Test2/Talking_head/Test02_Talking_Head_D07,36.58,640,480,uyvy422,29.526832509042592,497,N/A,145422889
|
| 46 |
+
Test2/Talking_head/Test02_Talking_Head_D08,35.7,640,480,uyvy422,29.8686674691382,495,N/A,147107662
|
| 47 |
+
Test2/Talking_head/Test02_Talking_Head_D09,64.84,640,480,uyvy422,25.0,423,N/A,123171185
|
| 48 |
+
Test2/Talking_head/Test02_Talking_Head_D10,33.8,640,480,uyvy422,29.79817694753435,502,N/A,146756343
|
| 49 |
+
Test2/Traffic/Test02_Traffic_D01,69.62,640,480,uyvy422,30.00030000300003,806,N/A,147640652
|
| 50 |
+
Test2/Traffic/Test02_Traffic_D02,25.39,640,480,uyvy422,29.985726794046034,623,N/A,147622799
|
| 51 |
+
Test2/Traffic/Test02_Traffic_D03,21.78,640,480,uyvy422,14.96508645330444,385,N/A,73747946
|
| 52 |
+
Test2/Traffic/Test02_Traffic_D04,24.69,640,480,uyvy422,14.846928170561512,365,N/A,73176104
|
| 53 |
+
Test2/Traffic/Test02_Traffic_D05,30.89,640,480,uyvy422,28.670300149372263,769,N/A,141103749
|
| 54 |
+
Test2/Traffic/Test02_Traffic_D06,34.66,640,480,uyvy422,24.169943708201103,627,N/A,118989884
|
| 55 |
+
Test2/Traffic/Test02_Traffic_D07,61.43,640,480,uyvy422,29.461155466517397,765,N/A,144997010
|
| 56 |
+
Test2/Traffic/Test02_Traffic_D08,29.35,640,480,uyvy422,29.87759150759339,775,N/A,147044072
|
| 57 |
+
Test2/Traffic/Test02_Traffic_D09,64.3,640,480,uyvy422,25.0,676,N/A,123062044
|
| 58 |
+
Test2/Traffic/Test02_Traffic_D10,27.35,640,480,uyvy422,29.5801978323631,830,N/A,145567971
|
| 59 |
+
Test3/City/Test03_City_D01,34.75,1280,720,yuv422p,31.579612202362156,467,N/A,200251238
|
| 60 |
+
Test3/City/Test03_City_D02,16.48,1280,720,yuv422p,29.97000002997,451,N/A,185766244
|
| 61 |
+
Test3/City/Test03_City_D03,57.62,1280,720,yuv422p,30.00003000003,449,N/A,217790187
|
| 62 |
+
Test3/City/Test03_City_D04,13.96,1280,720,yuv422p,21.124543973906963,317,N/A,134282343
|
| 63 |
+
Test3/City/Test03_City_D05,22.47,1280,720,yuv422p,29.97000002997,450,N/A,198475216
|
| 64 |
+
Test3/City/Test03_City_D06,12.85,1280,720,yuv422p,14.89999865900012,225,N/A,93168559
|
| 65 |
+
Test3/City/Test03_City_D07,35.74,1280,720,yuv422p,30.109961579689024,449,N/A,193687806
|
| 66 |
+
Test3/City/Test03_City_D08,30.01,1280,720,yuv422p,24.369975215735206,342,N/A,155896458
|
| 67 |
+
Test3/City/Test03_City_D09,30.86,1280,720,yuv422p,28.92003898421255,434,N/A,183175226
|
| 68 |
+
Test3/City/Test03_City_D10,50.12,1280,720,yuv422p,24.320012451846374,365,N/A,174664811
|
| 69 |
+
Test3/City/Test03_City_D11,15.47,1280,720,yuv422p,22.229978014551744,338,N/A,153227869
|
| 70 |
+
Test3/City/Test03_City_D12,20.65,1280,720,yuv422p,29.099967117037156,435,N/A,183346462
|
| 71 |
+
Test3/City/Test03_City_D13,32.65,1280,720,yuv422p,29.58002283577763,443,N/A,183525060
|
| 72 |
+
Test3/Newspaper/Test03_Newspaper_D01,50.49,1280,720,yuv422p,30.00030000300003,450,N/A,198362792
|
| 73 |
+
Test3/Newspaper/Test03_Newspaper_D02,36.79,1280,720,yuv422p,29.97000002997,450,N/A,192077253
|
| 74 |
+
Test3/Newspaper/Test03_Newspaper_D03,55.89,1280,720,yuv422p,30.00003000003,450,N/A,219704421
|
| 75 |
+
Test3/Newspaper/Test03_Newspaper_D04,39.98,1280,720,yuv422p,23.572841258129685,354,N/A,158130605
|
| 76 |
+
Test3/Newspaper/Test03_Newspaper_D05,40.1,1280,720,yuv422p,29.97000002997,450,N/A,216436075
|
| 77 |
+
Test3/Newspaper/Test03_Newspaper_D06,31.2,1280,720,yuv422p,14.89999865900012,223,N/A,95205424
|
| 78 |
+
Test3/Newspaper/Test03_Newspaper_D07,49.85,1280,720,yuv422p,30.059999759520004,451,N/A,200776026
|
| 79 |
+
Test3/Newspaper/Test03_Newspaper_D08,47.8,1280,720,yuv422p,24.690016838591482,369,N/A,163807078
|
| 80 |
+
Test3/Newspaper/Test03_Newspaper_D09,47.22,1280,720,yuv422p,29.43999246336193,442,N/A,195633752
|
| 81 |
+
Test3/Newspaper/Test03_Newspaper_D10,49.95,1280,720,yuv422p,25.0,375,N/A,192625835
|
| 82 |
+
Test3/Newspaper/Test03_Newspaper_D11,46.51,1280,720,yuv422p,23.910002749650317,359,N/A,164240340
|
| 83 |
+
Test3/Newspaper/Test03_Newspaper_D12,41.32,1280,720,yuv422p,29.099967117037156,440,N/A,196110999
|
| 84 |
+
Test3/Newspaper/Test03_Newspaper_D13,38.65,1280,720,yuv422p,29.58002283577763,447,N/A,195681347
|
| 85 |
+
Test3/Talking_head/Test03_Talking_Head_D01,39.03,1280,720,yuv422p,20.0,311,N/A,135741194
|
| 86 |
+
Test3/Talking_head/Test03_Talking_Head_D02,36.32,1280,720,yuv422p,29.97000002997,452,N/A,187716521
|
| 87 |
+
Test3/Talking_head/Test03_Talking_Head_D03,67.38,1280,720,yuv422p,30.00003000003,474,N/A,215142957
|
| 88 |
+
Test3/Talking_head/Test03_Talking_Head_D04,27.86,1280,720,yuv422p,14.09355867994092,224,N/A,91849967
|
| 89 |
+
Test3/Talking_head/Test03_Talking_Head_D05,36.98,1280,720,yuv422p,29.97000002997,483,N/A,199214875
|
| 90 |
+
Test3/Talking_head/Test03_Talking_Head_D06,28.2,1280,720,yuv422p,14.89999865900012,227,N/A,93037931
|
| 91 |
+
Test3/Talking_head/Test03_Talking_Head_D07,35.29,1280,720,yuv422p,30.140000301400004,484,N/A,188635588
|
| 92 |
+
Test3/Talking_head/Test03_Talking_Head_D08,46.1,1280,720,yuv422p,24.649970420035498,402,N/A,160949201
|
| 93 |
+
Test3/Talking_head/Test03_Talking_Head_D09,37.85,1280,720,yuv422p,29.520033770918634,483,N/A,183519186
|
| 94 |
+
Test3/Talking_head/Test03_Talking_Head_D10,53.33,1280,720,yuv422p,25.0,410,N/A,181725551
|
| 95 |
+
Test3/Talking_head/Test03_Talking_Head_D11,38.37,1280,720,yuv422p,23.5800116956858,380,N/A,160672704
|
| 96 |
+
Test3/Talking_head/Test03_Talking_Head_D12,20.86,1280,720,yuv422p,29.099967117037156,474,N/A,184125507
|
| 97 |
+
Test3/Talking_head/Test03_Talking_Head_D13,32.72,1280,720,yuv422p,29.54000307216032,463,N/A,185715308
|
| 98 |
+
Test4/City/Test04_City_D01,61.85,1280,720,yuv422p,29.97000002997,450,N/A,205383250
|
| 99 |
+
Test4/City/Test04_City_D02,70.56,1280,720,yuv422p,25.0,375,N/A,175280128
|
| 100 |
+
Test4/City/Test04_City_D03,68.6,1280,720,yuv422p,25.0,375,N/A,173065780
|
| 101 |
+
Test4/City/Test04_City_D04,69.0,1280,720,yuv422p,30.00003000003,450,N/A,211561627
|
| 102 |
+
Test4/City/Test04_City_D05,65.86,1280,720,yuv422p,29.97000002997,450,N/A,203815747
|
| 103 |
+
Test4/City/Test04_City_D06,-6.5,1280,720,yuv422p,10.739996498761142,161,N/A,66122142
|
| 104 |
+
Test4/City/Test04_City_D07,16.12,1280,720,yuv422p,14.979994217722233,225,N/A,93560120
|
| 105 |
+
Test4/City/Test04_City_D08,26.17,1280,720,yuv422p,14.969992649733609,225,N/A,93155497
|
| 106 |
+
Test4/City/Test04_City_D09,62.08,1280,720,yuv422p,30.109961579689024,452,N/A,193195273
|
| 107 |
+
Test4/City/Test04_City_D10,38.64,1280,720,yuv422p,24.9699736067379,375,N/A,168939665
|
| 108 |
+
Test4/City/Test04_City_D11,-1.11,1280,720,yuv422p,14.280002284800366,214,N/A,87472286
|
| 109 |
+
Test4/City/Test04_City_D12,35.17,1280,720,yuv422p,29.099967117037156,437,N/A,183800029
|
| 110 |
+
Test4/City/Test04_City_D13,52.28,1280,720,yuv422p,29.97000002997,450,N/A,196047241
|
| 111 |
+
Test4/City/Test04_City_D14,42.63,1280,720,yuv422p,23.669983620371333,355,N/A,166156025
|
| 112 |
+
Test4/Newspaper/Test04_Newspaper_D01,70.38,1280,720,yuv420p,29.97000002997,450,N/A,185348785
|
| 113 |
+
Test4/Newspaper/Test04_Newspaper_D02,82.05,1280,720,yuv420p,25.0,375,N/A,147589061
|
| 114 |
+
Test4/Newspaper/Test04_Newspaper_D03,81.33,1280,720,yuv420p,25.0,375,N/A,158364141
|
| 115 |
+
Test4/Newspaper/Test04_Newspaper_D04,82.52,1280,720,yuv420p,30.00003000003,450,N/A,178726708
|
| 116 |
+
Test4/Newspaper/Test04_Newspaper_D05,79.36,1280,720,yuv420p,29.97000002997,450,N/A,177247855
|
| 117 |
+
Test4/Newspaper/Test04_Newspaper_D06,5.08,1280,720,yuv420p,12.910005641672466,194,N/A,63545513
|
| 118 |
+
Test4/Newspaper/Test04_Newspaper_D07,32.42,1280,720,yuv420p,14.909995810291177,224,N/A,78299281
|
| 119 |
+
Test4/Newspaper/Test04_Newspaper_D08,29.73,1280,720,yuv420p,14.950007176003444,224,N/A,79367484
|
| 120 |
+
Test4/Newspaper/Test04_Newspaper_D09,87.46,1280,720,yuv422p,30.040043377822638,451,N/A,204396356
|
| 121 |
+
Test4/Newspaper/Test04_Newspaper_D10,52.99,1280,720,yuv422p,24.9600015974401,374,N/A,179937409
|
| 122 |
+
Test4/Newspaper/Test04_Newspaper_D11,4.14,1280,720,yuv422p,14.280002284800366,214,N/A,90652188
|
| 123 |
+
Test4/Newspaper/Test04_Newspaper_D12,37.95,1280,720,yuv422p,29.099967117037156,436,N/A,197268161
|
| 124 |
+
Test4/Newspaper/Test04_Newspaper_D13,64.41,1280,720,yuv422p,29.97000002997,450,N/A,203204890
|
| 125 |
+
Test4/Newspaper/Test04_Newspaper_D14,41.92,1280,720,yuv422p,23.98001026344439,360,N/A,169293586
|
| 126 |
+
Test4/Talking_head/Test04_Talking_Head_D01,51.43,1280,720,yuv422p,23.99002015161693,356,N/A,191365625
|
| 127 |
+
Test4/Talking_head/Test04_Talking_Head_D02,83.6,1280,720,yuv422p,25.0,388,N/A,179290718
|
| 128 |
+
Test4/Talking_head/Test04_Talking_Head_D03,77.09,1280,720,yuv422p,25.0,356,N/A,192197976
|
| 129 |
+
Test4/Talking_head/Test04_Talking_Head_D04,83.73,1280,720,yuv422p,30.00003000003,452,N/A,220309275
|
| 130 |
+
Test4/Talking_head/Test04_Talking_Head_D05,93.38,1280,720,yuv422p,29.97000002997,430,N/A,219208493
|
| 131 |
+
Test4/Talking_head/Test04_Talking_Head_D06,1.97,1280,720,yuv422p,9.790004405501982,143,N/A,59487727
|
| 132 |
+
Test4/Talking_head/Test04_Talking_Head_D07,31.15,1280,720,yuv422p,14.950007176003444,217,N/A,101396229
|
| 133 |
+
Test4/Talking_head/Test04_Talking_Head_D08,38.31,1280,720,yuv422p,14.950007176003444,208,N/A,101813809
|
| 134 |
+
Test4/Talking_head/Test04_Talking_Head_D09,75.11,1280,720,yuv422p,30.109961579689024,406,N/A,194418327
|
| 135 |
+
Test4/Talking_head/Test04_Talking_Head_D10,61.18,1280,720,yuv422p,25.0,347,N/A,175460155
|
| 136 |
+
Test4/Talking_head/Test04_Talking_Head_D11,4.5,1280,720,yuv422p,14.269996903410672,212,N/A,85471673
|
| 137 |
+
Test4/Talking_head/Test04_Talking_Head_D12,17.06,1280,720,yuv422p,29.099967117037156,464,N/A,186499540
|
| 138 |
+
Test4/Talking_head/Test04_Talking_Head_D13,59.4,1280,720,yuv422p,29.97000002997,391,N/A,198635780
|
| 139 |
+
Test4/Talking_head/Test04_Talking_Head_D14,52.76,1280,720,yuv422p,23.96001552609006,359,N/A,171322127
|
| 140 |
+
Test5/City/Test05_City_D01,26.07,1280,720,yuv422p,30.799,462,N/A,191284376
|
| 141 |
+
Test5/City/Test05_City_D02,76.43,1280,720,yuv422p,29.97,450,N/A,204954307
|
| 142 |
+
Test5/City/Test05_City_D03,68.62,1280,720,yuv422p,25.0,375,N/A,180625275
|
| 143 |
+
Test5/City/Test05_City_D04,63.85,1280,720,yuv422p,23.977,360,N/A,163864267
|
| 144 |
+
Test5/City/Test05_City_D05,42.92,1280,720,yuv422p,24.0,360,N/A,160360428
|
| 145 |
+
Test5/City/Test05_City_D06,75.32,1280,720,yuv422p,29.632,444,N/A,202747882
|
| 146 |
+
Test5/City/Test05_City_D07,66.67,1280,720,yuv422p,28.196,423,N/A,193632758
|
| 147 |
+
Test5/City/Test05_City_D08,63.76,1280,720,yuv422p,30.199,453,N/A,202115264
|
| 148 |
+
Test5/City/Test05_City_D09,79.61,1280,720,yuv422p,30.0,450,N/A,202952939
|
| 149 |
+
Test5/City/Test05_City_D10,65.29,1280,720,yuv422p,28.668,430,N/A,197165534
|
| 150 |
+
Test5/City/Test05_City_D11,73.55,1280,720,yuv422p,29.985,450,N/A,206657509
|
| 151 |
+
Test5/City/Test05_City_D12,64.24,1280,720,yuv422p,24.983,375,N/A,170552057
|
| 152 |
+
Test5/City/Test05_City_D13,62.65,1280,720,yuv422p,25.002,375,N/A,165563867
|
| 153 |
+
Test5/City/Test05_City_D14,64.27,1280,720,yuv422p,30.046,451,N/A,206917453
|
| 154 |
+
Test5/City/Test05_City_D15,71.29,1280,720,yuv422p,29.933,449,N/A,206315653
|
| 155 |
+
Test5/City/Test05_City_D16,76.85,1280,720,yuv422p,29.701,446,N/A,201601439
|
| 156 |
+
Test5/Television/Test05_Television_D01,11.45,1280,720,yuv422p,30.798,646,N/A,194931184
|
| 157 |
+
Test5/Television/Test05_Television_D02,42.29,1280,720,yuv422p,24.01,508,N/A,166889959
|
| 158 |
+
Test5/Television/Test05_Television_D03,63.93,1280,720,yuv422p,25.0,526,N/A,180855017
|
| 159 |
+
Test5/Television/Test05_Television_D04,55.0,1280,720,yuv422p,23.977,504,N/A,165682974
|
| 160 |
+
Test5/Television/Test05_Television_D05,48.27,1280,720,yuv422p,24.0,513,N/A,153812484
|
| 161 |
+
Test5/Television/Test05_Television_D06,33.32,1280,720,yuv422p,17.123,353,N/A,113562571
|
| 162 |
+
Test5/Television/Test05_Television_D07,43.05,1280,720,yuv422p,17.1,349,N/A,113975881
|
| 163 |
+
Test5/Television/Test05_Television_D08,27.84,1280,720,yuv422p,20.379,434,N/A,137560130
|
| 164 |
+
Test5/Television/Test05_Television_D09,33.59,1280,720,yuv422p,30.0,620,N/A,191785922
|
| 165 |
+
Test5/Television/Test05_Television_D10,22.5,1280,720,yuv422p,24.936,531,N/A,170276274
|
| 166 |
+
Test5/Television/Test05_Television_D11,23.3,1280,720,yuv422p,24.941,511,N/A,177151180
|
| 167 |
+
Test5/Television/Test05_Television_D12,35.12,1280,720,yuv422p,24.981,518,N/A,177211641
|
| 168 |
+
Test5/Television/Test05_Television_D13,27.91,1280,720,yuv422p,25.001,507,N/A,157243214
|
| 169 |
+
Test5/Television/Test05_Television_D14,18.45,1280,720,yuv422p,24.945,515,N/A,165452542
|
| 170 |
+
Test5/Television/Test05_Television_D15,36.1,1280,720,yuv422p,29.678,614,N/A,193160087
|
| 171 |
+
Test5/Television/Test05_Television_D16,45.88,1280,720,yuv422p,29.692,620,N/A,199157288
|
| 172 |
+
Test5/Talking_head/Test05_Talking_Head_D01,46.8,1280,720,yuv422p,30.799,462,N/A,201384988
|
| 173 |
+
Test5/Talking_head/Test05_Talking_Head_D02,65.4,1280,720,yuv422p,24.01,360,N/A,183976065
|
| 174 |
+
Test5/Talking_head/Test05_Talking_Head_D03,83.25,1280,720,yuv422p,25.0,375,N/A,183173305
|
| 175 |
+
Test5/Talking_head/Test05_Talking_Head_D04,80.15,1280,720,yuv422p,23.976,360,N/A,172980787
|
| 176 |
+
Test5/Talking_head/Test05_Talking_Head_D05,69.78,1280,720,yuv422p,24.0,360,N/A,153379556
|
| 177 |
+
Test5/Talking_head/Test05_Talking_Head_D06,71.0,1280,720,yuv422p,29.618,444,N/A,212524194
|
| 178 |
+
Test5/Talking_head/Test05_Talking_Head_D07,73.71,1280,720,yuv422p,19.894,298,N/A,133207174
|
| 179 |
+
Test5/Talking_head/Test05_Talking_Head_D08,61.01,1280,720,yuv422p,24.896,373,N/A,178021914
|
| 180 |
+
Test5/Talking_head/Test05_Talking_Head_D09,68.07,1280,720,yuv422p,30.0,450,N/A,210173529
|
| 181 |
+
Test5/Talking_head/Test05_Talking_Head_D10,60.05,1280,720,yuv422p,24.938,374,N/A,179286896
|
| 182 |
+
Test5/Talking_head/Test05_Talking_Head_D11,56.29,1280,720,yuv422p,24.943,374,N/A,189249186
|
| 183 |
+
Test5/Talking_head/Test05_Talking_Head_D12,68.36,1280,720,yuv422p,24.984,375,N/A,184086958
|
| 184 |
+
Test5/Talking_head/Test05_Talking_Head_D13,61.84,1280,720,yuv422p,25.002,375,N/A,163935642
|
| 185 |
+
Test5/Talking_head/Test05_Talking_Head_D14,67.29,1280,720,yuv422p,24.957,374,N/A,177892607
|
| 186 |
+
Test5/Talking_head/Test05_Talking_Head_D15,63.92,1280,720,yuv422p,29.967,449,N/A,211684223
|
| 187 |
+
Test5/Talking_head/Test05_Talking_Head_D16,72.57,1280,720,yuv422p,29.701,446,N/A,219596498
|
| 188 |
+
Test6/City/Test06_City_D01,17.23,1280,720,yuv422p,30.8,462,N/A,201295870
|
| 189 |
+
Test6/City/Test06_City_D02,72.11,1280,720,yuv422p,29.97,450,N/A,200177183
|
| 190 |
+
Test6/City/Test06_City_D03,71.76,1280,720,yuv422p,29.973,450,N/A,199016608
|
| 191 |
+
Test6/City/Test06_City_D04,67.94,1280,720,yuv422p,25.0,375,N/A,180452719
|
| 192 |
+
Test6/City/Test06_City_D05,61.12,1280,720,yuv422p,28.656,430,N/A,190486103
|
| 193 |
+
Test6/City/Test06_City_D06,55.78,1280,720,yuv422p,23.626,354,N/A,156358131
|
| 194 |
+
Test6/City/Test06_City_D07,58.78,1280,720,yuv422p,29.648,445,N/A,203160795
|
| 195 |
+
Test6/City/Test06_City_D08,72.68,1280,720,yuv422p,30.0,450,N/A,201245084
|
| 196 |
+
Test6/City/Test06_City_D09,-0.85,1280,720,yuv422p,14.914776964425274,224,N/A,86212342
|
| 197 |
+
Test6/City/Test06_City_D10,23.58,1280,720,yuv422p,27.134,407,N/A,164918256
|
| 198 |
+
Test6/City/Test06_City_D11,62.03,1280,720,yuv422p,29.843,448,N/A,203831058
|
| 199 |
+
Test6/City/Test06_City_D12,53.03,1280,720,yuv422p,24.985,375,N/A,174612656
|
| 200 |
+
Test6/City/Test06_City_D13,64.28,1280,720,yuv422p,27.505,413,N/A,193115666
|
| 201 |
+
Test6/City/Test06_City_D14,72.46,1280,720,yuv422p,29.848,448,N/A,209424498
|
| 202 |
+
Test6/City/Test06_City_D15,71.95,1280,720,yuv422p,29.854,448,N/A,210038621
|
| 203 |
+
Test6/City/Test06_City_D16,58.33,1280,720,yuv422p,29.736,446,N/A,197529459
|
| 204 |
+
Test6/Television/Test06_Television_D01,10.93,1280,720,yuv422p,30.799,678,N/A,186350554
|
| 205 |
+
Test6/Television/Test06_Television_D02,28.81,1280,720,yuv422p,24.01,528,N/A,156430230
|
| 206 |
+
Test6/Television/Test06_Television_D03,28.29,1280,720,yuv422p,24.003,528,N/A,152695527
|
| 207 |
+
Test6/Television/Test06_Television_D04,39.1,1280,720,yuv422p,25.0,550,N/A,173803695
|
| 208 |
+
Test6/Television/Test06_Television_D05,27.85,1280,720,yuv422p,17.123,377,N/A,107058209
|
| 209 |
+
Test6/Television/Test06_Television_D06,33.68,1280,720,yuv422p,19.942,439,N/A,125004561
|
| 210 |
+
Test6/Television/Test06_Television_D07,22.59,1280,720,yuv422p,14.828,326,N/A,98146119
|
| 211 |
+
Test6/Television/Test06_Television_D08,23.61,1280,720,yuv422p,29.999,660,N/A,178930918
|
| 212 |
+
Test6/Television/Test06_Television_D09,7.23,1280,720,yuv422p,10.670741467141585,235,N/A,61045623
|
| 213 |
+
Test6/Television/Test06_Television_D10,14.77,1280,720,yuv422p,24.938,549,N/A,145669484
|
| 214 |
+
Test6/Television/Test06_Television_D11,23.8,1280,720,yuv422p,29.843,657,N/A,195990898
|
| 215 |
+
Test6/Television/Test06_Television_D12,22.48,1280,720,yuv422p,24.983,550,N/A,173991973
|
| 216 |
+
Test6/Television/Test06_Television_D13,23.7,1280,720,yuv422p,24.942,549,N/A,176207541
|
| 217 |
+
Test6/Television/Test06_Television_D14,31.73,1280,720,yuv422p,29.865,657,N/A,189870189
|
| 218 |
+
Test6/Television/Test06_Television_D15,33.46,1280,720,yuv422p,29.731,654,N/A,194526673
|
| 219 |
+
Test6/Television/Test06_Television_D16,20.54,1280,720,yuv422p,28.972,637,N/A,179544818
|
| 220 |
+
Test6/Talking_head/Test06_Talking_head_D01,14.9,1280,720,yuv422p,30.8,462,N/A,197924438
|
| 221 |
+
Test6/Talking_head/Test06_Talking_head_D02,57.48,1280,720,yuv422p,24.009,360,N/A,171078770
|
| 222 |
+
Test6/Talking_head/Test06_Talking_head_D03,53.44,1280,720,yuv422p,24.004,360,N/A,169095749
|
| 223 |
+
Test6/Talking_head/Test06_Talking_head_D04,76.79,1280,720,yuv422p,25.0,375,N/A,185762439
|
| 224 |
+
Test6/Talking_head/Test06_Talking_head_D05,45.07,1280,720,yuv422p,17.123,257,N/A,112342993
|
| 225 |
+
Test6/Talking_head/Test06_Talking_head_D06,61.99,1280,720,yuv422p,23.764,356,N/A,154405725
|
| 226 |
+
Test6/Talking_head/Test06_Talking_head_D07,32.99,1280,720,yuv422p,14.865,223,N/A,101926731
|
| 227 |
+
Test6/Talking_head/Test06_Talking_head_D08,62.56,1280,720,yuv422p,30.0,450,N/A,190568888
|
| 228 |
+
Test6/Talking_head/Test06_Talking_head_D09,0.58,1280,720,yuv422p,12.173210172908277,183,N/A,71612786
|
| 229 |
+
Test6/Talking_head/Test06_Talking_head_D10,23.31,1280,720,yuv422p,24.938,374,N/A,149026989
|
| 230 |
+
Test6/Talking_head/Test06_Talking_head_D11,42.32,1280,720,yuv422p,29.753,446,N/A,214619067
|
| 231 |
+
Test6/Talking_head/Test06_Talking_head_D12,57.39,1280,720,yuv422p,24.981,375,N/A,187042629
|
| 232 |
+
Test6/Talking_head/Test06_Talking_head_D13,47.76,1280,720,yuv422p,24.943,374,N/A,188372842
|
| 233 |
+
Test6/Talking_head/Test06_Talking_head_D14,58.85,1280,720,yuv422p,29.763,446,N/A,214753163
|
| 234 |
+
Test6/Talking_head/Test06_Talking_head_D15,73.35,1280,720,yuv422p,29.813,447,N/A,208679860
|
| 235 |
+
Test6/Talking_head/Test06_Talking_head_D16,33.02,1280,720,yuv422p,29.918,449,N/A,196487865
|
metadata/KONVID_1K_metadata.csv
ADDED
|
@@ -0,0 +1,1201 @@
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|
| 1 |
+
vid,mos,width,height,pixfmt,framerate,nb_frames,bitdepth,bitrate
|
| 2 |
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|
| 3 |
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|
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|
| 6 |
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| 7 |
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| 8 |
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| 9 |
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|
| 10 |
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| 11 |
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|
| 12 |
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|
| 13 |
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| 14 |
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|
| 16 |
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| 17 |
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| 29 |
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| 31 |
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| 32 |
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| 34 |
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metadata/LIVE_QUALCOMM_metadata.csv
ADDED
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| 1 |
+
vid,mos,width,height,pixfmt,framerate,nb_frames,bitdepth,bitrate
|
| 2 |
+
Artifacts/0723_ManUnderTree_GS5_03_20150723_130016,52.56657483287041,1920,1080,yuv420p,25.0,450,8,14968659
|
| 3 |
+
Artifacts/0723_ManUnderTree_GS6_03_20150723_130100,57.28880925319935,1920,1080,yuv420p,25.0,450,8,17017680
|
| 4 |
+
Artifacts/0723_ManUnderTree_HTCOneVx_03_VIDEO0006,36.709288593516966,1920,1080,yuv420p,25.0,450,8,10913547
|
| 5 |
+
Artifacts/0723_ManUnderTree_Nokia1020_03_WP_20130426_22_12_02_Pro,45.67874272310085,1920,1080,yuv420p,25.0,450,8,16824402
|
| 6 |
+
Artifacts/0911nightScene_1020_WindowsPhone_20130305_21_59_44_Pro,21.187371167047402,1920,1080,yuv420p,25.0,450,8,16294698
|
| 7 |
+
Artifacts/0911nightScene_5s_IMG_0160,35.72522155502472,1920,1080,yuv420p,25.0,450,8,5016508
|
| 8 |
+
Artifacts/0911nightScene_gs5_20150911_194737,32.27546017222677,1920,1080,yuv420p,25.0,450,8,5889119
|
| 9 |
+
Artifacts/0911nightScene_note4_20150911_193809,36.546723521311996,1920,1080,yuv420p,25.0,449,8,5288434
|
| 10 |
+
Artifacts/0918_AxelFetchesBall_GS6_20150919_090332,58.23142265391711,1920,1080,yuv420p,25.0,450,8,8783424
|
| 11 |
+
Artifacts/0918_AxelFetchesBall_HTCOneVX_VIDEO0056,42.628796648669706,1920,1080,yuv420p,25.0,450,8,7418672
|
| 12 |
+
Artifacts/0918_AxelFetchesBall_LGG2_CAM00802,52.891660679374006,1920,1080,yuv420p,25.0,450,8,9193804
|
| 13 |
+
Artifacts/0918_AxelFetchesBall_OppoFind7_VID20150407042625,56.47030298915485,1920,1080,yuv420p,25.0,450,8,9502680
|
| 14 |
+
Artifacts/0918_DogsOnBeach_GS6_20150919_085853,65.94233767581703,1920,1080,yuv420p,25.0,450,8,7767715
|
| 15 |
+
Artifacts/0918_DogsOnBeach_HTCOneVX_VIDEO0055,50.378497196338884,1920,1080,yuv420p,25.0,450,8,6770660
|
| 16 |
+
Artifacts/0918_DogsOnBeach_LGG2_CAM00801,60.0145230621533,1920,1080,yuv420p,25.0,450,8,9702272
|
| 17 |
+
Artifacts/0918_DogsOnBeach_OppoFind7_VID20150407042141,61.24971954276459,1920,1080,yuv420p,25.0,450,8,8739423
|
| 18 |
+
Artifacts/0923_ChattingOverLunch_G2_CAM00813,45.67855937078179,1920,1080,yuv420p,25.0,450,8,4969939
|
| 19 |
+
Artifacts/0923_ChattingOverLunch_GS6_20150923_122025,60.43118323177181,1920,1080,yuv420p,25.0,449,8,4918916
|
| 20 |
+
Artifacts/0923_ChattingOverLunch_HTCOneVx_VIDEO0066,31.49041491004223,1920,1080,yuv420p,25.0,443,8,6373809
|
| 21 |
+
Artifacts/0923_ChattingOverLunch_Oppo_VID20150411074312,52.436942132562734,1920,1080,yuv420p,25.0,450,8,5382725
|
| 22 |
+
Artifacts/1006_DucksAndFish2_GS6_20151006_161642,51.0393821878597,1920,1080,yuv420p,25.0,450,8,21413355
|
| 23 |
+
Artifacts/1006_DucksAndFish2_HTCOneVX_VIDEO0093,31.435259054533738,1920,1080,yuv420p,25.0,410,8,14145289
|
| 24 |
+
Artifacts/1006_DucksAndFish2_LGG2_CAM00841,61.63231290029228,1920,1080,yuv420p,25.0,450,8,24833971
|
| 25 |
+
Artifacts/1006_DucksAndFish2_OppoFind7_VID20150424113906,21.403852037578215,1920,1080,yuv420p,25.0,450,8,17539428
|
| 26 |
+
Artifacts/1006_FollowTheTrain_GS6_20151006_155539,47.30872740564676,1920,1080,yuv420p,25.0,450,8,3819270
|
| 27 |
+
Artifacts/1006_FollowTheTrain_HTCOneVX_VIDEO0086,27.486434026248876,1920,1080,yuv420p,25.0,443,8,5858713
|
| 28 |
+
Artifacts/1006_FollowTheTrain_LGG2_CAM00834,33.939419249644835,1920,1080,yuv420p,25.0,450,8,4609660
|
| 29 |
+
Artifacts/1006_FollowTheTrain_OppoFind7_VID20150424111801,48.7312983952301,1920,1080,yuv420p,25.0,449,8,4095874
|
| 30 |
+
Artifacts/1006_PlaneLandsAfar2_GS6_20151006_170428,54.47172841710812,1920,1080,yuv420p,25.0,450,8,5607434
|
| 31 |
+
Artifacts/1006_PlaneLandsAfar2_HTCOneVX_VIDEO0099,43.13950764717603,1920,1080,yuv420p,25.0,450,8,5439148
|
| 32 |
+
Artifacts/1006_PlaneLandsAfar2_LGG2_CAM00847,43.7872800043708,1920,1080,yuv420p,25.0,450,8,7026262
|
| 33 |
+
Artifacts/1006_PlaneLandsAfar2_OppoFind7_VID20150424122651,57.22288718627244,1920,1080,yuv420p,25.0,450,8,5779210
|
| 34 |
+
Artifacts/1019_ColorfulKites2_GS6_20151020_140327,69.34239333030179,1920,1080,yuv420p,25.0,450,8,16883208
|
| 35 |
+
Artifacts/1019_ColorfulKites2_HTCOneVX_VIDEO0124,45.61583176940698,1920,1080,yuv420p,25.0,450,8,12856210
|
| 36 |
+
Artifacts/1019_ColorfulKites2_LGG2_CAM00872,63.17108813219617,1920,1080,yuv420p,25.0,450,8,15576910
|
| 37 |
+
Artifacts/1019_ColorfulKites2_OppoFind7_VID20150508092524,50.97310511796482,1920,1080,yuv420p,25.0,450,8,13782093
|
| 38 |
+
Color/0912_CheerLeader1_Nokia1080_20130306_17_46_21_Pro,41.529339991183875,1920,1080,yuv420p,25.0,450,8,7861337
|
| 39 |
+
Color/0912_Cheerleader1_GS5_20150912_153429,51.08416769793256,1920,1080,yuv420p,25.0,450,8,5102284
|
| 40 |
+
Color/0912_Cheerleader1_Note4_20150912_152444,46.853275685229434,1920,1080,yuv420p,25.0,450,8,5361990
|
| 41 |
+
Color/0912_Cheerleader1_iphone_IMG_0184,59.46973486351669,1920,1080,yuv420p,25.0,450,8,6021844
|
| 42 |
+
Color/0913playSand_GS5_20150913_183702,58.51617627453426,1920,1080,yuv420p,25.0,450,8,8795519
|
| 43 |
+
Color/0913playSand_Note4_20150913_182713,54.20213781634974,1920,1080,yuv420p,25.0,450,8,9420069
|
| 44 |
+
Color/0913playSand_iPhone5s_IMG_0251,72.96615929361013,1920,1080,yuv420p,25.0,450,8,8809588
|
| 45 |
+
Color/0918_CyclistsNear1_GS6_20150919_081325,52.81500748332389,1920,1080,yuv420p,25.0,450,8,7001542
|
| 46 |
+
Color/0918_CyclistsNear1_HTCOneVX_VIDEO0050,43.90725048088645,1920,1080,yuv420p,25.0,434,8,7622183
|
| 47 |
+
Color/0918_CyclistsNear1_LGG2_CAM00795,46.753110388198024,1920,1080,yuv420p,25.0,450,8,8748949
|
| 48 |
+
Color/0918_CyclistsNear1_OppoFind7_VID20150407033615,50.22621242945224,1920,1080,yuv420p,25.0,450,8,8139034
|
| 49 |
+
Color/0918_DelMarView_GS6_20150921_071851,57.249620671155895,1920,1080,yuv420p,25.0,450,8,2970305
|
| 50 |
+
Color/0918_DelMarView_HTCOneVX_VIDEO0062,45.79645003864048,1920,1080,yuv420p,25.0,450,8,3307562
|
| 51 |
+
Color/0918_DelMarView_LGG2_CAM00809,48.7408776505235,1920,1080,yuv420p,25.0,450,8,3928600
|
| 52 |
+
Color/0918_DelMarView_OppoFind7_VID20150409024137,60.963433745979756,1920,1080,yuv420p,25.0,450,8,3756970
|
| 53 |
+
Color/0923_RiverBed_GS5_20150925_165808,61.69082350125231,1920,1080,yuv420p,25.0,450,8,17880206
|
| 54 |
+
Color/0923_RiverBed_Nokia1020_WP_20130305_20_19_29_Pro,66.39664147071237,1920,1080,yuv420p,25.0,450,8,21431926
|
| 55 |
+
Color/0923_RiverBed_Note4_20150925_164759,64.23939021565612,1920,1080,yuv420p,25.0,450,8,18101756
|
| 56 |
+
Color/0923_RiverBed_iphone5_IMG_0280,63.16961373461168,1920,1080,yuv420p,25.0,450,8,15140448
|
| 57 |
+
Color/1006_EmergencyScene_GS6_20151006_160038,58.33545178737855,1920,1080,yuv420p,25.0,450,8,4309236
|
| 58 |
+
Color/1006_EmergencyScene_HTCOneVX_VIDEO0090,42.55847413392969,1920,1080,yuv420p,25.0,443,8,4784302
|
| 59 |
+
Color/1006_EmergencyScene_LGG2_CAM00838,66.8679745839632,1920,1080,yuv420p,25.0,450,8,5543329
|
| 60 |
+
Color/1006_EmergencyScene_OppoFind7_VID20150424112300,62.01071121403491,1920,1080,yuv420p,25.0,449,8,6352130
|
| 61 |
+
Color/1006_FamilyScooter_GS6_20151006_170632,60.28464598447542,1920,1080,yuv420p,25.0,450,8,6647704
|
| 62 |
+
Color/1006_FamilyScooter_HTCOneVX_VIDEO0101,38.8106247696991,1920,1080,yuv420p,25.0,431,8,5906480
|
| 63 |
+
Color/1006_FamilyScooter_LGG2_CAM00849,49.68892718051139,1920,1080,yuv420p,25.0,450,8,7382091
|
| 64 |
+
Color/1006_FamilyScooter_OppoFind7_VID20150424122855,51.17307547834518,1920,1080,yuv420p,25.0,450,8,6587700
|
| 65 |
+
Color/1019_FlyingKite3_GS6_20151020_141052,73.64276415152705,1920,1080,yuv420p,25.0,450,8,4891837
|
| 66 |
+
Color/1019_FlyingKite3_HTCOneVX_VIDEO0129,49.33731661081409,1920,1080,yuv420p,25.0,450,8,4888128
|
| 67 |
+
Color/1019_FlyingKite3_LGG2_CAM00877,55.74782132350435,1920,1080,yuv420p,25.0,450,8,5722855
|
| 68 |
+
Color/1019_FlyingKite3_OppoFind7_VID20150508093248,56.46153730303658,1920,1080,yuv420p,25.0,450,8,4640643
|
| 69 |
+
Color/1024_BadmintonAtBalboa_GS6_20151024_104414,56.589364068655456,1920,1080,yuv420p,25.0,450,8,3807252
|
| 70 |
+
Color/1024_BadmintonAtBalboa_HTCOneVx_VIDEO0139,38.4966864084717,1920,1080,yuv420p,25.0,358,8,5121475
|
| 71 |
+
Color/1024_BadmintonAtBalboa_LGG2_CAM00888,52.3694844910836,1920,1080,yuv420p,25.0,450,8,4837192
|
| 72 |
+
Color/1024_BadmintonAtBalboa_OppoFined7_VID20150512060558,56.55278041432343,1920,1080,yuv420p,25.0,449,8,5000425
|
| 73 |
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Exposure/0817_WalkIntoBarn_LGG2_CAM00718,51.146870112855,1920,1080,yuv420p,25.0,450,8,5750232
|
| 74 |
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Exposure/0817_WalkIntoBarn_Note4_20150817_110508,58.079428695078406,1920,1080,yuv420p,25.0,450,8,5081925
|
| 75 |
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Exposure/0817_WalkIntoBarn_OppoFind7_VID20150718144333,49.02405995062579,1920,1080,yuv420p,25.0,450,8,5487433
|
| 76 |
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Exposure/0817_WalkIntoBarn_iPhone5_IMG_0410,39.516765825233485,1920,1080,yuv420p,25.0,450,8,4575724
|
| 77 |
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Exposure/0911_DogBall_GS6_20150911_185459,65.10542900249484,1920,1080,yuv420p,25.0,450,8,1540251
|
| 78 |
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Exposure/0911_DogBall_HTCOneVx_VIDEO0045,49.7123541769036,1920,1080,yuv420p,25.0,444,8,3894801
|
| 79 |
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Exposure/0911_DogBall_LGG2_CAM00791,46.17375037863661,1920,1080,yuv420p,25.0,450,8,3169722
|
| 80 |
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Exposure/0911_DogBall_OppoFind7_VID20150330141759,66.94118272054322,1920,1080,yuv420p,25.0,450,8,3111195
|
| 81 |
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Exposure/0911panGarden_Gs5_20150911_153348,30.910356850986815,1920,1080,yuv420p,25.0,450,8,3335278
|
| 82 |
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Exposure/0911panGarden_Lumia1020_WindowsPhone_20130330_23_52_26_Pro,61.25422142212514,1920,1080,yuv420p,25.0,450,8,8982854
|
| 83 |
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Exposure/0911panGarden_Note4_20150911_152420,44.69628201977274,1920,1080,yuv420p,25.0,450,8,5512683
|
| 84 |
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Exposure/0911panGarden_iPhone5s_IMG_0132,56.68920900925609,1920,1080,yuv420p,25.0,450,8,4431147
|
| 85 |
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Exposure/0911skateBoard_1020_WindowsPhone_20130305_19_55_21_Pro,55.13130426481119,1920,1080,yuv420p,25.0,450,8,5227208
|
| 86 |
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Exposure/0911skateBoard_5s_IMG_0151,66.17622463832596,1920,1080,yuv420p,25.0,450,8,4106239
|
| 87 |
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Exposure/0911skateBoard_gs5_20150911_174325,49.66995169846537,1920,1080,yuv420p,25.0,450,8,3238850
|
| 88 |
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Exposure/0911skateBoard_note4_20150911_173356,61.56026393111605,1920,1080,yuv420p,25.0,450,8,4222174
|
| 89 |
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Exposure/0912_Burgers_GS5_20150912_145544,63.475870057217286,1920,1080,yuv420p,25.0,450,8,6808886
|
| 90 |
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Exposure/0912_Burgers_Nokia1020_20130306_17_07_38_Pro,64.9396364629356,1920,1080,yuv420p,25.0,450,8,9569184
|
| 91 |
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Exposure/0912_Burgers_Note4_20150912_144558,61.98320494013754,1920,1080,yuv420p,25.0,450,8,8051300
|
| 92 |
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Exposure/0912_Burgers_iphone5s_IMG_0167,64.38345299460406,1920,1080,yuv420p,25.0,450,8,7901587
|
| 93 |
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Exposure/0923_FollowTheBee_GS6_20150923_131355,51.45085729576881,1920,1080,yuv420p,25.0,450,8,9790900
|
| 94 |
+
Exposure/0923_FollowTheBee_HTCOneVx_VIDEO0072,33.04439956164501,1920,1080,yuv420p,25.0,450,8,7463049
|
| 95 |
+
Exposure/1019_TrafficFromAfar_GS6_20151017_173401,60.08891382298475,1920,1080,yuv420p,25.0,450,8,4217942
|
| 96 |
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Exposure/1019_TrafficFromAfar_HTCOneVX_VIDEO0102,38.676262337846296,1920,1080,yuv420p,25.0,446,8,5418395
|
| 97 |
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Exposure/1019_TrafficFromAfar_LGG2_CAM00850,43.920755609079976,1920,1080,yuv420p,25.0,450,8,7310759
|
| 98 |
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Exposure/1019_TrafficFromAfar_OppoFind7_VID20150505125608,43.74671562206279,1920,1080,yuv420p,25.0,450,8,4666448
|
| 99 |
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Exposure/1019_ViewFromMetro2_GS6_20151020_133722,39.88244836115969,1920,1080,yuv420p,25.0,450,8,11445469
|
| 100 |
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Exposure/1019_ViewFromMetro2_HTCOneVX_VIDEO0122,31.44885665910503,1920,1080,yuv420p,25.0,450,8,9125470
|
| 101 |
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Exposure/1019_ViewFromMetro2_LGG2_CAM00870,37.58488437787315,1920,1080,yuv420p,25.0,450,8,9920800
|
| 102 |
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Exposure/1019_ViewFromMetro2_OppoFind7_VID20150508085916,36.18976114116138,1920,1080,yuv420p,25.0,450,8,9638550
|
| 103 |
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Exposure/1022_QCOMSoccer2_GS6_20151022_174002,44.361660121662815,1920,1080,yuv420p,25.0,450,8,2750687
|
| 104 |
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Exposure/1022_QCOMSoccer2_HTCOneVx_VIDEO0137,29.11684686808589,1920,1080,yuv420p,25.0,524,8,4341552
|
| 105 |
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Exposure/1022_QCOMSoccer2_LGG2_CAM00886,26.395621305018185,1920,1080,yuv420p,25.0,450,8,4712838
|
| 106 |
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Exposure/1022_QCOMSoccer2_OppoFind7_VID20150510130154,45.88425102889353,1920,1080,yuv420p,25.0,450,8,4274640
|
| 107 |
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Focus/0723_OldTownShop_GS5_12_20150723_133424,51.65560419378202,1920,1080,yuv420p,25.0,450,8,8686800
|
| 108 |
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Focus/0723_OldTownShop_GS6_12_20150723_133507,64.9917581345629,1920,1080,yuv420p,25.0,450,8,8837752
|
| 109 |
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Focus/0723_OldTownShop_HTCOneVx_12_VIDEO0019,30.53679255118817,1920,1080,yuv420p,25.0,426,8,7636430
|
| 110 |
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Focus/0723_OldTownShop_Nokia1020_12_WP_20130426_22_46_05_Pro,52.969252792370625,1920,1080,yuv420p,25.0,450,8,10374856
|
| 111 |
+
Focus/0911driving_5s_IMG_0145,44.9586564442459,1920,1080,yuv420p,25.0,450,8,5299261
|
| 112 |
+
Focus/0911driving_gs5_20150911_170903,52.638967951340504,1920,1080,yuv420p,25.0,450,8,5912821
|
| 113 |
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Focus/0911driving_note4_20150911_165933,66.5912517517975,1920,1080,yuv420p,25.0,450,8,7090768
|
| 114 |
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Focus/0913boyWalkBy_GS5_20150913_182938,51.591805400221055,1920,1080,yuv420p,25.0,450,8,6489040
|
| 115 |
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Focus/0913boyWalkBy_Note4_20150913_181950,53.97028926187148,1920,1080,yuv420p,25.0,450,8,6309869
|
| 116 |
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Focus/0913boyWalkBy_iPhone5s_IMG_0245,62.812549213807614,1920,1080,yuv420p,25.0,450,8,7747264
|
| 117 |
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Focus/0923_BirdsFlyOnPier_GS5_20150925_165328,46.858952126850156,1920,1080,yuv420p,25.0,450,8,4000340
|
| 118 |
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Focus/0923_BirdsFlyOnPier_Nokia1020_WP_20130305_20_14_47_Pro,49.44336625534995,1920,1080,yuv420p,25.0,450,8,6732198
|
| 119 |
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Focus/0923_BirdsFlyOnPier_Note4_20150925_164318,34.77241986411562,1920,1080,yuv420p,25.0,450,8,4290634
|
| 120 |
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Focus/0923_BirdsFlyOnPier_iphone5_IMG_0277,24.644612569329546,1920,1080,yuv420p,25.0,450,8,3449271
|
| 121 |
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Focus/1006_ComplexTrain_GS6_20151006_155711,44.35577617160543,1920,1080,yuv420p,25.0,450,8,6611177
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metadata/LIVE_VQC_metadata.csv
ADDED
|
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|
| 1 |
+
vid,mos,width,height,pixfmt,framerate,nb_frames,bitdepth,bitrate
|
| 2 |
+
A001,80.232,1920,1080,yuvj420p,29.9929016799357,300,8,19924412
|
| 3 |
+
A002,57.3005,1920,1080,yuv420p,29.9800796812749,301,8,10882542
|
| 4 |
+
A003,78.1558,1920,1080,yuv420p,29.9750208159867,300,8,17935662
|
| 5 |
+
A004,59.3179,1920,1080,yuvj420p,29.999531779883,299,8,21669449
|
| 6 |
+
A005,47.1778,1920,1080,yuv420p,30.0,301,8,16022596
|
| 7 |
+
A006,84.6517,1920,1080,yuvj420p,30.0078093413014,301,8,21644543
|
| 8 |
+
A007,50.8675,1920,1080,yuv420p,29.9750208159867,300,8,16753618
|
| 9 |
+
A008,46.8902,1920,1080,yuv420p,29.9800133244504,300,8,15365270
|
| 10 |
+
A009,80.7925,1920,1080,yuv420p,29.9800133244504,300,8,16169425
|
| 11 |
+
A010,31.2785,1920,1080,yuvj420p,30.0081749845473,301,8,21698492
|
| 12 |
+
A011,87.2287,1920,1080,yuv420p,29.9801797700409,300,8,16987750
|
| 13 |
+
A012,47.7826,1920,1080,yuv420p,29.9001666657438,300,8,16928331
|
| 14 |
+
A013,83.4083,1920,1080,yuv420p,29.9800796812749,301,8,10728535
|
| 15 |
+
A014,69.9652,1920,1080,yuv420p,29.9800133244504,300,8,15307688
|
| 16 |
+
A015,78.3957,1920,1080,yuv420p,29.9882712539096,300,8,16994922
|
| 17 |
+
A016,37.8421,1920,1080,yuv420p,29.8974421077308,299,8,16921455
|
| 18 |
+
A017,65.4778,1920,1080,yuv420p,30.0089727825596,301,8,17088272
|
| 19 |
+
A018,76.0205,1920,1080,yuv420p,29.9800133244504,300,8,15496118
|
| 20 |
+
A019,70.843,1920,1080,yuv420p,29.9800796812749,301,8,10665206
|
| 21 |
+
A020,77.7258,1920,1080,yuv420p,29.9836422573907,300,8,16992500
|
| 22 |
+
A021,37.6011,1920,1080,yuv420p,29.8500509148026,299,8,16995255
|
| 23 |
+
A022,38.674,1920,1080,yuv420p,30.0049842166473,301,8,17087855
|
| 24 |
+
A023,69.0588,1920,1080,yuv420p,29.9800133244504,300,8,15177783
|
| 25 |
+
A024,71.962,1920,1080,yuv420p,29.9800133244504,300,8,14728795
|
| 26 |
+
A025,42.0856,1920,1080,yuv420p,29.9255850451876,300,8,16952804
|
| 27 |
+
A026,74.5397,1920,1080,yuv420p,29.9758195055988,300,8,16986253
|
| 28 |
+
A027,56.3388,1920,1080,yuv420p,29.9800133244504,300,8,15459406
|
| 29 |
+
A028,41.5304,1920,1080,yuv420p,29.8293706789812,299,8,10238678
|
| 30 |
+
A029,75.3861,1920,1080,yuv420p,29.9850074962519,300,8,15407115
|
| 31 |
+
A030,81.5278,1920,1080,yuv420p,30.0055491990213,301,8,17160957
|
| 32 |
+
A031,62.1042,1920,1080,yuv420p,29.9295989322893,300,8,16971495
|
| 33 |
+
A032,74.3189,1920,1080,yuv420p,29.9800796812749,301,8,10475913
|
| 34 |
+
A033,71.7097,1920,1080,yuv420p,29.9482892871642,300,8,16970358
|
| 35 |
+
A034,80.4271,1920,1080,yuv420p,29.9800796812749,301,8,10775987
|
| 36 |
+
A035,85.0968,1920,1080,yuv420p,29.9800796812749,301,8,11407334
|
| 37 |
+
A036,46.6608,1920,1080,yuv420p,29.9139475442311,300,8,16951276
|
| 38 |
+
A037,77.5858,1920,1080,yuv420p,29.9800796812749,301,8,10727557
|
| 39 |
+
A038,51.9195,1920,1080,yuv420p,29.925,300,8,5553312
|
| 40 |
+
A039,53.006,1920,1080,yuv420p,29.8236514522822,299,8,16881304
|
| 41 |
+
A040,50.3763,1920,1080,yuv420p,30.0039207781305,301,8,17086412
|
| 42 |
+
A041,35.1412,1920,1080,yuv420p,29.9861397398536,300,8,16993397
|
| 43 |
+
A042,76.8312,1920,1080,yuv420p,30.0091389958182,301,8,17272915
|
| 44 |
+
A043,38.1554,1920,1080,yuv420p,30.0049842166473,301,8,16772979
|
| 45 |
+
A044,53.4333,1920,1080,yuv420p,30.0,301,8,15194328
|
| 46 |
+
A045,81.9773,1920,1080,yuv420p,29.9954673516002,300,8,16996736
|
| 47 |
+
A046,69.1902,1920,1080,yuv420p,30.0089395401288,301,8,17136933
|
| 48 |
+
A047,58.4895,1920,1080,yuv420p,30.0089062977717,301,8,17093347
|
| 49 |
+
A048,72.3462,1920,1080,yuv420p,29.9800133244504,300,8,15730344
|
| 50 |
+
A049,72.2176,1920,1080,yuv420p,29.9800133244504,300,8,14740138
|
| 51 |
+
A050,48.0519,1920,1080,yuv420p,29.9934680891717,300,8,16991793
|
| 52 |
+
A051,80.2807,1920,1080,yuv420p,29.9750208159867,300,8,15744258
|
| 53 |
+
A052,44.8256,1920,1080,yuv420p,29.9800133244504,300,8,15000156
|
| 54 |
+
A053,67.0884,1920,1080,yuv420p,30.0086736033339,301,8,17132551
|
| 55 |
+
A054,76.1667,1920,1080,yuv420p,29.917,300,8,5144377
|
| 56 |
+
A055,53.8092,1920,1080,yuvj420p,26.1094871113473,262,8,16293288
|
| 57 |
+
A056,52.3594,1920,1080,yuv420p,30.0,300,8,7449955
|
| 58 |
+
A057,66.4892,1920,1080,yuv420p,29.9850074962519,300,8,15523422
|
| 59 |
+
A058,57.0,1920,1080,yuv420p,29.7980465725025,298,8,16946194
|
| 60 |
+
A059,74.2873,1920,1080,yuv420p,29.9667369220166,300,8,16964290
|
| 61 |
+
A060,63.1839,1920,1080,yuv420p,29.9742887212302,300,8,16983630
|
| 62 |
+
A061,56.4521,1920,1080,yuv420p,29.9937679615458,300,8,16996386
|
| 63 |
+
A062,47.5187,1920,1080,yuv420p,29.9216385532555,300,8,16945184
|
| 64 |
+
A063,44.8971,1920,1080,yuv420p,30.0058483159508,301,8,17087068
|
| 65 |
+
A064,49.8497,1920,1080,yuv420p,29.9615160971019,300,8,16972656
|
| 66 |
+
A065,81.8222,1920,1080,yuvj420p,30.0081085032944,301,8,21758790
|
| 67 |
+
A066,74.7647,1920,1080,yuv420p,29.9800133244504,300,8,15444400
|
| 68 |
+
A067,77.4551,1920,1080,yuv420p,29.9800796812749,301,8,11218637
|
| 69 |
+
A068,39.123,1920,1080,yuv420p,29.2834711074194,293,8,16612499
|
| 70 |
+
A069,49.4444,1920,1080,yuv420p,30.0,300,8,9519048
|
| 71 |
+
A070,65.4608,1920,1080,yuv420p,29.9800133244504,300,8,15198127
|
| 72 |
+
A071,74.1071,1920,1080,yuv420p,29.9800133244504,300,8,18229798
|
| 73 |
+
A072,20.4406,1920,1080,yuv420p,25.0,252,8,8964932
|
| 74 |
+
A073,91.1313,1920,1080,yuv420p,29.9800796812749,301,8,10923183
|
| 75 |
+
A074,57.3204,1920,1080,yuv420p,29.9850074962519,300,8,15409714
|
| 76 |
+
A075,58.1867,1920,1080,yuv420p,30.0089062977717,301,8,17079144
|
| 77 |
+
A076,64.7835,1920,1080,yuv420p,30.0089727825596,301,8,17092917
|
| 78 |
+
A077,74.4483,1920,1080,yuv420p,29.9800796812749,301,8,10847007
|
| 79 |
+
A078,38.8777,1920,1080,yuvj420p,29.9855253818391,299,8,21717661
|
| 80 |
+
A079,81.0943,1920,1080,yuvj420p,30.0000666668148,300,8,19867331
|
| 81 |
+
A080,39.2086,1920,1080,yuv420p,29.9543695104458,300,8,16971987
|
| 82 |
+
A081,75.7143,1920,1080,yuv420p,29.9800133244504,300,8,15212800
|
| 83 |
+
A082,87.0859,1920,1080,yuv420p,29.9750208159867,300,8,15338957
|
| 84 |
+
A083,46.3077,1920,1080,yuv420p,29.931,300,8,7455171
|
| 85 |
+
A084,55.3835,1920,1080,yuv420p,29.79,298,8,2465060
|
| 86 |
+
A085,70.8486,1920,1080,yuv420p,30.0088730554882,301,8,17102288
|
| 87 |
+
A086,35.2749,1920,1080,yuv420p,29.9800133244504,300,8,17013643
|
| 88 |
+
A087,56.6415,1920,1080,yuv420p,29.8408818973079,299,8,16912277
|
| 89 |
+
A088,43.4892,1920,1080,yuv420p,24.9325988743763,250,8,19560818
|
| 90 |
+
A089,46.9444,1920,1080,yuv420p,29.841,299,8,4302760
|
| 91 |
+
A090,86.1709,1920,1080,yuv420p,29.9800133244504,300,8,15530839
|
| 92 |
+
A091,46.2238,1920,1080,yuv420p,29.837,299,8,8526448
|
| 93 |
+
A092,71.095,1920,1080,yuv420p,29.4234336870056,295,8,16780516
|
| 94 |
+
A093,66.9632,1920,1080,yuv420p,29.9821439675926,300,8,16990067
|
| 95 |
+
A094,52.3441,1920,1080,yuv420p,30.0055824339412,301,8,17168699
|
| 96 |
+
A095,71.5491,1920,1080,yuv420p,29.9800133244504,300,8,15427788
|
| 97 |
+
A096,66.5815,1920,1080,yuv420p,29.9750208159867,300,8,17309467
|
| 98 |
+
A097,66.4593,1920,1080,yuv420p,29.9751037344398,301,8,11046218
|
| 99 |
+
A098,85.8091,1920,1080,yuv420p,30.0089062977717,301,8,17118899
|
| 100 |
+
A099,46.1517,1920,1080,yuvj420p,30.0084409125047,301,8,21714840
|
| 101 |
+
A100,46.6273,1920,1080,yuv420p,30.025037489734,301,8,16961137
|
| 102 |
+
A101,69.3657,1920,1080,yuv420p,29.9750208159867,300,8,17493724
|
| 103 |
+
A102,62.987,1920,1080,yuv420p,29.8223293998741,299,8,16933097
|
| 104 |
+
A103,71.6203,1920,1080,yuv420p,29.8507462686567,299,8,16992718
|
| 105 |
+
A104,50.385,1920,1080,yuv420p,29.9360698596996,300,8,16963448
|
| 106 |
+
A105,37.8824,1920,1080,yuv420p,29.9800133244504,300,8,15042215
|
| 107 |
+
A106,72.642,1920,1080,yuv420p,30.3467718120805,303,8,4546073
|
| 108 |
+
A107,81.6325,1920,1080,yuv420p,29.9800133244504,300,8,15519907
|
| 109 |
+
A108,35.8462,1920,1080,yuvj420p,30.0832870627429,301,8,21757909
|
| 110 |
+
A109,36.5086,1920,1080,yuv420p,29.791,298,8,3648772
|
| 111 |
+
A110,87.8585,1920,1080,yuv420p,29.9800796812749,301,8,10767615
|
| 112 |
+
B001,27.2485,1280,720,yuv420p,19.561507300634,196,8,7880020
|
| 113 |
+
B002,73.4245,1280,720,yuv420p,30.0055824339412,301,8,10609223
|
| 114 |
+
B003,49.1111,1280,720,yuv420p,30.0058815515334,301,8,11514298
|
| 115 |
+
B004,60.6719,1280,720,yuv420p,30.0809821346282,301,8,10014957
|
| 116 |
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| 492 |
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| 495 |
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| 497 |
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| 498 |
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| 499 |
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G063,29.0845,404,720,yuv420p,20.1666666666667,204,8,2135118
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G064,11.3333,404,720,yuv420p,20.1666666666667,202,8,1042338
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G065,68.6814,404,720,yuv420p,24.25,243,8,1365433
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G066,11.9079,404,720,yuv420p,20.2020202020202,204,8,1001169
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| 504 |
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G067,57.2944,404,720,yuv420p,29.8333333333333,299,8,1147950
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G068,59.4638,404,720,yuv420p,29.8507462686567,299,8,1877276
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G069,66.1244,404,720,yuv420p,29.8333333333333,299,8,1032572
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G070,79.5904,404,720,yuvj420p,30.0,302,8,5971384
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G071,48.3714,404,720,yuv420p,30.0,300,8,5919311
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| 509 |
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G072,47.7432,404,720,yuv420p,29.97002997003,300,8,1479618
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G073,18.6333,404,720,yuv420p,30.0,300,8,2177430
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G074,83.1826,404,720,yuv420p,29.97002997003,300,8,2205880
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G075,64.293,404,720,yuv420p,29.851,300,8,1847991
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G076,80.0361,404,720,yuv420p,29.839,299,8,1347538
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G077,85.2383,404,720,yuv420p,29.8333333333333,299,8,3337300
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G079,15.3576,404,720,yuv420p,30.0,301,8,834032
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G080,26.2575,404,720,yuv420p,29.9166666666667,300,8,910002
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G081,46.7325,404,720,yuv420p,24.0,241,8,2495439
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G118,65.9,404,720,yuv420p,29.97002997003,301,8,3985286
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G119,76.8795,404,720,yuv420p,29.97002997003,301,8,1929960
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| 569 |
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M001,36.9665,568,320,yuv420p,30.0,300,8,759733
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| 572 |
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M003,21.1084,568,320,yuv420p,30.0,300,8,697685
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| 573 |
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N001,67.445,360,480,yuv420p,28.6856571714143,287,8,687151
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| 574 |
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O001,27.8,480,360,yuv420p,24.0119561607439,241,8,721305
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| 575 |
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O002,31.0169,480,360,yuv420p,25.2988047808765,254,8,648200
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| 576 |
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P001,64.399,272,480,yuv420p,30.0,300,8,728060
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| 577 |
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P002,60.1489,272,480,yuv420p,30.0,300,8,742869
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| 578 |
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| 579 |
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| 580 |
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P005,74.1421,272,480,yuv420p,30.0,300,8,734968
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| 581 |
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P006,54.2271,272,480,yuv420p,30.0,300,8,727947
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| 582 |
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P007,38.2139,272,480,yuv420p,30.0,300,8,721308
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| 583 |
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P008,38.4545,272,480,yuv420p,30.0,300,8,721492
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| 584 |
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P009,55.9663,272,480,yuv420p,30.0,300,8,744878
|
| 585 |
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Q001,61.1466,240,320,yuv420p,30.0200133422282,301,8,771281
|
| 586 |
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R001,72.4848,320,240,yuv420p,30.0200133422282,301,8,770681
|
metadata/LSVQ_TEST_1080P_metadata.csv
ADDED
|
The diff for this file is too large to render.
See raw diff
|
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|
metadata/LSVQ_TEST_metadata.csv
ADDED
|
The diff for this file is too large to render.
See raw diff
|
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|
metadata/LSVQ_TRAIN_metadata.csv
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
metadata/YOUTUBE_UGC_1080P_metadata.csv
ADDED
|
@@ -0,0 +1,295 @@
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| 1 |
+
vid,category,resolution,x_Subjects,MOSFull,MOSChunk00,MOSChunk05,MOSChunk10,stdFull,stdChunk00,stdChunk05,stdChunk10,width,height,pixfmt,framerate,nb_frames,bitdepth,bitrate
|
| 2 |
+
Animation_1080P-05f8,Animation,1080,146,3.9010000000000002,3.7569999999999997,3.6710000000000003,3.728,0.871,0.7340000000000001,0.733,0.738,1920,1080,yuv420p,25,,,
|
| 3 |
+
Animation_1080P-0c4f,Animation,1080,127,3.94,3.8310000000000004,4.01,4.031000000000001,0.529,0.527,0.626,0.6920000000000001,1920,1080,yuv420p,23.97602398,,,
|
| 4 |
+
Animation_1080P-18f5,Animation,1080,139,4.158,4.177,3.815,4.02,0.579,0.5870000000000001,0.612,0.6829999999999999,1920,1080,yuv420p,29.97002997,,,
|
| 5 |
+
Animation_1080P-209f,Animation,1080,136,4.189,4.138,4.112,4.224,0.622,0.606,0.594,0.509,1920,1080,yuv420p,25,,,
|
| 6 |
+
Animation_1080P-21dd,Animation,1080,115,3.7910000000000004,3.719,3.733,3.62,0.486,0.526,0.48100000000000004,0.588,1920,1080,yuv420p,23.97602398,,,
|
| 7 |
+
Animation_1080P-2fbe,Animation,1080,105,3.346,3.4560000000000004,3.341,3.324,0.82,0.578,0.721,0.64,1920,1080,yuv420p,23.97602398,,,
|
| 8 |
+
Animation_1080P-2fff,Animation,1080,124,4.369,4.343,4.288,4.465,0.517,0.517,0.637,0.47600000000000003,1920,1080,yuv420p,25,,,
|
| 9 |
+
Animation_1080P-3d67,Animation,1080,118,2.778,2.2969999999999997,2.313,2.201,0.907,1.005,0.978,0.857,1920,1080,yuv420p,4,,,
|
| 10 |
+
Animation_1080P-3dbf,Animation,1080,142,4.444,4.3260000000000005,4.319,4.212,0.514,0.57,0.578,0.568,1920,1080,yuv420p,24,,,
|
| 11 |
+
Animation_1080P-3e01,Animation,1080,135,4.045,4.172,4.26,4.132,0.654,0.67,0.589,0.688,1920,1080,yuv420p,12,,,
|
| 12 |
+
Animation_1080P-4214,Animation,1080,119,3.51,3.3789999999999996,3.3680000000000003,3.415,0.7509999999999999,0.863,0.872,0.83,1920,1080,yuv420p,25,,,
|
| 13 |
+
Animation_1080P-4be3,Animation,1080,124,3.565,3.491,3.7060000000000004,3.508,0.49700000000000005,0.5920000000000001,0.584,0.525,1920,1080,yuv422p10le,23.97602398,,,
|
| 14 |
+
Animation_1080P-4ea8,Animation,1080,139,4.121,4.314,3.835,3.61,0.625,0.509,0.522,0.586,1920,1080,yuv420p,24,,,
|
| 15 |
+
Animation_1080P-5083,Animation,1080,111,3.252,2.45,2.491,2.4859999999999998,0.778,0.972,0.996,1.006,1920,1080,yuv420p,25,,,
|
| 16 |
+
Animation_1080P-58d3,Animation,1080,148,4.243,4.395,3.965,4.024,0.556,0.465,0.503,0.5479999999999999,1920,1080,yuv420p,29.97002997,,,
|
| 17 |
+
Animation_1080P-5dd8,Animation,1080,115,3.958,3.946,4.006,4.2330000000000005,0.562,0.552,0.62,0.494,1920,1080,yuv420p,29.97002997,,,
|
| 18 |
+
Animation_1080P-646f,Animation,1080,143,3.3760000000000003,3.248,3.228,3.383,0.7090000000000001,0.698,0.736,0.7609999999999999,1920,1080,yuv420p,29.97002997,,,
|
| 19 |
+
Animation_1080P-6a33,Animation,1080,143,2.052,1.868,1.755,1.919,1.075,1.037,1.003,1.035,1920,1080,yuv420p,23.97602398,,,
|
| 20 |
+
Animation_1080P-6ec0,Animation,1080,134,4.124,3.923,3.8289999999999997,4.2219999999999995,0.568,0.527,0.541,0.598,1920,1080,yuv420p,29.97002997,,,
|
| 21 |
+
CoverSong_1080P-0188,CoverSong,1080,138,2.383,2.205,2.224,2.2840000000000003,1.0959999999999999,1.122,1.079,1.088,1920,1080,yuv420p,25,,,
|
| 22 |
+
CoverSong_1080P-033d,CoverSong,1080,127,4.433,4.3580000000000005,4.4719999999999995,4.412,0.46799999999999997,0.48200000000000004,0.431,0.495,1920,1080,yuv420p,30,,,
|
| 23 |
+
CoverSong_1080P-0a86,CoverSong,1080,137,4.228,4.258,4.041,4.085,0.447,0.451,0.516,0.5710000000000001,1920,1080,yuv420p,30,,,
|
| 24 |
+
CoverSong_1080P-0cdc,CoverSong,1080,135,2.741,2.45,2.553,2.539,0.695,0.593,0.608,0.5920000000000001,1920,1080,yuv420p,29.97002997,,,
|
| 25 |
+
CoverSong_1080P-0f33,CoverSong,1080,120,3.221,3.198,3.0780000000000003,3.0589999999999997,0.494,0.46799999999999997,0.652,0.632,1920,1080,yuv420p,30,,,
|
| 26 |
+
CoverSong_1080P-0f3a,CoverSong,1080,130,4.3580000000000005,4.4,4.3469999999999995,4.385,0.478,0.415,0.493,0.431,1440,1080,yuv420p,25,,,
|
| 27 |
+
CoverSong_1080P-1963,CoverSong,1080,126,3.8819999999999997,3.778,3.612,3.502,0.539,0.536,0.5429999999999999,0.618,1920,1080,yuv420p,29.97002997,,,
|
| 28 |
+
CoverSong_1080P-1b08,CoverSong,1080,103,3.812,3.75,3.71,3.71,0.48100000000000004,0.43200000000000005,0.465,0.479,1920,1080,yuv420p,25,,,
|
| 29 |
+
CoverSong_1080P-1b0c,CoverSong,1080,129,3.763,3.795,3.714,3.801,0.496,0.449,0.45899999999999996,0.431,1920,1080,yuv420p,29.724,,,
|
| 30 |
+
CoverSong_1080P-2207,CoverSong,1080,119,3.675,3.611,4.086,4.079,0.511,0.514,0.602,0.59,1440,1080,yuv420p,25,,,
|
| 31 |
+
CoverSong_1080P-237f,CoverSong,1080,132,3.8930000000000002,3.7089999999999996,3.613,4.1339999999999995,0.5379999999999999,0.511,0.489,0.637,1920,1080,yuv420p,25,,,
|
| 32 |
+
CoverSong_1080P-3409,CoverSong,1080,133,4.331,4.283,4.289,4.345,0.489,0.47700000000000004,0.551,0.486,1920,1080,yuv420p,25,,,
|
| 33 |
+
CoverSong_1080P-3499,CoverSong,1080,105,4.1930000000000005,4.228,4.273,4.3069999999999995,0.386,0.52,0.47600000000000003,0.456,1920,1080,yuv420p,25,,,
|
| 34 |
+
CoverSong_1080P-3aac,CoverSong,1080,112,3.3810000000000002,3.5239999999999996,3.407,3.4410000000000003,0.489,0.545,0.498,0.507,1920,1080,yuv420p,24,,,
|
| 35 |
+
CoverSong_1080P-3df8,CoverSong,1080,124,3.827,3.727,3.7510000000000003,3.877,0.507,0.469,0.425,0.444,1920,1080,yuv420p,29.97002997,,,
|
| 36 |
+
CoverSong_1080P-4bad,CoverSong,1080,136,4.298,4.321000000000001,4.379,4.364,0.45799999999999996,0.503,0.45799999999999996,0.478,1920,1080,yuv420p,60,,,
|
| 37 |
+
CoverSong_1080P-5430,CoverSong,1080,137,3.969,3.728,4.046,4.19,0.47700000000000004,0.479,0.5820000000000001,0.5579999999999999,1920,1080,yuv422p10le,29.97002997,,,
|
| 38 |
+
CoverSong_1080P-5456,CoverSong,1080,112,3.736,3.8289999999999997,3.778,3.762,0.445,0.51,0.541,0.523,1920,1080,yuv420p,29.97002997,,,
|
| 39 |
+
CoverSong_1080P-5cae,CoverSong,1080,117,3.2089999999999996,3.2910000000000004,3.267,3.267,0.662,0.544,0.445,0.53,1920,1080,yuv420p,30,,,
|
| 40 |
+
CoverSong_1080P-5e69,CoverSong,1080,124,3.29,3.46,3.4330000000000003,3.42,0.507,0.564,0.499,0.48100000000000004,1920,1080,yuv420p,30,,,
|
| 41 |
+
CoverSong_1080P-5f92,CoverSong,1080,104,4.043,4.273,4.245,4.196000000000001,0.489,0.45799999999999996,0.484,0.485,1920,1080,yuv420p,29.97002997,,,
|
| 42 |
+
CoverSong_1080P-6f26,CoverSong,1080,137,4.288,4.328,4.151,4.1160000000000005,0.48700000000000004,0.488,0.591,0.584,1440,1080,yuv420p,25,,,
|
| 43 |
+
Gaming_1080P-0ce6,Gaming,1080,111,3.968,3.93,4.223,4.019,0.5329999999999999,0.565,0.54,0.48100000000000004,1920,1080,yuv420p,30,,,
|
| 44 |
+
Gaming_1080P-0ef8,Gaming,1080,108,3.781,4.047,3.835,3.759,0.48100000000000004,0.608,0.446,0.551,1920,1080,yuv420p,29.97002997,,,
|
| 45 |
+
Gaming_1080P-12d4,Gaming,1080,134,4.494,4.422,4.354,4.122,0.449,0.479,0.5429999999999999,0.6990000000000001,1920,1080,yuv420p,30,,,
|
| 46 |
+
Gaming_1080P-13e3,Gaming,1080,126,4.434,4.513,4.504,4.535,0.46,0.413,0.467,0.409,1920,1080,yuv420p,59.94006309,,,
|
| 47 |
+
Gaming_1080P-1704,Gaming,1080,140,4.295,4.302,4.07,4.176,0.637,0.474,0.544,0.5329999999999999,1920,1080,yuv420p,60,,,
|
| 48 |
+
Gaming_1080P-173a,Gaming,1080,115,3.013,3.13,3.3169999999999997,3.147,0.7,0.777,0.742,0.726,1920,1080,yuv420p,30,,,
|
| 49 |
+
Gaming_1080P-190d,Gaming,1080,125,3.898,3.758,3.9539999999999997,3.667,0.557,0.511,0.591,0.579,1920,1080,yuv420p,59,,,
|
| 50 |
+
Gaming_1080P-2221,Gaming,1080,124,4.239,4.183,4.192,4.1419999999999995,0.563,0.605,0.588,0.565,1920,1080,yuv420p,51.73900293,,,
|
| 51 |
+
Gaming_1080P-223e,Gaming,1080,137,4.283,4.207,4.461,4.34,0.493,0.358,0.433,0.524,1920,1080,yuv420p,29.97002997,,,
|
| 52 |
+
Gaming_1080P-26dc,Gaming,1080,130,3.5410000000000004,3.397,3.437,3.7239999999999998,0.8109999999999999,0.8420000000000001,0.77,0.637,1920,1080,yuv420p,29.97002997,,,
|
| 53 |
+
Gaming_1080P-277c,Gaming,1080,122,3.884,3.9810000000000003,3.7460000000000004,3.89,0.523,0.67,0.579,0.599,1920,1080,yuv420p,25,,,
|
| 54 |
+
Gaming_1080P-2927,Gaming,1080,135,4.441,4.477,4.487,4.468999999999999,0.44,0.469,0.41,0.457,1920,1080,yuv420p,60,,,
|
| 55 |
+
Gaming_1080P-29b1,Gaming,1080,124,3.8289999999999997,4.289,3.9619999999999997,3.574,0.498,0.541,0.542,0.6,1920,1080,yuv420p,29.97002997,,,
|
| 56 |
+
Gaming_1080P-2e97,Gaming,1080,133,4.391,4.324,4.29,4.279,0.48700000000000004,0.467,0.478,0.46299999999999997,1920,1080,yuv420p,60,,,
|
| 57 |
+
Gaming_1080P-35fa,Gaming,1080,117,3.035,2.991,2.9339999999999997,2.6689999999999996,0.637,0.7040000000000001,0.654,0.7,1920,1080,yuv420p,30,,,
|
| 58 |
+
Gaming_1080P-3a9d,Gaming,1080,116,3.872,3.9760000000000004,4.087,4.056,0.5529999999999999,0.518,0.597,0.598,1920,1080,yuv420p,23.97602398,,,
|
| 59 |
+
Gaming_1080P-3bf7,Gaming,1080,130,3.245,2.845,2.838,2.946,0.763,0.6629999999999999,0.6779999999999999,0.679,1920,1080,yuv420p,60,,,
|
| 60 |
+
Gaming_1080P-3d58,Gaming,1080,124,3.782,3.96,3.915,4.066,0.47,0.6779999999999999,0.5379999999999999,0.568,1920,1080,yuv420p,59,,,
|
| 61 |
+
Gaming_1080P-44c4,Gaming,1080,129,3.839,3.73,3.758,3.798,0.657,0.585,0.578,0.58,1920,1080,yuv420p,59,,,
|
| 62 |
+
Gaming_1080P-45af,Gaming,1080,138,4.373,4.499,4.488,4.353,0.486,0.396,0.401,0.429,1920,1080,yuv420p,29.97002997,,,
|
| 63 |
+
Gaming_1080P-45fc,Gaming,1080,124,3.695,3.592,3.678,3.8360000000000003,0.624,0.638,0.555,0.564,1920,1080,yuv420p,60,,,
|
| 64 |
+
Gaming_1080P-4865,Gaming,1080,132,4.355,4.284,4.302,4.09,0.48,0.532,0.495,0.461,1920,1080,yuv420p,59.94006309,,,
|
| 65 |
+
Gaming_1080P-51fc,Gaming,1080,114,4.377,4.312,4.386,4.213,0.531,0.49700000000000005,0.494,0.575,1920,1080,yuv420p,60,,,
|
| 66 |
+
Gaming_1080P-564e,Gaming,1080,120,4.322,4.19,4.234,4.348,0.525,0.35600000000000004,0.49700000000000005,0.439,1920,1080,yuv420p,60,,,
|
| 67 |
+
Gaming_1080P-57ca,Gaming,1080,136,4.409,4.289,4.3580000000000005,4.39,0.505,0.598,0.594,0.505,1920,1080,yuv420p,51.73900293,,,
|
| 68 |
+
Gaming_1080P-58ba,Gaming,1080,115,3.952,3.861,3.87,3.713,0.456,0.485,0.446,0.39799999999999996,1440,1080,yuv420p,25,,,
|
| 69 |
+
Gaming_1080P-5ae7,Gaming,1080,110,3.3280000000000003,3.4539999999999997,2.8760000000000003,2.7689999999999997,0.701,0.708,0.6829999999999999,0.755,1920,1080,yuv420p,60,,,
|
| 70 |
+
Gaming_1080P-6530,Gaming,1080,115,3.92,4.098,3.3539999999999996,3.5010000000000003,0.475,0.624,0.69,0.609,1920,1080,yuv420p,25,,,
|
| 71 |
+
Gaming_1080P-6578,Gaming,1080,154,4.399,4.413,4.412,4.379,0.504,0.425,0.46399999999999997,0.46299999999999997,1920,1080,yuv420p,60,,,
|
| 72 |
+
Gaming_1080P-66d2,Gaming,1080,129,4.1419999999999995,4.178999999999999,4.249,3.989,0.628,0.624,0.59,0.602,1920,1080,yuv420p,30,,,
|
| 73 |
+
Gaming_1080P-698a,Gaming,1080,118,3.7430000000000003,3.799,3.6180000000000003,3.625,0.579,0.561,0.557,0.562,1920,1080,yuv420p,60,,,
|
| 74 |
+
Gaming_1080P-6d53,Gaming,1080,118,3.821,4.021,4.156000000000001,4.14,0.552,0.503,0.603,0.591,1920,1080,yuv420p,60,,,
|
| 75 |
+
Gaming_1080P-6db2,Gaming,1080,118,3.475,3.2030000000000003,3.302,3.1439999999999997,0.741,0.7959999999999999,0.779,0.735,1920,1080,yuv420p,29.97002997,,,
|
| 76 |
+
Gaming_1080P-6dc6,Gaming,1080,111,4.602,4.44,4.417,4.471,0.401,0.40700000000000003,0.456,0.43200000000000005,1920,1080,yuv420p,30,,,
|
| 77 |
+
Gaming_1080P-6e45,Gaming,1080,118,2.517,2.412,2.273,2.3369999999999997,1.047,0.9740000000000001,0.9520000000000001,0.985,1920,1080,yuv420p,30,,,
|
| 78 |
+
Gaming_1080P-71a5,Gaming,1080,114,4.482,4.562,4.543,4.547,0.47700000000000004,0.42700000000000005,0.42200000000000004,0.402,1920,1080,yuv420p,39,,,
|
| 79 |
+
Gaming_1080P-72c8,Gaming,1080,119,4.3660000000000005,4.408,4.412,4.5089999999999995,0.503,0.39299999999999996,0.446,0.39799999999999996,1920,1080,yuv420p,30,,,
|
| 80 |
+
Gaming_1080P-777b,Gaming,1080,129,4.05,3.773,3.9760000000000004,3.8289999999999997,0.635,0.508,0.5710000000000001,0.626,1920,1080,yuv420p,59,,,
|
| 81 |
+
Gaming_1080P-7a1e,Gaming,1080,116,4.024,3.927,3.9210000000000003,4.127,0.504,0.606,0.5870000000000001,0.607,1920,1080,yuv420p,29.97002997,,,
|
| 82 |
+
HDR_1080P-0d31,HDR,1080,139,4.373,4.06,4.102,3.7769999999999997,0.507,0.483,0.569,0.504,1920,1080,yuv420p10le,60,,,
|
| 83 |
+
HDR_1080P-13eb,HDR,1080,125,3.761,3.647,3.799,3.667,0.484,0.59,0.74,0.529,1920,1080,yuv420p10le,60,,,
|
| 84 |
+
HDR_1080P-1be2,HDR,1080,122,3.7560000000000002,3.906,3.738,3.735,0.495,0.5329999999999999,0.547,0.485,1920,1080,yuv420p10le,60,,,
|
| 85 |
+
HDR_1080P-1e5b,HDR,1080,128,1.242,1.3230000000000002,1.3869999999999998,1.3869999999999998,0.431,0.513,0.6579999999999999,0.6509999999999999,1920,1080,yuv420p10le,60,,,
|
| 86 |
+
HDR_1080P-206d,HDR,1080,109,3.7910000000000004,3.6919999999999997,3.971,3.8810000000000002,0.5760000000000001,0.66,0.6859999999999999,0.569,1920,1080,yuv420p10le,59.94006309,,,
|
| 87 |
+
HDR_1080P-2d32,HDR,1080,141,4.503,4.467,4.303,4.553,0.488,0.44,0.605,0.483,1920,1080,yuv420p10le,23.97602398,,,
|
| 88 |
+
HDR_1080P-3181,HDR,1080,114,3.7769999999999997,3.718,3.412,3.444,0.594,0.5429999999999999,0.6809999999999999,0.711,1920,1080,yuv420p12le,5,,,
|
| 89 |
+
HDR_1080P-33e3,HDR,1080,141,4.45,4.436,4.227,4.156000000000001,0.467,0.449,0.55,0.5670000000000001,1920,1080,yuv420p10le,60,,,
|
| 90 |
+
HDR_1080P-3521,HDR,1080,132,3.895,4.013,4.191,4.123,0.495,0.53,0.5329999999999999,0.597,1920,1080,yuv420p10le,60,,,
|
| 91 |
+
HDR_1080P-35f4,HDR,1080,125,4.16,4.051,3.9539999999999997,3.96,0.6,0.618,0.5579999999999999,0.529,1920,1080,yuv420p10le,60,,,
|
| 92 |
+
HDR_1080P-3749,HDR,1080,131,4.08,3.949,4.015,3.89,0.5870000000000001,0.47,0.55,0.523,1920,1080,yuv420p10le,60,,,
|
| 93 |
+
HDR_1080P-3a4a,HDR,1080,127,3.824,3.9789999999999996,3.7110000000000003,3.972,0.43799999999999994,0.41100000000000003,0.5429999999999999,0.515,1920,1080,yuv420p10le,60,,,
|
| 94 |
+
HDR_1080P-46a4,HDR,1080,122,3.582,3.667,3.3760000000000003,3.432,0.728,0.755,0.76,0.8,1920,1080,yuv420p10le,50,,,
|
| 95 |
+
HDR_1080P-49d6,HDR,1080,148,4.5889999999999995,4.637,4.531000000000001,4.6,0.395,0.401,0.47100000000000003,0.425,1920,1080,yuv420p10le,23.97602398,,,
|
| 96 |
+
HDR_1080P-4f4a,HDR,1080,125,3.51,3.363,3.366,3.5460000000000003,0.838,0.728,0.79,0.743,1920,1080,yuv420p10le,59.94006309,,,
|
| 97 |
+
HDR_1080P-548b,HDR,1080,130,4.626,4.757,4.655,4.672,0.41200000000000003,0.35,0.392,0.37200000000000005,1920,1080,yuv422p10le,25,,,
|
| 98 |
+
HDR_1080P-549e,HDR,1080,127,4.064,3.9339999999999997,3.928,3.977,0.633,0.5710000000000001,0.579,0.488,1920,1080,yuv420p10le,60,,,
|
| 99 |
+
HDR_1080P-55c4,HDR,1080,124,4.106,3.264,4.35,4.405,0.584,0.898,0.581,0.48,1920,1080,yuv420p10le,60,,,
|
| 100 |
+
HDR_1080P-601b,HDR,1080,129,4.3180000000000005,4.362,4.277,4.427,0.578,0.452,0.611,0.529,1920,1080,yuv420p10le,59.94006309,,,
|
| 101 |
+
HDR_1080P-687e,HDR,1080,134,2.6289999999999996,3.895,2.452,1.5230000000000001,1.093,0.498,1.06,0.755,1920,1080,yuv420p10le,60,,,
|
| 102 |
+
HDR_1080P-68c8,HDR,1080,124,3.804,3.6830000000000003,3.7439999999999998,3.7739999999999996,0.648,0.605,0.622,0.591,1920,1080,yuv420p10le,60,,,
|
| 103 |
+
HDR_1080P-69de,HDR,1080,124,4.181,4.005,4.005,4.087,0.488,0.40399999999999997,0.41,0.385,1920,1080,yuv420p10le,24,,,
|
| 104 |
+
HDR_1080P-6eb7,HDR,1080,134,4.046,3.847,3.89,3.8280000000000003,0.63,0.465,0.465,0.461,1920,1080,yuv420p10le,60,,,
|
| 105 |
+
HDR_1080P-7825,HDR,1080,118,4.513999999999999,4.498,4.46,4.455,0.45299999999999996,0.43200000000000005,0.413,0.424,1920,1080,yuv420p10le,24,,,
|
| 106 |
+
HowTo_1080P-0267,HowTo,1080,144,4.234,4.168,4.184,4.231,0.56,0.623,0.599,0.607,1920,1080,yuv420p,30,,,
|
| 107 |
+
HowTo_1080P-03fd,HowTo,1080,121,3.517,3.44,3.41,3.4410000000000003,0.46799999999999997,0.483,0.478,0.526,1920,1080,yuv420p,30,,,
|
| 108 |
+
HowTo_1080P-13aa,HowTo,1080,126,4.336,4.428,4.328,4.001,0.48100000000000004,0.436,0.42200000000000004,0.484,1920,1080,yuv420p,23.97602398,,,
|
| 109 |
+
HowTo_1080P-1f3b,HowTo,1080,152,4.185,4.367,4.346,4.312,0.574,0.474,0.484,0.5,1920,1080,yuv420p,30,,,
|
| 110 |
+
HowTo_1080P-36a9,HowTo,1080,116,4.427,4.208,4.16,4.14,0.445,0.626,0.645,0.569,1920,1080,yuv420p,31.13900135,,,
|
| 111 |
+
HowTo_1080P-4d7b,HowTo,1080,113,4.199,3.897,4.308,4.397,0.40700000000000003,0.517,0.517,0.483,1920,1080,yuv420p,29.97002997,,,
|
| 112 |
+
HowTo_1080P-52bb,HowTo,1080,130,4.272,4.492,4.417,4.281000000000001,0.55,0.434,0.47600000000000003,0.547,1920,1080,yuv444p,15,,,
|
| 113 |
+
HowTo_1080P-55d1,HowTo,1080,133,4.102,3.988,3.95,3.947,0.593,0.517,0.461,0.526,1920,1080,yuv420p,30.02100162,,,
|
| 114 |
+
HowTo_1080P-63e4,HowTo,1080,139,3.948,4.198,3.978,3.904,0.47700000000000004,0.6,0.486,0.5429999999999999,1920,1080,yuv420p,30,,,
|
| 115 |
+
HowTo_1080P-63ec,HowTo,1080,133,4.229,4.167,4.453,4.331,0.5579999999999999,0.65,0.47700000000000004,0.491,1920,1080,yuv420p,29.97002997,,,
|
| 116 |
+
HowTo_1080P-64f7,HowTo,1080,151,4.454,4.157,4.367,4.416,0.46799999999999997,0.602,0.478,0.522,1920,1080,yuv420p,25,,,
|
| 117 |
+
HowTo_1080P-6cbf,HowTo,1080,131,1.915,1.8559999999999999,1.848,1.649,0.8009999999999999,0.691,0.667,0.589,1920,1080,yuv422p,29.97002997,,,
|
| 118 |
+
HowTo_1080P-7399,HowTo,1080,151,4.194,4.146,4.171,4.184,0.608,0.41200000000000003,0.605,0.659,1920,1080,yuv420p,30,,,
|
| 119 |
+
HowTo_1080P-763c,HowTo,1080,126,3.957,3.841,3.9760000000000004,4.4239999999999995,0.465,0.499,0.509,0.47200000000000003,1920,1080,yuv420p,59.94006309,,,
|
| 120 |
+
HowTo_1080P-7cf2,HowTo,1080,117,3.717,3.716,3.8489999999999998,3.6039999999999996,0.493,0.515,0.527,0.565,1920,1080,yuv420p,29.901999999999997,,,
|
| 121 |
+
HowTo_1080P-7f18,HowTo,1080,133,3.718,3.7889999999999997,3.88,3.752,0.499,0.536,0.604,0.6459999999999999,1920,1080,yuv420p,29.97002997,,,
|
| 122 |
+
Lecture_1080P-011f,Lecture,1080,122,3.59,3.636,3.637,3.614,0.5479999999999999,0.441,0.527,0.486,1440,1080,yuv420p,25,,,
|
| 123 |
+
Lecture_1080P-0201,Lecture,1080,121,3.826,3.4930000000000003,3.6,3.548,0.535,0.586,0.489,0.608,1920,1080,yuv420p,30,,,
|
| 124 |
+
Lecture_1080P-0c8a,Lecture,1080,127,4.55,4.532,4.496,4.494,0.44299999999999995,0.395,0.39799999999999996,0.42100000000000004,1920,1080,yuv420p,23.97602398,,,
|
| 125 |
+
Lecture_1080P-1709,Lecture,1080,133,4.172,4.151,4.235,4.159,0.665,0.619,0.555,0.63,1920,1080,yuv420p,25,,,
|
| 126 |
+
Lecture_1080P-1969,Lecture,1080,118,3.6489999999999996,3.5780000000000003,3.6460000000000004,3.616,0.451,0.495,0.444,0.474,1920,1080,yuv420p,30,,,
|
| 127 |
+
LiveMusic_1080P-14af,LiveMusic,1080,139,4.45,4.418,4.477,4.516,0.423,0.47600000000000003,0.442,0.389,1920,1080,yuv420p,25,,,
|
| 128 |
+
LiveMusic_1080P-157b,LiveMusic,1080,122,4.36,4.414,4.181,4.183,0.43,0.5,0.637,0.62,1920,1080,yuv420p,25,,,
|
| 129 |
+
LiveMusic_1080P-1ace,LiveMusic,1080,118,4.162,4.2589999999999995,4.315,4.224,0.562,0.588,0.489,0.484,1920,1080,yuv420p,25,,,
|
| 130 |
+
LiveMusic_1080P-21dd,LiveMusic,1080,117,3.819,3.9019999999999997,3.693,3.815,0.488,0.5379999999999999,0.48700000000000004,0.5489999999999999,1440,1080,yuv420p,29.97002997,,,
|
| 131 |
+
LiveMusic_1080P-28fe,LiveMusic,1080,112,3.693,3.625,3.714,3.815,0.469,0.41700000000000004,0.475,0.49200000000000005,1920,1080,yuv420p,25,,,
|
| 132 |
+
LiveMusic_1080P-2930,LiveMusic,1080,129,3.7760000000000002,3.767,3.9139999999999997,4.0280000000000005,0.479,0.446,0.527,0.5329999999999999,1920,1080,yuv420p,25,,,
|
| 133 |
+
LiveMusic_1080P-2f7f,LiveMusic,1080,126,3.326,3.426,3.076,3.134,0.737,0.725,0.799,0.77,1920,1080,yuv420p,25,,,
|
| 134 |
+
LiveMusic_1080P-3549,LiveMusic,1080,134,3.906,3.8819999999999997,3.909,3.964,0.537,0.475,0.45299999999999996,0.512,1440,1080,yuv420p,29.97002997,,,
|
| 135 |
+
LiveMusic_1080P-3f95,LiveMusic,1080,104,4.05,4.274,4.281000000000001,4.1419999999999995,0.45,0.457,0.47700000000000004,0.57,1920,1080,yuv420p,29.97002997,,,
|
| 136 |
+
LiveMusic_1080P-51f6,LiveMusic,1080,126,3.819,3.84,3.8560000000000003,3.8760000000000003,0.496,0.502,0.475,0.442,1920,1080,yuv420p,25,,,
|
| 137 |
+
LiveMusic_1080P-541f,LiveMusic,1080,133,4.1739999999999995,4.114,3.9960000000000004,4.105,0.561,0.506,0.47700000000000004,0.574,1920,1080,yuv420p,29.97002997,,,
|
| 138 |
+
LiveMusic_1080P-59b3,LiveMusic,1080,124,3.443,3.8939999999999997,3.475,3.345,0.519,0.6609999999999999,0.552,0.49700000000000005,1440,1080,yuv420p,29.97002997,,,
|
| 139 |
+
LiveMusic_1080P-6bbe,LiveMusic,1080,128,4.329,4.296,4.357,4.426,0.446,0.595,0.52,0.479,1920,1080,yuv420p,24,,,
|
| 140 |
+
LiveMusic_1080P-6d1a,LiveMusic,1080,122,3.78,3.641,3.592,3.6319999999999997,0.495,0.484,0.47600000000000003,0.489,1920,1080,yuv420p,29.97002997,,,
|
| 141 |
+
LiveMusic_1080P-6fe2,LiveMusic,1080,124,3.281,3.0789999999999997,3.141,3.0269999999999997,0.562,0.621,0.644,0.6,1920,1080,yuv420p,25,,,
|
| 142 |
+
LiveMusic_1080P-77e8,LiveMusic,1080,112,3.346,2.427,2.016,2.88,0.708,0.935,0.932,0.727,1920,1080,yuv420p,29.97002997,,,
|
| 143 |
+
LiveMusic_1080P-7948,LiveMusic,1080,126,3.838,3.752,3.685,3.8680000000000003,0.509,0.579,0.534,0.479,1920,1080,yuv420p,29.97002997,,,
|
| 144 |
+
LiveMusic_1080P-7ead,LiveMusic,1080,109,3.5589999999999997,3.4130000000000003,3.353,3.353,0.431,0.579,0.5920000000000001,0.508,1920,1080,yuv420p,29.97002997,,,
|
| 145 |
+
LyricVideo_1080P-0075,LyricVideo,1080,109,2.8139999999999996,2.742,2.4930000000000003,2.141,1.145,1.18,1.199,1.031,1920,1080,yuv420p,29.97002997,,,
|
| 146 |
+
LyricVideo_1080P-0625,LyricVideo,1080,135,4.3,4.43,4.377,4.401,0.607,0.485,0.48100000000000004,0.496,1920,1080,yuv420p,29.97002997,,,
|
| 147 |
+
LyricVideo_1080P-16b6,LyricVideo,1080,152,4.381,4.438,4.386,4.367,0.491,0.475,0.502,0.45799999999999996,1920,1080,yuv420p,30,,,
|
| 148 |
+
LyricVideo_1080P-1b04,LyricVideo,1080,134,3.803,3.75,3.71,3.8369999999999997,0.546,0.483,0.45799999999999996,0.426,1440,1080,yuv420p,29.97002997,,,
|
| 149 |
+
LyricVideo_1080P-1d75,LyricVideo,1080,140,4.2989999999999995,4.28,4.263,4.279,0.63,0.5820000000000001,0.607,0.51,1920,1080,yuv420p,29.97002997,,,
|
| 150 |
+
LyricVideo_1080P-2946,LyricVideo,1080,136,4.177,3.95,4.1739999999999995,3.9410000000000003,0.609,0.49,0.498,0.508,1920,1080,yuv420p,25,,,
|
| 151 |
+
LyricVideo_1080P-3803,LyricVideo,1080,113,3.95,3.9130000000000003,4.11,3.984,0.5770000000000001,0.535,0.6559999999999999,0.47100000000000003,1920,1080,yuv420p,29.97002997,,,
|
| 152 |
+
LyricVideo_1080P-41ee,LyricVideo,1080,136,4.071000000000001,3.878,3.7680000000000002,4.121,0.603,0.607,0.607,0.62,1920,1080,yuv420p,50,,,
|
| 153 |
+
LyricVideo_1080P-4b40,LyricVideo,1080,121,3.636,3.484,3.241,3.1510000000000002,0.506,0.7759999999999999,0.667,0.659,1920,1080,yuv420p,25,,,
|
| 154 |
+
LyricVideo_1080P-5461,LyricVideo,1080,121,4.031000000000001,3.8080000000000003,3.865,3.8089999999999997,0.635,0.522,0.423,0.47700000000000004,1920,1080,yuv420p,30,,,
|
| 155 |
+
LyricVideo_1080P-584f,LyricVideo,1080,123,3.188,1.91,3.075,4.234,0.68,0.8240000000000001,0.674,0.6459999999999999,1920,1080,yuv420p,25,,,
|
| 156 |
+
LyricVideo_1080P-63d4,LyricVideo,1080,140,4.221,4.194,4.1610000000000005,4.063,0.618,0.638,0.6729999999999999,0.675,1920,1080,yuv420p,29.97002997,,,
|
| 157 |
+
LyricVideo_1080P-63d9,LyricVideo,1080,114,3.512,3.5460000000000003,3.668,3.3169999999999997,0.632,0.523,0.518,0.659,1440,1080,yuv420p,25,,,
|
| 158 |
+
LyricVideo_1080P-725e,LyricVideo,1080,149,4.399,4.442,4.367,4.262,0.491,0.483,0.575,0.613,1920,1080,yuv420p,29.97002997,,,
|
| 159 |
+
LyricVideo_1080P-756a,LyricVideo,1080,127,4.228,4.3180000000000005,4.335,4.191,0.48100000000000004,0.562,0.531,0.609,1920,1080,yuv420p,29.97002997,,,
|
| 160 |
+
MusicVideo_1080P-04b6,MusicVideo,1080,129,4.288,4.126,4.155,4.07,0.46399999999999997,0.41700000000000004,0.379,0.46399999999999997,1920,1080,yuv422p10le,23.97602398,,,
|
| 161 |
+
MusicVideo_1080P-0706,MusicVideo,1080,130,4.084,3.8810000000000002,3.85,3.8,0.617,0.607,0.61,0.474,1920,1080,yuv422p10le,23.97602398,,,
|
| 162 |
+
MusicVideo_1080P-0860,MusicVideo,1080,128,3.963,3.984,3.978,4.103,0.542,0.5379999999999999,0.528,0.419,1920,1080,yuv420p,29.97002997,,,
|
| 163 |
+
MusicVideo_1080P-106d,MusicVideo,1080,112,3.259,3.44,3.0780000000000003,2.945,0.727,0.531,0.64,0.64,1440,1080,yuv420p,29.97002997,,,
|
| 164 |
+
MusicVideo_1080P-16e6,MusicVideo,1080,139,4.349,4.474,4.548,4.592,0.5720000000000001,0.479,0.473,0.418,1920,1080,yuv420p,25,,,
|
| 165 |
+
MusicVideo_1080P-18f5,MusicVideo,1080,116,3.8289999999999997,3.793,3.247,3.7510000000000003,0.563,0.531,0.665,0.601,1920,1080,yuv420p,25,,,
|
| 166 |
+
MusicVideo_1080P-1a53,MusicVideo,1080,124,3.577,3.279,3.197,3.27,0.574,0.7709999999999999,0.759,0.774,1920,1080,yuv420p,29.97002997,,,
|
| 167 |
+
MusicVideo_1080P-24f5,MusicVideo,1080,144,4.283,4.346,4.3,4.2219999999999995,0.478,0.506,0.485,0.575,1920,1080,yuv420p,24,,,
|
| 168 |
+
MusicVideo_1080P-2b2b,MusicVideo,1080,136,4.436,4.51,4.51,4.484,0.445,0.42700000000000005,0.44299999999999995,0.42200000000000004,1920,1080,yuv420p,25,,,
|
| 169 |
+
MusicVideo_1080P-2d1c,MusicVideo,1080,108,3.616,3.585,3.694,3.685,0.506,0.535,0.48200000000000004,0.461,1440,1080,yuv420p,29.97002997,,,
|
| 170 |
+
MusicVideo_1080P-34ff,MusicVideo,1080,118,3.658,3.4739999999999998,3.452,3.3080000000000003,0.505,0.508,0.498,0.515,1440,1080,yuv420p,25,,,
|
| 171 |
+
MusicVideo_1080P-3a50,MusicVideo,1080,119,3.69,3.5580000000000003,3.38,3.498,0.564,0.57,0.769,0.623,1920,1080,yuv420p,25,,,
|
| 172 |
+
MusicVideo_1080P-453f,MusicVideo,1080,133,4.372,4.622,4.5680000000000005,4.481,0.59,0.436,0.455,0.508,1920,1080,yuv420p,25,,,
|
| 173 |
+
MusicVideo_1080P-4671,MusicVideo,1080,129,3.083,3.008,3.009,3.395,0.6609999999999999,0.5920000000000001,0.551,0.524,1920,1080,yuv420p,25,,,
|
| 174 |
+
MusicVideo_1080P-5072,MusicVideo,1080,144,4.19,4.021,3.997,4.045,0.594,0.514,0.478,0.526,1920,1080,yuv420p,23.97602398,,,
|
| 175 |
+
MusicVideo_1080P-55af,MusicVideo,1080,122,3.748,3.6860000000000004,3.846,3.906,0.523,0.474,0.433,0.523,1920,1080,yuv422p10le,24,,,
|
| 176 |
+
MusicVideo_1080P-6260,MusicVideo,1080,110,3.042,2.613,2.003,2.833,0.66,0.8,0.807,0.736,1920,1080,yuv420p,25,,,
|
| 177 |
+
MusicVideo_1080P-65c6,MusicVideo,1080,107,4.066,3.9730000000000003,4.223,4.013,0.40700000000000003,0.524,0.5710000000000001,0.478,1920,1080,yuv420p,25,,,
|
| 178 |
+
MusicVideo_1080P-6c1d,MusicVideo,1080,142,4.323,4.3,4.356,4.262,0.512,0.565,0.449,0.483,1920,1080,yuv420p,25,,,
|
| 179 |
+
MusicVideo_1080P-6c88,MusicVideo,1080,110,3.318,2.991,2.9330000000000003,2.7560000000000002,0.516,0.586,0.6559999999999999,0.595,1920,1080,yuv420p,29.97002997,,,
|
| 180 |
+
MusicVideo_1080P-6fb1,MusicVideo,1080,116,3.492,3.5010000000000003,3.676,3.786,0.5329999999999999,0.507,0.528,0.504,1440,1080,yuv420p,25,,,
|
| 181 |
+
MusicVideo_1080P-7265,MusicVideo,1080,115,3.5660000000000003,3.595,3.551,3.1239999999999997,0.516,0.52,0.5489999999999999,0.799,1920,1080,yuv420p,25,,,
|
| 182 |
+
MusicVideo_1080P-7706,MusicVideo,1080,126,2.925,2.917,3.05,2.978,0.774,0.696,0.693,0.759,1920,1080,yuv420p,29.97002997,,,
|
| 183 |
+
MusicVideo_1080P-7f2e,MusicVideo,1080,109,3.75,3.772,3.633,3.588,0.5760000000000001,0.6970000000000001,0.63,0.5770000000000001,1920,1080,yuv422p10le,24,,,
|
| 184 |
+
NewsClip_1080P-00c3,NewsClip,1080,141,4.421,4.415,4.458,4.44,0.462,0.42200000000000004,0.405,0.465,1920,1080,yuv420p,50,,,
|
| 185 |
+
NewsClip_1080P-02fd,NewsClip,1080,145,4.159,4.216,4.268,4.215,0.596,0.495,0.474,0.496,1920,1080,yuv420p,25,,,
|
| 186 |
+
NewsClip_1080P-06df,NewsClip,1080,124,4.489,4.559,4.3839999999999995,4.283,0.40299999999999997,0.379,0.486,0.489,1920,1080,yuv420p,25,,,
|
| 187 |
+
NewsClip_1080P-0abf,NewsClip,1080,122,3.6719999999999997,3.503,3.182,3.4589999999999996,0.501,0.474,0.631,0.48700000000000004,1920,1080,yuv420p,29.97002997,,,
|
| 188 |
+
NewsClip_1080P-1db0,NewsClip,1080,152,4.141,4.138999999999999,4.081,4.234,0.584,0.628,0.49,0.534,1920,1080,yuv420p,25,,,
|
| 189 |
+
NewsClip_1080P-22b3,NewsClip,1080,113,3.812,3.572,3.293,3.65,0.479,0.54,0.534,0.506,1440,1080,yuv420p,25,,,
|
| 190 |
+
NewsClip_1080P-27fb,NewsClip,1080,124,4.002,3.958,3.968,3.935,0.495,0.49200000000000005,0.48200000000000004,0.48100000000000004,1920,1080,yuv420p,25,,,
|
| 191 |
+
NewsClip_1080P-2eb0,NewsClip,1080,131,4.484,4.533,4.503,4.566,0.413,0.406,0.424,0.409,1920,1080,yuv420p,29.97002997,,,
|
| 192 |
+
NewsClip_1080P-3427,NewsClip,1080,131,4.382,4.513999999999999,4.34,4.073,0.48700000000000004,0.4,0.491,0.447,1920,1080,yuv420p,25,,,
|
| 193 |
+
NewsClip_1080P-3c7c,NewsClip,1080,113,4.462,4.459,4.282,4.188,0.493,0.45399999999999996,0.523,0.574,1920,1080,yuv420p,25,,,
|
| 194 |
+
NewsClip_1080P-4241,NewsClip,1080,129,3.9360000000000004,3.713,3.513,4.0969999999999995,0.507,0.55,0.525,0.637,1920,1080,yuv420p,25,,,
|
| 195 |
+
NewsClip_1080P-48ae,NewsClip,1080,135,3.887,3.765,3.819,3.763,0.406,0.489,0.48100000000000004,0.5,1920,1080,yuv420p,29.97002997,,,
|
| 196 |
+
NewsClip_1080P-5b53,NewsClip,1080,126,4.45,4.433,4.382,4.418,0.425,0.43,0.46399999999999997,0.47,1920,1080,yuv420p,59.94006309,,,
|
| 197 |
+
NewsClip_1080P-5be1,NewsClip,1080,123,4.065,4.2330000000000005,4.312,3.86,0.535,0.621,0.502,0.45899999999999996,1920,1080,yuv420p,25,,,
|
| 198 |
+
NewsClip_1080P-632e,NewsClip,1080,113,3.517,3.137,3.375,3.7119999999999997,0.495,0.6579999999999999,0.49700000000000005,0.562,1920,1080,yuv420p,29.97002997,,,
|
| 199 |
+
NewsClip_1080P-67dc,NewsClip,1080,112,3.977,3.963,3.8510000000000004,4.053999999999999,0.46299999999999997,0.38,0.467,0.5720000000000001,1920,1080,yuv420p,25,,,
|
| 200 |
+
NewsClip_1080P-7816,NewsClip,1080,141,3.855,3.702,3.76,3.7560000000000002,0.503,0.47,0.48100000000000004,0.457,1920,1080,yuv420p,25,,,
|
| 201 |
+
Sports_1080P-0063,Sports,1080,113,3.447,3.465,3.484,3.33,0.612,0.609,0.55,0.716,1920,1080,yuv420p,25,,,
|
| 202 |
+
Sports_1080P-0640,Sports,1080,117,3.5239999999999996,3.455,3.359,3.022,0.511,0.49,0.507,0.631,1920,1080,yuv420p,29.97002997,,,
|
| 203 |
+
Sports_1080P-08e1,Sports,1080,135,4.224,4.262,3.987,4.079,0.56,0.532,0.485,0.581,1920,1080,yuv420p,25,,,
|
| 204 |
+
Sports_1080P-0d0c,Sports,1080,108,3.98,4.06,4.1739999999999995,4.015,0.516,0.562,0.435,0.528,1920,1080,yuv420p,60,,,
|
| 205 |
+
Sports_1080P-15d1,Sports,1080,114,4.57,4.5760000000000005,4.542,4.551,0.39299999999999996,0.397,0.385,0.41,1920,1080,yuv420p,29.97002997,,,
|
| 206 |
+
Sports_1080P-19d8,Sports,1080,123,4.4719999999999995,4.365,4.452,4.466,0.42200000000000004,0.604,0.493,0.456,1920,1080,yuv420p,30,,,
|
| 207 |
+
Sports_1080P-1ae3,Sports,1080,123,3.352,3.35,3.3539999999999996,3.313,0.6829999999999999,0.495,0.512,0.647,1440,1080,yuv420p,29.97002997,,,
|
| 208 |
+
Sports_1080P-1bf7,Sports,1080,127,4.49,4.4639999999999995,4.335,4.309,0.496,0.451,0.46299999999999997,0.5,1920,1080,yuv420p,30,,,
|
| 209 |
+
Sports_1080P-1d78,Sports,1080,122,3.378,3.495,3.31,3.265,0.718,0.598,0.7120000000000001,0.685,1920,1080,yuv420p,29.97002997,,,
|
| 210 |
+
Sports_1080P-241e,Sports,1080,131,4.4110000000000005,4.2330000000000005,4.473,4.407,0.461,0.604,0.457,0.47700000000000004,1920,1080,yuv420p,29.97002997,,,
|
| 211 |
+
Sports_1080P-2524,Sports,1080,128,3.662,3.6,3.589,3.548,0.516,0.466,0.455,0.446,1920,1080,yuv420p,59.94006309,,,
|
| 212 |
+
Sports_1080P-28a6,Sports,1080,130,4.452,4.478,4.452,4.388999999999999,0.45299999999999996,0.423,0.409,0.431,1920,1080,yuv420p,29.97002997,,,
|
| 213 |
+
Sports_1080P-2a21,Sports,1080,118,3.2310000000000003,3.415,3.3,3.048,0.624,0.599,0.669,0.716,1920,1080,yuv420p,29.97002997,,,
|
| 214 |
+
Sports_1080P-3a3b,Sports,1080,115,3.6689999999999996,3.486,3.3,3.208,0.524,0.552,0.564,0.5429999999999999,1440,1080,yuv420p,25,,,
|
| 215 |
+
Sports_1080P-3db7,Sports,1080,118,4.021,3.8939999999999997,3.758,3.534,0.526,0.542,0.47200000000000003,0.653,1440,1080,yuv420p,25,,,
|
| 216 |
+
Sports_1080P-3eb0,Sports,1080,113,3.819,3.8689999999999998,3.694,3.597,0.508,0.43,0.441,0.456,1920,1080,yuv420p,25,,,
|
| 217 |
+
Sports_1080P-43e2,Sports,1080,148,4.414,4.454,4.436,4.435,0.42100000000000004,0.419,0.478,0.44,1920,1080,yuv420p,29.97002997,,,
|
| 218 |
+
Sports_1080P-46ed,Sports,1080,119,3.4930000000000003,2.9930000000000003,2.985,3.509,0.5710000000000001,0.642,0.605,0.518,1920,1080,yuv420p,25,,,
|
| 219 |
+
Sports_1080P-47e9,Sports,1080,126,4.106,3.656,3.9589999999999996,4.268,0.41200000000000003,0.515,0.48700000000000004,0.45399999999999996,1920,1080,yuv420p,25,,,
|
| 220 |
+
Sports_1080P-49c5,Sports,1080,139,4.1419999999999995,4.263,3.863,3.946,0.588,0.501,0.491,0.5479999999999999,1920,1080,yuv420p,29.97002997,,,
|
| 221 |
+
Sports_1080P-4e05,Sports,1080,106,3.445,3.458,3.451,3.447,0.547,0.503,0.628,0.526,1920,1080,yuv420p,25,,,
|
| 222 |
+
Sports_1080P-53a0,Sports,1080,113,3.429,3.412,3.4219999999999997,3.135,0.61,0.5489999999999999,0.618,0.655,1920,1080,yuv420p,30,,,
|
| 223 |
+
Sports_1080P-5d25,Sports,1080,123,3.478,3.6060000000000003,3.142,3.252,0.779,0.6509999999999999,0.735,0.764,1920,1080,yuv420p,29.97002997,,,
|
| 224 |
+
Sports_1080P-6571,Sports,1080,115,3.6519999999999997,3.5410000000000004,3.7569999999999997,3.695,0.44799999999999995,0.53,0.436,0.484,1920,1080,yuv420p,29.97002997,,,
|
| 225 |
+
Sports_1080P-6710,Sports,1080,141,4.335,4.317,4.417,4.303,0.428,0.474,0.444,0.583,1920,1080,yuv420p,25,,,
|
| 226 |
+
Sports_1080P-679d,Sports,1080,114,4.3660000000000005,4.402,4.223,4.249,0.44799999999999995,0.435,0.498,0.39799999999999996,1920,1080,yuv420p,29.97002997,,,
|
| 227 |
+
Sports_1080P-7203,Sports,1080,123,3.91,3.915,3.8560000000000003,3.908,0.46799999999999997,0.521,0.547,0.515,1920,1080,yuv420p,30.07,,,
|
| 228 |
+
Sports_1080P-7584,Sports,1080,128,3.755,3.6630000000000003,3.516,3.6439999999999997,0.47200000000000003,0.452,0.473,0.469,1920,1080,yuv420p,25,,,
|
| 229 |
+
Sports_1080P-76a2,Sports,1080,123,4.053999999999999,3.8939999999999997,3.911,3.958,0.44,0.49,0.514,0.52,1920,1080,yuv420p,59.94006309,,,
|
| 230 |
+
Sports_1080P-7dba,Sports,1080,119,3.718,3.7680000000000002,3.889,3.917,0.508,0.529,0.539,0.539,1440,1080,yuv420p,25,,,
|
| 231 |
+
TelevisionClip_1080P-0604,TelevisionClip,1080,114,3.765,4.008,3.637,3.721,0.518,0.616,0.498,0.493,1920,1080,yuv420p,25,,,
|
| 232 |
+
TelevisionClip_1080P-3758,TelevisionClip,1080,128,4.313,4.265,4.197,4.354,0.499,0.5,0.627,0.42,1920,1080,yuv420p,30,,,
|
| 233 |
+
TelevisionClip_1080P-39e3,TelevisionClip,1080,137,4.607,4.561,4.515,4.612,0.413,0.395,0.435,0.368,1440,1080,yuv420p,25,,,
|
| 234 |
+
TelevisionClip_1080P-3b9b,TelevisionClip,1080,135,4.251,4.404,4.402,4.117,0.596,0.48200000000000004,0.445,0.546,1920,1080,yuv420p,29.97002997,,,
|
| 235 |
+
TelevisionClip_1080P-3d10,TelevisionClip,1080,145,4.052,4.0169999999999995,4.1610000000000005,4.3260000000000005,0.616,0.6459999999999999,0.561,0.5429999999999999,1920,1080,yuv420p,29.97002997,,,
|
| 236 |
+
TelevisionClip_1080P-3d83,TelevisionClip,1080,124,3.3819999999999997,3.195,3.102,3.1460000000000004,0.521,0.636,0.608,0.63,1920,1080,yuv420p,16.632,,,
|
| 237 |
+
TelevisionClip_1080P-3e42,TelevisionClip,1080,137,1.989,1.6440000000000001,1.67,1.837,0.861,0.5670000000000001,0.6,0.682,1440,1080,yuv420p,29.97002997,,,
|
| 238 |
+
TelevisionClip_1080P-401e,TelevisionClip,1080,123,4.227,4.2989999999999995,4.331,4.2989999999999995,0.359,0.474,0.504,0.47700000000000004,1920,1080,yuv420p,25,,,
|
| 239 |
+
TelevisionClip_1080P-4c24,TelevisionClip,1080,132,4.334,4.369,4.287,4.328,0.526,0.444,0.45799999999999996,0.439,1920,1080,yuv420p,23.97602398,,,
|
| 240 |
+
TelevisionClip_1080P-525d,TelevisionClip,1080,143,4.273,4.478,4.47,4.244,0.556,0.498,0.42700000000000005,0.544,1920,1080,yuv420p,29.97002997,,,
|
| 241 |
+
TelevisionClip_1080P-5278,TelevisionClip,1080,125,4.099,4.056,4.045,3.906,0.617,0.624,0.634,0.46799999999999997,1920,1080,yuv420p,24.97502498,,,
|
| 242 |
+
TelevisionClip_1080P-5e68,TelevisionClip,1080,134,4.604,4.593,4.581,4.593,0.409,0.382,0.4,0.40399999999999997,1920,1080,yuv420p,29.97002997,,,
|
| 243 |
+
TelevisionClip_1080P-6026,TelevisionClip,1080,134,4.415,4.414,4.475,4.435,0.46,0.46399999999999997,0.39399999999999996,0.433,1920,1080,yuv420p,25,,,
|
| 244 |
+
TelevisionClip_1080P-6282,TelevisionClip,1080,115,4.199,4.4030000000000005,4.199,4.212,0.389,0.43799999999999994,0.583,0.48,1920,1080,yuv420p,29.97002997,,,
|
| 245 |
+
TelevisionClip_1080P-63e6,TelevisionClip,1080,128,4.228,4.013,4.147,4.288,0.478,0.525,0.419,0.46,1920,1080,yuv420p,29.97002997,,,
|
| 246 |
+
TelevisionClip_1080P-68c6,TelevisionClip,1080,132,4.561,4.6610000000000005,4.658,4.613,0.436,0.374,0.387,0.387,1920,1080,yuv420p,25,,,
|
| 247 |
+
VerticalVideo_1080P-04d4,VerticalVideo,1080,106,4.15,4.293,4.195,4.317,0.429,0.502,0.455,0.484,1080,1920,yuv420p,29.85,,,
|
| 248 |
+
VerticalVideo_1080P-1105,VerticalVideo,1080,127,3.8280000000000003,3.826,3.861,3.891,0.501,0.45299999999999996,0.455,0.47,1080,1920,yuv420p,30,,,
|
| 249 |
+
VerticalVideo_1080P-1ac1,VerticalVideo,1080,122,3.9789999999999996,3.7680000000000002,3.645,3.8110000000000004,0.677,0.5379999999999999,0.611,0.6679999999999999,1080,1920,yuv420p,29.951999999999998,,,
|
| 250 |
+
VerticalVideo_1080P-1c86,VerticalVideo,1080,136,1.848,1.4869999999999999,1.3219999999999998,1.5490000000000002,1.0270000000000001,0.7190000000000001,0.583,0.8140000000000001,1080,1920,yuv420p,30,,,
|
| 251 |
+
VerticalVideo_1080P-2195,VerticalVideo,1080,119,2.891,2.679,2.786,2.758,0.647,0.556,0.754,0.737,1088,1920,yuv420p,30,,,
|
| 252 |
+
VerticalVideo_1080P-2f00,VerticalVideo,1080,135,4.36,4.386,4.386,4.423,0.478,0.48200000000000004,0.425,0.484,1080,1920,yuv420p,30,,,
|
| 253 |
+
VerticalVideo_1080P-34ba,VerticalVideo,1080,134,3.3819999999999997,3.511,3.264,,0.703,0.606,0.7879999999999999,,1080,1920,yuv420p,111.71,,,
|
| 254 |
+
VerticalVideo_1080P-360f,VerticalVideo,1080,159,4.189,4.232,4.2139999999999995,4.104,0.564,0.61,0.527,0.515,1080,2220,yuv420p,37.402,,,
|
| 255 |
+
VerticalVideo_1080P-3709,VerticalVideo,1080,123,4.367,4.2669999999999995,4.399,4.386,0.466,0.591,0.501,0.447,1080,1920,yuv420p,30,,,
|
| 256 |
+
VerticalVideo_1080P-3a9b,VerticalVideo,1080,127,4.4319999999999995,4.427,4.541,4.439,0.466,0.49,0.431,0.433,1080,1920,yuv420p,78.06701031,,,
|
| 257 |
+
VerticalVideo_1080P-3d96,VerticalVideo,1080,143,4.452,4.383,4.402,,0.456,0.5529999999999999,0.547,,1080,1920,yuv420p,29.97002997,,,
|
| 258 |
+
VerticalVideo_1080P-4591,VerticalVideo,1080,114,3.4389999999999996,3.491,3.615,3.366,0.509,0.504,0.534,0.523,1080,1920,yuv420p,29.791,,,
|
| 259 |
+
VerticalVideo_1080P-4925,VerticalVideo,1080,129,4.496,4.379,4.5,4.503,0.45299999999999996,0.5710000000000001,0.44299999999999995,0.408,1080,1920,yuv420p,30,,,
|
| 260 |
+
VerticalVideo_1080P-4b92,VerticalVideo,1080,123,4.119,4.251,4.301,4.261,0.39299999999999996,0.479,0.475,0.556,1080,1920,yuv420p,30,,,
|
| 261 |
+
VerticalVideo_1080P-4da5,VerticalVideo,1080,134,4.257,4.141,4.4030000000000005,4.244,0.603,0.614,0.488,0.521,1440,2560,yuv420p,88,,,
|
| 262 |
+
VerticalVideo_1080P-6e19,VerticalVideo,1080,113,2.84,2.576,2.52,2.353,0.846,0.8079999999999999,0.8690000000000001,0.6829999999999999,1088,1920,yuv420p,12.030999999999999,,,
|
| 263 |
+
VerticalVideo_1080P-73b6,VerticalVideo,1080,111,4.034,3.9810000000000003,4.157,4.013999999999999,0.529,0.503,0.629,0.512,1080,1920,yuv420p,59.94006309,,,
|
| 264 |
+
VerticalVideo_1080P-766b,VerticalVideo,1080,106,3.305,3.347,3.1830000000000003,3.2319999999999998,0.449,0.45399999999999996,0.52,0.48700000000000004,1080,1920,yuv420p,29.97002997,,,
|
| 265 |
+
VerticalVideo_1080P-7c5c,VerticalVideo,1080,121,3.78,3.778,3.806,4.013,0.512,0.525,0.515,0.631,1080,1920,yuv420p,59.94006309,,,
|
| 266 |
+
VerticalVideo_1080P-7cf3,VerticalVideo,1080,117,3.955,4.005,3.9189999999999996,4.042,0.424,0.44299999999999995,0.45899999999999996,0.39899999999999997,1080,1920,yuv420p,30,,,
|
| 267 |
+
Vlog_1080P-010b,Vlog,1080,154,4.185,3.722,4.223,4.391,0.563,0.6509999999999999,0.604,0.485,1920,1080,yuv420p,30,,,
|
| 268 |
+
Vlog_1080P-1609,Vlog,1080,121,2.528,2.5269999999999997,2.6710000000000003,2.551,0.8079999999999999,1.03,1.0270000000000001,0.9470000000000001,1920,1080,yuv420p,29.97002997,,,
|
| 269 |
+
Vlog_1080P-18da,Vlog,1080,116,4.442,4.037,4.261,4.612,0.495,0.6809999999999999,0.6409999999999999,0.42700000000000005,1920,1080,yuv420p,24,,,
|
| 270 |
+
Vlog_1080P-19bb,Vlog,1080,119,3.076,2.8810000000000002,3.0389999999999997,3.233,0.8170000000000001,0.728,0.7120000000000001,0.773,1920,1080,yuv420p,29.97002997,,,
|
| 271 |
+
Vlog_1080P-1a53,Vlog,1080,134,4.474,4.489,4.41,4.315,0.469,0.389,0.418,0.44299999999999995,1920,1080,yuv420p,59.94006309,,,
|
| 272 |
+
Vlog_1080P-1df9,Vlog,1080,125,4.1339999999999995,4.1419999999999995,4.394,4.332,0.37200000000000005,0.415,0.46299999999999997,0.41700000000000004,1920,1080,yuv420p,60,,,
|
| 273 |
+
Vlog_1080P-1e70,Vlog,1080,132,2.4419999999999997,2.775,2.87,2.198,0.953,0.774,0.797,0.9079999999999999,1920,1080,yuv420p,30,,,
|
| 274 |
+
Vlog_1080P-1f0a,Vlog,1080,118,3.872,3.8289999999999997,4.138999999999999,3.967,0.627,0.588,0.645,0.741,1920,1080,yuv420p,24,,,
|
| 275 |
+
Vlog_1080P-21f5,Vlog,1080,111,3.327,3.304,3.4589999999999996,3.72,0.636,0.659,0.55,0.54,1920,1080,yuv420p,29.97002997,,,
|
| 276 |
+
Vlog_1080P-2394,Vlog,1080,119,4.641,4.662,4.737,4.595,0.377,0.392,0.325,0.42,1920,1080,yuv420p,24,,,
|
| 277 |
+
Vlog_1080P-23cb,Vlog,1080,114,3.069,3.2060000000000004,3.074,2.7680000000000002,0.748,0.7020000000000001,0.652,0.784,1920,1080,yuv420p,29.97002997,,,
|
| 278 |
+
Vlog_1080P-25de,Vlog,1080,139,1.9140000000000001,1.814,1.8430000000000002,1.8940000000000001,0.7090000000000001,0.6659999999999999,0.679,0.785,1920,1080,yuv420p,50,,,
|
| 279 |
+
Vlog_1080P-26dc,Vlog,1080,124,3.685,3.552,3.359,2.9330000000000003,0.5870000000000001,0.564,0.685,0.696,1920,1080,yuv420p,29.97002997,,,
|
| 280 |
+
Vlog_1080P-2cda,Vlog,1080,127,4.428999999999999,4.448,4.381,4.109,0.48,0.43799999999999994,0.439,0.43200000000000005,1920,1080,yuv420p,25,,,
|
| 281 |
+
Vlog_1080P-34cb,Vlog,1080,118,2.428,2.715,2.0580000000000003,1.699,0.995,0.7829999999999999,0.851,0.809,1920,1080,yuv420p,59.94006309,,,
|
| 282 |
+
Vlog_1080P-35cd,Vlog,1080,112,3.196,3.4810000000000003,3.528,3.55,0.596,0.542,0.465,0.484,1920,1080,yuv420p,30,,,
|
| 283 |
+
Vlog_1080P-3744,Vlog,1080,117,3.948,4.186,4.398,3.4989999999999997,0.574,0.40399999999999997,0.44799999999999995,0.624,1920,1080,yuv420p,50,,,
|
| 284 |
+
Vlog_1080P-45c9,Vlog,1080,104,2.888,2.886,2.7569999999999997,2.801,0.696,0.659,0.642,0.583,1920,1080,yuv420p,23.97602398,,,
|
| 285 |
+
Vlog_1080P-4ba9,Vlog,1080,112,3.908,4.163,3.977,4.189,0.523,0.6,0.7,0.589,1920,1080,yuv420p,24,,,
|
| 286 |
+
Vlog_1080P-4f26,Vlog,1080,126,3.9619999999999997,3.803,3.716,3.69,0.484,0.511,0.495,0.42100000000000004,1920,1080,yuv420p,25,,,
|
| 287 |
+
Vlog_1080P-52fe,Vlog,1080,115,3.838,3.5180000000000002,3.3,3.15,0.5329999999999999,0.54,0.585,0.623,1920,1080,yuv420p,25,,,
|
| 288 |
+
Vlog_1080P-5904,Vlog,1080,128,4.365,4.538,4.459,4.391,0.47,0.413,0.48,0.434,1920,1080,yuv420p,60,,,
|
| 289 |
+
Vlog_1080P-5f28,Vlog,1080,108,2.8110000000000004,2.582,2.91,3.168,0.728,0.7659999999999999,0.594,0.627,1920,1080,yuv420p,59.94006309,,,
|
| 290 |
+
Vlog_1080P-62fc,Vlog,1080,136,4.255,4.2589999999999995,4.1419999999999995,4.271,0.5329999999999999,0.504,0.43700000000000006,0.493,1920,1080,yuv420p,60,,,
|
| 291 |
+
Vlog_1080P-64b6,Vlog,1080,108,4.192,4.0760000000000005,4.0169999999999995,3.8939999999999997,0.5760000000000001,0.40399999999999997,0.46799999999999997,0.451,1088,1088,yuv420p,60,,,
|
| 292 |
+
Vlog_1080P-6686,Vlog,1080,137,3.926,3.9360000000000004,3.205,2.998,0.701,0.528,0.6509999999999999,0.6609999999999999,1920,1080,yuv422p10le,59.94006309,,,
|
| 293 |
+
Vlog_1080P-687a,Vlog,1080,125,3.3739999999999997,3.216,3.346,3.241,0.7340000000000001,0.736,0.698,0.715,1920,1080,yuv422p10le,29.97002997,,,
|
| 294 |
+
Vlog_1080P-7062,Vlog,1080,126,3.944,4.175,4.181,4.032,0.584,0.62,0.583,0.708,1920,1080,yuv420p,25,,,
|
| 295 |
+
Vlog_1080P-7e8c,Vlog,1080,115,3.042,2.541,3.4939999999999998,3.426,0.662,0.95,0.52,0.551,1920,1080,yuv420p,29.97002997,,,
|
metadata/YOUTUBE_UGC_2160P_metadata.csv
ADDED
|
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|
| 1 |
+
vid,category,resolution,x_Subjects,MOSFull,MOSChunk00,MOSChunk05,MOSChunk10,stdFull,stdChunk00,stdChunk05,stdChunk10,width,height,pixfmt,framerate,nb_frames,bitdepth,bitrate
|
| 2 |
+
Animation_2160P-41dc,Animation,2160,116,3.512,3.0610000000000004,3.3339999999999996,3.6689999999999996,0.743,0.713,0.76,0.695,3840,2160,yuv420p,15,,,
|
| 3 |
+
Animation_2160P-6f3b,Animation,2160,128,4.082,3.73,3.687,3.804,0.612,0.586,0.615,0.7909999999999999,3032,2304,yuv420p,24,,,
|
| 4 |
+
Gaming_2160P-2436,Gaming,2160,135,4.473,4.592,4.545,4.519,0.5710000000000001,0.452,0.465,0.531,3840,2160,yuv420p,60,,,
|
| 5 |
+
Gaming_2160P-2b92,Gaming,2160,114,3.6319999999999997,3.508,3.4560000000000004,3.555,0.615,0.627,0.599,0.57,4096,2160,yuv420p,25,,,
|
| 6 |
+
Gaming_2160P-2cb1,Gaming,2160,129,4.481,4.544,4.575,4.463,0.517,0.46799999999999997,0.449,0.48700000000000004,3840,2160,yuv420p,59.94006309,,,
|
| 7 |
+
Gaming_2160P-2dc4,Gaming,2160,111,4.383,4.438,4.618,4.471,0.664,0.627,0.44299999999999995,0.547,3840,2160,yuv420p,59.94006309,,,
|
| 8 |
+
Gaming_2160P-3002,Gaming,2160,108,4.337,4.3580000000000005,4.283,4.336,0.485,0.45799999999999996,0.597,0.48200000000000004,3840,2160,yuv420p,24,,,
|
| 9 |
+
Gaming_2160P-31f6,Gaming,2160,133,4.311,4.317,4.263,4.316,0.517,0.469,0.578,0.615,3840,2160,yuv420p,60,,,
|
| 10 |
+
Gaming_2160P-348d,Gaming,2160,129,4.4430000000000005,4.387,4.367,4.374,0.469,0.452,0.499,0.504,3840,2160,yuv420p,29.97002997,,,
|
| 11 |
+
Gaming_2160P-34a1,Gaming,2160,132,4.414,4.538,4.365,4.447,0.502,0.444,0.536,0.47700000000000004,3840,2160,yuv420p,60,,,
|
| 12 |
+
Gaming_2160P-387f,Gaming,2160,115,4.513,4.426,4.494,4.48,0.447,0.501,0.435,0.475,3840,2160,yuv420p,29.97002997,,,
|
| 13 |
+
Gaming_2160P-3a25,Gaming,2160,127,4.502,4.513999999999999,4.601,4.539,0.44299999999999995,0.449,0.44,0.54,3840,2160,yuv420p,60,,,
|
| 14 |
+
Gaming_2160P-3aec,Gaming,2160,132,4.646,4.611000000000001,4.374,4.46,0.386,0.40399999999999997,0.622,0.47700000000000004,3840,2160,yuv420p,60,,,
|
| 15 |
+
Gaming_2160P-3ce4,Gaming,2160,119,4.529,4.418,4.494,4.465,0.466,0.518,0.52,0.503,3840,2160,yuv420p,60,,,
|
| 16 |
+
Gaming_2160P-4139,Gaming,2160,107,3.923,3.57,3.45,3.9939999999999998,0.597,0.701,0.757,0.688,3840,2160,yuv420p,29.97002997,,,
|
| 17 |
+
Gaming_2160P-416f,Gaming,2160,133,4.332,4.409,4.327,4.395,0.595,0.465,0.5479999999999999,0.494,3840,2160,yuv420p,29.97002997,,,
|
| 18 |
+
Gaming_2160P-4258,Gaming,2160,130,4.067,3.7939999999999996,3.847,4.152,0.662,0.605,0.64,0.654,3840,2160,yuv420p,60,,,
|
| 19 |
+
Gaming_2160P-5c33,Gaming,2160,115,2.8339999999999996,2.7110000000000003,2.9539999999999997,2.468,0.9129999999999999,0.914,0.767,0.9109999999999999,3840,2160,yuv420p,29.97002997,,,
|
| 20 |
+
Gaming_2160P-5c53,Gaming,2160,140,4.309,4.329,4.322,4.353,0.6,0.596,0.568,0.569,3840,2160,yuv420p,29.97002997,,,
|
| 21 |
+
Gaming_2160P-673d,Gaming,2160,148,4.165,4.248,4.024,4.209,0.564,0.552,0.495,0.585,3840,2160,yuv420p,30,,,
|
| 22 |
+
Gaming_2160P-67b0,Gaming,2160,136,4.204,4.235,4.36,4.277,0.607,0.5660000000000001,0.521,0.636,3840,2160,yuv420p,60,,,
|
| 23 |
+
Gaming_2160P-6bb6,Gaming,2160,131,4.537,4.534,4.615,4.5360000000000005,0.473,0.426,0.38799999999999996,0.444,3840,2160,yuv420p,60,,,
|
| 24 |
+
Gaming_2160P-6cd8,Gaming,2160,143,4.31,4.2139999999999995,4.285,4.556,0.589,0.5539999999999999,0.5710000000000001,0.423,3840,2160,yuv420p,50,,,
|
| 25 |
+
Gaming_2160P-7e80,Gaming,2160,117,4.1080000000000005,3.9,4.2410000000000005,4.043,0.46,0.562,0.602,0.461,3840,2160,yuv420p,30,,,
|
| 26 |
+
Gaming_2160P-7ff0,Gaming,2160,133,4.238,4.3660000000000005,4.172,4.1819999999999995,0.604,0.502,0.597,0.647,3840,2160,yuv420p,60,,,
|
| 27 |
+
HDR_2160P-06ae,HDR,2160,130,4.474,4.4319999999999995,4.382,4.3839999999999995,0.552,0.604,0.59,0.618,3840,2160,yuv420p10le,24,,,
|
| 28 |
+
HDR_2160P-15e2,HDR,2160,104,4.191,4.113,4.144,4.225,0.621,0.621,0.609,0.5920000000000001,3840,2160,yuv422p10le,59.94006309,,,
|
| 29 |
+
HDR_2160P-2a72,HDR,2160,138,4.113,4.023,3.846,4.0760000000000005,0.733,0.757,0.639,0.711,3840,2160,yuv422p10le,59.94006309,,,
|
| 30 |
+
HDR_2160P-3663,HDR,2160,110,4.34,4.3469999999999995,4.263,4.364,0.7,0.569,0.718,0.626,3840,2160,yuv422p10le,59.94006309,,,
|
| 31 |
+
HDR_2160P-40ab,HDR,2160,132,4.105,4.113,4.06,4.127,0.611,0.5770000000000001,0.705,0.695,3840,2160,yuv420p10le,25,,,
|
| 32 |
+
HDR_2160P-4581,HDR,2160,118,3.9789999999999996,4.132,4.091,3.87,0.515,0.603,0.6509999999999999,0.608,3840,2160,yuv420p10le,24,,,
|
| 33 |
+
HDR_2160P-4dcd,HDR,2160,124,3.763,3.639,3.623,3.6319999999999997,0.652,0.728,0.857,0.9520000000000001,3840,2160,yuv422p10le,59.94006309,,,
|
| 34 |
+
HDR_2160P-5275,HDR,2160,126,4.206,4.248,4.263999999999999,4.1160000000000005,0.628,0.654,0.604,0.642,3840,2160,yuv420p10le,29.97002997,,,
|
| 35 |
+
HDR_2160P-5926,HDR,2160,113,4.386,4.607,4.3469999999999995,4.397,0.622,0.501,0.6859999999999999,0.625,3840,2160,yuv420p10le,29.97002997,,,
|
| 36 |
+
HDR_2160P-5e25,HDR,2160,132,4.209,4.086,4.2,4.213,0.638,0.7090000000000001,0.5479999999999999,0.624,3840,2160,yuv420p10le,29.97002997,,,
|
| 37 |
+
HDR_2160P-664d,HDR,2160,123,3.753,3.733,3.391,3.017,0.6509999999999999,0.649,0.713,0.742,3840,2160,yuv420p10le,25,,,
|
| 38 |
+
HDR_2160P-6ed1,HDR,2160,109,3.7239999999999998,3.74,3.907,3.969,0.7929999999999999,0.767,0.743,0.752,3840,2160,yuv422p10le,59.94006309,,,
|
| 39 |
+
HDR_2160P-6eeb,HDR,2160,129,4.402,4.341,4.379,4.428999999999999,0.5660000000000001,0.607,0.625,0.512,4096,2160,yuv422p10le,23.97602398,,,
|
| 40 |
+
HDR_2160P-6fab,HDR,2160,110,3.443,3.4960000000000004,3.455,3.3710000000000004,0.764,0.716,0.7759999999999999,0.852,3840,2160,yuv422p10le,59.94006309,,,
|
| 41 |
+
HDR_2160P-70ca,HDR,2160,122,3.905,4.0569999999999995,3.95,3.752,0.741,0.643,0.772,0.715,3840,2160,yuv422p10le,59.94006309,,,
|
| 42 |
+
Sports_2160P-0455,Sports,2160,126,4.287,4.364,4.204,4.191,0.56,0.591,0.75,0.6509999999999999,3840,2160,yuv420p,29.97002997,,,
|
| 43 |
+
Sports_2160P-086d,Sports,2160,132,4.376,4.373,4.563,4.579,0.561,0.595,0.41,0.439,3840,2160,yuv420p,23.97602398,,,
|
| 44 |
+
Sports_2160P-1261,Sports,2160,132,4.114,4.376,4.209,3.99,0.63,0.467,0.58,0.547,3840,2160,yuv420p,29.97002997,,,
|
| 45 |
+
Sports_2160P-1733,Sports,2160,136,4.373,4.137,4.5680000000000005,4.5569999999999995,0.5820000000000001,0.6629999999999999,0.447,0.444,3840,2160,yuv420p,30,,,
|
| 46 |
+
Sports_2160P-1b70,Sports,2160,133,4.508,4.4910000000000005,4.393,4.468,0.491,0.552,0.6,0.552,3840,2160,yuv420p,29.97002997,,,
|
| 47 |
+
Sports_2160P-1ddc,Sports,2160,122,3.466,3.549,3.417,3.41,0.546,0.564,0.514,0.736,3840,2160,yuv420p,29.97002997,,,
|
| 48 |
+
Sports_2160P-210c,Sports,2160,130,4.462,4.474,4.375,4.409,0.5670000000000001,0.561,0.631,0.593,3840,2160,yuv420p,29.97002997,,,
|
| 49 |
+
Sports_2160P-2568,Sports,2160,124,4.401,4.434,4.4,4.365,0.496,0.46,0.504,0.509,3840,2160,yuv420p,29.97002997,,,
|
| 50 |
+
Sports_2160P-2626,Sports,2160,139,4.388,4.479,4.321000000000001,4.273,0.498,0.56,0.603,0.61,3840,2160,yuv420p,30,,,
|
| 51 |
+
Sports_2160P-279f,Sports,2160,120,4.526,4.588,4.62,4.5089999999999995,0.414,0.43799999999999994,0.442,0.62,3840,2160,yuv420p,29.97002997,,,
|
| 52 |
+
Sports_2160P-2a83,Sports,2160,127,4.002,4.095,4.128,4.117,0.775,0.614,0.5920000000000001,0.637,3840,2160,yuv420p,29.97002997,,,
|
| 53 |
+
Sports_2160P-2e1d,Sports,2160,136,4.457,4.506,4.551,4.396,0.579,0.591,0.43200000000000005,0.557,3840,2160,yuv420p,30,,,
|
| 54 |
+
Sports_2160P-2eda,Sports,2160,133,4.3069999999999995,4.331,4.331,4.402,0.594,0.62,0.6629999999999999,0.599,3840,2160,yuv420p,29.97002997,,,
|
| 55 |
+
Sports_2160P-300d,Sports,2160,110,3.67,3.6,3.508,3.5789999999999997,0.546,0.5539999999999999,0.672,0.485,3840,2160,yuv420p,29.97002997,,,
|
| 56 |
+
Sports_2160P-324e,Sports,2160,140,4.317,4.238,4.2669999999999995,4.25,0.5770000000000001,0.621,0.643,0.626,3840,2160,yuv420p,25,,,
|
| 57 |
+
Sports_2160P-349c,Sports,2160,131,4.302,4.364,4.47,4.3660000000000005,0.609,0.605,0.609,0.64,3840,2160,yuv420p,29.97002997,,,
|
| 58 |
+
Sports_2160P-3794,Sports,2160,106,3.158,3.1060000000000003,3.0860000000000003,2.891,0.679,0.606,0.594,0.6659999999999999,3840,2160,yuv420p,59.94006309,,,
|
| 59 |
+
Sports_2160P-391a,Sports,2160,132,4.281000000000001,4.37,4.482,4.456,0.6579999999999999,0.632,0.594,0.565,3840,2160,yuv420p,29.97002997,,,
|
| 60 |
+
Sports_2160P-3a9a,Sports,2160,105,3.633,3.535,3.577,4.131,0.8420000000000001,0.888,0.835,0.638,3840,2160,yuv420p,59.94006309,,,
|
| 61 |
+
Sports_2160P-3d85,Sports,2160,124,4.5169999999999995,4.378,4.388,4.463,0.569,0.614,0.614,0.599,3840,2160,yuv420p,29.97002997,,,
|
| 62 |
+
Sports_2160P-4024,Sports,2160,127,4.053999999999999,3.9330000000000003,3.783,3.655,0.6729999999999999,0.593,0.593,0.647,3840,2160,yuv420p,29.97002997,,,
|
| 63 |
+
Sports_2160P-4201,Sports,2160,129,4.012,4.1339999999999995,3.759,3.9930000000000003,0.728,0.654,0.83,0.597,3840,2160,yuv420p,60,,,
|
| 64 |
+
Sports_2160P-49f1,Sports,2160,134,4.287,4.298,4.374,4.352,0.612,0.6920000000000001,0.593,0.586,3840,2160,yuv420p,29.97002997,,,
|
| 65 |
+
Sports_2160P-4aae,Sports,2160,124,4.323,4.447,4.428999999999999,4.428,0.526,0.452,0.442,0.479,3840,2160,yuv420p,59.94006309,,,
|
| 66 |
+
Sports_2160P-4e9f,Sports,2160,136,4.405,4.438,4.4510000000000005,4.439,0.617,0.51,0.47700000000000004,0.449,3840,2160,yuv420p,29.97002997,,,
|
| 67 |
+
Sports_2160P-5ef3,Sports,2160,119,4.209,4.272,4.301,4.066,0.696,0.6679999999999999,0.634,0.83,3840,2160,yuv420p,29.97002997,,,
|
| 68 |
+
Sports_2160P-69b9,Sports,2160,122,4.564,4.425,4.511,4.44,0.47100000000000003,0.5579999999999999,0.49700000000000005,0.563,3840,2160,yuv420p,30,,,
|
| 69 |
+
Sports_2160P-6b28,Sports,2160,137,4.391,4.324,4.3919999999999995,4.1819999999999995,0.434,0.48200000000000004,0.485,0.5920000000000001,3840,2160,yuv420p,29.97002997,,,
|
| 70 |
+
Sports_2160P-7165,Sports,2160,112,4.351,4.291,4.322,4.5360000000000005,0.561,0.613,0.594,0.49,3840,2160,yuv420p,29.97002997,,,
|
| 71 |
+
Sports_2160P-7a11,Sports,2160,132,4.373,4.428,4.385,4.486000000000001,0.605,0.636,0.541,0.527,3840,2160,yuv420p,29.97002997,,,
|
| 72 |
+
Sports_2160P-7af7,Sports,2160,115,3.95,4.024,3.992,3.8960000000000004,0.47200000000000003,0.513,0.7,0.462,3840,2160,yuv420p,29.97002997,,,
|
| 73 |
+
Sports_2160P-7af8,Sports,2160,128,3.563,3.409,3.12,3.25,0.8009999999999999,0.7809999999999999,0.9309999999999999,0.946,3840,2160,yuv420p,59.94006309,,,
|
| 74 |
+
Sports_2160P-7bbe,Sports,2160,130,4.343,4.37,4.46,4.436,0.573,0.603,0.539,0.593,3840,2160,yuv420p,29.97002997,,,
|
| 75 |
+
VerticalVideo_2160P-6d62,VerticalVideo,2160,111,4.199,4.034,4.086,4.172,0.633,0.777,0.705,0.6920000000000001,2160,3840,yuv420p,30,,,
|
| 76 |
+
Vlog_2160P-030a,Vlog,2160,131,2.448,2.648,2.661,2.526,0.9,0.8490000000000001,0.777,0.701,3840,2160,yuv420p,23.97602398,,,
|
| 77 |
+
Vlog_2160P-0577,Vlog,2160,105,4.27,4.205,4.482,4.592,0.664,0.6709999999999999,0.495,0.47,3840,2160,yuv420p,29.97002997,,,
|
| 78 |
+
Vlog_2160P-059c,Vlog,2160,123,4.441,4.409,4.525,4.363,0.521,0.583,0.465,0.633,3840,2160,yuv420p,29.97002997,,,
|
| 79 |
+
Vlog_2160P-09c9,Vlog,2160,129,2.8760000000000003,1.89,4.157,4.416,1.2329999999999999,0.7440000000000001,0.76,0.584,3840,2160,yuv420p,29.97002997,,,
|
| 80 |
+
Vlog_2160P-13a1,Vlog,2160,121,3.859,3.514,3.859,4.152,0.569,0.762,0.601,0.595,3840,2160,yuv420p,29.97002997,,,
|
| 81 |
+
Vlog_2160P-13fe,Vlog,2160,106,3.992,4.111000000000001,3.917,3.798,0.5429999999999999,0.606,0.547,0.595,3840,2160,yuv420p,25,,,
|
| 82 |
+
Vlog_2160P-19f9,Vlog,2160,134,3.9189999999999996,4.122,4.194,4.117,0.7020000000000001,0.605,0.611,0.6920000000000001,3840,2160,yuv420p,30,,,
|
| 83 |
+
Vlog_2160P-1ff6,Vlog,2160,113,3.29,3.3789999999999996,2.79,2.8810000000000002,0.721,0.795,0.7659999999999999,0.795,3840,2160,yuv420p,23.97602398,,,
|
| 84 |
+
Vlog_2160P-255c,Vlog,2160,113,4.4430000000000005,4.38,4.263,4.157,0.495,0.5489999999999999,0.63,0.728,3840,2160,yuv420p,25,,,
|
| 85 |
+
Vlog_2160P-2953,Vlog,2160,135,3.467,3.437,3.614,3.6830000000000003,0.664,0.6629999999999999,0.63,0.526,3840,2160,yuv420p,23.97602398,,,
|
| 86 |
+
Vlog_2160P-2b2d,Vlog,2160,126,3.906,4.005,4.0680000000000005,3.986,0.866,0.794,0.69,0.584,3840,2160,yuv420p,25,,,
|
| 87 |
+
Vlog_2160P-3019,Vlog,2160,138,4.578,4.586,4.5760000000000005,4.527,0.452,0.457,0.498,0.518,3840,2160,yuv420p,29.97002997,,,
|
| 88 |
+
Vlog_2160P-310b,Vlog,2160,116,3.8569999999999998,3.6460000000000004,3.886,4.297,0.586,0.5489999999999999,0.602,0.535,3840,2160,yuv420p,30,,,
|
| 89 |
+
Vlog_2160P-327a,Vlog,2160,117,3.983,3.7910000000000004,3.77,3.8569999999999998,0.521,0.757,0.619,0.604,3840,2160,yuv420p,30,,,
|
| 90 |
+
Vlog_2160P-342a,Vlog,2160,113,4.321000000000001,4.283,4.488,4.39,0.59,0.5479999999999999,0.436,0.61,3840,2160,yuv420p,60,,,
|
| 91 |
+
Vlog_2160P-3a75,Vlog,2160,122,4.212,4.258,4.428,4.349,0.631,0.585,0.49200000000000005,0.511,3840,2160,yuv420p,30,,,
|
| 92 |
+
Vlog_2160P-408f,Vlog,2160,115,3.79,3.931,3.859,3.855,0.488,0.47700000000000004,0.495,0.506,3840,2160,yuv420p,23.97602398,,,
|
| 93 |
+
Vlog_2160P-416c,Vlog,2160,145,4.019,3.9789999999999996,3.9330000000000003,3.963,0.721,0.48,0.508,0.545,3840,2160,yuv420p,30,,,
|
| 94 |
+
Vlog_2160P-4362,Vlog,2160,119,4.553999999999999,4.618,4.625,4.505,0.503,0.52,0.508,0.574,3840,2160,yuv420p,29.97002997,,,
|
| 95 |
+
Vlog_2160P-4419,Vlog,2160,126,4.324,4.457,4.189,4.198,0.555,0.542,0.439,0.594,3840,2160,yuv420p,25,,,
|
| 96 |
+
Vlog_2160P-4655,Vlog,2160,125,3.887,4.196000000000001,4.263,3.6239999999999997,0.6729999999999999,0.621,0.6709999999999999,0.89,3840,2160,yuv420p,25,,,
|
| 97 |
+
Vlog_2160P-4f98,Vlog,2160,125,3.47,3.29,2.847,3.148,0.713,0.778,0.825,0.6990000000000001,3840,2160,yuv420p,30,,,
|
| 98 |
+
Vlog_2160P-522f,Vlog,2160,132,4.324,4.363,4.382,4.3919999999999995,0.5429999999999999,0.595,0.551,0.574,3840,2160,yuv420p,30,,,
|
| 99 |
+
Vlog_2160P-56e4,Vlog,2160,136,4.289,4.065,4.266,4.272,0.485,0.607,0.5870000000000001,0.6559999999999999,3840,2160,yuv420p,30,,,
|
| 100 |
+
Vlog_2160P-5874,Vlog,2160,134,4.101,4.296,3.92,4.062,0.659,0.556,0.6459999999999999,0.752,3840,2160,yuv420p,25,,,
|
| 101 |
+
Vlog_2160P-62b2,Vlog,2160,115,3.39,3.3760000000000003,3.154,3.177,0.7090000000000001,0.7390000000000001,0.7,0.684,3840,2160,yuv420p,30,,,
|
| 102 |
+
Vlog_2160P-6629,Vlog,2160,129,4.27,4.213,4.2860000000000005,4.229,0.6409999999999999,0.655,0.664,0.644,3840,2160,yuv420p,29.97002997,,,
|
| 103 |
+
Vlog_2160P-6f92,Vlog,2160,114,3.886,3.764,3.681,3.529,0.532,0.565,0.569,0.746,3840,2160,yuv420p,29.97002997,,,
|
| 104 |
+
Vlog_2160P-700c,Vlog,2160,118,3.8810000000000002,4.14,3.7030000000000003,3.6719999999999997,0.625,0.619,0.738,0.84,3840,2160,yuv420p,30,,,
|
| 105 |
+
Vlog_2160P-70d6,Vlog,2160,110,3.0669999999999997,3.074,3.188,3.012,0.698,0.6729999999999999,0.679,0.708,3840,2160,yuv420p,30,,,
|
| 106 |
+
Vlog_2160P-7324,Vlog,2160,116,4.194,4.209,3.802,4.196000000000001,0.713,0.634,1.041,0.8490000000000001,3840,2160,yuv420p,25,,,
|
| 107 |
+
Vlog_2160P-77d8,Vlog,2160,104,3.667,3.3480000000000003,3.6430000000000002,3.3480000000000003,0.599,0.797,0.6729999999999999,0.727,3840,2160,yuv420p,23.97602398,,,
|
| 108 |
+
Vlog_2160P-7b10,Vlog,2160,121,3.864,3.654,3.951,3.823,0.5770000000000001,0.7290000000000001,0.7509999999999999,0.528,3840,2160,yuv420p,30,,,
|
| 109 |
+
Vlog_2160P-7b5c,Vlog,2160,104,3.61,2.9330000000000003,2.747,3.13,0.726,0.736,0.637,0.6759999999999999,3840,2160,yuv420p,30,,,
|
| 110 |
+
Vlog_2160P-7bfb,Vlog,2160,125,4.206,4.396,4.127,4.058,0.665,0.493,0.6,0.7070000000000001,3840,2160,yuv420p,30,,,
|
| 111 |
+
Vlog_2160P-7f05,Vlog,2160,118,3.111,2.9160000000000004,2.9160000000000004,3.056,0.63,0.716,0.687,0.669,3840,2160,yuv420p,30,,,
|
metadata/YOUTUBE_UGC_360P_metadata.csv
ADDED
|
@@ -0,0 +1,225 @@
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|
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|
|
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|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
| 1 |
+
vid,category,resolution,x_Subjects,MOSFull,MOSChunk00,MOSChunk05,MOSChunk10,stdFull,stdChunk00,stdChunk05,stdChunk10,width,height,pixfmt,framerate,nb_frames,bitdepth,bitrate
|
| 2 |
+
Animation_360P-08c9,Animation,360,112,3.6519999999999997,3.6060000000000003,3.7260000000000004,3.622,0.456,0.47600000000000003,0.514,0.552,640,360,yuv420p,30,,,
|
| 3 |
+
Animation_360P-188f,Animation,360,125,3.702,3.44,3.697,3.6639999999999997,0.45799999999999996,0.564,0.52,0.485,640,360,yuv420p,29.97002997,,,
|
| 4 |
+
Animation_360P-24d4,Animation,360,121,3.435,3.292,3.397,3.42,0.516,0.594,0.562,0.5589999999999999,480,360,yuv420p,24,,,
|
| 5 |
+
Animation_360P-3e40,Animation,360,140,2.353,2.239,1.896,1.962,0.9279999999999999,0.649,0.747,0.81,640,360,yuv420p,30,,,
|
| 6 |
+
Animation_360P-3e52,Animation,360,115,3.6510000000000002,3.707,3.523,3.4610000000000003,0.508,0.485,0.5379999999999999,0.5379999999999999,640,360,yuv420p,29.97002997,,,
|
| 7 |
+
Animation_360P-47cc,Animation,360,133,3.282,3.306,3.333,3.09,0.6709999999999999,0.613,0.655,0.682,640,360,yuv420p,30,,,
|
| 8 |
+
Animation_360P-4b4c,Animation,360,130,3.003,3.1260000000000003,3.002,3.0460000000000003,0.816,0.612,0.649,0.66,640,360,yuv420p,30,,,
|
| 9 |
+
Animation_360P-4edc,Animation,360,106,3.682,3.627,3.699,3.6889999999999996,0.597,0.563,0.513,0.5820000000000001,640,360,yuv420p,30,,,
|
| 10 |
+
Animation_360P-5712,Animation,360,122,3.552,3.659,3.658,3.555,0.5329999999999999,0.622,0.621,0.632,640,360,yuv420p,30,,,
|
| 11 |
+
Animation_360P-5de0,Animation,360,117,3.429,3.307,3.451,3.431,0.523,0.5770000000000001,0.5429999999999999,0.551,640,360,yuv420p,30,,,
|
| 12 |
+
Animation_360P-631c,Animation,360,108,2.2359999999999998,2.056,2.019,1.891,0.6890000000000001,0.574,0.56,0.598,640,360,yuv420p,15,,,
|
| 13 |
+
Animation_360P-69e0,Animation,360,120,3.3819999999999997,3.2319999999999998,3.304,2.9010000000000002,0.599,0.733,0.755,0.784,640,360,yuv420p,29.97002997,,,
|
| 14 |
+
Animation_360P-7856,Animation,360,109,2.7510000000000003,2.647,2.6689999999999996,2.386,0.684,0.6940000000000001,0.7140000000000001,0.6779999999999999,640,360,yuv420p,29.97002997,,,
|
| 15 |
+
Animation_360P-794f,Animation,360,124,3.362,3.304,3.236,3.338,0.6409999999999999,0.561,0.586,0.55,640,360,yuv420p,29.97002997,,,
|
| 16 |
+
CoverSong_360P-0a9d,CoverSong,360,130,2.5380000000000003,2.2840000000000003,2.218,2.224,0.752,0.521,0.461,0.466,640,360,yuv420p,8.761000000000001,,,
|
| 17 |
+
CoverSong_360P-0aa2,CoverSong,360,123,2.511,2.3680000000000003,2.26,2.306,0.5720000000000001,0.495,0.45899999999999996,0.43200000000000005,640,360,yuv420p,30,,,
|
| 18 |
+
CoverSong_360P-11f9,CoverSong,360,120,2.79,2.6069999999999998,2.7539999999999996,2.647,0.55,0.542,0.56,0.58,640,360,yuv420p,16.522000000000002,,,
|
| 19 |
+
CoverSong_360P-13d2,CoverSong,360,104,2.5780000000000003,2.441,2.404,2.414,0.667,0.506,0.563,0.511,640,360,yuv420p,25,,,
|
| 20 |
+
CoverSong_360P-14b9,CoverSong,360,114,2.633,2.536,2.536,2.568,0.578,0.516,0.446,0.504,640,360,yuv420p,29.97002997,,,
|
| 21 |
+
CoverSong_360P-1b2b,CoverSong,360,116,3.409,3.159,3.252,3.063,0.5710000000000001,0.498,0.521,0.628,640,360,yuv420p,29.97002997,,,
|
| 22 |
+
CoverSong_360P-2146,CoverSong,360,128,2.273,2.254,2.256,2.2769999999999997,0.6759999999999999,0.45399999999999996,0.452,0.516,640,360,yuv420p,15.879000000000001,,,
|
| 23 |
+
CoverSong_360P-2b4d,CoverSong,360,125,2.7960000000000003,2.737,2.821,2.7060000000000004,0.5329999999999999,0.478,0.589,0.469,480,360,yuv420p,30,,,
|
| 24 |
+
CoverSong_360P-3218,CoverSong,360,129,2.427,2.281,2.3609999999999998,2.4290000000000003,0.7120000000000001,0.597,0.557,0.545,640,360,yuv420p,21,,,
|
| 25 |
+
CoverSong_360P-4add,CoverSong,360,128,2.855,2.6489999999999996,2.761,2.657,0.621,0.515,0.529,0.5,640,360,yuv420p,29.97002997,,,
|
| 26 |
+
CoverSong_360P-53a6,CoverSong,360,127,3.235,3.255,3.1889999999999996,3.023,0.506,0.528,0.555,0.6679999999999999,640,360,yuv420p,30,,,
|
| 27 |
+
CoverSong_360P-59d1,CoverSong,360,126,2.8280000000000003,2.421,2.437,2.42,0.667,0.44799999999999995,0.552,0.517,640,360,yuv420p,30,,,
|
| 28 |
+
CoverSong_360P-5a24,CoverSong,360,114,2.7889999999999997,2.675,2.8560000000000003,2.9419999999999997,0.6559999999999999,0.5379999999999999,0.568,0.615,640,360,yuv420p,29.97002997,,,
|
| 29 |
+
CoverSong_360P-5d20,CoverSong,360,119,2.943,2.7569999999999997,2.718,2.708,0.642,0.595,0.502,0.602,480,360,yuv420p,30,,,
|
| 30 |
+
CoverSong_360P-67ec,CoverSong,360,117,2.404,2.211,2.7939999999999996,2.9419999999999997,0.581,0.55,0.6,0.5660000000000001,640,360,yuv420p,29.97002997,,,
|
| 31 |
+
CoverSong_360P-69ef,CoverSong,360,118,3.145,3.033,3.218,3.011,0.609,0.5820000000000001,0.507,0.585,480,360,yuv420p,30,,,
|
| 32 |
+
CoverSong_360P-6d6a,CoverSong,360,104,3.273,3.24,3.194,3.3169999999999997,0.58,0.478,0.447,0.537,640,360,yuv420p,14.758,,,
|
| 33 |
+
CoverSong_360P-6da7,CoverSong,360,120,2.763,2.75,2.655,2.648,0.61,0.524,0.466,0.514,640,360,yuv420p,30,,,
|
| 34 |
+
Gaming_360P-043e,Gaming,360,106,2.582,2.655,2.5380000000000003,2.4130000000000003,0.605,0.7809999999999999,0.69,0.6990000000000001,450,360,yuv420p,30,,,
|
| 35 |
+
Gaming_360P-0b98,Gaming,360,134,3.14,2.812,2.583,3.415,0.728,0.7,0.934,0.616,640,360,yuv420p,30,,,
|
| 36 |
+
Gaming_360P-187a,Gaming,360,126,3.133,3.0010000000000003,3.198,3.062,0.667,0.6559999999999999,0.589,0.654,640,360,yuv420p,29.97002997,,,
|
| 37 |
+
Gaming_360P-215f,Gaming,360,120,2.8689999999999998,2.775,2.748,2.745,0.677,0.659,0.721,0.573,640,360,yuv420p,30,,,
|
| 38 |
+
Gaming_360P-2164,Gaming,360,111,3.245,3.034,2.9539999999999997,3.0039999999999996,0.6970000000000001,0.629,0.6709999999999999,0.6409999999999999,640,360,yuv422p,25,,,
|
| 39 |
+
Gaming_360P-21d2,Gaming,360,127,2.372,2.498,2.45,2.5660000000000003,0.6709999999999999,0.865,0.8440000000000001,0.848,480,360,yuv420p,25,,,
|
| 40 |
+
Gaming_360P-2330,Gaming,360,120,2.6069999999999998,2.2840000000000003,2.458,2.475,0.7390000000000001,0.57,0.7559999999999999,0.536,640,360,yuv420p,29.97002997,,,
|
| 41 |
+
Gaming_360P-279f,Gaming,360,127,2.841,2.895,2.983,2.9419999999999997,0.59,0.853,0.731,0.6990000000000001,640,360,yuv420p,30,,,
|
| 42 |
+
Gaming_360P-2f99,Gaming,360,129,3.281,3.4130000000000003,3.43,3.145,0.7090000000000001,0.48100000000000004,0.524,0.647,640,360,yuv420p,45.335,,,
|
| 43 |
+
Gaming_360P-3794,Gaming,360,113,1.475,1.5430000000000001,1.621,1.5590000000000002,0.547,0.622,0.71,0.6559999999999999,640,360,yuv420p,18.506,,,
|
| 44 |
+
Gaming_360P-3eb6,Gaming,360,145,3.1839999999999997,3.2760000000000002,3.103,3.108,0.6579999999999999,0.6579999999999999,0.67,0.6890000000000001,640,360,yuv420p,59.79099678,,,
|
| 45 |
+
Gaming_360P-48b0,Gaming,360,110,3.427,3.4339999999999997,3.287,3.245,0.5,0.521,0.5670000000000001,0.569,480,360,yuv420p,24.919,,,
|
| 46 |
+
Gaming_360P-4f09,Gaming,360,124,3.5060000000000002,3.417,3.511,3.3160000000000003,0.634,0.604,0.611,0.619,640,360,yuv420p,30,,,
|
| 47 |
+
Gaming_360P-56fe,Gaming,360,127,2.439,2.293,2.041,2.305,0.767,0.705,0.665,0.5710000000000001,640,360,yuv420p,24,,,
|
| 48 |
+
Gaming_360P-586d,Gaming,360,109,3.0260000000000002,2.813,2.827,2.911,0.743,0.648,0.67,0.647,640,360,yuv420p,60.04899777,,,
|
| 49 |
+
Gaming_360P-5e0f,Gaming,360,123,3.012,2.9560000000000004,3.049,3.1860000000000004,0.695,0.66,0.65,0.71,464,360,yuv420p,30,,,
|
| 50 |
+
Gaming_360P-6207,Gaming,360,113,3.3489999999999998,3.3680000000000003,3.252,3.31,0.613,0.527,0.6809999999999999,0.613,640,360,yuv420p,30,,,
|
| 51 |
+
Gaming_360P-63e6,Gaming,360,117,2.636,2.543,2.362,2.397,0.726,0.722,0.595,0.624,640,360,yuv420p,24.453000000000003,,,
|
| 52 |
+
Gaming_360P-73c7,Gaming,360,124,3.247,3.2439999999999998,3.235,3.323,0.67,0.557,0.588,0.5710000000000001,640,360,yuv420p,23.97602398,,,
|
| 53 |
+
Gaming_360P-7975,Gaming,360,127,2.761,2.765,2.7460000000000004,2.417,0.665,0.679,0.581,0.573,640,360,yuv420p,30,,,
|
| 54 |
+
Gaming_360P-7acb,Gaming,360,106,3.0589999999999997,3.08,2.862,2.927,0.6,0.627,0.605,0.664,640,360,yuv420p,30,,,
|
| 55 |
+
HowTo_360P-041c,HowTo,360,122,3.214,2.906,2.8560000000000003,2.969,0.511,0.607,0.555,0.614,640,360,yuv420p,30,,,
|
| 56 |
+
HowTo_360P-0562,HowTo,360,126,3.261,3.27,3.225,3.168,0.49200000000000005,0.479,0.495,0.494,640,360,yuv420p,25,,,
|
| 57 |
+
HowTo_360P-06be,HowTo,360,122,3.6010000000000004,3.509,3.443,3.466,0.452,0.47600000000000003,0.499,0.485,640,360,yuv420p,25,,,
|
| 58 |
+
HowTo_360P-09f8,HowTo,360,110,2.951,2.8160000000000003,2.839,2.8710000000000004,0.602,0.645,0.637,0.614,640,360,yuv420p,30,,,
|
| 59 |
+
HowTo_360P-127f,HowTo,360,114,3.332,2.9789999999999996,2.702,2.825,0.5489999999999999,0.623,0.5479999999999999,0.615,640,360,yuv420p,25,,,
|
| 60 |
+
HowTo_360P-1823,HowTo,360,125,3.056,2.612,2.8339999999999996,2.8569999999999998,0.584,0.5770000000000001,0.679,0.612,640,360,yuv420p,30,,,
|
| 61 |
+
HowTo_360P-18e7,HowTo,360,123,2.78,2.903,2.775,2.602,0.632,0.662,0.659,0.652,640,360,yuv420p,15,,,
|
| 62 |
+
HowTo_360P-1dba,HowTo,360,126,1.9069999999999998,1.742,1.828,1.9169999999999998,0.764,0.619,0.625,0.742,640,360,yuv420p,28.318,,,
|
| 63 |
+
HowTo_360P-2074,HowTo,360,127,3.536,3.455,3.537,3.448,0.452,0.47600000000000003,0.447,0.44,640,360,yuv420p,30,,,
|
| 64 |
+
HowTo_360P-2a3b,HowTo,360,114,2.589,2.448,2.445,2.8360000000000003,0.652,0.7120000000000001,0.653,0.73,640,360,yuv420p,15,,,
|
| 65 |
+
HowTo_360P-2d35,HowTo,360,124,2.847,2.833,2.66,2.685,0.632,0.6709999999999999,0.5660000000000001,0.514,640,360,yuv420p,29.97002997,,,
|
| 66 |
+
HowTo_360P-2fd5,HowTo,360,127,2.049,1.52,1.682,1.811,1.0270000000000001,0.607,0.74,0.82,640,360,yuv420p,26.7,,,
|
| 67 |
+
HowTo_360P-3aa6,HowTo,360,126,3.0189999999999997,3.025,2.984,3.156,0.629,0.631,0.611,0.508,640,360,yuv420p,23.97602398,,,
|
| 68 |
+
HowTo_360P-4276,HowTo,360,115,3.448,3.29,3.283,3.273,0.489,0.498,0.504,0.5589999999999999,640,360,yuv420p,30,,,
|
| 69 |
+
HowTo_360P-4e4c,HowTo,360,119,3.005,2.745,3.009,3.227,0.615,0.58,0.625,0.57,640,360,yuv420p,25,,,
|
| 70 |
+
HowTo_360P-55e9,HowTo,360,140,3.4960000000000004,3.542,3.654,3.7289999999999996,0.496,0.444,0.51,0.465,640,360,yuv420p,29.97002997,,,
|
| 71 |
+
HowTo_360P-5da7,HowTo,360,122,3.425,3.304,3.392,3.34,0.5760000000000001,0.5379999999999999,0.518,0.499,640,360,yuv420p,30,,,
|
| 72 |
+
HowTo_360P-6093,HowTo,360,113,3.205,3.173,3.023,3.2560000000000002,0.513,0.474,0.616,0.462,640,360,yuv420p,25,,,
|
| 73 |
+
HowTo_360P-6a0e,HowTo,360,139,2.104,2.01,1.901,1.835,0.85,0.894,0.758,0.732,640,360,yuv420p,30,,,
|
| 74 |
+
HowTo_360P-7083,HowTo,360,131,3.031,2.948,2.824,2.898,0.636,0.601,0.5429999999999999,0.574,640,360,yuv420p,29.97002997,,,
|
| 75 |
+
HowTo_360P-7dcd,HowTo,360,144,3.035,3.069,3.057,2.997,0.62,0.62,0.6679999999999999,0.62,640,360,yuv420p,24,,,
|
| 76 |
+
HowTo_360P-7fb1,HowTo,360,114,3.322,3.26,3.155,3.264,0.5489999999999999,0.581,0.659,0.54,640,360,yuv420p,30,,,
|
| 77 |
+
Lecture_360P-03bc,Lecture,360,130,2.762,2.492,2.638,2.7939999999999996,0.71,0.831,0.705,0.61,640,360,yuv420p,25,,,
|
| 78 |
+
Lecture_360P-051a,Lecture,360,131,2.64,2.448,2.454,2.3409999999999997,0.644,0.664,0.599,0.621,480,360,yuv420p,25,,,
|
| 79 |
+
Lecture_360P-114f,Lecture,360,128,4.172,4.037,4.197,3.8280000000000003,0.588,0.568,0.524,0.47700000000000004,640,360,yuv420p,25,,,
|
| 80 |
+
Lecture_360P-20c3,Lecture,360,116,3.133,3.029,3.1,2.955,0.513,0.5670000000000001,0.601,0.596,640,360,yuv420p,14.842,,,
|
| 81 |
+
Lecture_360P-2276,Lecture,360,124,3.742,3.8080000000000003,3.6860000000000004,3.682,0.49700000000000005,0.483,0.45799999999999996,0.495,640,360,yuv420p,25,,,
|
| 82 |
+
Lecture_360P-27db,Lecture,360,122,3.092,2.992,3.011,3.045,0.625,0.618,0.622,0.564,640,360,yuv420p,15,,,
|
| 83 |
+
Lecture_360P-2c55,Lecture,360,115,3.717,3.5789999999999997,3.736,3.676,0.433,0.491,0.475,0.503,640,360,yuv420p,29.97002997,,,
|
| 84 |
+
Lecture_360P-2d1f,Lecture,360,112,3.62,3.4939999999999998,3.3960000000000004,3.432,0.46299999999999997,0.51,0.488,0.47200000000000003,640,360,yuv420p,30,,,
|
| 85 |
+
Lecture_360P-30eb,Lecture,360,117,3.35,3.122,3.208,3.375,0.494,0.541,0.52,0.517,640,360,yuv420p,29.97002997,,,
|
| 86 |
+
Lecture_360P-311d,Lecture,360,110,3.603,3.4939999999999998,3.477,3.5260000000000002,0.474,0.46399999999999997,0.49700000000000005,0.499,640,360,yuv420p,25,,,
|
| 87 |
+
Lecture_360P-3590,Lecture,360,120,3.7030000000000003,3.623,3.6039999999999996,3.516,0.49700000000000005,0.5329999999999999,0.478,0.516,640,360,yuv420p,29.97002997,,,
|
| 88 |
+
Lecture_360P-44c2,Lecture,360,122,3.389,3.3680000000000003,3.168,3.253,0.486,0.501,0.475,0.55,640,360,yuv420p,29.97002997,,,
|
| 89 |
+
Lecture_360P-4bb4,Lecture,360,122,3.822,3.73,3.6310000000000002,3.508,0.43200000000000005,0.45899999999999996,0.496,0.483,640,360,yuv420p,25,,,
|
| 90 |
+
Lecture_360P-4f00,Lecture,360,133,3.1719999999999997,3.074,3.0780000000000003,3.114,0.6859999999999999,0.598,0.575,0.5,640,360,yuv420p,15,,,
|
| 91 |
+
Lecture_360P-506c,Lecture,360,130,3.168,3.055,2.865,2.662,0.633,0.617,0.579,0.57,640,360,yuv420p,11.710999999999999,,,
|
| 92 |
+
Lecture_360P-5594,Lecture,360,116,3.367,3.406,3.483,3.408,0.506,0.516,0.54,0.56,640,360,yuv420p,29.97002997,,,
|
| 93 |
+
Lecture_360P-5779,Lecture,360,129,3.069,3.102,3.042,3.0439999999999996,0.598,0.574,0.5489999999999999,0.5920000000000001,540,360,yuv420p,30,,,
|
| 94 |
+
Lecture_360P-5aea,Lecture,360,124,3.088,2.865,2.7039999999999997,2.915,0.679,0.613,0.632,0.675,640,360,yuv420p,29.97002997,,,
|
| 95 |
+
Lecture_360P-5d0a,Lecture,360,135,3.4160000000000004,3.242,3.346,3.2969999999999997,0.485,0.47600000000000003,0.514,0.40700000000000003,640,360,yuv420p,25,,,
|
| 96 |
+
Lecture_360P-6656,Lecture,360,132,3.458,3.074,3.472,3.6239999999999997,0.498,0.7190000000000001,0.47600000000000003,0.522,640,360,yuv420p,29.97002997,,,
|
| 97 |
+
Lecture_360P-6d29,Lecture,360,134,2.99,2.67,2.958,3.017,0.57,0.532,0.605,0.574,640,360,yuv420p,30,,,
|
| 98 |
+
Lecture_360P-7550,Lecture,360,129,3.033,3.0060000000000002,3.1630000000000003,3.407,0.568,0.564,0.618,0.528,640,360,yuv420p,15.072000000000001,,,
|
| 99 |
+
Lecture_360P-7a57,Lecture,360,115,3.4930000000000003,3.292,3.3289999999999997,2.978,0.47,0.5,0.5529999999999999,0.614,640,360,yuv420p,29.97002997,,,
|
| 100 |
+
Lecture_360P-7f7e,Lecture,360,103,3.679,3.637,3.335,3.3480000000000003,0.52,0.514,0.508,0.59,640,360,yuv420p,25,,,
|
| 101 |
+
LiveMusic_360P-121f,LiveMusic,360,121,3.43,3.43,3.5039999999999996,3.58,0.509,0.436,0.465,0.42,640,360,yuv420p,29.97002997,,,
|
| 102 |
+
LiveMusic_360P-1d94,LiveMusic,360,122,3.028,3.123,2.9989999999999997,2.944,0.622,0.612,0.578,0.602,640,360,yuv420p,25,,,
|
| 103 |
+
LiveMusic_360P-22c5,LiveMusic,360,130,3.2310000000000003,3.275,3.278,3.2310000000000003,0.5,0.49700000000000005,0.531,0.544,640,360,yuv420p,30,,,
|
| 104 |
+
LiveMusic_360P-2508,LiveMusic,360,126,3.0039999999999996,3.015,2.91,3.015,0.623,0.625,0.598,0.5820000000000001,640,360,yuv420p,29.97002997,,,
|
| 105 |
+
LiveMusic_360P-265c,LiveMusic,360,114,2.339,1.835,1.861,1.911,0.588,0.594,0.628,0.6409999999999999,480,360,yuv420p,30,,,
|
| 106 |
+
LiveMusic_360P-2b0f,LiveMusic,360,127,3.4010000000000002,3.358,3.366,3.122,0.444,0.466,0.507,0.598,640,360,yuv420p,25,,,
|
| 107 |
+
LiveMusic_360P-3979,LiveMusic,360,132,1.838,1.633,1.68,2.146,0.718,0.727,0.7440000000000001,0.867,640,360,yuv420p,29.97002997,,,
|
| 108 |
+
LiveMusic_360P-3b96,LiveMusic,360,132,2.358,2.0380000000000003,1.994,2.0380000000000003,0.977,0.812,0.762,0.8420000000000001,640,360,yuv420p,25,,,
|
| 109 |
+
LiveMusic_360P-3be0,LiveMusic,360,123,1.72,1.787,1.719,1.6680000000000001,0.579,0.632,0.7190000000000001,0.755,480,360,yuv420p,25,,,
|
| 110 |
+
LiveMusic_360P-48d5,LiveMusic,360,138,3.325,3.275,3.588,3.498,0.457,0.556,0.48,0.48200000000000004,640,360,yuv420p,29.97002997,,,
|
| 111 |
+
LiveMusic_360P-5281,LiveMusic,360,124,3.596,3.5589999999999997,3.575,3.576,0.45399999999999996,0.489,0.47200000000000003,0.455,640,360,yuv420p,30,,,
|
| 112 |
+
LiveMusic_360P-54d0,LiveMusic,360,115,3.5589999999999997,3.6039999999999996,3.612,3.658,0.461,0.447,0.493,0.444,640,360,yuv420p,30,,,
|
| 113 |
+
LiveMusic_360P-5b57,LiveMusic,360,118,2.773,2.7689999999999997,2.891,2.781,0.583,0.504,0.618,0.579,640,360,yuv420p,25,,,
|
| 114 |
+
LiveMusic_360P-6266,LiveMusic,360,121,3.383,3.32,3.323,3.5189999999999997,0.506,0.546,0.489,0.49700000000000005,640,360,yuv420p,29.97002997,,,
|
| 115 |
+
LiveMusic_360P-6640,LiveMusic,360,128,3.11,2.909,3.116,2.8280000000000003,0.525,0.645,0.565,0.655,640,360,yuv420p,24,,,
|
| 116 |
+
LiveMusic_360P-6a65,LiveMusic,360,113,2.427,2.471,2.269,2.0269999999999997,0.586,0.596,0.531,0.581,480,360,yuv420p,25,,,
|
| 117 |
+
LiveMusic_360P-7483,LiveMusic,360,140,3.063,2.997,2.949,3.043,0.601,0.616,0.575,0.603,640,360,yuv420p,29.97002997,,,
|
| 118 |
+
LyricVideo_360P-0ac7,LyricVideo,360,111,2.964,2.988,2.8539999999999996,2.86,0.625,0.604,0.612,0.58,480,360,yuv420p,25,,,
|
| 119 |
+
LyricVideo_360P-17ce,LyricVideo,360,125,2.83,2.523,2.395,2.6210000000000004,0.705,0.595,0.505,0.5579999999999999,480,360,yuv420p,25,,,
|
| 120 |
+
LyricVideo_360P-3afc,LyricVideo,360,120,2.685,2.676,2.971,2.873,0.56,0.6920000000000001,0.65,0.61,480,360,yuv420p,25,,,
|
| 121 |
+
LyricVideo_360P-5868,LyricVideo,360,116,3.1069999999999998,2.6430000000000002,2.98,3.3339999999999996,0.6990000000000001,0.863,0.7070000000000001,0.649,640,360,yuv420p,29.97002997,,,
|
| 122 |
+
LyricVideo_360P-5e87,LyricVideo,360,132,2.9989999999999997,2.968,3.0239999999999996,3.009,0.637,0.598,0.5479999999999999,0.614,640,360,yuv420p,6,,,
|
| 123 |
+
MusicVideo_360P-08cb,MusicVideo,360,126,3.4010000000000002,3.307,3.135,2.926,0.584,0.446,0.589,0.628,640,360,yuv420p,24.956,,,
|
| 124 |
+
MusicVideo_360P-17e4,MusicVideo,360,123,3.233,3.3169999999999997,3.1210000000000004,3.1430000000000002,0.635,0.546,0.664,0.625,640,360,yuv420p,29.97002997,,,
|
| 125 |
+
MusicVideo_360P-24b0,MusicVideo,360,113,3.512,3.283,2.96,3.3939999999999997,0.539,0.6970000000000001,0.715,0.525,640,360,yuv420p,30,,,
|
| 126 |
+
MusicVideo_360P-2fcb,MusicVideo,360,128,3.6189999999999998,3.6830000000000003,3.602,3.693,0.51,0.457,0.436,0.457,640,360,yuv420p,25,,,
|
| 127 |
+
MusicVideo_360P-462f,MusicVideo,360,116,3.51,3.653,3.364,3.07,0.48700000000000004,0.586,0.5760000000000001,0.624,640,360,yuv420p,29.97002997,,,
|
| 128 |
+
MusicVideo_360P-4de7,MusicVideo,360,127,2.968,2.9819999999999998,2.8369999999999997,2.839,0.589,0.5710000000000001,0.545,0.556,480,360,yuv420p,29.97002997,,,
|
| 129 |
+
MusicVideo_360P-5358,MusicVideo,360,125,3.069,2.63,2.88,2.909,0.612,0.524,0.667,0.609,640,360,yuv420p,29.97002997,,,
|
| 130 |
+
MusicVideo_360P-5578,MusicVideo,360,120,2.873,2.7510000000000003,2.638,2.6639999999999997,0.665,0.62,0.6629999999999999,0.639,480,360,yuv420p,29.97002997,,,
|
| 131 |
+
MusicVideo_360P-5699,MusicVideo,360,116,3.7760000000000002,3.714,3.7439999999999998,3.638,0.42200000000000004,0.479,0.47600000000000003,0.478,640,360,yuv420p,30,,,
|
| 132 |
+
MusicVideo_360P-5f07,MusicVideo,360,120,2.9210000000000003,2.6830000000000003,2.918,2.763,0.629,0.503,0.5539999999999999,0.47700000000000004,640,360,yuv420p,23.97602398,,,
|
| 133 |
+
MusicVideo_360P-5f8d,MusicVideo,360,103,2.437,2.202,2.374,2.1,0.574,0.556,0.59,0.5329999999999999,640,360,yuv420p,29.97002997,,,
|
| 134 |
+
MusicVideo_360P-648c,MusicVideo,360,115,3.272,2.998,2.928,2.86,0.68,0.713,0.7490000000000001,0.8220000000000001,640,360,yuv420p,29.97002997,,,
|
| 135 |
+
MusicVideo_360P-7b94,MusicVideo,360,107,2.44,2.24,2.259,2.392,0.524,0.627,0.604,0.75,640,360,yuv420p,23.97602398,,,
|
| 136 |
+
NewsClip_360P-0376,NewsClip,360,132,3.095,3.105,3.1180000000000003,3.247,0.562,0.608,0.63,0.532,640,360,yuv420p,30,,,
|
| 137 |
+
NewsClip_360P-0c17,NewsClip,360,136,2.968,2.66,2.7089999999999996,2.677,0.624,0.45,0.45899999999999996,0.465,480,360,yuv420p,23.97602398,,,
|
| 138 |
+
NewsClip_360P-0ff8,NewsClip,360,114,3.5180000000000002,3.533,3.447,3.449,0.598,0.48,0.581,0.506,640,360,yuv420p,30,,,
|
| 139 |
+
NewsClip_360P-1093,NewsClip,360,117,3.1,3.252,3.148,3.1630000000000003,0.583,0.446,0.461,0.48,480,360,yuv420p,25,,,
|
| 140 |
+
NewsClip_360P-12fc,NewsClip,360,115,2.9019999999999997,2.918,2.905,2.965,0.586,0.615,0.601,0.642,480,360,yuv420p,23.97602398,,,
|
| 141 |
+
NewsClip_360P-1b1c,NewsClip,360,118,3.514,3.178,3.366,3.389,0.49,0.613,0.507,0.53,640,360,yuv420p,25,,,
|
| 142 |
+
NewsClip_360P-1e1c,NewsClip,360,117,3.2539999999999996,2.9210000000000003,3.1319999999999997,2.998,0.541,0.593,0.41100000000000003,0.573,480,360,yuv420p,25,,,
|
| 143 |
+
NewsClip_360P-1eae,NewsClip,360,126,3.467,3.5260000000000002,3.467,3.5669999999999997,0.501,0.483,0.486,0.452,640,360,yuv420p,30,,,
|
| 144 |
+
NewsClip_360P-22ce,NewsClip,360,106,2.698,2.654,2.747,2.552,0.6659999999999999,0.632,0.679,0.599,480,360,yuv420p,25,,,
|
| 145 |
+
NewsClip_360P-274e,NewsClip,360,115,3.332,3.18,3.301,3.215,0.46,0.494,0.505,0.47100000000000003,480,360,yuv420p,25,,,
|
| 146 |
+
NewsClip_360P-2986,NewsClip,360,124,2.9019999999999997,2.676,2.592,2.68,0.648,0.6459999999999999,0.612,0.621,640,360,yuv420p,25,,,
|
| 147 |
+
NewsClip_360P-2c91,NewsClip,360,111,2.859,2.884,2.728,2.502,0.614,0.637,0.527,0.47100000000000003,480,320,yuv420p,25,,,
|
| 148 |
+
NewsClip_360P-311a,NewsClip,360,113,2.447,2.062,2.235,2.492,0.633,0.81,0.672,0.708,480,360,yuv420p,23.97602398,,,
|
| 149 |
+
NewsClip_360P-3451,NewsClip,360,137,2.121,2.323,2.134,2.211,0.733,0.627,0.561,0.47100000000000003,480,320,yuv420p,25,,,
|
| 150 |
+
NewsClip_360P-4288,NewsClip,360,124,3.3930000000000002,3.4419999999999997,3.548,3.5010000000000003,0.507,0.5,0.434,0.455,640,352,yuv420p,25,,,
|
| 151 |
+
NewsClip_360P-439a,NewsClip,360,113,3.282,3.245,3.0980000000000003,3.165,0.486,0.51,0.64,0.522,480,320,yuv420p,25,,,
|
| 152 |
+
NewsClip_360P-4417,NewsClip,360,137,2.924,2.773,2.7939999999999996,2.7960000000000003,0.64,0.619,0.6609999999999999,0.688,480,320,yuv420p,25,,,
|
| 153 |
+
NewsClip_360P-5052,NewsClip,360,132,2.095,2.248,2.175,2.292,0.675,0.557,0.562,0.519,640,360,yuv420p,29.97002997,,,
|
| 154 |
+
NewsClip_360P-5752,NewsClip,360,145,3.346,3.214,3.265,3.339,0.5479999999999999,0.501,0.478,0.46299999999999997,480,360,yuv420p,25,,,
|
| 155 |
+
NewsClip_360P-5bcc,NewsClip,360,140,2.864,2.824,2.862,2.765,0.602,0.629,0.621,0.703,480,360,yuv420p,25,,,
|
| 156 |
+
NewsClip_360P-5f3b,NewsClip,360,123,2.298,2.1959999999999997,2.158,2.261,0.583,0.653,0.647,0.616,480,360,yuv420p,25,,,
|
| 157 |
+
NewsClip_360P-66ae,NewsClip,360,118,3.218,3.17,3.014,3.029,0.529,0.513,0.5760000000000001,0.511,480,360,yuv420p,25,,,
|
| 158 |
+
NewsClip_360P-67ce,NewsClip,360,116,3.196,2.9219999999999997,2.72,2.965,0.49,0.594,0.502,0.5770000000000001,640,360,yuv420p,23.97602398,,,
|
| 159 |
+
NewsClip_360P-7a96,NewsClip,360,120,2.291,1.946,1.94,1.827,0.6559999999999999,0.627,0.574,0.618,480,360,yuv420p,25,,,
|
| 160 |
+
Sports_360P-02c3,Sports,360,120,3.3960000000000004,3.389,3.2960000000000003,3.3480000000000003,0.502,0.447,0.46399999999999997,0.469,640,360,yuv420p,25,,,
|
| 161 |
+
Sports_360P-0c66,Sports,360,124,3.097,2.9210000000000003,2.937,2.966,0.629,0.605,0.6459999999999999,0.63,720,360,yuv420p,29.97002997,,,
|
| 162 |
+
Sports_360P-0dda,Sports,360,126,2.8680000000000003,2.552,2.588,2.555,0.664,0.49,0.502,0.49,768,432,yuv420p,24.961,,,
|
| 163 |
+
Sports_360P-11b7,Sports,360,117,2.401,2.307,2.5980000000000003,2.701,0.622,0.654,0.597,0.7170000000000001,480,360,yuv420p,29.97002997,,,
|
| 164 |
+
Sports_360P-1803,Sports,360,119,3.593,3.2460000000000004,3.486,3.717,0.485,0.62,0.47700000000000004,0.418,720,400,yuv420p,29.97002997,,,
|
| 165 |
+
Sports_360P-1d5c,Sports,360,130,3.781,3.825,3.77,3.2439999999999998,0.442,0.442,0.49,0.5670000000000001,640,360,yuv420p,25,,,
|
| 166 |
+
Sports_360P-2725,Sports,360,114,2.862,2.906,2.903,2.926,0.611,0.508,0.5720000000000001,0.569,640,352,yuv420p,30,,,
|
| 167 |
+
Sports_360P-27b0,Sports,360,123,2.82,2.839,2.62,2.87,0.628,0.6729999999999999,0.5329999999999999,0.602,640,352,yuv420p,30.0230011,,,
|
| 168 |
+
Sports_360P-2ace,Sports,360,140,3.444,3.2,3.3139999999999996,3.321,0.494,0.462,0.45299999999999996,0.493,720,412,yuv420p,25,,,
|
| 169 |
+
Sports_360P-2e48,Sports,360,108,2.7439999999999998,2.715,2.662,2.736,0.698,0.563,0.609,0.552,512,384,yuv420p,25,,,
|
| 170 |
+
Sports_360P-301d,Sports,360,118,3.667,3.825,3.761,3.048,0.508,0.45,0.456,0.625,720,400,yuv420p,29.97002997,,,
|
| 171 |
+
Sports_360P-32d3,Sports,360,132,3.012,2.8689999999999998,2.813,2.531,0.623,0.6579999999999999,0.628,0.56,640,352,yuv420p,25,,,
|
| 172 |
+
Sports_360P-3960,Sports,360,118,2.787,2.8760000000000003,2.7880000000000003,2.642,0.525,0.575,0.588,0.53,480,360,yuv420p,29.97002997,,,
|
| 173 |
+
Sports_360P-3e68,Sports,360,107,2.508,2.5839999999999996,2.44,2.6189999999999998,0.7040000000000001,0.713,0.58,0.5579999999999999,640,360,yuv420p,30,,,
|
| 174 |
+
Sports_360P-4545,Sports,360,117,2.432,2.2569999999999997,2.197,2.201,0.746,0.621,0.527,0.593,624,464,yuv420p,25,,,
|
| 175 |
+
Sports_360P-4802,Sports,360,131,2.582,2.553,2.404,2.6710000000000003,0.647,0.524,0.5589999999999999,0.639,628,468,yuv420p,25,,,
|
| 176 |
+
Sports_360P-4ad7,Sports,360,130,2.327,2.042,2.333,2.191,0.537,0.6970000000000001,0.626,0.5820000000000001,632,468,yuv420p,25,,,
|
| 177 |
+
Sports_360P-50fd,Sports,360,118,3.011,2.7110000000000003,2.7969999999999997,2.764,0.642,0.552,0.613,0.6679999999999999,718,366,yuv420p,29.97002997,,,
|
| 178 |
+
Sports_360P-5252,Sports,360,122,2.5380000000000003,2.545,2.156,2.37,0.696,0.71,0.679,0.532,624,464,yuv420p,25,,,
|
| 179 |
+
Sports_360P-5ded,Sports,360,134,3.784,3.7260000000000004,3.716,3.818,0.491,0.522,0.489,0.498,684,384,yuv420p,30,,,
|
| 180 |
+
Sports_360P-61f6,Sports,360,126,3.3930000000000002,3.673,3.6630000000000003,3.44,0.511,0.556,0.493,0.583,720,400,yuv420p,25,,,
|
| 181 |
+
Sports_360P-65f3,Sports,360,145,2.826,2.7430000000000003,2.634,2.516,0.6629999999999999,0.616,0.608,0.586,360,360,yuv420p,29.97002997,,,
|
| 182 |
+
Sports_360P-6b4f,Sports,360,120,2.9810000000000003,3.0189999999999997,2.9530000000000003,2.966,0.618,0.586,0.621,0.599,720,406,yuv420p,25,,,
|
| 183 |
+
Sports_360P-6c6f,Sports,360,110,2.556,2.378,2.602,2.474,0.698,0.627,0.7120000000000001,0.642,480,360,yuv420p,30,,,
|
| 184 |
+
Sports_360P-6f62,Sports,360,118,3.594,3.545,3.533,3.701,0.45799999999999996,0.47200000000000003,0.479,0.518,640,360,yuv420p,25,,,
|
| 185 |
+
Sports_360P-7f50,Sports,360,145,1.963,1.805,1.874,2.004,0.643,0.603,0.5760000000000001,0.633,624,464,yuv420p,25,,,
|
| 186 |
+
TelevisionClip_360P-0951,TelevisionClip,360,114,2.511,2.451,2.9539999999999997,2.705,0.5539999999999999,0.509,0.58,0.665,642,360,yuv420p,17.846,,,
|
| 187 |
+
TelevisionClip_360P-11d5,TelevisionClip,360,120,2.842,2.7689999999999997,2.469,2.721,0.61,0.693,0.551,0.58,540,360,yuv420p,30.23900119,,,
|
| 188 |
+
TelevisionClip_360P-29f1,TelevisionClip,360,114,2.5810000000000004,2.046,2.484,2.927,0.574,0.726,0.584,0.621,480,360,yuv420p,29.97002997,,,
|
| 189 |
+
TelevisionClip_360P-3b9a,TelevisionClip,360,134,2.883,2.688,2.8310000000000004,2.862,0.621,0.51,0.539,0.5710000000000001,480,360,yuv420p,29.97002997,,,
|
| 190 |
+
TelevisionClip_360P-74dd,TelevisionClip,360,106,3.58,3.477,3.66,3.72,0.474,0.55,0.434,0.45399999999999996,640,360,yuv420p,29.97002997,,,
|
| 191 |
+
TelevisionClip_360P-7b23,TelevisionClip,360,125,2.997,2.8160000000000003,2.715,3.188,0.601,0.5720000000000001,0.591,0.545,640,360,yuv420p,29.97002997,,,
|
| 192 |
+
VerticalVideo_360P-0d29,VerticalVideo,360,116,2.8089999999999997,2.614,2.605,2.495,0.701,0.63,0.7190000000000001,0.738,480,640,yuv420p,26.39,,,
|
| 193 |
+
VerticalVideo_360P-1424,VerticalVideo,360,126,3.7319999999999998,3.7289999999999996,3.705,3.8339999999999996,0.491,0.493,0.496,0.503,480,800,yuv420p,26.296,,,
|
| 194 |
+
VerticalVideo_360P-145e,VerticalVideo,360,127,2.6060000000000003,2.2319999999999998,2.512,2.059,0.763,0.851,0.8190000000000001,0.723,480,640,yuv420p,25.035,,,
|
| 195 |
+
VerticalVideo_360P-2bb0,VerticalVideo,360,121,3.073,3.057,3.302,3.3110000000000004,0.647,0.642,0.51,0.48200000000000004,480,720,yuv420p,29.73,,,
|
| 196 |
+
VerticalVideo_360P-2fa3,VerticalVideo,360,136,4.331,4.41,4.396,4.369,0.491,0.45,0.456,0.444,540,720,yuv420p,28.311,,,
|
| 197 |
+
VerticalVideo_360P-3936,VerticalVideo,360,121,3.7939999999999996,3.92,3.8310000000000004,3.728,0.449,0.43799999999999994,0.424,0.408,270,480,yuv420p,29.97002997,,,
|
| 198 |
+
VerticalVideo_360P-3b4e,VerticalVideo,360,119,3.576,3.365,3.5239999999999996,3.412,0.5539999999999999,0.69,0.5710000000000001,0.5770000000000001,480,640,yuv420p,24.611,,,
|
| 199 |
+
VerticalVideo_360P-42f1,VerticalVideo,360,114,3.603,3.673,3.48,3.3760000000000003,0.49200000000000005,0.519,0.525,0.525,406,720,yuv420p,30,,,
|
| 200 |
+
VerticalVideo_360P-52c1,VerticalVideo,360,114,2.352,1.9869999999999999,1.893,2.289,0.657,0.691,0.6559999999999999,0.5429999999999999,202,360,yuv420p,29.97002997,,,
|
| 201 |
+
VerticalVideo_360P-5429,VerticalVideo,360,105,3.616,3.3939999999999997,3.4810000000000003,3.408,0.526,0.51,0.51,0.5710000000000001,352,640,yuv420p,25,,,
|
| 202 |
+
VerticalVideo_360P-54f7,VerticalVideo,360,123,4.146,4.1160000000000005,4.07,3.986,0.39899999999999997,0.5770000000000001,0.6,0.5429999999999999,480,800,yuv420p,53.448,,,
|
| 203 |
+
VerticalVideo_360P-579c,VerticalVideo,360,119,3.0580000000000003,3.127,3.136,2.853,0.8029999999999999,0.706,0.732,0.7959999999999999,600,600,yuv420p,12,,,
|
| 204 |
+
VerticalVideo_360P-634f,VerticalVideo,360,124,4.523,4.428999999999999,4.4719999999999995,4.327,0.42,0.439,0.415,0.462,432,768,yuv420p,30.25299891,,,
|
| 205 |
+
VerticalVideo_360P-6490,VerticalVideo,360,107,2.9730000000000003,2.795,2.8680000000000003,2.7089999999999996,0.73,0.7659999999999999,0.722,0.7829999999999999,480,720,yuv420p,10.157,,,
|
| 206 |
+
VerticalVideo_360P-694d,VerticalVideo,360,118,3.4539999999999997,3.383,3.346,3.4360000000000004,0.45399999999999996,0.489,0.49700000000000005,0.488,480,640,yuv420p,15.134,,,
|
| 207 |
+
VerticalVideo_360P-6ff2,VerticalVideo,360,125,3.543,3.259,3.2030000000000003,3.1839999999999997,0.516,0.6,0.5770000000000001,0.536,480,640,yuv420p,29.165,,,
|
| 208 |
+
VerticalVideo_360P-70a9,VerticalVideo,360,132,2.8139999999999996,2.847,2.847,2.733,0.7140000000000001,0.6559999999999999,0.72,0.7609999999999999,480,720,yuv420p,20,,,
|
| 209 |
+
VerticalVideo_360P-7ba8,VerticalVideo,360,121,3.563,3.23,3.237,3.5660000000000003,0.535,0.698,0.7190000000000001,0.5660000000000001,720,720,yuv420p,29.97002997,,,
|
| 210 |
+
VerticalVideo_360P-7ec3,VerticalVideo,360,129,3.909,3.875,3.886,3.885,0.515,0.545,0.529,0.521,406,720,yuv420p,30,,,
|
| 211 |
+
Vlog_360P-1e5b,Vlog,360,123,3.266,3.2710000000000004,3.1839999999999997,3.012,0.574,0.551,0.54,0.583,640,360,yuv420p,30,,,
|
| 212 |
+
Vlog_360P-22a0,Vlog,360,130,3.286,3.037,3.0980000000000003,3,0.528,0.618,0.6409999999999999,0.6559999999999999,640,360,yuv420p,30,,,
|
| 213 |
+
Vlog_360P-2973,Vlog,360,109,2.9019999999999997,2.742,2.741,2.798,0.628,0.531,0.535,0.5429999999999999,640,360,yuv420p,29.97002997,,,
|
| 214 |
+
Vlog_360P-2e9d,Vlog,360,124,2.7460000000000004,2.79,2.8680000000000003,2.773,0.561,0.608,0.594,0.614,640,360,yuv420p,25,,,
|
| 215 |
+
Vlog_360P-3121,Vlog,360,125,2.775,2.5839999999999996,2.734,2.481,0.727,0.578,0.6609999999999999,0.721,640,360,yuv420p,30,,,
|
| 216 |
+
Vlog_360P-3bc2,Vlog,360,111,3.944,3.6519999999999997,3.585,3.818,0.47100000000000003,0.539,0.544,0.499,640,360,yuv420p,30,,,
|
| 217 |
+
Vlog_360P-433e,Vlog,360,133,3.198,3.197,3.01,3.071,0.58,0.617,0.6509999999999999,0.654,640,360,yuv420p,30,,,
|
| 218 |
+
Vlog_360P-4697,Vlog,360,118,3.551,3.091,3.312,3.109,0.56,0.645,0.603,0.6920000000000001,640,360,yuv420p,30,,,
|
| 219 |
+
Vlog_360P-4795,Vlog,360,121,2.479,2.401,2.22,2.533,0.629,0.547,0.607,0.589,640,360,yuv420p,30,,,
|
| 220 |
+
Vlog_360P-4ad1,Vlog,360,109,3.016,3.029,3.17,3.1910000000000003,0.5529999999999999,0.588,0.5539999999999999,0.505,640,360,yuv420p,30,,,
|
| 221 |
+
Vlog_360P-4d71,Vlog,360,103,3.052,3.102,3.3510000000000004,3.0239999999999996,0.615,0.625,0.662,0.787,640,360,yuv420p,30,,,
|
| 222 |
+
Vlog_360P-6279,Vlog,360,104,3.26,2.986,2.965,2.9960000000000004,0.555,0.584,0.601,0.5920000000000001,640,360,yuv420p,30,,,
|
| 223 |
+
Vlog_360P-7334,Vlog,360,143,3.266,3.245,3.008,3.213,0.674,0.55,0.614,0.679,640,360,yuv420p,30,,,
|
| 224 |
+
Vlog_360P-76ae,Vlog,360,109,2.6060000000000003,2.3930000000000002,2.6889999999999996,2.853,0.667,0.6459999999999999,0.596,0.643,640,360,yuv420p,30,,,
|
| 225 |
+
Vlog_360P-7efe,Vlog,360,146,2.912,2.781,2.915,2.992,0.6679999999999999,0.624,0.675,0.609,640,360,yuv420p,30,,,
|
metadata/YOUTUBE_UGC_480P_metadata.csv
ADDED
|
@@ -0,0 +1,266 @@
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|
| 1 |
+
vid,category,resolution,x_Subjects,MOSFull,MOSChunk00,MOSChunk05,MOSChunk10,stdFull,stdChunk00,stdChunk05,stdChunk10,width,height,pixfmt,framerate,nb_frames,bitdepth,bitrate
|
| 2 |
+
Animation_480P-046c,Animation,480,119,3.15,3.022,3.123,3.075,0.7190000000000001,0.598,0.5870000000000001,0.581,640,480,yuv420p,29.97002997,,,
|
| 3 |
+
Animation_480P-073c,Animation,480,107,3.407,3.123,3.113,2.9530000000000003,0.65,0.7170000000000001,0.693,0.755,640,480,yuv420p,25,,,
|
| 4 |
+
Animation_480P-087e,Animation,480,126,3.525,3.432,3.2319999999999998,3.353,0.523,0.565,0.72,0.601,720,480,yuv420p,15.415,,,
|
| 5 |
+
Animation_480P-08a5,Animation,480,104,3.42,3.19,3.535,3.52,0.625,0.6679999999999999,0.662,0.564,640,480,yuv420p,25,,,
|
| 6 |
+
Animation_480P-0d04,Animation,480,135,3.111,3.096,3.0210000000000004,2.862,0.7020000000000001,0.7290000000000001,0.725,0.76,856,480,yuv420p,30,,,
|
| 7 |
+
Animation_480P-1da7,Animation,480,106,3.1830000000000003,2.912,2.9589999999999996,3.228,0.679,0.7440000000000001,0.752,0.726,928,480,yuv420p,9.39,,,
|
| 8 |
+
Animation_480P-35ee,Animation,480,120,3.29,3.313,3.174,1.7,0.644,0.608,0.552,1.054,720,480,yuv420p,30,,,
|
| 9 |
+
Animation_480P-3fdf,Animation,480,106,3.465,3.4139999999999997,3.245,3.2,0.545,0.612,0.657,0.617,640,480,yuv420p,25,,,
|
| 10 |
+
Animation_480P-4b86,Animation,480,131,3.495,3.32,3.2460000000000004,3.2569999999999997,0.535,0.547,0.601,0.6679999999999999,720,480,yuv420p,29.97002997,,,
|
| 11 |
+
Animation_480P-4e36,Animation,480,116,3.303,3.4,3.389,3.3560000000000003,0.649,0.556,0.536,0.597,640,480,yuv420p,25,,,
|
| 12 |
+
Animation_480P-52af,Animation,480,133,3.656,3.64,3.6189999999999998,3.78,0.503,0.45799999999999996,0.49,0.528,854,480,yuv420p,29.97002997,,,
|
| 13 |
+
Animation_480P-66c0,Animation,480,125,3.4530000000000003,3.537,3.5039999999999996,3.373,0.5379999999999999,0.606,0.517,0.56,720,480,yuv420p,29.905,,,
|
| 14 |
+
Animation_480P-6e23,Animation,480,114,3.7310000000000003,3.654,3.4989999999999997,3.589,0.504,0.5660000000000001,0.7,0.627,854,480,yuv420p,29.97002997,,,
|
| 15 |
+
Animation_480P-6ef6,Animation,480,115,3.6069999999999998,3.5069999999999997,3.302,3.247,0.489,0.522,0.513,0.556,640,480,yuv420p,25,,,
|
| 16 |
+
Animation_480P-70d4,Animation,480,120,3.8480000000000003,3.9,3.793,3.6239999999999997,0.524,0.469,0.462,0.514,720,480,yuv420p,30,,,
|
| 17 |
+
Animation_480P-791b,Animation,480,137,3.286,3.142,3.2289999999999996,3.139,0.591,0.6409999999999999,0.6729999999999999,0.674,640,480,yuv420p,29.97002997,,,
|
| 18 |
+
Animation_480P-7a31,Animation,480,117,3.487,3.28,3.341,3.438,0.524,0.612,0.7440000000000001,0.53,640,480,yuv420p,8,,,
|
| 19 |
+
Animation_480P-7ef2,Animation,480,119,3.1639999999999997,3.123,3.1260000000000003,3.4810000000000003,0.5870000000000001,0.674,0.664,0.5660000000000001,854,480,yuv420p,25,,,
|
| 20 |
+
CoverSong_480P-0170,CoverSong,480,109,3.1180000000000003,2.915,2.763,2.844,0.569,0.665,0.578,0.627,640,480,yuv420p,15.11,,,
|
| 21 |
+
CoverSong_480P-1019,CoverSong,480,115,3.815,3.509,3.6919999999999997,3.523,0.43,0.442,0.48100000000000004,0.451,640,480,yuv420p,25,,,
|
| 22 |
+
CoverSong_480P-1109,CoverSong,480,114,2.826,2.762,2.573,2.271,0.614,0.607,0.563,0.584,640,480,yuv420p,25,,,
|
| 23 |
+
CoverSong_480P-2142,CoverSong,480,119,3.25,3.2319999999999998,3.2030000000000003,3.1519999999999997,0.532,0.53,0.483,0.66,854,480,yuv420p,29.97002997,,,
|
| 24 |
+
CoverSong_480P-2207,CoverSong,480,125,2.9339999999999997,2.935,2.931,2.945,0.782,0.7290000000000001,0.737,0.7509999999999999,640,480,yuv420p,30,,,
|
| 25 |
+
CoverSong_480P-2d2c,CoverSong,480,123,3.8369999999999997,3.7430000000000003,3.6919999999999997,3.6310000000000002,0.485,0.451,0.42700000000000005,0.447,640,480,yuv420p,30,,,
|
| 26 |
+
CoverSong_480P-3076,CoverSong,480,127,1.71,1.505,1.5290000000000001,1.524,0.779,0.519,0.59,0.607,640,480,yuv420p,29.87100103,,,
|
| 27 |
+
CoverSong_480P-35e1,CoverSong,480,104,3.608,3.437,3.5269999999999997,3.428,0.522,0.515,0.5589999999999999,0.496,640,480,yuv420p,30,,,
|
| 28 |
+
CoverSong_480P-3e4b,CoverSong,480,132,2.877,2.609,2.609,2.6210000000000004,0.6940000000000001,0.6679999999999999,0.6409999999999999,0.752,640,480,yuv420p,29.97002997,,,
|
| 29 |
+
CoverSong_480P-3e92,CoverSong,480,139,4.235,4.277,4.1739999999999995,4.2589999999999995,0.35,0.457,0.526,0.456,640,480,yuv420p,50,,,
|
| 30 |
+
CoverSong_480P-3f3b,CoverSong,480,120,3.0269999999999997,2.575,2.467,2.658,0.657,0.489,0.483,0.485,640,480,yuv420p,30,,,
|
| 31 |
+
CoverSong_480P-41a5,CoverSong,480,112,3.27,3.3089999999999997,3.264,3.392,0.456,0.518,0.557,0.425,640,480,yuv420p,16.457,,,
|
| 32 |
+
CoverSong_480P-4d34,CoverSong,480,113,2.677,2.498,2.526,2.479,0.67,0.424,0.485,0.503,640,480,yuv420p,29.916999999999998,,,
|
| 33 |
+
CoverSong_480P-53f4,CoverSong,480,118,3.1689999999999996,3.153,3.122,3.153,0.483,0.499,0.46399999999999997,0.539,640,480,yuv422p,30,,,
|
| 34 |
+
CoverSong_480P-59f2,CoverSong,480,121,3.418,3.404,3.342,3.508,0.517,0.546,0.5329999999999999,0.48200000000000004,720,480,yuv420p,10.965,,,
|
| 35 |
+
CoverSong_480P-5b62,CoverSong,480,135,3.056,2.8739999999999997,2.866,2.889,0.617,0.597,0.599,0.6,640,480,yuyv422,24.71,,,
|
| 36 |
+
CoverSong_480P-60a6,CoverSong,480,113,2.369,2.6289999999999996,2.661,2.715,0.457,0.593,0.69,0.629,640,480,yuv420p,29.619,,,
|
| 37 |
+
CoverSong_480P-64d0,CoverSong,480,131,3.917,3.859,3.833,3.832,0.47100000000000003,0.527,0.519,0.431,640,480,yuv420p,25,,,
|
| 38 |
+
CoverSong_480P-6c3e,CoverSong,480,115,2.502,2.0340000000000003,1.93,2.011,0.763,0.687,0.5479999999999999,0.622,720,480,yuv420p,29.97002997,,,
|
| 39 |
+
CoverSong_480P-6c50,CoverSong,480,107,2.7289999999999996,2.895,2.8139999999999996,2.73,0.5379999999999999,0.586,0.617,0.568,640,480,yuv422p,30,,,
|
| 40 |
+
CoverSong_480P-7f6d,CoverSong,480,131,3.048,3.0189999999999997,2.9210000000000003,2.96,0.619,0.503,0.612,0.5770000000000001,854,480,yuv420p,29.97002997,,,
|
| 41 |
+
Gaming_480P-0109,Gaming,480,118,2.436,2.406,2.536,2.468,0.585,0.51,0.518,0.55,640,480,yuv420p,24.846,,,
|
| 42 |
+
Gaming_480P-09ab,Gaming,480,128,3.795,3.747,3.8289999999999997,3.78,0.5670000000000001,0.5710000000000001,0.552,0.511,848,480,yuv420p,30,,,
|
| 43 |
+
Gaming_480P-0a03,Gaming,480,124,2.676,2.5810000000000004,2.484,2.559,0.7490000000000001,0.725,0.735,0.742,720,480,yuv411p,29.97002997,,,
|
| 44 |
+
Gaming_480P-0d23,Gaming,480,113,3.466,3.378,3.3310000000000004,3.358,0.583,0.546,0.68,0.6579999999999999,640,480,yuv420p,29.6,,,
|
| 45 |
+
Gaming_480P-0dbc,Gaming,480,114,2.9730000000000003,2.8510000000000004,2.968,2.938,0.611,0.57,0.616,0.61,720,480,yuv422p,29.97002997,,,
|
| 46 |
+
Gaming_480P-14fc,Gaming,480,124,3.822,3.7319999999999998,3.813,3.84,0.503,0.574,0.45299999999999996,0.48700000000000004,854,480,yuv420p,29.97002997,,,
|
| 47 |
+
Gaming_480P-1542,Gaming,480,104,2.7760000000000002,2.545,2.7060000000000004,2.636,0.657,0.622,0.645,0.633,854,480,yuv420p,29.97002997,,,
|
| 48 |
+
Gaming_480P-20ca,Gaming,480,117,2.891,2.95,2.861,2.693,0.731,0.72,0.72,0.67,720,480,yuv411p,29.97002997,,,
|
| 49 |
+
Gaming_480P-3a44,Gaming,480,112,3.043,2.9189999999999996,2.957,3.062,0.607,0.672,0.655,0.648,720,480,yuv420p,29.97002997,,,
|
| 50 |
+
Gaming_480P-445b,Gaming,480,106,3.714,3.825,3.8289999999999997,3.648,0.523,0.46799999999999997,0.512,0.48200000000000004,640,480,yuv420p,29.97002997,,,
|
| 51 |
+
Gaming_480P-4560,Gaming,480,114,3.904,3.8139999999999996,3.9419999999999997,3.91,0.527,0.466,0.52,0.474,854,480,yuv420p,59.545,,,
|
| 52 |
+
Gaming_480P-5a5a,Gaming,480,130,3.478,3.216,3.373,3.522,0.583,0.7140000000000001,0.6759999999999999,0.562,854,480,yuv420p,25,,,
|
| 53 |
+
Gaming_480P-61ee,Gaming,480,115,3.6310000000000002,3.435,3.46,3.24,0.503,0.491,0.502,0.667,640,480,pal8,70,,,
|
| 54 |
+
Gaming_480P-626a,Gaming,480,122,3.786,3.701,3.498,3.6319999999999997,0.498,0.48,0.52,0.505,856,480,yuv420p,30,,,
|
| 55 |
+
Gaming_480P-6491,Gaming,480,122,3.013,2.875,3.009,3.115,0.695,0.6659999999999999,0.6759999999999999,0.625,720,480,yuv420p,29.932,,,
|
| 56 |
+
Gaming_480P-6548,Gaming,480,126,2.8710000000000004,2.61,2.528,3.077,0.727,0.7909999999999999,0.764,0.6970000000000001,640,480,yuv420p,29.97002997,,,
|
| 57 |
+
Gaming_480P-6a5a,Gaming,480,124,3.468,3.312,3.42,3.483,0.512,0.574,0.564,0.532,854,480,yuv420p,29.97002997,,,
|
| 58 |
+
Gaming_480P-6c92,Gaming,480,140,2.3480000000000003,2.21,2.281,2.4,0.899,0.69,0.889,0.619,864,480,yuv420p,29.706,,,
|
| 59 |
+
Gaming_480P-6cdc,Gaming,480,127,2.425,2.23,2.162,2.463,0.527,0.484,0.493,0.504,640,480,yuv420p,29.97002997,,,
|
| 60 |
+
Gaming_480P-6d1e,Gaming,480,117,2.674,2.569,2.6310000000000002,2.678,0.632,0.66,0.737,0.672,566,480,yuv420p,29.97002997,,,
|
| 61 |
+
Gaming_480P-6f4b,Gaming,480,120,3.67,3.588,3.509,3.603,0.494,0.537,0.53,0.493,864,480,yuv420p,30,,,
|
| 62 |
+
Gaming_480P-75f7,Gaming,480,127,2.798,2.7880000000000003,3.0060000000000002,2.9930000000000003,0.728,0.8109999999999999,0.674,0.65,640,480,yuv420p,29.97002997,,,
|
| 63 |
+
Gaming_480P-7a08,Gaming,480,128,3.792,3.7439999999999998,3.5380000000000003,3.698,0.5670000000000001,0.5870000000000001,0.7090000000000001,0.542,640,480,yuv420p,35,,,
|
| 64 |
+
Gaming_480P-7ccb,Gaming,480,118,3.5810000000000004,3.4530000000000003,3.609,3.6069999999999998,0.506,0.512,0.517,0.517,852,480,yuv420p,30.02100162,,,
|
| 65 |
+
Gaming_480P-7e76,Gaming,480,117,2.397,2.146,2.336,2.306,0.695,0.888,0.652,0.616,720,480,yuv411p,29.97002997,,,
|
| 66 |
+
HowTo_480P-04f1,HowTo,480,134,3.4539999999999997,3.187,3.26,3.438,0.489,0.526,0.511,0.425,640,480,yuv420p,30,,,
|
| 67 |
+
HowTo_480P-0cb3,HowTo,480,132,3.592,3.5269999999999997,3.5410000000000004,3.5589999999999997,0.503,0.512,0.45899999999999996,0.511,640,480,yuv422p,30,,,
|
| 68 |
+
HowTo_480P-0d00,HowTo,480,142,3.998,4.046,4.043,4.0280000000000005,0.625,0.634,0.631,0.665,640,480,yuv420p,25,,,
|
| 69 |
+
HowTo_480P-0eb3,HowTo,480,121,2.5069999999999997,2.339,2.5269999999999997,2.552,0.563,0.5870000000000001,0.557,0.525,848,480,yuv422p,30,,,
|
| 70 |
+
HowTo_480P-118d,HowTo,480,145,1.462,1.3130000000000002,1.294,1.27,0.76,0.56,0.519,0.485,640,480,yuv420p,30,,,
|
| 71 |
+
HowTo_480P-15c1,HowTo,480,116,3.264,3.361,3.202,3.247,0.698,0.63,0.653,0.687,848,480,yuv420p,18.785,,,
|
| 72 |
+
HowTo_480P-221e,HowTo,480,113,3.283,3.2,3.306,3.2289999999999996,0.5760000000000001,0.6409999999999999,0.6609999999999999,0.674,640,480,yuv420p,15,,,
|
| 73 |
+
HowTo_480P-2610,HowTo,480,121,2.9810000000000003,2.8760000000000003,3.0980000000000003,2.978,0.5429999999999999,0.637,0.635,0.648,640,480,yuv420p,1.9980019980000001,,,
|
| 74 |
+
HowTo_480P-32c9,HowTo,480,134,2.51,2.3930000000000002,2.532,2.295,0.73,0.723,0.76,0.664,640,480,yuv420p,25,,,
|
| 75 |
+
HowTo_480P-3435,HowTo,480,126,3.93,3.971,4.148,4.061,0.483,0.547,0.537,0.563,640,480,yuv420p,59.94006309,,,
|
| 76 |
+
HowTo_480P-470b,HowTo,480,118,3.858,3.7,3.8139999999999996,3.8,0.547,0.511,0.513,0.473,640,480,yuv420p,29.97002997,,,
|
| 77 |
+
HowTo_480P-4948,HowTo,480,120,2.551,2.4530000000000003,2.498,2.655,0.611,0.5720000000000001,0.523,0.5710000000000001,640,480,yuv420p,1.994,,,
|
| 78 |
+
HowTo_480P-4a28,HowTo,480,127,3.659,3.6519999999999997,3.6510000000000002,3.469,0.47600000000000003,0.501,0.524,0.528,720,480,yuv420p,29.97002997,,,
|
| 79 |
+
HowTo_480P-4b6a,HowTo,480,106,2.763,2.5789999999999997,2.722,2.549,0.6890000000000001,0.579,0.7,0.6990000000000001,640,480,yuv420p,1.966,,,
|
| 80 |
+
HowTo_480P-4c99,HowTo,480,111,2.94,2.7510000000000003,2.793,3.1710000000000003,0.616,0.648,0.6729999999999999,0.608,850,480,yuv420p,30,,,
|
| 81 |
+
HowTo_480P-56a5,HowTo,480,103,4.085,3.8819999999999997,3.969,4.228,0.522,0.49700000000000005,0.5379999999999999,0.527,640,480,yuv420p,30,,,
|
| 82 |
+
HowTo_480P-60b3,HowTo,480,109,3.576,3.429,3.015,3.3960000000000004,0.531,0.528,0.698,0.545,640,480,yuv420p,15,,,
|
| 83 |
+
HowTo_480P-63a2,HowTo,480,128,3.5589999999999997,3.59,3.534,3.531,0.51,0.48,0.528,0.539,720,480,yuv411p,29.97002997,,,
|
| 84 |
+
HowTo_480P-64a2,HowTo,480,118,2.8360000000000003,3.6310000000000002,2.915,1.883,0.682,0.456,0.64,0.782,854,480,yuv420p,29.97002997,,,
|
| 85 |
+
HowTo_480P-7579,HowTo,480,110,4.079,3.883,3.992,4.08,0.596,0.465,0.473,0.43,854,480,yuv420p,29.97002997,,,
|
| 86 |
+
HowTo_480P-7c11,HowTo,480,127,3.267,3.2030000000000003,3.109,3.174,0.537,0.529,0.585,0.531,720,480,yuv420p,29.97002997,,,
|
| 87 |
+
Lecture_480P-02f2,Lecture,480,109,3.261,3.179,3.0869999999999997,3.2430000000000003,0.532,0.7020000000000001,0.642,0.5379999999999999,720,480,yuv420p,29.97002997,,,
|
| 88 |
+
Lecture_480P-11df,Lecture,480,124,3.514,3.4619999999999997,3.383,3.4610000000000003,0.496,0.442,0.46299999999999997,0.449,720,480,yuv420p,29.97002997,,,
|
| 89 |
+
Lecture_480P-181a,Lecture,480,134,1.785,1.58,2.031,1.9269999999999998,0.698,0.6970000000000001,0.9109999999999999,0.698,640,480,yuv420p,29.230999999999998,,,
|
| 90 |
+
Lecture_480P-1ec3,Lecture,480,119,3.1860000000000004,3.083,3.0260000000000002,3.063,0.6629999999999999,0.6990000000000001,0.69,0.6679999999999999,720,480,yuv420p,15,,,
|
| 91 |
+
Lecture_480P-2513,Lecture,480,112,1.879,1.903,2.017,1.7919999999999998,0.613,0.5589999999999999,0.48200000000000004,0.575,640,480,yuv420p,25,,,
|
| 92 |
+
Lecture_480P-2655,Lecture,480,121,3.261,3.002,2.9139999999999997,2.7,0.547,0.583,0.605,0.593,640,480,yuv422p,30,,,
|
| 93 |
+
Lecture_480P-2ed0,Lecture,480,139,1.453,1.456,1.272,1.2790000000000001,0.767,0.8270000000000001,0.605,0.557,640,480,yuv420p,25,,,
|
| 94 |
+
Lecture_480P-369f,Lecture,480,138,4.1930000000000005,4.05,3.866,3.758,0.505,0.43799999999999994,0.5660000000000001,0.49,856,480,yuv420p,24,,,
|
| 95 |
+
Lecture_480P-3bdc,Lecture,480,114,3.168,2.904,3.013,2.991,0.552,0.624,0.626,0.556,640,480,yuv420p,15,,,
|
| 96 |
+
Lecture_480P-41b7,Lecture,480,113,3.3280000000000003,3.082,3.258,3.161,0.568,0.495,0.531,0.534,640,480,yuv420p,29.97002997,,,
|
| 97 |
+
Lecture_480P-4272,Lecture,480,119,3.457,3.363,3.284,3.1439999999999997,0.511,0.5,0.542,0.621,640,480,yuv420p,10,,,
|
| 98 |
+
Lecture_480P-42c3,Lecture,480,127,3.451,3.312,3.22,3.438,0.544,0.525,0.64,0.562,638,480,yuv420p,30,,,
|
| 99 |
+
Lecture_480P-4bc3,Lecture,480,130,3.93,3.9810000000000003,4.004,3.9330000000000003,0.483,0.46,0.414,0.446,854,480,yuv420p,30,,,
|
| 100 |
+
Lecture_480P-4c86,Lecture,480,116,3.6660000000000004,3.6919999999999997,3.236,3.0380000000000003,0.43700000000000006,0.45799999999999996,0.519,0.583,720,480,yuv411p,29.97002997,,,
|
| 101 |
+
Lecture_480P-5aee,Lecture,480,124,2.6060000000000003,2.6460000000000004,2.708,2.66,0.611,0.551,0.579,0.608,352,480,yuv420p,29.97002997,,,
|
| 102 |
+
Lecture_480P-5cd7,Lecture,480,116,4.024,4,3.785,3.79,0.39899999999999997,0.495,0.451,0.537,720,480,yuv420p,29.97002997,,,
|
| 103 |
+
Lecture_480P-5f3a,Lecture,480,120,2.324,2.33,2.285,2.435,0.565,0.575,0.614,0.6,352,480,yuv420p,29.97002997,,,
|
| 104 |
+
Lecture_480P-6191,Lecture,480,125,3.502,3.2880000000000003,3.315,3.323,0.526,0.529,0.519,0.522,640,480,yuv420p,25,,,
|
| 105 |
+
Lecture_480P-6b1e,Lecture,480,104,3.3680000000000003,3.142,3.125,3.292,0.5760000000000001,0.633,0.623,0.629,640,480,yuv420p,10,,,
|
| 106 |
+
Lecture_480P-71c0,Lecture,480,125,3.2489999999999997,3.196,3.2689999999999997,3.3169999999999997,0.649,0.518,0.5479999999999999,0.498,640,480,yuv420p,25,,,
|
| 107 |
+
Lecture_480P-71d6,Lecture,480,128,3.275,3.077,3.323,2.99,0.486,0.6609999999999999,0.5539999999999999,0.619,640,480,yuv420p,29.97002997,,,
|
| 108 |
+
Lecture_480P-7205,Lecture,480,126,3.417,3.322,3.3110000000000004,3.344,0.473,0.433,0.501,0.483,640,480,yuv420p,25,,,
|
| 109 |
+
Lecture_480P-73f6,Lecture,480,117,3.2560000000000002,3.0580000000000003,3.025,2.9819999999999998,0.584,0.607,0.615,0.5770000000000001,640,480,yuv420p,29.97002997,,,
|
| 110 |
+
Lecture_480P-74cf,Lecture,480,118,3.428,3.1319999999999997,3.207,3.304,0.46299999999999997,0.48100000000000004,0.441,0.46399999999999997,720,480,yuv420p,29.97002997,,,
|
| 111 |
+
Lecture_480P-74ea,Lecture,480,126,3.258,3.449,3.508,3.424,0.657,0.54,0.505,0.462,640,480,yuv420p,25,,,
|
| 112 |
+
Lecture_480P-7d77,Lecture,480,134,3.5980000000000003,3.3989999999999996,3.408,3.484,0.486,0.516,0.48100000000000004,0.46,640,480,yuv420p,25,,,
|
| 113 |
+
Lecture_480P-7e55,Lecture,480,122,3.5380000000000003,3.4989999999999997,3.4960000000000004,3.483,0.565,0.568,0.637,0.525,642,480,yuv420p,30,,,
|
| 114 |
+
Lecture_480P-7eec,Lecture,480,121,3.015,2.972,2.957,2.8680000000000003,0.612,0.664,0.618,0.594,854,480,yuv420p,29.97002997,,,
|
| 115 |
+
LiveMusic_480P-0638,LiveMusic,480,135,2.909,2.8989999999999996,2.822,2.781,0.639,0.6920000000000001,0.621,0.617,852,480,yuv420p,29.97002997,,,
|
| 116 |
+
LiveMusic_480P-1a91,LiveMusic,480,120,3.173,2.971,3.195,3.1239999999999997,0.603,0.639,0.517,0.51,720,480,yuv420p,29.97002997,,,
|
| 117 |
+
LiveMusic_480P-2019,LiveMusic,480,125,3.6310000000000002,3.57,3.4560000000000004,3.397,0.5539999999999999,0.529,0.47100000000000003,0.48700000000000004,640,480,yuv420p,30,,,
|
| 118 |
+
LiveMusic_480P-2a5e,LiveMusic,480,121,3.089,3.043,2.967,2.99,0.608,0.522,0.544,0.598,640,480,yuv420p,29.97002997,,,
|
| 119 |
+
LiveMusic_480P-38f3,LiveMusic,480,121,2.9730000000000003,2.918,2.912,2.9810000000000003,0.601,0.619,0.643,0.617,640,480,yuv422p,30,,,
|
| 120 |
+
LiveMusic_480P-3d03,LiveMusic,480,119,2.7289999999999996,2.775,2.8280000000000003,2.0709999999999997,0.757,0.6920000000000001,0.562,0.8220000000000001,720,480,yuv420p,29.97002997,,,
|
| 121 |
+
LiveMusic_480P-459c,LiveMusic,480,107,2.755,2.7039999999999997,2.7039999999999997,2.781,0.652,0.51,0.612,0.635,640,480,yuv420p,29.97002997,,,
|
| 122 |
+
LiveMusic_480P-474a,LiveMusic,480,134,1.4909999999999999,1.208,1.3459999999999999,1.3940000000000001,0.733,0.32899999999999996,0.415,0.44799999999999995,640,480,yuv420p,25,,,
|
| 123 |
+
LiveMusic_480P-4c3a,LiveMusic,480,130,2.907,2.5980000000000003,2.69,2.799,0.6,0.49200000000000005,0.465,0.5329999999999999,640,480,yuv420p,25,,,
|
| 124 |
+
LiveMusic_480P-4f88,LiveMusic,480,133,3.267,3.228,3.1260000000000003,3.0239999999999996,0.653,0.5539999999999999,0.609,0.632,720,480,yuv420p,29.97002997,,,
|
| 125 |
+
LiveMusic_480P-559d,LiveMusic,480,124,3.695,4.11,3.968,3.63,0.484,0.647,0.684,0.488,640,480,yuv420p,29.97002997,,,
|
| 126 |
+
LiveMusic_480P-58fb,LiveMusic,480,121,3.502,3.417,3.343,3.438,0.456,0.502,0.5870000000000001,0.49200000000000005,640,480,yuv422p,29.97002997,,,
|
| 127 |
+
LiveMusic_480P-61ef,LiveMusic,480,132,3.153,3.159,2.97,2.83,0.602,0.49700000000000005,0.564,0.534,640,480,yuv420p,25,,,
|
| 128 |
+
LiveMusic_480P-6542,LiveMusic,480,119,3.2489999999999997,3.21,3.228,3.0789999999999997,0.47,0.501,0.445,0.56,640,480,yuv420p,29.97002997,,,
|
| 129 |
+
LiveMusic_480P-65ca,LiveMusic,480,122,3.6910000000000003,3.612,3.556,3.589,0.436,0.531,0.5539999999999999,0.45299999999999996,640,480,yuv420p,30,,,
|
| 130 |
+
LyricVideo_480P-0a5b,LyricVideo,480,109,3.247,3.043,3.19,3.053,0.544,0.614,0.522,0.599,640,480,yuv420p,25,,,
|
| 131 |
+
LyricVideo_480P-0f72,LyricVideo,480,129,2.064,2.125,1.939,2.294,0.695,0.542,0.6609999999999999,0.622,848,480,yuv420p,29.97002997,,,
|
| 132 |
+
LyricVideo_480P-1484,LyricVideo,480,113,3.145,3.713,3.68,2.491,0.67,0.509,0.5379999999999999,0.9840000000000001,854,480,yuv420p,29.97002997,,,
|
| 133 |
+
LyricVideo_480P-200a,LyricVideo,480,121,3.063,3.158,2.9419999999999997,2.9019999999999997,0.6779999999999999,0.677,0.629,0.6509999999999999,640,480,yuv420p,25,,,
|
| 134 |
+
LyricVideo_480P-2c50,LyricVideo,480,129,2.985,2.8360000000000003,2.866,2.9610000000000003,0.696,0.637,0.6509999999999999,0.5820000000000001,640,480,yuv420p,30,,,
|
| 135 |
+
LyricVideo_480P-3b2c,LyricVideo,480,123,3.472,3.322,3.2960000000000003,3.2739999999999996,0.535,0.57,0.607,0.5589999999999999,720,480,yuv420p,29.97002997,,,
|
| 136 |
+
LyricVideo_480P-3ccf,LyricVideo,480,105,2.639,2.465,2.494,2.342,0.645,0.46299999999999997,0.596,0.513,640,480,yuv420p,25,,,
|
| 137 |
+
LyricVideo_480P-4346,LyricVideo,480,129,3.193,2.105,3.2439999999999998,3.537,0.616,0.76,0.47100000000000003,0.485,854,480,yuv420p,29.97002997,,,
|
| 138 |
+
LyricVideo_480P-51e4,LyricVideo,480,129,3.179,3.1060000000000003,3.1239999999999997,3.141,0.573,0.657,0.608,0.614,640,480,yuv420p,30,,,
|
| 139 |
+
LyricVideo_480P-5c17,LyricVideo,480,123,3.344,3.437,3.29,2.8760000000000003,0.5,0.7070000000000001,0.6859999999999999,0.7709999999999999,640,480,yuv420p,30,,,
|
| 140 |
+
LyricVideo_480P-6fca,LyricVideo,480,137,2.572,2.448,2.397,2.289,0.754,0.563,0.569,0.645,848,480,yuv420p,29.97002997,,,
|
| 141 |
+
LyricVideo_480P-7919,LyricVideo,480,107,2.508,2.452,2.51,2.54,0.514,0.531,0.498,0.561,640,480,yuv420p,30,,,
|
| 142 |
+
MusicVideo_480P-0448,MusicVideo,480,107,3.4530000000000003,3.468,3.1639999999999997,3.13,0.523,0.556,0.611,0.598,640,480,yuv420p,24.962,,,
|
| 143 |
+
MusicVideo_480P-12fb,MusicVideo,480,115,3.0589999999999997,3.16,3.198,3.117,0.611,0.515,0.483,0.602,640,480,yuv420p,25,,,
|
| 144 |
+
MusicVideo_480P-184c,MusicVideo,480,113,3.7260000000000004,3.721,3.7680000000000002,3.678,0.478,0.41200000000000003,0.44799999999999995,0.43,640,480,yuv420p,25,,,
|
| 145 |
+
MusicVideo_480P-1eee,MusicVideo,480,132,3.0610000000000004,2.838,3.025,3.1060000000000003,0.5870000000000001,0.539,0.579,0.5479999999999999,640,480,yuv420p,30,,,
|
| 146 |
+
MusicVideo_480P-2de0,MusicVideo,480,137,3.47,3.395,3.361,3.408,0.525,0.5589999999999999,0.491,0.47600000000000003,656,480,yuv420p,25,,,
|
| 147 |
+
MusicVideo_480P-3aa2,MusicVideo,480,109,2.767,2.474,2.289,2.497,0.742,0.529,0.531,0.59,854,480,yuv420p,29.97002997,,,
|
| 148 |
+
MusicVideo_480P-3c8b,MusicVideo,480,119,2.908,2.69,2.713,2.7289999999999996,0.6659999999999999,0.762,0.764,0.7509999999999999,640,480,yuv410p,25,,,
|
| 149 |
+
MusicVideo_480P-41ce,MusicVideo,480,119,3.766,3.764,3.67,3.613,0.46799999999999997,0.48100000000000004,0.423,0.509,720,480,yuv411p,29.97002997,,,
|
| 150 |
+
MusicVideo_480P-483b,MusicVideo,480,119,1.5390000000000001,1.61,1.6440000000000001,1.507,0.7020000000000001,0.807,0.8370000000000001,0.741,720,480,yuv420p,29.97002997,,,
|
| 151 |
+
MusicVideo_480P-4cc8,MusicVideo,480,127,3.346,3.492,3.458,3.428,0.6920000000000001,0.623,0.5329999999999999,0.5589999999999999,854,480,yuv420p,29.97002997,,,
|
| 152 |
+
MusicVideo_480P-5461,MusicVideo,480,149,4.272,4.279,4.185,4.031000000000001,0.48200000000000004,0.442,0.526,0.486,854,480,yuv420p,29.97002997,,,
|
| 153 |
+
MusicVideo_480P-5830,MusicVideo,480,120,3.045,3.005,2.889,2.702,0.636,0.595,0.6579999999999999,0.593,640,480,yuv420p,30,,,
|
| 154 |
+
MusicVideo_480P-6026,MusicVideo,480,121,3.033,2.813,2.877,2.7739999999999996,0.608,0.46299999999999997,0.508,0.485,640,480,yuv420p,30,,,
|
| 155 |
+
MusicVideo_480P-61ba,MusicVideo,480,140,1.756,1.349,1.581,1.633,0.8640000000000001,0.5,0.711,0.7140000000000001,720,480,yuv420p,29.97002997,,,
|
| 156 |
+
MusicVideo_480P-66b2,MusicVideo,480,124,2.7089999999999996,2.6260000000000003,1.9509999999999998,2.057,0.843,0.836,0.706,0.858,640,480,yuv420p,15,,,
|
| 157 |
+
MusicVideo_480P-6fb6,MusicVideo,480,115,3.611,3.5639999999999996,3.47,3.662,0.5479999999999999,0.49700000000000005,0.531,0.5489999999999999,640,480,yuv420p,24.97502498,,,
|
| 158 |
+
MusicVideo_480P-7643,MusicVideo,480,121,3.093,3.017,2.945,3.275,0.613,0.579,0.6,0.511,1080,480,yuv420p,30,,,
|
| 159 |
+
MusicVideo_480P-7955,MusicVideo,480,116,3.509,3.4610000000000003,3.569,3.485,0.519,0.473,0.48,0.49200000000000005,854,480,yuv420p,30,,,
|
| 160 |
+
NewsClip_480P-0269,NewsClip,480,105,3.04,2.678,2.905,2.884,0.711,0.531,0.5920000000000001,0.623,720,480,yuv420p,29.97002997,,,
|
| 161 |
+
NewsClip_480P-0ce5,NewsClip,480,121,3.265,3.1519999999999997,2.957,2.6239999999999997,0.474,0.527,0.542,0.49200000000000005,720,480,yuv420p,29.97002997,,,
|
| 162 |
+
NewsClip_480P-15fa,NewsClip,480,120,3.5810000000000004,3.4739999999999998,3.6519999999999997,3.658,0.42200000000000004,0.49,0.48100000000000004,0.505,640,480,yuv420p,25,,,
|
| 163 |
+
NewsClip_480P-2407,NewsClip,480,130,2.688,2.636,2.582,2.593,0.575,0.5660000000000001,0.504,0.531,640,480,yuv422p,30,,,
|
| 164 |
+
NewsClip_480P-2642,NewsClip,480,121,2.688,2.625,2.5980000000000003,2.418,0.53,0.622,0.662,0.539,600,480,yuv420p,24.97502498,,,
|
| 165 |
+
NewsClip_480P-28eb,NewsClip,480,120,2.998,2.951,2.9739999999999998,2.889,0.5660000000000001,0.627,0.633,0.634,728,480,yuv420p,30,,,
|
| 166 |
+
NewsClip_480P-2b9b,NewsClip,480,113,2.421,2.314,2.331,2.221,0.7,0.655,0.818,0.873,640,480,yuv422p,29.97002997,,,
|
| 167 |
+
NewsClip_480P-2ba7,NewsClip,480,123,2.7910000000000004,2.537,2.381,2.585,0.5920000000000001,0.547,0.5,0.484,640,480,yuv420p,25,,,
|
| 168 |
+
NewsClip_480P-31bd,NewsClip,480,126,3.61,3.4610000000000003,3.596,3.592,0.467,0.507,0.47200000000000003,0.528,854,480,yuv420p,30,,,
|
| 169 |
+
NewsClip_480P-3672,NewsClip,480,123,3.802,3.653,3.418,3.4819999999999998,0.523,0.42200000000000004,0.475,0.486,640,480,yuv420p,25,,,
|
| 170 |
+
NewsClip_480P-36d0,NewsClip,480,110,3.764,3.972,3.6630000000000003,3.55,0.494,0.508,0.423,0.456,640,480,yuv420p,25,,,
|
| 171 |
+
NewsClip_480P-4072,NewsClip,480,123,2.481,2.2880000000000003,2.525,2.426,0.606,0.532,0.625,0.605,600,480,yuv420p,24.962,,,
|
| 172 |
+
NewsClip_480P-41b1,NewsClip,480,114,3.623,3.41,3.472,3.5189999999999997,0.456,0.445,0.41700000000000004,0.44299999999999995,854,480,yuv420p,25,,,
|
| 173 |
+
NewsClip_480P-437c,NewsClip,480,118,3.011,2.947,3.1210000000000004,3.127,0.562,0.601,0.607,0.606,720,480,yuv420p,29.97002997,,,
|
| 174 |
+
NewsClip_480P-49c9,NewsClip,480,112,3.975,3.798,3.7880000000000003,3.8569999999999998,0.493,0.473,0.43200000000000005,0.466,640,480,yuv420p,25,,,
|
| 175 |
+
NewsClip_480P-4a9f,NewsClip,480,136,2.983,3.156,2.927,3.1489999999999996,0.583,0.495,0.612,0.518,720,480,yuv420p,29.97002997,,,
|
| 176 |
+
NewsClip_480P-4e77,NewsClip,480,117,3.45,3.576,3.298,2.798,0.502,0.523,0.544,0.602,854,480,yuv420p,25,,,
|
| 177 |
+
NewsClip_480P-543f,NewsClip,480,122,3.423,3.466,3.1060000000000003,2.997,0.491,0.452,0.5489999999999999,0.644,856,480,yuv420p,25,,,
|
| 178 |
+
NewsClip_480P-5a3b,NewsClip,480,110,2.237,1.8659999999999999,1.9809999999999999,2.522,0.8859999999999999,0.7190000000000001,0.7929999999999999,0.737,640,480,yuv420p,25,,,
|
| 179 |
+
NewsClip_480P-5e7f,NewsClip,480,111,3.105,3.1039999999999996,3.1319999999999997,3.22,0.62,0.5770000000000001,0.49700000000000005,0.505,640,480,yuv420p,25,,,
|
| 180 |
+
NewsClip_480P-606e,NewsClip,480,114,2.9210000000000003,2.73,2.835,2.988,0.613,0.621,0.6629999999999999,0.594,856,480,yuv420p,25,,,
|
| 181 |
+
NewsClip_480P-6615,NewsClip,480,124,3.51,3.301,3.199,3.275,0.47600000000000003,0.43700000000000006,0.514,0.498,854,480,yuv420p,29.97002997,,,
|
| 182 |
+
NewsClip_480P-696e,NewsClip,480,132,3.102,2.8369999999999997,3.134,3.2239999999999998,0.6709999999999999,0.6970000000000001,0.679,0.5660000000000001,640,480,yuv420p,25,,,
|
| 183 |
+
NewsClip_480P-6bfa,NewsClip,480,105,3.793,3.884,3.8139999999999996,3.845,0.493,0.523,0.473,0.447,640,480,yuv420p,25,,,
|
| 184 |
+
NewsClip_480P-7232,NewsClip,480,128,2.991,2.924,2.804,2.805,0.644,0.594,0.622,0.5870000000000001,856,480,yuv420p,25,,,
|
| 185 |
+
NewsClip_480P-72b8,NewsClip,480,122,3.407,3.375,3.23,3.429,0.49,0.469,0.506,0.508,640,480,yuv420p,25,,,
|
| 186 |
+
NewsClip_480P-7a0d,NewsClip,480,124,3.7539999999999996,3.83,3.67,3.742,0.523,0.507,0.523,0.503,640,480,yuv420p,25,,,
|
| 187 |
+
Sports_480P-0623,Sports,480,128,3.1060000000000003,3.092,2.938,2.8939999999999997,0.682,0.662,0.64,0.6609999999999999,720,576,yuv420p,25,,,
|
| 188 |
+
Sports_480P-0827,Sports,480,108,2.9139999999999997,2.7769999999999997,2.7489999999999997,2.637,0.6629999999999999,0.623,0.534,0.526,720,576,yuv420p,25,,,
|
| 189 |
+
Sports_480P-0af9,Sports,480,133,3.765,3.693,3.675,3.639,0.504,0.502,0.525,0.514,640,480,yuv420p,29.97002997,,,
|
| 190 |
+
Sports_480P-0bb9,Sports,480,114,3.4189999999999996,3.182,3.575,3.659,0.532,0.644,0.596,0.46399999999999997,720,576,yuv420p,25,,,
|
| 191 |
+
Sports_480P-0efe,Sports,480,131,3.673,3.6039999999999996,3.678,3.635,0.47,0.478,0.489,0.5,848,480,yuv420p,59.94006309,,,
|
| 192 |
+
Sports_480P-1019,Sports,480,117,3.3160000000000003,3.125,3.245,3.193,0.627,0.546,0.6,0.649,768,576,yuv420p,25,,,
|
| 193 |
+
Sports_480P-1056,Sports,480,114,3.1489999999999996,3.008,3.05,2.8819999999999997,0.659,0.632,0.6459999999999999,0.622,720,576,yuv420p,25,,,
|
| 194 |
+
Sports_480P-11ec,Sports,480,121,1.6119999999999999,1.54,1.4340000000000002,1.449,0.607,0.561,0.45,0.508,720,480,yuv420p,29.97002997,,,
|
| 195 |
+
Sports_480P-1396,Sports,480,133,3.4589999999999996,3.3110000000000004,3.4139999999999997,3.093,0.551,0.5529999999999999,0.47700000000000004,0.617,720,576,yuv420p,25,,,
|
| 196 |
+
Sports_480P-19e4,Sports,480,112,3.835,3.718,3.772,3.822,0.457,0.499,0.43,0.484,854,480,yuv420p,29.97002997,,,
|
| 197 |
+
Sports_480P-1d1b,Sports,480,121,2.177,2.522,2.594,2.555,0.7290000000000001,0.662,0.775,0.708,768,576,yuv420p,25,,,
|
| 198 |
+
Sports_480P-1fe9,Sports,480,142,1.706,1.7380000000000002,1.757,1.909,0.612,0.597,0.657,0.6629999999999999,720,480,yuv420p,29.97002997,,,
|
| 199 |
+
Sports_480P-2053,Sports,480,117,3.1489999999999996,2.787,2.898,3.469,0.635,0.6509999999999999,0.659,0.465,720,576,yuv420p,25,,,
|
| 200 |
+
Sports_480P-211f,Sports,480,131,2.715,2.7119999999999997,2.5869999999999997,2.676,0.68,0.706,0.7340000000000001,0.6759999999999999,352,480,yuv420p,29.97002997,,,
|
| 201 |
+
Sports_480P-2dfe,Sports,480,105,2.401,2.3280000000000003,2.295,2.309,0.5589999999999999,0.62,0.617,0.602,720,576,yuv420p,29.97002997,,,
|
| 202 |
+
Sports_480P-3195,Sports,480,121,3.537,3.094,3.235,3.4419999999999997,0.541,0.6759999999999999,0.575,0.527,720,576,yuv420p,25,,,
|
| 203 |
+
Sports_480P-3404,Sports,480,117,3.533,3.207,3.287,3.3569999999999998,0.484,0.47700000000000004,0.467,0.466,854,480,yuv420p,24.746,,,
|
| 204 |
+
Sports_480P-35ea,Sports,480,105,3.668,3.4389999999999996,3.51,3.5010000000000003,0.439,0.594,0.511,0.557,720,480,yuv420p,29.97002997,,,
|
| 205 |
+
Sports_480P-3ca4,Sports,480,139,2.3890000000000002,2.041,2.097,2.122,0.736,0.718,0.46799999999999997,0.505,720,576,yuv420p,25,,,
|
| 206 |
+
Sports_480P-3dfd,Sports,480,126,3.498,3.38,3.234,3.177,0.54,0.545,0.5670000000000001,0.611,640,480,yuv420p,29.97002997,,,
|
| 207 |
+
Sports_480P-3f50,Sports,480,127,4.234,4.32,4.242,3.9219999999999997,0.397,0.43,0.369,0.511,960,540,yuv420p,25,,,
|
| 208 |
+
Sports_480P-41a5,Sports,480,141,1.97,1.845,1.585,1.528,0.635,0.614,0.599,0.544,854,480,yuv420p,25,,,
|
| 209 |
+
Sports_480P-44e5,Sports,480,129,3.17,2.991,3.332,3.2760000000000002,0.693,0.648,0.534,0.508,720,576,yuv420p,25,,,
|
| 210 |
+
Sports_480P-4cf8,Sports,480,119,2.023,1.7280000000000002,1.8130000000000002,2.053,0.866,0.5379999999999999,0.6609999999999999,0.564,720,576,yuv420p,25,,,
|
| 211 |
+
Sports_480P-5224,Sports,480,115,2.866,2.915,2.688,2.516,0.645,0.519,0.5429999999999999,0.5710000000000001,640,480,yuv420p,29.662,,,
|
| 212 |
+
Sports_480P-5871,Sports,480,126,2.698,2.553,2.637,2.7230000000000003,0.5710000000000001,0.557,0.625,0.6990000000000001,640,480,yuv420p,29.97002997,,,
|
| 213 |
+
Sports_480P-5f38,Sports,480,135,3.7710000000000004,3.679,3.6310000000000002,3.592,0.495,0.431,0.486,0.503,360,640,yuv420p,15.245999999999999,,,
|
| 214 |
+
Sports_480P-6508,Sports,480,120,3.448,3.154,3.34,3.154,0.515,0.591,0.537,0.634,720,576,yuv420p,25,,,
|
| 215 |
+
Sports_480P-6e41,Sports,480,115,3.306,3.213,3.3689999999999998,3.2260000000000004,0.625,0.594,0.5920000000000001,0.608,720,576,yuv420p,25,,,
|
| 216 |
+
Sports_480P-77e3,Sports,480,118,3.053,3.074,3.147,3.0660000000000003,0.612,0.619,0.63,0.612,720,480,yuv420p,29.97002997,,,
|
| 217 |
+
Sports_480P-7f7e,Sports,480,146,1.953,1.808,1.641,1.848,0.655,0.55,0.509,0.593,720,576,yuv420p,29.97002997,,,
|
| 218 |
+
TelevisionClip_480P-09d8,TelevisionClip,480,136,2.908,2.7119999999999997,2.727,2.5660000000000003,0.633,0.478,0.588,0.62,640,480,yuv420p,30,,,
|
| 219 |
+
TelevisionClip_480P-0e46,TelevisionClip,480,109,2.8810000000000002,2.8739999999999997,2.6519999999999997,2.4090000000000003,0.631,0.612,0.539,0.5329999999999999,640,480,yuv422p,29.97002997,,,
|
| 220 |
+
TelevisionClip_480P-19d3,TelevisionClip,480,111,3.4419999999999997,3.464,3.385,3.495,0.522,0.517,0.499,0.515,854,480,yuv420p,25,,,
|
| 221 |
+
TelevisionClip_480P-1b49,TelevisionClip,480,117,3.1830000000000003,2.9219999999999997,2.855,2.9130000000000003,0.512,0.623,0.523,0.605,640,480,yuv422p,29.97002997,,,
|
| 222 |
+
TelevisionClip_480P-27ca,TelevisionClip,480,115,3.475,3.56,3.5069999999999997,3.4419999999999997,0.45799999999999996,0.5,0.488,0.466,640,480,yuv422p,30,,,
|
| 223 |
+
TelevisionClip_480P-2ead,TelevisionClip,480,118,2.371,2.2640000000000002,2.251,1.926,0.69,0.555,0.499,0.612,722,480,yuv420p,25,,,
|
| 224 |
+
TelevisionClip_480P-30b6,TelevisionClip,480,107,2.609,2.784,2.793,2.832,0.535,0.643,0.627,0.65,640,480,yuv420p,29.97002997,,,
|
| 225 |
+
TelevisionClip_480P-3284,TelevisionClip,480,108,3.1689999999999996,2.9610000000000003,2.908,2.975,0.537,0.605,0.601,0.6,656,480,yuv420p,29.97002997,,,
|
| 226 |
+
TelevisionClip_480P-3617,TelevisionClip,480,122,2.312,1.599,2.585,2.84,0.653,0.659,0.65,0.64,848,480,yuv422p,30,,,
|
| 227 |
+
TelevisionClip_480P-373d,TelevisionClip,480,147,3.0439999999999996,2.969,2.573,2.476,0.633,0.605,0.47,0.5329999999999999,720,480,yuv420p,29.97002997,,,
|
| 228 |
+
TelevisionClip_480P-3c64,TelevisionClip,480,106,3.4560000000000004,3.2710000000000004,3.279,3.221,0.485,0.431,0.451,0.483,640,480,yuv420p,29.97002997,,,
|
| 229 |
+
TelevisionClip_480P-415c,TelevisionClip,480,122,3.5439999999999996,3.307,3.2889999999999997,3.1289999999999996,0.451,0.434,0.42,0.502,720,480,yuv420p,29.97002997,,,
|
| 230 |
+
TelevisionClip_480P-436c,TelevisionClip,480,117,2.823,2.823,2.657,2.8169999999999997,0.6609999999999999,0.605,0.506,0.586,368,480,yuv420p,29.97002997,,,
|
| 231 |
+
TelevisionClip_480P-4c6b,TelevisionClip,480,126,3.85,3.7260000000000004,3.718,3.8,0.469,0.462,0.391,0.439,720,480,yuv420p,29.97002997,,,
|
| 232 |
+
TelevisionClip_480P-59f0,TelevisionClip,480,123,2.8960000000000004,2.7539999999999996,2.7439999999999998,2.93,0.644,0.499,0.545,0.608,640,480,yuv420p,29.97002997,,,
|
| 233 |
+
TelevisionClip_480P-723e,TelevisionClip,480,118,2.8819999999999997,2.88,2.719,2.819,0.62,0.696,0.65,0.652,640,480,yuv420p,29.97002997,,,
|
| 234 |
+
VerticalVideo_480P-1bb9,VerticalVideo,480,114,3.825,3.5789999999999997,3.7539999999999996,3.845,0.5539999999999999,0.5479999999999999,0.501,0.45899999999999996,768,1024,yuv420p,30,,,
|
| 235 |
+
VerticalVideo_480P-2aa1,VerticalVideo,480,139,4.402,4.404,4.44,4.383,0.39399999999999996,0.41700000000000004,0.391,0.41700000000000004,608,1080,yuv420p,30,,,
|
| 236 |
+
VerticalVideo_480P-2fa4,VerticalVideo,480,121,4.133,4.137,4.253,4.324,0.389,0.55,0.534,0.42100000000000004,608,1080,yuv420p,29.804000000000002,,,
|
| 237 |
+
VerticalVideo_480P-34b9,VerticalVideo,480,113,3.5269999999999997,3.515,3.4410000000000003,3.554,0.456,0.49700000000000005,0.475,0.47600000000000003,480,856,yuv420p,30.022,,,
|
| 238 |
+
VerticalVideo_480P-3a6a,VerticalVideo,480,114,4.337,4.383,4.405,4.316,0.348,0.47,0.503,0.483,480,854,yuv420p,160,,,
|
| 239 |
+
VerticalVideo_480P-419c,VerticalVideo,480,143,3.78,3.588,3.5639999999999996,3.641,0.447,0.45399999999999996,0.475,0.48,480,848,yuv420p,30,,,
|
| 240 |
+
VerticalVideo_480P-467e,VerticalVideo,480,127,3.383,3.205,3.157,3.216,0.52,0.6729999999999999,0.705,0.623,480,854,yuv420p,30,,,
|
| 241 |
+
VerticalVideo_480P-51b7,VerticalVideo,480,140,2.299,2.763,1.7069999999999999,1.374,0.872,0.61,0.6409999999999999,0.48,608,1080,yuv420p,14.011,,,
|
| 242 |
+
VerticalVideo_480P-550d,VerticalVideo,480,112,3.832,3.647,,,0.544,0.7879999999999999,,,608,1080,yuv420p,270.36,,,
|
| 243 |
+
VerticalVideo_480P-572a,VerticalVideo,480,135,3.4989999999999997,3.502,3.471,3.576,0.473,0.405,0.43799999999999994,0.426,480,856,yuv420p,30.022,,,
|
| 244 |
+
VerticalVideo_480P-7278,VerticalVideo,480,133,3.005,2.863,2.9330000000000003,2.9,0.588,0.611,0.609,0.6409999999999999,612,1088,yuv420p,26.511,,,
|
| 245 |
+
VerticalVideo_480P-790a,VerticalVideo,480,124,4.248,4.173,4.261,4.363,0.526,0.606,0.622,0.521,720,1080,yuv420p,46.856,,,
|
| 246 |
+
Vlog_480P-08c7,Vlog,480,120,3.591,3.2539999999999996,3.242,3.571,0.446,0.486,0.46399999999999997,0.49700000000000005,854,480,yuv420p,29.97002997,,,
|
| 247 |
+
Vlog_480P-0980,Vlog,480,129,2.755,2.803,2.807,2.612,0.623,0.613,0.645,0.5720000000000001,720,480,yuv420p,29.97002997,,,
|
| 248 |
+
Vlog_480P-0ddc,Vlog,480,114,4.075,3.9760000000000004,4.215,3.891,0.414,0.532,0.59,0.519,720,480,yuv420p,30.136,,,
|
| 249 |
+
Vlog_480P-1b39,Vlog,480,130,2.325,2.266,2.352,2.0980000000000003,0.672,0.5579999999999999,0.597,0.552,640,480,yuv420p,29.97002997,,,
|
| 250 |
+
Vlog_480P-206f,Vlog,480,116,2.779,2.556,2.421,2.508,0.732,0.659,0.94,0.695,640,480,yuv420p,30,,,
|
| 251 |
+
Vlog_480P-279d,Vlog,480,124,3.373,3.323,3.38,3.403,0.58,0.501,0.479,0.466,720,480,yuv411p,29.97002997,,,
|
| 252 |
+
Vlog_480P-2cad,Vlog,480,132,3.2439999999999998,3.1719999999999997,3.263,3.261,0.63,0.688,0.701,0.7,720,480,yuv411p,29.97002997,,,
|
| 253 |
+
Vlog_480P-34ba,Vlog,480,122,3.6660000000000004,3.696,3.772,3.665,0.475,0.434,0.45899999999999996,0.449,640,480,yuv420p,29.97002997,,,
|
| 254 |
+
Vlog_480P-476b,Vlog,480,119,3.431,3.245,3.343,3.4160000000000004,0.505,0.649,0.45399999999999996,0.508,854,480,yuv420p,29.97002997,,,
|
| 255 |
+
Vlog_480P-4beb,Vlog,480,125,2.512,2.4130000000000003,2.418,2.395,0.639,0.578,0.569,0.5529999999999999,640,480,yuv420p,30,,,
|
| 256 |
+
Vlog_480P-5275,Vlog,480,123,3.12,3.2739999999999996,3.082,3.05,0.5489999999999999,0.555,0.64,0.604,640,480,yuv420p,25,,,
|
| 257 |
+
Vlog_480P-535d,Vlog,480,110,2.846,2.825,2.8289999999999997,2.8289999999999997,0.493,0.605,0.49,0.537,720,480,yuv420p,29.97002997,,,
|
| 258 |
+
Vlog_480P-59dc,Vlog,480,116,3.2060000000000004,3.285,3.236,3.3569999999999998,0.45,0.5,0.495,0.5329999999999999,640,480,yuv420p,30.026,,,
|
| 259 |
+
Vlog_480P-5dfe,Vlog,480,110,3.8,3.803,3.7539999999999996,3.225,0.5,0.415,0.386,0.6779999999999999,854,480,yuv420p,29.97002997,,,
|
| 260 |
+
Vlog_480P-5e1c,Vlog,480,121,2.5810000000000004,2.229,,,1.01,1.005,,,640,480,yuv420p,1.9980019980000001,,,
|
| 261 |
+
Vlog_480P-6395,Vlog,480,103,3.755,3.679,3.6830000000000003,3.647,0.46,0.485,0.532,0.569,640,480,yuv420p,30,,,
|
| 262 |
+
Vlog_480P-6c60,Vlog,480,112,2.487,2.6660000000000004,2.682,2.435,0.624,0.8270000000000001,0.737,0.65,640,480,yuv420p,30,,,
|
| 263 |
+
Vlog_480P-7237,Vlog,480,130,3.89,3.752,3.7969999999999997,3.702,0.446,0.446,0.48200000000000004,0.431,854,480,yuv420p,29.97002997,,,
|
| 264 |
+
Vlog_480P-7615,Vlog,480,108,2.588,2.878,2.752,2.865,0.746,0.7070000000000001,0.591,0.7020000000000001,640,480,yuv420p,30,,,
|
| 265 |
+
Vlog_480P-7754,Vlog,480,118,3.944,4.0969999999999995,4.055,4.124,0.428,0.386,0.38299999999999995,0.36200000000000004,854,480,yuv420p,29.97002997,,,
|
| 266 |
+
Vlog_480P-7d0c,Vlog,480,119,3.577,3.614,3.707,3.426,0.501,0.43,0.447,0.5,640,480,yuv422p,30,,,
|
metadata/YOUTUBE_UGC_720P_metadata.csv
ADDED
|
@@ -0,0 +1,257 @@
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|
| 1 |
+
vid,category,resolution,x_Subjects,MOSFull,MOSChunk00,MOSChunk05,MOSChunk10,stdFull,stdChunk00,stdChunk05,stdChunk10,width,height,pixfmt,framerate,nb_frames,bitdepth,bitrate
|
| 2 |
+
Animation_720P-0116,Animation,720,135,4.053999999999999,4.074,4.037,4.11,0.62,0.607,0.605,0.594,1280,720,yuv420p,24,,,
|
| 3 |
+
Animation_720P-01b3,Animation,720,112,3.638,3.46,3.2910000000000004,3.278,0.589,0.5539999999999999,0.701,0.6779999999999999,1280,720,yuv420p,25,,,
|
| 4 |
+
Animation_720P-06a6,Animation,720,123,3.9419999999999997,4.141,4.128,4.169,0.552,0.598,0.555,0.5579999999999999,1280,720,yuv420p,30,,,
|
| 5 |
+
Animation_720P-0acc,Animation,720,122,3.8760000000000003,3.7710000000000004,3.8810000000000002,3.886,0.501,0.597,0.521,0.5,1280,720,yuv420p,23.97602398,,,
|
| 6 |
+
Animation_720P-13b7,Animation,720,136,4.3180000000000005,4.095,4.19,3.898,0.46799999999999997,0.5760000000000001,0.501,0.545,1280,720,yuv420p,30,,,
|
| 7 |
+
Animation_720P-1a6d,Animation,720,129,4.283,4.285,4.251,4.261,0.526,0.507,0.542,0.574,1280,720,yuv420p,25,,,
|
| 8 |
+
Animation_720P-2525,Animation,720,106,3.73,3.719,3.696,3.804,0.498,0.43,0.496,0.496,1280,720,yuv420p,25,,,
|
| 9 |
+
Animation_720P-2abf,Animation,720,127,3.615,3.5210000000000004,3.366,3.488,0.6859999999999999,0.615,0.7440000000000001,0.6970000000000001,1280,720,yuv420p,25,,,
|
| 10 |
+
Animation_720P-3adc,Animation,720,118,3.575,3.404,3.467,3.463,0.48200000000000004,0.59,0.5579999999999999,0.589,1280,720,yuv420p,10,,,
|
| 11 |
+
Animation_720P-412a,Animation,720,145,4.249,4.2780000000000005,4.244,4.129,0.599,0.675,0.628,0.653,1280,720,yuv420p,23.97602398,,,
|
| 12 |
+
Animation_720P-41cf,Animation,720,131,3.862,3.89,3.926,4.077,0.46399999999999997,0.58,0.5529999999999999,0.624,1280,720,yuv420p,25,,,
|
| 13 |
+
Animation_720P-41d6,Animation,720,141,4.148,4.176,4.195,4.269,0.619,0.62,0.607,0.542,960,720,yuv420p,30,,,
|
| 14 |
+
Animation_720P-4268,Animation,720,123,3.846,3.682,3.7319999999999998,3.793,0.537,0.568,0.491,0.5429999999999999,1280,720,yuv420p,29.97002997,,,
|
| 15 |
+
Animation_720P-431c,Animation,720,135,4.396,4.41,4.084,4.206,0.49,0.457,0.507,0.43200000000000005,1280,720,yuv420p,25,,,
|
| 16 |
+
Animation_720P-57d9,Animation,720,121,3.522,2.7510000000000003,3.572,3.533,0.513,0.657,0.527,0.49200000000000005,1280,720,yuv420p,29.97002997,,,
|
| 17 |
+
Animation_720P-620f,Animation,720,127,4.522,4.64,4.641,4.501,0.47700000000000004,0.375,0.385,0.433,1280,720,yuv420p,25,,,
|
| 18 |
+
Animation_720P-6372,Animation,720,124,4.422,4.221,3.832,4.282,0.47700000000000004,0.615,0.564,0.507,1280,720,yuv420p,30,,,
|
| 19 |
+
Animation_720P-79ee,Animation,720,152,2.197,1.9680000000000002,2.2030000000000003,2.166,0.9109999999999999,0.645,0.609,0.604,1280,720,yuv420p,24,,,
|
| 20 |
+
Animation_720P-7b29,Animation,720,125,3.862,3.7760000000000002,3.687,3.804,0.47700000000000004,0.595,0.507,0.48,1280,720,yuv420p,24,,,
|
| 21 |
+
Animation_720P-7e7d,Animation,720,114,3.589,3.365,3.403,3.4819999999999998,0.63,0.784,0.8190000000000001,0.774,1280,720,yuv420p,30,,,
|
| 22 |
+
Animation_720P-7ff2,Animation,720,136,4.124,4.156000000000001,3.866,3.904,0.627,0.616,0.581,0.581,1280,720,yuv420p,30,,,
|
| 23 |
+
CoverSong_720P-014c,CoverSong,720,120,2.6180000000000003,2.359,2.387,2.4730000000000003,0.66,0.493,0.616,0.62,1280,720,yuv420p,29.97002997,,,
|
| 24 |
+
CoverSong_720P-0239,CoverSong,720,112,2.52,2.595,2.479,2.432,0.6,0.6459999999999999,0.5379999999999999,0.5760000000000001,1280,720,yuv420p,30,,,
|
| 25 |
+
CoverSong_720P-10f1,CoverSong,720,118,2.805,2.9389999999999996,2.772,2.532,0.787,0.7929999999999999,0.8640000000000001,1.088,1280,720,yuv420p,29.97002997,,,
|
| 26 |
+
CoverSong_720P-1393,CoverSong,720,116,4.059,3.88,3.8789999999999996,3.846,0.42700000000000005,0.46799999999999997,0.48200000000000004,0.478,1280,720,yuv420p,30,,,
|
| 27 |
+
CoverSong_720P-2d05,CoverSong,720,119,3.2239999999999998,3.0869999999999997,2.9960000000000004,3.3930000000000002,0.643,0.61,0.655,0.537,1280,720,yuv420p,25,,,
|
| 28 |
+
CoverSong_720P-4006,CoverSong,720,118,3.4139999999999997,3.397,3.3760000000000003,3.427,0.534,0.506,0.537,0.47,1280,720,yuv420p,25,,,
|
| 29 |
+
CoverSong_720P-449f,CoverSong,720,108,3.4960000000000004,3.532,3.477,3.403,0.525,0.573,0.514,0.475,1280,720,yuv420p,29.97002997,,,
|
| 30 |
+
CoverSong_720P-5689,CoverSong,720,133,2.3169999999999997,2.656,2.4930000000000003,2.426,1.0270000000000001,0.9179999999999999,1.117,0.981,960,720,yuv420p,29.97002997,,,
|
| 31 |
+
CoverSong_720P-5ba2,CoverSong,720,128,2.8339999999999996,2.8710000000000004,2.792,2.9,0.605,0.634,0.5579999999999999,0.561,1280,720,yuv420p,29.97002997,,,
|
| 32 |
+
CoverSong_720P-60d3,CoverSong,720,134,2.991,2.7939999999999996,2.7880000000000003,2.8,0.5770000000000001,0.49200000000000005,0.462,0.47100000000000003,1280,720,yuv420p,30.12,,,
|
| 33 |
+
CoverSong_720P-6626,CoverSong,720,123,3.3160000000000003,2.952,3.0389999999999997,3.155,0.479,0.588,0.58,0.53,1280,720,yuv420p,15,,,
|
| 34 |
+
CoverSong_720P-6b8c,CoverSong,720,123,2.988,2.8160000000000003,2.888,2.924,0.665,0.639,0.6940000000000001,0.648,1280,720,yuv420p,25,,,
|
| 35 |
+
CoverSong_720P-6d6a,CoverSong,720,115,3.52,3.4589999999999996,3.492,3.3480000000000003,0.5489999999999999,0.5529999999999999,0.511,0.496,1280,720,yuv420p,30,,,
|
| 36 |
+
CoverSong_720P-6d81,CoverSong,720,120,3.145,3.049,3.096,3.092,0.5760000000000001,0.633,0.5579999999999999,0.498,1280,720,yuv420p,29.97002997,,,
|
| 37 |
+
CoverSong_720P-7360,CoverSong,720,132,2.798,2.682,2.502,2.521,0.639,0.5,0.47600000000000003,0.49,1280,720,yuv420p,29.97002997,,,
|
| 38 |
+
CoverSong_720P-7539,CoverSong,720,145,2.3480000000000003,1.868,2.077,2.6710000000000003,0.9359999999999999,0.802,0.9670000000000001,0.821,1280,720,yuv420p,24,,,
|
| 39 |
+
Gaming_720P-0fba,Gaming,720,120,4.0889999999999995,3.77,3.763,3.785,0.61,0.521,0.52,0.565,1280,720,yuv420p,60,,,
|
| 40 |
+
Gaming_720P-0fdb,Gaming,720,115,3.263,2.91,3.071,3.26,0.6890000000000001,0.7659999999999999,0.728,0.6809999999999999,1280,720,yuv420p,30,,,
|
| 41 |
+
Gaming_720P-103a,Gaming,720,127,4.056,3.932,4.001,4.006,0.61,0.495,0.405,0.503,1280,720,yuv420p,30,,,
|
| 42 |
+
Gaming_720P-1cbb,Gaming,720,124,4.01,3.892,4.051,3.937,0.5329999999999999,0.535,0.419,0.491,1280,720,yuv420p,30,,,
|
| 43 |
+
Gaming_720P-221d,Gaming,720,135,3.252,3.2060000000000004,3.2110000000000003,3.242,0.66,0.628,0.667,0.637,1280,720,yuv420p,26.934,,,
|
| 44 |
+
Gaming_720P-25aa,Gaming,720,127,2.952,2.924,2.9,2.855,0.8859999999999999,0.773,0.757,0.8270000000000001,1280,720,yuv420p,29.97002997,,,
|
| 45 |
+
Gaming_720P-2dbe,Gaming,720,138,4.327,4.229,4.217,4.1339999999999995,0.489,0.584,0.5489999999999999,0.596,1280,720,yuv420p,29.97002997,,,
|
| 46 |
+
Gaming_720P-312f,Gaming,720,125,3.826,3.7910000000000004,3.761,3.7089999999999996,0.5,0.545,0.541,0.512,1280,720,yuv420p,60.04899777,,,
|
| 47 |
+
Gaming_720P-324d,Gaming,720,113,3.145,2.755,3.076,3.043,0.843,0.8909999999999999,0.8190000000000001,0.8640000000000001,3840,720,yuv420p,29.896,,,
|
| 48 |
+
Gaming_720P-3524,Gaming,720,114,3.11,2.94,2.501,2.225,0.728,0.7879999999999999,0.674,0.785,1280,720,yuv420p,30,,,
|
| 49 |
+
Gaming_720P-40b2,Gaming,720,124,3.855,3.943,3.861,3.7910000000000004,0.435,0.508,0.518,0.491,1280,720,yuv420p,29.97002997,,,
|
| 50 |
+
Gaming_720P-469a,Gaming,720,143,4.124,4.2010000000000005,3.977,3.917,0.575,0.52,0.5529999999999999,0.475,1280,720,yuv420p,60,,,
|
| 51 |
+
Gaming_720P-4813,Gaming,720,109,4.29,4.144,4.0889999999999995,3.908,0.433,0.526,0.434,0.569,1280,720,yuv420p,60.18,,,
|
| 52 |
+
Gaming_720P-493e,Gaming,720,113,3.5669999999999997,3.688,3.694,3.24,0.565,0.64,0.672,0.725,1280,720,yuv420p,29.97002997,,,
|
| 53 |
+
Gaming_720P-4cda,Gaming,720,122,3.62,3.446,3.593,3.445,0.512,0.574,0.505,0.465,1280,720,yuv420p,30,,,
|
| 54 |
+
Gaming_720P-5973,Gaming,720,123,4.067,4.122,4.1610000000000005,4.082,0.474,0.612,0.603,0.588,1280,720,yuv420p,30,,,
|
| 55 |
+
Gaming_720P-5ba2,Gaming,720,107,4.281000000000001,4.247,4.35,4.246,0.564,0.48100000000000004,0.48,0.503,1280,720,yuv420p,30,,,
|
| 56 |
+
Gaming_720P-6403,Gaming,720,121,4.023,4.013999999999999,4.221,3.964,0.445,0.516,0.402,0.531,1280,720,yuv420p,60,,,
|
| 57 |
+
Gaming_720P-64b0,Gaming,720,108,4.396,4.285,4.3839999999999995,4.44,0.48,0.511,0.483,0.48,1280,720,yuv420p,29.97002997,,,
|
| 58 |
+
Gaming_720P-6625,Gaming,720,120,3.813,3.5660000000000003,3.8789999999999996,3.738,0.47600000000000003,0.722,0.57,0.583,1280,720,yuv420p,29.97002997,,,
|
| 59 |
+
Gaming_720P-6658,Gaming,720,123,3.715,3.7230000000000003,3.522,3.4419999999999997,0.498,0.502,0.449,0.444,1280,720,yuv420p,30,,,
|
| 60 |
+
Gaming_720P-6a45,Gaming,720,120,3.967,4.1160000000000005,4.188,4.109,0.532,0.627,0.609,0.56,1280,720,yuv420p,30,,,
|
| 61 |
+
Gaming_720P-7afd,Gaming,720,125,2.4859999999999998,2.283,2.722,2.602,0.755,0.637,0.588,0.541,1280,720,yuv420p,30,,,
|
| 62 |
+
HowTo_720P-017a,HowTo,720,106,3.762,3.74,3.718,3.835,0.49200000000000005,0.45799999999999996,0.455,0.45399999999999996,1280,720,yuv420p,30,,,
|
| 63 |
+
HowTo_720P-06eb,HowTo,720,143,4.13,3.986,4.091,3.9360000000000004,0.624,0.706,0.6940000000000001,0.564,1280,720,yuv420p,30,,,
|
| 64 |
+
HowTo_720P-0a67,HowTo,720,149,4.315,4.303999999999999,4.319,4.301,0.455,0.516,0.466,0.506,1280,720,yuv420p,29.97002997,,,
|
| 65 |
+
HowTo_720P-0b01,HowTo,720,119,4.111000000000001,4.088,3.9989999999999997,3.9160000000000004,0.40399999999999997,0.441,0.374,0.47,1280,720,yuv420p,23.97602398,,,
|
| 66 |
+
HowTo_720P-0c47,HowTo,720,140,4.364,4.242,3.929,4.052,0.44299999999999995,0.449,0.489,0.455,1280,720,yuv420p,25,,,
|
| 67 |
+
HowTo_720P-111e,HowTo,720,112,3.938,4.0569999999999995,4.1,3.985,0.45,0.5870000000000001,0.61,0.43799999999999994,1280,720,yuv420p,30,,,
|
| 68 |
+
HowTo_720P-12f7,HowTo,720,130,3.3,3.1239999999999997,3.138,2.926,0.522,0.47600000000000003,0.523,0.599,1280,720,yuv420p,60,,,
|
| 69 |
+
HowTo_720P-21c6,HowTo,720,117,2.838,2.71,2.772,2.867,0.593,0.596,0.609,0.604,1280,720,yuv422p,29.97002997,,,
|
| 70 |
+
HowTo_720P-269e,HowTo,720,107,3.6430000000000002,3.653,3.639,3.575,0.614,0.682,0.615,0.484,1280,720,yuv420p,20,,,
|
| 71 |
+
HowTo_720P-2cc6,HowTo,720,137,2.8139999999999996,2.577,2.852,2.741,0.691,0.6459999999999999,0.62,0.619,1280,720,yuv420p,30.156,,,
|
| 72 |
+
HowTo_720P-37d0,HowTo,720,147,4.428999999999999,4.349,4.388999999999999,4.5760000000000005,0.46799999999999997,0.47100000000000003,0.42100000000000004,0.44799999999999995,1280,720,yuv420p,29.97002997,,,
|
| 73 |
+
HowTo_720P-3813,HowTo,720,129,3.072,2.93,2.985,3.074,0.637,0.6459999999999999,0.648,0.655,1280,720,yuv420p,25,,,
|
| 74 |
+
HowTo_720P-3a5d,HowTo,720,117,2.536,2.371,2.3080000000000003,1.824,0.585,0.64,0.596,0.698,1280,720,yuv420p,29.97002997,,,
|
| 75 |
+
HowTo_720P-479b,HowTo,720,114,3.62,3.5839999999999996,3.5239999999999996,3.655,0.456,0.518,0.496,0.467,1280,720,yuv420p,29.97002997,,,
|
| 76 |
+
HowTo_720P-483c,HowTo,720,125,3.485,3.301,3.1069999999999998,3.505,0.516,0.595,0.645,0.639,1280,720,yuv420p,25,,,
|
| 77 |
+
HowTo_720P-4d55,HowTo,720,122,3.947,4.249,4.149,4.316,0.475,0.46,0.423,0.462,1280,720,yuv420p,29.97002997,,,
|
| 78 |
+
HowTo_720P-4d9e,HowTo,720,117,3.688,3.238,3.2539999999999996,3.18,0.603,0.594,0.534,0.638,1280,720,yuv420p,30,,,
|
| 79 |
+
HowTo_720P-6323,HowTo,720,114,3.7319999999999998,3.468,3.392,3.4989999999999997,0.431,0.46799999999999997,0.505,0.46399999999999997,1280,720,yuv420p,29.97002997,,,
|
| 80 |
+
HowTo_720P-6791,HowTo,720,134,2.263,2.408,1.8619999999999999,1.73,1.063,0.991,0.841,0.816,1280,720,yuv420p,30,,,
|
| 81 |
+
HowTo_720P-7782,HowTo,720,114,3.761,3.7430000000000003,3.49,3.718,0.475,0.499,0.51,0.56,1280,720,yuv420p,30,,,
|
| 82 |
+
HowTo_720P-7878,HowTo,720,140,3.674,3.6839999999999997,3.321,3.523,0.578,0.556,0.701,0.563,1280,720,yuv420p,30,,,
|
| 83 |
+
HowTo_720P-7c38,HowTo,720,118,4.074,4.188,3.87,3.929,0.674,0.508,0.523,0.485,1280,720,yuv420p,30,,,
|
| 84 |
+
Lecture_720P-003a,Lecture,720,117,3.812,3.8689999999999998,3.853,3.878,0.45899999999999996,0.503,0.493,0.474,1280,720,yuv420p,24,,,
|
| 85 |
+
Lecture_720P-07e0,Lecture,720,130,4.082,4.044,3.826,3.906,0.359,0.40399999999999997,0.439,0.518,1280,720,yuv420p,30,,,
|
| 86 |
+
Lecture_720P-094d,Lecture,720,108,3.616,3.548,3.502,3.398,0.49200000000000005,0.486,0.494,0.44799999999999995,1280,720,yuv420p,19.933,,,
|
| 87 |
+
Lecture_720P-0f64,Lecture,720,132,3.58,3.452,3.44,3.5,0.5720000000000001,0.596,0.602,0.547,1280,720,yuv420p,24,,,
|
| 88 |
+
Lecture_720P-1033,Lecture,720,124,3.5410000000000004,3.465,3.608,3.6460000000000004,0.491,0.49700000000000005,0.469,0.48200000000000004,1280,720,yuv420p,25,,,
|
| 89 |
+
Lecture_720P-10bc,Lecture,720,120,3.116,3.157,3.113,2.986,0.637,0.521,0.596,0.6509999999999999,1280,720,yuv420p,23.351,,,
|
| 90 |
+
Lecture_720P-11e7,Lecture,720,117,2.569,2.434,2.4130000000000003,2.451,0.546,0.568,0.512,0.545,1280,720,yuv420p,25,,,
|
| 91 |
+
Lecture_720P-1e62,Lecture,720,127,3.958,3.752,3.655,3.904,0.511,0.46,0.48700000000000004,0.48200000000000004,1280,720,yuv420p,30,,,
|
| 92 |
+
Lecture_720P-1f22,Lecture,720,132,4.2330000000000005,4.178999999999999,4.113,4.025,0.591,0.605,0.638,0.7040000000000001,1280,720,yuv420p,30,,,
|
| 93 |
+
Lecture_720P-2442,Lecture,720,126,3.39,3.545,3.3960000000000004,3.55,0.46299999999999997,0.5820000000000001,0.54,0.546,1280,720,yuv420p,24,,,
|
| 94 |
+
Lecture_720P-2b0f,Lecture,720,117,3.852,4.172,4.065,,0.57,0.659,0.669,,1152,720,yuv420p,7.96,,,
|
| 95 |
+
Lecture_720P-2f38,Lecture,720,129,3.552,3.448,3.446,3.512,0.547,0.499,0.5,0.46799999999999997,1280,720,yuv420p,30,,,
|
| 96 |
+
Lecture_720P-2f8c,Lecture,720,122,3.839,3.807,3.79,3.822,0.44,0.516,0.502,0.46799999999999997,1280,720,yuv420p,30,,,
|
| 97 |
+
Lecture_720P-3b7f,Lecture,720,124,4.106,4.013999999999999,3.9760000000000004,3.985,0.361,0.5660000000000001,0.48700000000000004,0.486,1280,720,yuv420p,29.97002997,,,
|
| 98 |
+
Lecture_720P-3f9f,Lecture,720,120,3.4789999999999996,3.385,3.5810000000000004,3.6639999999999997,0.508,0.561,0.518,0.479,956,720,yuv420p,30,,,
|
| 99 |
+
Lecture_720P-4c87,Lecture,720,129,3.7430000000000003,3.78,3.864,3.7489999999999997,0.479,0.491,0.478,0.508,1280,720,yuv420p,29.97002997,,,
|
| 100 |
+
Lecture_720P-50b9,Lecture,720,112,3.5789999999999997,3.457,3.4410000000000003,3.427,0.46799999999999997,0.47100000000000003,0.589,0.515,1280,720,yuv420p,16.98301698,,,
|
| 101 |
+
Lecture_720P-5120,Lecture,720,144,4.295,4.256,4.132,4.133,0.457,0.489,0.588,0.37799999999999995,1280,720,yuv420p,29.97002997,,,
|
| 102 |
+
Lecture_720P-53a1,Lecture,720,117,3.5860000000000003,3.5580000000000003,3.6310000000000002,3.53,0.474,0.524,0.5660000000000001,0.51,1280,720,yuv420p,29.97002997,,,
|
| 103 |
+
Lecture_720P-5725,Lecture,720,130,4.262,4.309,4.176,4.327,0.46799999999999997,0.447,0.5920000000000001,0.503,1280,720,yuv420p,25,,,
|
| 104 |
+
Lecture_720P-5c7e,Lecture,720,122,3.846,3.781,3.7939999999999996,3.719,0.528,0.43700000000000006,0.45299999999999996,0.451,1280,720,yuv420p,23.97602398,,,
|
| 105 |
+
Lecture_720P-6016,Lecture,720,115,3.043,2.975,3.0389999999999997,3.074,0.623,0.5870000000000001,0.633,0.5720000000000001,1280,720,yuv420p,29.97002997,,,
|
| 106 |
+
Lecture_720P-6951,Lecture,720,106,4.163,3.838,3.888,3.802,0.552,0.48200000000000004,0.525,0.507,1280,720,yuv420p,29.97,,,
|
| 107 |
+
Lecture_720P-7189,Lecture,720,137,3.8689999999999998,4.025,3.947,3.931,0.465,0.36700000000000005,0.47600000000000003,0.489,1280,720,yuv420p,25,,,
|
| 108 |
+
Lecture_720P-7e40,Lecture,720,115,4.246,4.198,4.197,4.406000000000001,0.56,0.627,0.693,0.43200000000000005,1280,720,yuv420p,29.97002997,,,
|
| 109 |
+
LiveMusic_720P-0cd5,LiveMusic,720,109,4.042,4.248,4.078,4.0760000000000005,0.449,0.506,0.601,0.581,1280,720,yuv420p,23.97602398,,,
|
| 110 |
+
LiveMusic_720P-0d9f,LiveMusic,720,108,3.319,3.108,3.3139999999999996,3.2510000000000003,0.47600000000000003,0.7070000000000001,0.5579999999999999,0.66,1280,720,yuv420p,25,,,
|
| 111 |
+
LiveMusic_720P-2620,LiveMusic,720,121,3.721,3.608,3.58,3.552,0.65,0.48,0.496,0.47,1280,720,yuv420p,30,,,
|
| 112 |
+
LiveMusic_720P-267b,LiveMusic,720,116,3.525,3.4360000000000004,3.5180000000000002,3.068,0.508,0.46299999999999997,0.496,0.608,1280,720,yuv420p,25,,,
|
| 113 |
+
LiveMusic_720P-289b,LiveMusic,720,151,2.436,2.101,2.05,2.233,1.002,0.8,0.818,0.966,1280,720,yuv420p,59.94006309,,,
|
| 114 |
+
LiveMusic_720P-3320,LiveMusic,720,113,3.45,3.142,3.116,2.783,0.5720000000000001,0.5479999999999999,0.639,0.561,1280,720,yuv420p,29.97002997,,,
|
| 115 |
+
LiveMusic_720P-4ae2,LiveMusic,720,110,3.285,3.237,3.1660000000000004,3.3110000000000004,0.405,0.46799999999999997,0.519,0.445,1280,720,yuv420p,25,,,
|
| 116 |
+
LiveMusic_720P-58db,LiveMusic,720,124,4.382,4.315,4.218,4.127,0.42100000000000004,0.455,0.479,0.525,1280,720,yuv420p,25,,,
|
| 117 |
+
LiveMusic_720P-6343,LiveMusic,720,115,3.304,3.259,3.2030000000000003,3.2230000000000003,0.5539999999999999,0.569,0.556,0.545,1280,720,yuv422p,30,,,
|
| 118 |
+
LiveMusic_720P-6452,LiveMusic,720,123,3.0980000000000003,2.911,2.957,2.972,0.631,0.65,0.583,0.573,1280,720,yuv422p,30,,,
|
| 119 |
+
LiveMusic_720P-653e,LiveMusic,720,117,3.112,3.083,3.286,3.355,0.613,0.6859999999999999,0.581,0.478,1280,720,yuv420p,25,,,
|
| 120 |
+
LiveMusic_720P-65f6,LiveMusic,720,116,4.019,3.7960000000000003,3.909,3.7939999999999996,0.39,0.475,0.44,0.46399999999999997,1280,720,yuv420p,25,,,
|
| 121 |
+
LiveMusic_720P-66df,LiveMusic,720,117,4.592,4.579,4.656000000000001,4.553,0.41200000000000003,0.41,0.39,0.402,1280,720,yuv420p,29.97002997,,,
|
| 122 |
+
LiveMusic_720P-71c5,LiveMusic,720,125,3.605,3.3510000000000004,3.577,3.661,0.518,0.5579999999999999,0.47700000000000004,0.506,1280,720,yuv420p,25,,,
|
| 123 |
+
LyricVideo_720P-068d,LyricVideo,720,123,3.825,4.041,3.975,4.084,0.5770000000000001,0.6759999999999999,0.6829999999999999,0.618,1280,720,yuv420p,29.97002997,,,
|
| 124 |
+
LyricVideo_720P-0940,LyricVideo,720,118,3.045,3.259,3.037,2.998,0.631,0.557,0.622,0.601,1280,720,yuv420p,30,,,
|
| 125 |
+
LyricVideo_720P-09ed,LyricVideo,720,129,3.0260000000000002,2.762,3.176,3.907,0.762,0.733,0.701,0.6579999999999999,1280,720,yuv420p,29.97002997,,,
|
| 126 |
+
LyricVideo_720P-0ae4,LyricVideo,720,121,3.717,3.563,3.682,3.673,0.479,0.541,0.507,0.489,1280,720,yuv420p,29.97002997,,,
|
| 127 |
+
LyricVideo_720P-1c89,LyricVideo,720,140,3.93,3.83,3.873,3.943,0.517,0.532,0.49700000000000005,0.528,1280,720,yuv420p,29.97002997,,,
|
| 128 |
+
LyricVideo_720P-2d24,LyricVideo,720,131,4.199,4.064,4.045,3.9810000000000003,0.672,0.684,0.674,0.743,1280,720,yuv420p,29.97002997,,,
|
| 129 |
+
LyricVideo_720P-36fb,LyricVideo,720,122,3.839,3.886,3.78,3.778,0.512,0.45899999999999996,0.515,0.504,1280,720,yuv420p,30,,,
|
| 130 |
+
LyricVideo_720P-4253,LyricVideo,720,137,4.066,3.784,3.86,3.823,0.6559999999999999,0.541,0.544,0.61,1280,720,yuv420p,23.97602398,,,
|
| 131 |
+
LyricVideo_720P-47a9,LyricVideo,720,112,2.8160000000000003,2.331,2.258,2.727,0.852,1.005,0.925,0.8220000000000001,1280,720,yuv420p,23.97602398,,,
|
| 132 |
+
LyricVideo_720P-59ed,LyricVideo,720,135,4.315,4.2780000000000005,4.3260000000000005,4.322,0.522,0.5870000000000001,0.532,0.529,1280,720,yuv420p,50,,,
|
| 133 |
+
LyricVideo_720P-5d3a,LyricVideo,720,127,3.9389999999999996,4.266,4.298,4.3660000000000005,0.513,0.479,0.595,0.447,960,720,yuv420p,29.97002997,,,
|
| 134 |
+
LyricVideo_720P-6f0c,LyricVideo,720,116,2.803,2.506,2.678,2.682,0.7340000000000001,0.6609999999999999,0.733,0.7120000000000001,1280,720,yuv420p,30,,,
|
| 135 |
+
LyricVideo_720P-6fc9,LyricVideo,720,124,2.6710000000000003,2.364,2.426,2.427,0.568,0.491,0.488,0.474,1280,720,yuv420p,30,,,
|
| 136 |
+
LyricVideo_720P-739a,LyricVideo,720,125,2.877,2.772,2.563,2.667,0.708,0.774,0.6920000000000001,0.667,1280,720,yuv420p,25,,,
|
| 137 |
+
LyricVideo_720P-74a0,LyricVideo,720,136,4.415,4.252,4.203,4.206,0.425,0.56,0.602,0.513,1280,720,yuv420p,29.97002997,,,
|
| 138 |
+
LyricVideo_720P-7f44,LyricVideo,720,142,4.196000000000001,4.118,4.16,4.202,0.574,0.677,0.612,0.627,1280,720,yuv420p,25,,,
|
| 139 |
+
MusicVideo_720P-0355,MusicVideo,720,116,1.9069999999999998,2.009,1.985,2.115,1.01,1.07,1.0390000000000001,1.064,1280,720,yuv420p,29.97002997,,,
|
| 140 |
+
MusicVideo_720P-0752,MusicVideo,720,136,4.365,4.367,4.272,4.0360000000000005,0.495,0.40399999999999997,0.42100000000000004,0.502,1280,720,yuv420p,23.97602398,,,
|
| 141 |
+
MusicVideo_720P-14f0,MusicVideo,720,120,2.3819999999999997,2.574,2.4859999999999998,2.633,0.6559999999999999,0.604,0.622,0.639,1280,720,yuv420p,30,,,
|
| 142 |
+
MusicVideo_720P-2d7d,MusicVideo,720,126,2.988,2.897,2.145,3.103,0.708,0.757,0.85,0.823,1280,720,yuv420p,60,,,
|
| 143 |
+
MusicVideo_720P-3698,MusicVideo,720,119,4.378,4.331,4.421,4.282,0.512,0.423,0.41600000000000004,0.445,1280,720,yuv420p,29.97002997,,,
|
| 144 |
+
MusicVideo_720P-3b11,MusicVideo,720,109,3.6,3.4610000000000003,3.4360000000000004,3.55,0.484,0.526,0.499,0.55,960,720,yuv420p,29.97002997,,,
|
| 145 |
+
MusicVideo_720P-3c4c,MusicVideo,720,152,4.135,4.085,4.1160000000000005,3.963,0.583,0.434,0.405,0.469,1280,720,yuv420p,25,,,
|
| 146 |
+
MusicVideo_720P-44c1,MusicVideo,720,136,4.499,4.406000000000001,4.496,4.462,0.42200000000000004,0.441,0.4,0.402,1280,720,yuv420p,23.97602398,,,
|
| 147 |
+
MusicVideo_720P-4895,MusicVideo,720,135,4.173,3.998,3.73,3.812,0.606,0.539,0.557,0.5529999999999999,1280,720,yuv420p,59.94006309,,,
|
| 148 |
+
MusicVideo_720P-4ad2,MusicVideo,720,117,3.7560000000000002,3.775,3.767,3.8160000000000003,0.44,0.46799999999999997,0.44799999999999995,0.479,1280,720,yuv420p,29.97002997,,,
|
| 149 |
+
MusicVideo_720P-575d,MusicVideo,720,144,3.242,3.5069999999999997,3.103,3.1510000000000002,0.762,0.616,0.775,0.753,1280,720,yuv422p10le,30,,,
|
| 150 |
+
MusicVideo_720P-5c9c,MusicVideo,720,120,2.471,2.273,2.367,2.204,0.737,0.595,0.685,0.759,1280,720,yuv420p,50,,,
|
| 151 |
+
MusicVideo_720P-62df,MusicVideo,720,127,4.246,4.296,4.248,4.021,0.602,0.441,0.45799999999999996,0.58,1280,720,yuv420p,25,,,
|
| 152 |
+
MusicVideo_720P-734f,MusicVideo,720,140,3.8480000000000003,3.7439999999999998,4.335,4.002,0.5379999999999999,0.649,0.52,0.494,1280,720,yuv420p,23.97602398,,,
|
| 153 |
+
MusicVideo_720P-7bcf,MusicVideo,720,121,3.536,3.319,3.3930000000000002,3.103,0.517,0.537,0.513,0.612,1280,720,yuv420p,59.94006309,,,
|
| 154 |
+
NewsClip_720P-04ba,NewsClip,720,133,3.49,3.346,3.262,3.053,0.489,0.491,0.46299999999999997,0.665,1280,720,yuv420p,25,,,
|
| 155 |
+
NewsClip_720P-0c81,NewsClip,720,127,3.8930000000000002,3.739,3.8169999999999997,3.745,0.529,0.551,0.439,0.465,1280,720,yuv420p,25,,,
|
| 156 |
+
NewsClip_720P-1971,NewsClip,720,134,4.272,3.9619999999999997,3.801,4.013,0.483,0.537,0.5329999999999999,0.507,1280,720,yuv420p,29.97002997,,,
|
| 157 |
+
NewsClip_720P-2182,NewsClip,720,117,3.4939999999999998,3.44,3.489,3.614,0.54,0.496,0.49,0.49200000000000005,1280,720,yuv420p,29.97002997,,,
|
| 158 |
+
NewsClip_720P-23e0,NewsClip,720,115,4.11,4.2219999999999995,4.158,3.963,0.366,0.456,0.545,0.49700000000000005,1280,720,yuv420p,25,,,
|
| 159 |
+
NewsClip_720P-2882,NewsClip,720,118,3.667,3.573,3.6239999999999997,3.5410000000000004,0.47100000000000003,0.446,0.45299999999999996,0.444,1280,720,yuv420p,25,,,
|
| 160 |
+
NewsClip_720P-35d9,NewsClip,720,116,3.017,3.0869999999999997,2.81,2.6310000000000002,0.6940000000000001,0.733,0.555,0.677,1280,720,yuv420p,25,,,
|
| 161 |
+
NewsClip_720P-37f7,NewsClip,720,129,3.716,3.84,3.803,3.813,0.466,0.47700000000000004,0.5379999999999999,0.512,1280,720,yuv420p,29.97002997,,,
|
| 162 |
+
NewsClip_720P-4603,NewsClip,720,125,3.4539999999999997,3.279,3.477,3.2939999999999996,0.49,0.456,0.508,0.547,1280,720,yuv420p,25,,,
|
| 163 |
+
NewsClip_720P-4e87,NewsClip,720,136,4.368,4.477,4.448,4.363,0.469,0.431,0.431,0.473,1280,720,yuv420p,29.97002997,,,
|
| 164 |
+
NewsClip_720P-5564,NewsClip,720,146,4.377,4.512,4.422,4.656000000000001,0.45299999999999996,0.418,0.457,0.385,1280,720,yuv420p,25,,,
|
| 165 |
+
NewsClip_720P-5787,NewsClip,720,109,4.4830000000000005,4.32,3.821,3.79,0.45,0.484,0.446,0.515,1280,720,yuv420p,29.97002997,,,
|
| 166 |
+
NewsClip_720P-579b,NewsClip,720,110,2.866,3.0239999999999996,3.0180000000000002,2.461,0.639,0.642,0.618,0.555,1280,720,yuv420p,29.97002997,,,
|
| 167 |
+
NewsClip_720P-5d89,NewsClip,720,137,4.48,4.349,4.414,4.462,0.436,0.44799999999999995,0.43200000000000005,0.42200000000000004,1280,720,yuv420p,29.955,,,
|
| 168 |
+
NewsClip_720P-6016,NewsClip,720,122,4.522,4.3260000000000005,4.218999999999999,4.372,0.39899999999999997,0.435,0.531,0.474,1280,720,yuv420p,25,,,
|
| 169 |
+
NewsClip_720P-6106,NewsClip,720,110,2.9960000000000004,2.838,2.924,3.12,0.634,0.679,0.674,0.659,1280,720,yuv420p,30,,,
|
| 170 |
+
NewsClip_720P-672c,NewsClip,720,121,3.987,4.052,3.864,3.846,0.496,0.413,0.465,0.47700000000000004,1280,720,yuv420p,25,,,
|
| 171 |
+
NewsClip_720P-6a19,NewsClip,720,116,3.5469999999999997,2.9930000000000003,3.484,3.687,0.5,0.609,0.51,0.514,1280,720,yuv420p,29.97002997,,,
|
| 172 |
+
NewsClip_720P-6cde,NewsClip,720,128,4.297,4.439,4.345,4.357,0.586,0.431,0.465,0.529,1280,720,yuv420p,24,,,
|
| 173 |
+
NewsClip_720P-72d2,NewsClip,720,109,4.357,4.3389999999999995,3.5639999999999996,3.758,0.507,0.494,0.506,0.5489999999999999,1280,720,yuv420p,29.97002997,,,
|
| 174 |
+
NewsClip_720P-739b,NewsClip,720,148,4.007,3.925,3.84,3.9819999999999998,0.608,0.499,0.47200000000000003,0.46299999999999997,1280,720,yuv420p,29.97002997,,,
|
| 175 |
+
NewsClip_720P-7745,NewsClip,720,129,4.5280000000000005,4.465,4.49,4.431,0.446,0.433,0.43200000000000005,0.449,1280,720,yuv420p,29.97002997,,,
|
| 176 |
+
NewsClip_720P-7b7e,NewsClip,720,119,4.145,4.038,4.2410000000000005,4.1080000000000005,0.613,0.509,0.452,0.384,1280,720,yuv420p,29.97002997,,,
|
| 177 |
+
NewsClip_720P-7b97,NewsClip,720,109,2.803,2.025,1.825,2.866,0.861,0.851,0.7859999999999999,0.713,1280,720,yuv420p,29.97002997,,,
|
| 178 |
+
NewsClip_720P-7c67,NewsClip,720,125,4.434,4.497,4.412,3.911,0.474,0.436,0.48700000000000004,0.524,1280,720,yuv420p,29.97002997,,,
|
| 179 |
+
NewsClip_720P-7e56,NewsClip,720,124,4.41,4.402,3.83,3.875,0.42,0.38,0.517,0.493,1280,720,yuv420p,29.97002997,,,
|
| 180 |
+
Sports_720P-00a1,Sports,720,112,4.061,4.313,4.335,4.296,0.447,0.445,0.478,0.473,1280,960,yuv420p,48,,,
|
| 181 |
+
Sports_720P-0104,Sports,720,141,4.223,4.272,4.327,4.365,0.603,0.556,0.47,0.451,1280,720,yuv420p,25,,,
|
| 182 |
+
Sports_720P-058f,Sports,720,137,4.28,4.224,4.309,4.287,0.52,0.619,0.5429999999999999,0.52,1280,720,yuv420p,119.88016529999999,,,
|
| 183 |
+
Sports_720P-07d0,Sports,720,129,3.863,3.813,3.665,3.595,0.47100000000000003,0.501,0.45899999999999996,0.542,1280,720,yuv420p,29.97002997,,,
|
| 184 |
+
Sports_720P-0b9e,Sports,720,130,3.9419999999999997,3.763,3.8369999999999997,3.9389999999999996,0.48200000000000004,0.544,0.46,0.547,1280,720,yuv420p,50,,,
|
| 185 |
+
Sports_720P-17fb,Sports,720,128,3.591,3.32,3.415,3.364,0.525,0.68,0.652,0.6459999999999999,1280,720,yuv420p,30.052,,,
|
| 186 |
+
Sports_720P-2191,Sports,720,128,3.7889999999999997,3.971,3.917,3.951,0.551,0.499,0.495,0.537,1280,720,yuv420p,59.94006309,,,
|
| 187 |
+
Sports_720P-2234,Sports,720,142,4.16,4.073,4.292,4.186,0.561,0.375,0.485,0.511,1280,720,yuv420p,29.97002997,,,
|
| 188 |
+
Sports_720P-2632,Sports,720,112,3.884,3.687,3.662,3.785,0.491,0.525,0.52,0.48200000000000004,1280,720,yuv420p,25,,,
|
| 189 |
+
Sports_720P-2c06,Sports,720,117,3.978,3.5810000000000004,3.8339999999999996,3.8360000000000003,0.42100000000000004,0.44299999999999995,0.515,0.431,1280,720,yuv420p,25,,,
|
| 190 |
+
Sports_720P-2c80,Sports,720,115,3.136,3.235,3.1060000000000003,3.182,0.654,0.515,0.627,0.615,1280,720,yuv420p,29.795,,,
|
| 191 |
+
Sports_720P-3072,Sports,720,131,3.18,3.217,3.071,2.9610000000000003,0.679,0.675,0.6940000000000001,0.747,1280,720,yuv420p,29.97002997,,,
|
| 192 |
+
Sports_720P-3338,Sports,720,115,4.4830000000000005,4.371,4.31,4.367,0.426,0.451,0.434,0.408,1280,720,yuv420p,24,,,
|
| 193 |
+
Sports_720P-33c6,Sports,720,124,2.985,2.991,2.9160000000000004,2.978,0.624,0.6,0.5539999999999999,0.621,1280,720,yuv422p,30,,,
|
| 194 |
+
Sports_720P-38a1,Sports,720,129,3.165,2.782,2.792,3.3689999999999998,0.695,0.616,0.626,0.54,1280,720,yuv420p,30,,,
|
| 195 |
+
Sports_720P-3eb4,Sports,720,115,3.802,3.866,3.8160000000000003,3.917,0.47100000000000003,0.496,0.503,0.489,1280,720,yuv420p,25,,,
|
| 196 |
+
Sports_720P-3ffe,Sports,720,127,3.011,2.8789999999999996,3.09,2.827,0.6629999999999999,0.606,0.626,0.655,1280,720,yuv420p,30.03,,,
|
| 197 |
+
Sports_720P-4b69,Sports,720,113,3.903,3.7030000000000003,3.6180000000000003,3.615,0.498,0.541,0.59,0.501,1280,720,yuv420p,30,,,
|
| 198 |
+
Sports_720P-4c6f,Sports,720,112,4.105,3.701,3.9,3.9219999999999997,0.389,0.444,0.41,0.501,1280,720,yuv420p,25,,,
|
| 199 |
+
Sports_720P-50cf,Sports,720,132,3.372,3.2969999999999997,3.2889999999999997,3.335,0.546,0.489,0.515,0.546,1280,720,yuv420p,24,,,
|
| 200 |
+
Sports_720P-531c,Sports,720,112,4.555,4.515,4.532,4.473,0.38799999999999996,0.409,0.405,0.46399999999999997,1280,720,yuv420p,29.97002997,,,
|
| 201 |
+
Sports_720P-5833,Sports,720,119,3.205,3.0380000000000003,3.0189999999999997,3.5610000000000004,0.6829999999999999,0.6729999999999999,0.737,0.593,1280,720,yuv420p,29.97002997,,,
|
| 202 |
+
Sports_720P-5ae1,Sports,720,141,4.333,4.287,4.244,4.381,0.52,0.601,0.619,0.502,1280,720,yuv420p,30,,,
|
| 203 |
+
Sports_720P-5bfd,Sports,720,127,4.677,4.667,4.627,4.531000000000001,0.413,0.373,0.39,0.38799999999999996,1280,960,yuv420p,47.95205479,,,
|
| 204 |
+
Sports_720P-5e39,Sports,720,121,3.685,3.636,3.6839999999999997,3.4419999999999997,0.469,0.436,0.424,0.452,1280,720,yuv420p,29.97002997,,,
|
| 205 |
+
Sports_720P-5ea4,Sports,720,144,4.238,4.257,4.1819999999999995,4.2139999999999995,0.544,0.525,0.43,0.439,1280,720,yuv420p,50,,,
|
| 206 |
+
Sports_720P-62ba,Sports,720,124,2.965,2.8819999999999997,2.883,3.014,0.5870000000000001,0.635,0.648,0.63,1280,720,yuv420p,30,,,
|
| 207 |
+
Sports_720P-675b,Sports,720,133,3.201,3.2260000000000004,3.247,3.377,0.677,0.5479999999999999,0.574,0.595,1280,720,yuv420p,25,,,
|
| 208 |
+
Sports_720P-69a0,Sports,720,117,3.878,4.0569999999999995,3.873,3.782,0.504,0.6459999999999999,0.6,0.5670000000000001,1280,720,yuv420p,29.97002997,,,
|
| 209 |
+
Sports_720P-6bb7,Sports,720,123,3.8819999999999997,3.87,3.7769999999999997,3.6519999999999997,0.493,0.474,0.496,0.469,1280,720,yuv420p,25,,,
|
| 210 |
+
Sports_720P-6d04,Sports,720,122,3.783,3.7510000000000003,3.7230000000000003,3.674,0.45799999999999996,0.5489999999999999,0.54,0.5429999999999999,1280,720,yuv420p,59.94006309,,,
|
| 211 |
+
Sports_720P-7ba7,Sports,720,122,3.5660000000000003,3.451,3.426,3.516,0.5539999999999999,0.49,0.575,0.5329999999999999,1280,720,yuv420p,30,,,
|
| 212 |
+
TelevisionClip_720P-1862,TelevisionClip,720,127,3.238,3.1289999999999996,3.073,3.1319999999999997,0.636,0.6940000000000001,0.655,0.667,1280,720,yuv420p,29.97002997,,,
|
| 213 |
+
TelevisionClip_720P-19de,TelevisionClip,720,148,4.346,4.398,4.413,4.141,0.473,0.456,0.441,0.634,1280,720,yuv420p,25,,,
|
| 214 |
+
TelevisionClip_720P-1b61,TelevisionClip,720,131,4.2989999999999995,4.386,4.418,4.395,0.49200000000000005,0.489,0.442,0.436,1280,720,yuv420p,29.97002997,,,
|
| 215 |
+
TelevisionClip_720P-31ce,TelevisionClip,720,131,4.324,4.167,4.406000000000001,4.244,0.449,0.31,0.43200000000000005,0.465,1280,720,yuv420p,24,,,
|
| 216 |
+
TelevisionClip_720P-3f4c,TelevisionClip,720,115,2.515,1.902,1.903,,0.83,0.684,0.698,,1280,720,yuv420p,53.56900212,,,
|
| 217 |
+
TelevisionClip_720P-44d1,TelevisionClip,720,112,3.839,3.7460000000000004,3.819,3.687,0.498,0.47200000000000003,0.5670000000000001,0.488,1280,720,yuv420p,30,,,
|
| 218 |
+
TelevisionClip_720P-4edb,TelevisionClip,720,110,4.015,3.807,3.76,3.7460000000000004,0.504,0.475,0.485,0.44799999999999995,1280,720,yuv420p,23.97602398,,,
|
| 219 |
+
TelevisionClip_720P-5e93,TelevisionClip,720,136,4.292,4.3180000000000005,4.091,4.1419999999999995,0.465,0.486,0.594,0.376,1280,720,yuv420p,25,,,
|
| 220 |
+
TelevisionClip_720P-7c06,TelevisionClip,720,150,4.396,4.417,4.395,4.434,0.43200000000000005,0.473,0.44299999999999995,0.43700000000000006,1280,720,yuv420p,29.97002997,,,
|
| 221 |
+
VerticalVideo_720P-0750,VerticalVideo,720,124,3.71,3.537,3.571,3.718,0.56,0.77,0.71,0.44,720,1280,yuv420p,29.9,,,
|
| 222 |
+
VerticalVideo_720P-0dac,VerticalVideo,720,104,2.9339999999999997,2.605,2.847,2.759,0.684,0.627,0.58,0.6579999999999999,720,1280,yuv420p,29.97002997,,,
|
| 223 |
+
VerticalVideo_720P-0f61,VerticalVideo,720,118,4.522,4.544,4.539,4.496,0.465,0.41600000000000004,0.41,0.436,740,1324,yuv420p,60,,,
|
| 224 |
+
VerticalVideo_720P-19fa,VerticalVideo,720,132,4.269,4.24,4.2780000000000005,4.218,0.59,0.581,0.604,0.5760000000000001,720,1280,yuv420p,29.954,,,
|
| 225 |
+
VerticalVideo_720P-1ada,VerticalVideo,720,135,3.532,3.341,3.4560000000000004,3.593,0.5579999999999999,0.718,0.57,0.45299999999999996,720,1280,yuv420p,29.888,,,
|
| 226 |
+
VerticalVideo_720P-2efc,VerticalVideo,720,113,3.523,3.431,3.384,3.147,0.5479999999999999,0.6920000000000001,0.579,0.636,720,1280,yuv420p,28.112,,,
|
| 227 |
+
VerticalVideo_720P-42f2,VerticalVideo,720,129,3.199,3.2960000000000003,3.0810000000000004,2.895,0.618,0.5539999999999999,0.613,0.569,720,1280,yuv420p,25.747,,,
|
| 228 |
+
VerticalVideo_720P-456c,VerticalVideo,720,114,3.7110000000000003,4.004,3.907,3.765,0.507,0.384,0.478,0.439,720,1280,yuv420p,30,,,
|
| 229 |
+
VerticalVideo_720P-4730,VerticalVideo,720,117,4.107,4.357,4.325,4.052,0.47200000000000003,0.462,0.42,0.401,720,1280,yuv420p,30,,,
|
| 230 |
+
VerticalVideo_720P-4ca7,VerticalVideo,720,128,4.442,4.445,4.434,4.397,0.515,0.456,0.474,0.49700000000000005,748,1332,yuv420p,60,,,
|
| 231 |
+
VerticalVideo_720P-6580,VerticalVideo,720,121,2.9789999999999996,3.0669999999999997,2.9339999999999997,2.784,0.618,0.654,0.598,0.631,720,1280,yuv420p,9.064,,,
|
| 232 |
+
VerticalVideo_720P-665d,VerticalVideo,720,104,4.023,4.178999999999999,4.236000000000001,4.098,0.49700000000000005,0.591,0.5820000000000001,0.633,720,1280,yuv420p,29.724,,,
|
| 233 |
+
VerticalVideo_720P-669d,VerticalVideo,720,152,4.176,4.314,4.263,4.364,0.639,0.505,0.609,0.48100000000000004,720,1280,yuv420p,30,,,
|
| 234 |
+
VerticalVideo_720P-6bf7,VerticalVideo,720,127,4.261,4.266,4.227,4.135,0.544,0.544,0.524,0.568,720,1280,yuv420p,30.10099889,,,
|
| 235 |
+
VerticalVideo_720P-7517,VerticalVideo,720,131,4.271,4.162,4.132,3.978,0.47100000000000003,0.522,0.5710000000000001,0.569,750,1334,yuv420p,25,,,
|
| 236 |
+
VerticalVideo_720P-7859,VerticalVideo,720,107,3.603,3.1710000000000003,3.3989999999999996,3.5610000000000004,0.593,0.6829999999999999,0.501,0.544,720,1280,yuv420p,30.018,,,
|
| 237 |
+
VerticalVideo_720P-7c1d,VerticalVideo,720,130,4.502,4.375,4.495,4.452,0.461,0.579,0.439,0.526,720,1280,yuv420p,50.71300448,,,
|
| 238 |
+
Vlog_720P-033a,Vlog,720,129,4.302,4.26,4.357,4.256,0.479,0.45899999999999996,0.488,0.529,1280,720,yuv420p,25,,,
|
| 239 |
+
Vlog_720P-03d5,Vlog,720,121,3.605,3.77,3.2119999999999997,3.1060000000000003,0.5489999999999999,0.45799999999999996,0.754,0.701,1280,720,yuv420p,30,,,
|
| 240 |
+
Vlog_720P-03f9,Vlog,720,109,3.972,3.955,3.906,3.88,0.446,0.48,0.436,0.47700000000000004,1280,720,yuv420p,23.97602398,,,
|
| 241 |
+
Vlog_720P-0d79,Vlog,720,110,3.805,3.574,4.024,4.245,0.479,0.511,0.603,0.474,1280,720,yuv420p,29.97002997,,,
|
| 242 |
+
Vlog_720P-11c5,Vlog,720,122,3.8160000000000003,3.741,3.73,3.773,0.53,0.48,0.484,0.488,1280,720,yuv420p,30,,,
|
| 243 |
+
Vlog_720P-135c,Vlog,720,115,3.872,3.759,3.7030000000000003,3.74,0.461,0.509,0.49200000000000005,0.485,1280,720,yuv420p,25,,,
|
| 244 |
+
Vlog_720P-141f,Vlog,720,105,3.128,2.815,2.908,3.11,0.6859999999999999,0.768,0.75,0.7040000000000001,1280,720,yuv420p,30,,,
|
| 245 |
+
Vlog_720P-155f,Vlog,720,133,4.316,4.4030000000000005,4.331,3.8369999999999997,0.493,0.439,0.484,0.462,1280,720,yuv420p,29.97002997,,,
|
| 246 |
+
Vlog_720P-2929,Vlog,720,116,3.918,3.8680000000000003,3.889,3.867,0.512,0.5529999999999999,0.518,0.602,1280,720,yuv420p,30,,,
|
| 247 |
+
Vlog_720P-329f,Vlog,720,125,3.7,3.824,3.832,3.6710000000000003,0.47100000000000003,0.515,0.456,0.479,1280,720,yuv420p,24,,,
|
| 248 |
+
Vlog_720P-32b2,Vlog,720,120,4.365,4.2860000000000005,4.338,4.351,0.465,0.473,0.475,0.423,1280,720,yuv420p,29.97002997,,,
|
| 249 |
+
Vlog_720P-343d,Vlog,720,127,3.667,3.597,3.951,3.835,0.541,0.593,0.684,0.636,1280,720,yuv420p,30,,,
|
| 250 |
+
Vlog_720P-372d,Vlog,720,121,4.047,3.78,3.904,4.145,0.495,0.467,0.46299999999999997,0.409,1280,720,yuv420p,29.97002997,,,
|
| 251 |
+
Vlog_720P-3e9c,Vlog,720,114,3.8819999999999997,3.7680000000000002,3.7460000000000004,3.7539999999999996,0.524,0.469,0.467,0.49700000000000005,1280,720,yuv420p,25,,,
|
| 252 |
+
Vlog_720P-4e3d,Vlog,720,116,2.7430000000000003,2.725,2.86,2.895,0.779,0.76,0.706,0.736,1280,720,yuv420p,30,,,
|
| 253 |
+
Vlog_720P-5364,Vlog,720,113,2.7939999999999996,2.815,2.63,2.655,0.7859999999999999,0.688,0.775,0.765,1280,720,yuv420p,30,,,
|
| 254 |
+
Vlog_720P-561e,Vlog,720,133,4.438,4.465,4.46,4.373,0.45899999999999996,0.44799999999999995,0.426,0.508,1280,720,yuv420p,30,,,
|
| 255 |
+
Vlog_720P-5d08,Vlog,720,126,3.783,3.739,3.78,3.7739999999999996,0.555,0.5,0.499,0.491,732,720,yuv420p,24,,,
|
| 256 |
+
Vlog_720P-60f8,Vlog,720,134,4.335,4.1610000000000005,4.192,4.11,0.488,0.39799999999999996,0.371,0.581,1280,720,yuv420p,25,,,
|
| 257 |
+
Vlog_720P-6d56,Vlog,720,115,3.7760000000000002,3.905,3.675,3.301,0.48100000000000004,0.466,0.507,0.488,1280,720,yuv420p,29.97002997,,,
|
metadata/YOUTUBE_UGC_metadata.csv
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
metadata/greyscale_report/LSVQ_TEST_1080P_greyscale_metadata.csv
ADDED
|
@@ -0,0 +1,11 @@
|
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|
|
|
|
|
|
|
| 1 |
+
Index,vid,Is Greyscale
|
| 2 |
+
499,yfcc-batch3/2330,True
|
| 3 |
+
860,yfcc-batch6/4918,True
|
| 4 |
+
1523,yfcc-batch13/10413,True
|
| 5 |
+
1744,yfcc-batch15/12074,True
|
| 6 |
+
2190,yfcc-batch19/14801,True
|
| 7 |
+
2504,yfcc-batch22/17326,True
|
| 8 |
+
2744,yfcc-batch25/19754,True
|
| 9 |
+
2893,ia-batch4/second-step-relax,True
|
| 10 |
+
3208,ia-batch14/youtube-555AGMud2Z0,True
|
| 11 |
+
3326,ia-batch18/INTERLICHTSPIELHAUS1565563742646,True
|
metadata/greyscale_report/LSVQ_TEST_greyscale_metadata.csv
ADDED
|
@@ -0,0 +1,65 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
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|
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|
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|
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|
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|
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|
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|
|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
Index,vid,Is Greyscale
|
| 2 |
+
1528,yfcc-batch11/8468,True
|
| 3 |
+
2098,yfcc-batch15/11512,True
|
| 4 |
+
2142,yfcc-batch15/11754,True
|
| 5 |
+
2376,yfcc-batch17/13861,True
|
| 6 |
+
2384,yfcc-batch17/13899,True
|
| 7 |
+
2464,yfcc-batch18/14400,True
|
| 8 |
+
2914,yfcc-batch21/16884,True
|
| 9 |
+
3573,ia-batch1/youtube-N83tiF5vT98,True
|
| 10 |
+
3701,ia-batch1/Upper_Lafayette_on_the_Move_Episode_17B003,True
|
| 11 |
+
3856,ia-batch2/youtube-tUGuqn9RpPk,True
|
| 12 |
+
3902,ia-batch3/youtube-j_zXDoRR1p4,True
|
| 13 |
+
3940,ia-batch3/youtube-Mmu-OW7zkOE,True
|
| 14 |
+
4080,ia-batch4/ClassicToonFestival,True
|
| 15 |
+
4153,ia-batch4/Rainy_Day_5_12_14,True
|
| 16 |
+
4198,ia-batch5/youtube-TUdzadVges0,True
|
| 17 |
+
4213,ia-batch5/youtube-r21mUuCXbt0,True
|
| 18 |
+
4229,ia-batch5/100ThousandPoetsForChangeSanPedroBelize2014,True
|
| 19 |
+
4261,ia-batch5/youtube-vxe1foIXUuw,True
|
| 20 |
+
4287,ia-batch5/youtube-6X713ZrVas,True
|
| 21 |
+
4357,ia-batch6/youtube-QGe0DAyy6B0,True
|
| 22 |
+
4383,ia-batch6/youtube-9MCs6pmiYvA,True
|
| 23 |
+
4413,ia-batch6/youtube-OfMJbuBToR4,True
|
| 24 |
+
4422,ia-batch6/youtube-r5cLJN5ew2c,True
|
| 25 |
+
4577,ia-batch7/youtube-q_ZooUiCmYA,True
|
| 26 |
+
4584,ia-batch7/youtube-5QxQZR3C1QA,True
|
| 27 |
+
4632,ia-batch8/youtube-tCTi9bMS4gw,True
|
| 28 |
+
4655,ia-batch8/youtube-Lo15zqrTD-c,True
|
| 29 |
+
4657,ia-batch8/TheForcedExtraditionOfSouthSlavicPeopleByAllies,True
|
| 30 |
+
4719,ia-batch8/youtube-t3yytvZUeFU,True
|
| 31 |
+
4768,ia-batch8/wycemi-Roosevelt_Diggs_-_Don_t_Count_On_Me,True
|
| 32 |
+
4883,ia-batch9/youtube-dlZfrCx-rp4,True
|
| 33 |
+
4888,ia-batch9/youtube-U5VMwryXVTc,True
|
| 34 |
+
4959,ia-batch10/youtube-gtCgcmu1nz4,True
|
| 35 |
+
4978,ia-batch10/2013-04-20-SDA_Speed_Runs,True
|
| 36 |
+
5026,ia-batch10/youtube-EWRYsSjNTfM,True
|
| 37 |
+
5126,ia-batch11/youtube-Tsno8o7L1uk,True
|
| 38 |
+
5136,ia-batch11/TheEffectsOfNuclearWar,True
|
| 39 |
+
5169,ia-batch11/TheFemaleOfTheSpecies,True
|
| 40 |
+
5256,ia-batch11/aocla-AOC_Community_Quotes-_Patrick_Seaux,True
|
| 41 |
+
5569,ia-batch13/wycemi-Matt_Gabriel_-_Irish_Eyes_songs_from_the_second_floor,True
|
| 42 |
+
5665,ia-batch14/78254GermanFortification,True
|
| 43 |
+
5756,ia-batch15/youtube-yC7EENXNdPA,True
|
| 44 |
+
5778,ia-batch15/RedHotMamma1934,True
|
| 45 |
+
5830,ia-batch15/youtube-otZNt5Duylk,True
|
| 46 |
+
5870,ia-batch15/William_Fitzsimmons_The_Winter_From_Her_Leaving_LIve_at_KDHX_5_1_11_HD,True
|
| 47 |
+
6093,ia-batch17/hbsnsch88876,True
|
| 48 |
+
6201,ia-batch18/LesAventuresDeRobinsonCruso,True
|
| 49 |
+
6215,ia-batch18/FoodAndNutrition,True
|
| 50 |
+
6240,ia-batch18/LeMerveilleuxEventailVivant_163,True
|
| 51 |
+
6258,ia-batch18/ANATURALBORNGAMBLER1916BertWilliams,True
|
| 52 |
+
6378,ia-batch19/grtvmiv-The_David_Thom_Band_-_Lucille_Live_at_WYCE,True
|
| 53 |
+
6504,ia-batch19/Film_of_the_Century_of_the_Week_Episode_1,True
|
| 54 |
+
6569,ia-batch20/youtube-n-H6b2PUO40,True
|
| 55 |
+
6580,ia-batch20/youtube-EpNLUrU61y0,True
|
| 56 |
+
6737,ia-batch21/youtube-IdWaNAbCMf0,True
|
| 57 |
+
6827,ia-batch22/youtube-7PNZU0mzGPI,True
|
| 58 |
+
6854,ia-batch22/youtube-9k19kg90BBY,True
|
| 59 |
+
6923,ia-batch22/youtube-RGShRQpL1rQ,True
|
| 60 |
+
6933,ia-batch22/youtube-xRTUuRF8sP0,True
|
| 61 |
+
6959,ia-batch22/youtube-Eb4pukiJUak,True
|
| 62 |
+
7036,ia-batch23/Thao_Mirah_Folks_Live_at_KDHX_6_15_11,True
|
| 63 |
+
7115,ia-batch23/wacama-Moo,True
|
| 64 |
+
7144,ia-batch24/youtube-wQ8NQyk-Hpw,True
|
| 65 |
+
7166,ia-batch24/Sega_Mega-CD_Longplay_078_Prize_Fighter,True
|
metadata/greyscale_report/LSVQ_TRAIN_greyscale_metadata.csv
ADDED
|
@@ -0,0 +1,210 @@
|
|
|
|
|
|
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|
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|
|
| 1 |
+
Index,vid,Is Greyscale
|
| 2 |
+
520,yfcc-batch2/1395,True
|
| 3 |
+
727,yfcc-batch2/867,True
|
| 4 |
+
771,yfcc-batch2/940,True
|
| 5 |
+
839,yfcc-batch2/1037,True
|
| 6 |
+
841,yfcc-batch2/1045,True
|
| 7 |
+
1164,yfcc-batch3/2334,True
|
| 8 |
+
1392,yfcc-batch3/1847,True
|
| 9 |
+
1446,yfcc-batch3/1924,True
|
| 10 |
+
2033,yfcc-batch4/2758,True
|
| 11 |
+
2774,yfcc-batch6/4635,True
|
| 12 |
+
2815,yfcc-batch6/4691,True
|
| 13 |
+
3098,yfcc-batch6/4303,True
|
| 14 |
+
4369,yfcc-batch9/6940,True
|
| 15 |
+
4861,yfcc-batch9/6824,True
|
| 16 |
+
4986,yfcc-batch10/7836,True
|
| 17 |
+
6692,yfcc-batch13/10491,True
|
| 18 |
+
7049,yfcc-batch13/10170,True
|
| 19 |
+
8235,yfcc-batch15/11747,True
|
| 20 |
+
9099,yfcc-batch17/13807,True
|
| 21 |
+
9335,yfcc-batch17/13321,True
|
| 22 |
+
9423,yfcc-batch17/13446,True
|
| 23 |
+
9591,yfcc-batch18/14509,True
|
| 24 |
+
9809,yfcc-batch18/14003,True
|
| 25 |
+
10284,yfcc-batch19/15498,True
|
| 26 |
+
10508,yfcc-batch19/15003,True
|
| 27 |
+
11240,yfcc-batch21/16891,True
|
| 28 |
+
11594,yfcc-batch21/16586,True
|
| 29 |
+
11645,yfcc-batch21/16665,True
|
| 30 |
+
11807,yfcc-batch22/17710,True
|
| 31 |
+
12178,yfcc-batch22/17431,True
|
| 32 |
+
12181,yfcc-batch22/17432,True
|
| 33 |
+
12914,yfcc-batch24/19329,True
|
| 34 |
+
13323,yfcc-batch24/19093,True
|
| 35 |
+
13501,yfcc-batch25/19818,True
|
| 36 |
+
13619,ia-batch1/youtube-xZoH2oztnU,True
|
| 37 |
+
13711,ia-batch1/youtube-AGJ32TfXbE0,True
|
| 38 |
+
13846,ia-batch1/BeauSabreurTrailer,True
|
| 39 |
+
13859,ia-batch1/yorkmba99_hotmail_Mon,True
|
| 40 |
+
13917,ia-batch1/bliptv-20131031-022004-Black5video-FarmerMovingSouthBR1952566,True
|
| 41 |
+
13974,ia-batch1/youtube-LE0PzrIisEk,True
|
| 42 |
+
14141,ia-batch1/youtube-ggskYcM-U3A,True
|
| 43 |
+
14158,ia-batch1/ScottPhillipsWalkingBlues1985,True
|
| 44 |
+
14295,ia-batch2/TheCrystalBall1943,True
|
| 45 |
+
14395,ia-batch2/youtube-7Nm7aC_dkx8,True
|
| 46 |
+
14607,ia-batch2/RidersOfTheRockies,True
|
| 47 |
+
14627,ia-batch2/aocla-AOC_Community_Quotes-_Candace_Gulotta-Haggart,True
|
| 48 |
+
14668,ia-batch2/AnArcadianMaidPreview,True
|
| 49 |
+
14676,ia-batch2/arthurlipsett,True
|
| 50 |
+
14840,ia-batch3/youtube-UIIUlJ4Wg5o,True
|
| 51 |
+
14875,ia-batch3/ClassicToons_201601,True
|
| 52 |
+
14933,ia-batch3/cobok-The_Way_We_Worked_-_Smithsonian_Exhibit_Coming_to_Broken_Arrow,True
|
| 53 |
+
14951,ia-batch3/youtube-qxRyk1QCfQs,True
|
| 54 |
+
15104,ia-batch3/1912DirectorUnknownPetticoatCampFlorenceLaBadieWilliamGarwood,True
|
| 55 |
+
15129,ia-batch3/youtube-oxtaSn217JQ,True
|
| 56 |
+
15214,ia-batch3/1933DeadwoodPass,True
|
| 57 |
+
15220,ia-batch3/ncgvtvca-Smith_and_Macarthy,True
|
| 58 |
+
15306,ia-batch3/youtube-ec7Hy4eHHyc,True
|
| 59 |
+
15402,ia-batch3/SwingYouSinners,True
|
| 60 |
+
15452,ia-batch4/youtube-rap_98LxkjA,True
|
| 61 |
+
15459,ia-batch4/MVI505449,True
|
| 62 |
+
15474,ia-batch4/youtube-FmleJsklmAA,True
|
| 63 |
+
15643,ia-batch4/CutTheGoat1921,True
|
| 64 |
+
15707,ia-batch4/youtube-7UNg2UZW2as,True
|
| 65 |
+
15796,ia-batch4/FightingBlood_201401,True
|
| 66 |
+
15803,ia-batch4/MVI50542126,True
|
| 67 |
+
15835,ia-batch4/TheBarnDance,True
|
| 68 |
+
15849,ia-batch4/ClassicToons_201511,True
|
| 69 |
+
15900,ia-batch4/TightRopeTricks1933,True
|
| 70 |
+
16074,ia-batch5/58270_1411111978_s01_Rubber_cultuurmaatschappij_Amsterdam_Deli_ok_van_Sumatra,True
|
| 71 |
+
16128,ia-batch5/O_Sister_Brother-Babylon,True
|
| 72 |
+
16144,ia-batch5/youtube-IXccrEcnZD4,True
|
| 73 |
+
16154,ia-batch5/youtube-BTb-BBjjzCo,True
|
| 74 |
+
16421,ia-batch5/youtube-T9aRA5RZAq8,True
|
| 75 |
+
16460,ia-batch5/youtube-u4n_kfmF2jk,True
|
| 76 |
+
16493,ia-batch5/youtube-7rYtt_cJkWg,True
|
| 77 |
+
16517,ia-batch5/youtube-aUTM5R1_eeI,True
|
| 78 |
+
16627,ia-batch5/youtube-MGlkqDU4hBI,True
|
| 79 |
+
16645,ia-batch5/youtube-lMReJJTs460,True
|
| 80 |
+
16735,ia-batch6/youtube-XTeL4lMDZ2U,True
|
| 81 |
+
16745,ia-batch6/youtube-Fkyq106eeJ0,True
|
| 82 |
+
16801,ia-batch6/youtube-Gt1eXWQbmWA,True
|
| 83 |
+
17053,ia-batch6/MemoriaDeCaranguejo,True
|
| 84 |
+
17055,ia-batch6/SoundieFrankParisHisMarionettes,True
|
| 85 |
+
17127,ia-batch6/youtube-3vtsbU-WtX4,True
|
| 86 |
+
17172,ia-batch6/THEDANGERGIRL1916GloriaSwansonBobbyVernon,True
|
| 87 |
+
17192,ia-batch6/youtube-foMVeph2ggQ,True
|
| 88 |
+
17310,ia-batch7/youtube-gNTZKkMgVdU,True
|
| 89 |
+
17370,ia-batch7/youtube-GaJi_d860KE,True
|
| 90 |
+
17426,ia-batch7/28MVI00272,True
|
| 91 |
+
17521,ia-batch7/youtube-cd7I0eUKw7U,True
|
| 92 |
+
17625,ia-batch7/youtube-BSzDS6COYVU,True
|
| 93 |
+
17793,ia-batch7/sdys1125578,True
|
| 94 |
+
17801,ia-batch7/cactvma-Directions_to_Your_Room_TRAILER,True
|
| 95 |
+
17851,ia-batch7/youtube-v1tkNu7zuFU,True
|
| 96 |
+
17934,ia-batch8/youtube-wWQirQUJgAw,True
|
| 97 |
+
18139,ia-batch8/27MVI00271,True
|
| 98 |
+
18242,ia-batch8/ADayInTheHayfields1904,True
|
| 99 |
+
18440,ia-batch8/youtube-YXjaRF8gv1c,True
|
| 100 |
+
18577,ia-batch9/PreviewABeastAtBay,True
|
| 101 |
+
18695,ia-batch9/youtube-HTlc6WvUn1I,True
|
| 102 |
+
18772,ia-batch9/youtube-EhweuFY3CUo,True
|
| 103 |
+
18872,ia-batch9/BSOHEPISODE294A,True
|
| 104 |
+
18996,ia-batch9/youtube-QEA4uLukdqI,True
|
| 105 |
+
19154,ia-batch10/youtube-COBrODvPOyo,True
|
| 106 |
+
19298,ia-batch10/1936MARYELLENBUTEPARABOLA,True
|
| 107 |
+
19349,ia-batch10/youtube-6PPcZDEpEI8,True
|
| 108 |
+
19439,ia-batch10/youtube-VGWpP1xx8nk,True
|
| 109 |
+
19496,ia-batch10/TheGallopinGaucho,True
|
| 110 |
+
19513,ia-batch10/youtube-xDaReF1f9vs,True
|
| 111 |
+
19565,ia-batch10/50-50com_oldschool_skatejam_2001,True
|
| 112 |
+
19618,ia-batch10/ClassicCartoons,True
|
| 113 |
+
19698,ia-batch11/75062_14157030_s01_Hurdle_Jumping_by_Trained_Dogs,True
|
| 114 |
+
19790,ia-batch11/witm090065,True
|
| 115 |
+
19817,ia-batch11/youtube-FGT6xOaY-fc,True
|
| 116 |
+
19849,ia-batch11/ws46fg45_201704,True
|
| 117 |
+
19936,ia-batch11/c50ic4,True
|
| 118 |
+
19966,ia-batch11/youtube-EZ9w_3ieY3I,True
|
| 119 |
+
20088,ia-batch11/youtube-aqbGGBKr2pQ,True
|
| 120 |
+
20094,ia-batch11/youtube-p7bO3JcZFEw,True
|
| 121 |
+
20172,ia-batch11/Mono_-_Rubbing_a_poem_by_Stephen_Dunn_read_by_Steve_Dunning,True
|
| 122 |
+
20207,ia-batch11/tp510633,True
|
| 123 |
+
20417,ia-batch12/CartoonCluster105,True
|
| 124 |
+
20464,ia-batch12/AaronDubrowUSHISTORYUNIT2PART2ROUGHCUT,True
|
| 125 |
+
20483,ia-batch12/DIGitMediaArtsExposition_21,True
|
| 126 |
+
20544,ia-batch12/1936SantaFeBound,True
|
| 127 |
+
20607,ia-batch12/youtube-8XGizxVg-p4,True
|
| 128 |
+
20702,ia-batch12/youtube-bRzA329Z5Fk,True
|
| 129 |
+
20720,ia-batch12/70624ADayWithTheSeminolesMos,True
|
| 130 |
+
20772,ia-batch12/MiezarumonosGbBatmanIn1006.42,True
|
| 131 |
+
20774,ia-batch12/youtube-ynKu-Yuhbt0,True
|
| 132 |
+
21040,ia-batch13/youtube-C0NZObyv45I,True
|
| 133 |
+
21043,ia-batch13/ToonFair_167,True
|
| 134 |
+
21130,ia-batch13/Mono_-_Before_the_Sky_Darkens_a_poem_by_Stephen_Dunn_read_by_Steve_Dunning,True
|
| 135 |
+
21167,ia-batch13/TF-14,True
|
| 136 |
+
21177,ia-batch13/youtube-Os6snXW2ubQ,True
|
| 137 |
+
21202,ia-batch13/youtube-o7E_EbBWDWY,True
|
| 138 |
+
21269,ia-batch13/youtube-EySUzeJg-Zs,True
|
| 139 |
+
21296,ia-batch13/28531_1411112332_s01_Hoe_men_filmster_wordt,True
|
| 140 |
+
21332,ia-batch13/youtube-fIz7ckxYAFE,True
|
| 141 |
+
21340,ia-batch13/youtube-go1BQcaad7k,True
|
| 142 |
+
21354,ia-batch13/78034WWIINews,True
|
| 143 |
+
21671,ia-batch14/youtube-wQ8NQyk-Hpw,True
|
| 144 |
+
21674,ia-batch14/cactvma-Bridgeside_Bars_10_-_Decoy,True
|
| 145 |
+
21686,ia-batch14/youtube-S4QhFiavEUs,True
|
| 146 |
+
21830,ia-batch14/youtube-jCEpjRXvAno,True
|
| 147 |
+
21884,ia-batch14/AndysAnimalAlphabet,True
|
| 148 |
+
21941,ia-batch14/youtube-E9dv4Ziqmq0,True
|
| 149 |
+
21953,ia-batch14/youtube-XkXZsDUmZLE,True
|
| 150 |
+
21977,ia-batch14/4SmokeAtmosphereHdFreeStockFootageDigital.meals,True
|
| 151 |
+
22098,ia-batch14/youtube-XndKGb8bB7k,True
|
| 152 |
+
22125,ia-batch14/vimeo-26948527,True
|
| 153 |
+
22208,ia-batch15/youtube-SrBerR9np9M,True
|
| 154 |
+
22386,ia-batch15/youtube-GFMcXXe89ak,True
|
| 155 |
+
22422,ia-batch15/FB-01,True
|
| 156 |
+
22462,ia-batch15/the_support_640_r1,True
|
| 157 |
+
22494,ia-batch15/SoundieGingerHarmonTicaTiTicaTa,True
|
| 158 |
+
22513,ia-batch15/Therevuecreative-PositionYourselfForSuccess131-2,True
|
| 159 |
+
22531,ia-batch15/youtube-Ph2Js2rPhV4,True
|
| 160 |
+
22587,ia-batch15/nctv-Night_of_the_Living_Dead,True
|
| 161 |
+
22606,ia-batch15/CartoonFavorites_125,True
|
| 162 |
+
22842,ia-batch16/InsainmentMix,True
|
| 163 |
+
22946,ia-batch16/GbOperationCIn1105.57ByVentuz,True
|
| 164 |
+
23059,ia-batch16/LifeofanAmericanFiremanVideoEssay,True
|
| 165 |
+
23139,ia-batch16/grtvmiv-Mandolin_Orange_-_House_of_Stone,True
|
| 166 |
+
23170,ia-batch16/19011902MassiveCongregationsLeavingChurchEnglandspeedCorrectedWAddedSound_201808,True
|
| 167 |
+
23253,ia-batch16/Flickr-4981563895,True
|
| 168 |
+
23312,ia-batch16/ClassicToonTheater5,True
|
| 169 |
+
24386,ia-batch18/kodakSalesTrainingConference,True
|
| 170 |
+
24443,ia-batch18/KikoTheKangarooDangerOnIceakaTheHayRide8mm_20180826,True
|
| 171 |
+
24589,ia-batch18/htm1950654,True
|
| 172 |
+
24638,ia-batch19/hismajestyincommand1935,True
|
| 173 |
+
24916,ia-batch19/02CheatonMe,True
|
| 174 |
+
25066,ia-batch19/GopherCG-LaplacianBlendIError678,True
|
| 175 |
+
25121,ia-batch19/am-Tim_Kennedy_-_Medicine_Man,True
|
| 176 |
+
25155,ia-batch19/C.L.A.S.S._3_21_16,True
|
| 177 |
+
25366,ia-batch20/youtube-FhLb2v-x_Lo,True
|
| 178 |
+
25550,ia-batch20/youtube-RuJ6aaBNTJ8,True
|
| 179 |
+
25675,ia-batch20/youtube-9prZvpCRyW0,True
|
| 180 |
+
25744,ia-batch20/McAllen_State_of_the_City_2011_-_Scott_Crane,True
|
| 181 |
+
26180,ia-batch21/youtube-Ugv8Yvv1ulg,True
|
| 182 |
+
26233,ia-batch21/vimeo-31173808,True
|
| 183 |
+
26375,ia-batch21/vimeo-119720349,True
|
| 184 |
+
26596,ia-batch22/youtube-1lOBwKz6yfE,True
|
| 185 |
+
26687,ia-batch22/youtube-LacHVggZXv8,True
|
| 186 |
+
26758,ia-batch22/NazisCrushPoland,True
|
| 187 |
+
26896,ia-batch22/youtube-rOqvDytojpU,True
|
| 188 |
+
26981,ia-batch22/youtube-Y__LuZEPkbk,True
|
| 189 |
+
27041,ia-batch22/youtube-asEgQZhShLk,True
|
| 190 |
+
27052,ia-batch22/Mono_-_Poems_by_Jack_Gilbert_and_B.H._Fairchild,True
|
| 191 |
+
27183,ia-batch23/youtube-RwbkhDsRMHg,True
|
| 192 |
+
27222,ia-batch23/Sega_Mega-CD_Longplay_078_Prize_Fighter,True
|
| 193 |
+
27292,ia-batch23/youtube-wGXyNOcv3Xk,True
|
| 194 |
+
27307,ia-batch23/ToonClassics_93,True
|
| 195 |
+
27373,ia-batch23/youtube-krpt2QD306I,True
|
| 196 |
+
27436,ia-batch23/youtube-2ETGUcSMDKg,True
|
| 197 |
+
27583,ia-batch23/youtube-1IdTVcpBbRk,True
|
| 198 |
+
27625,ia-batch23/The_Undersea_Kingdom_Chapter_3_-_Presented_by_North_Metro_TV_and_DaVinci_Academy,True
|
| 199 |
+
27646,ia-batch23/youtube-CYUKvzDJTJw,True
|
| 200 |
+
27747,ia-batch24/Mono_-_Poems_by_Jack_Gilbert_and_B.H._Fairchild,True
|
| 201 |
+
27794,ia-batch24/70624ADayWithTheSeminolesMos,True
|
| 202 |
+
27802,ia-batch24/TF-14,True
|
| 203 |
+
27821,ia-batch24/nppany-2019_1_23_New_Paltz_Village_Board,True
|
| 204 |
+
27851,ia-batch24/FB-01,True
|
| 205 |
+
27867,ia-batch24/Mono_-_Rubbing_a_poem_by_Stephen_Dunn_read_by_Steve_Dunning,True
|
| 206 |
+
27925,ia-batch24/FightingBlood_201401,True
|
| 207 |
+
27934,ia-batch24/Gavin_drawing,True
|
| 208 |
+
27937,ia-batch24/Mono_-_Before_the_Sky_Darkens_a_poem_by_Stephen_Dunn_read_by_Steve_Dunning,True
|
| 209 |
+
27947,ia-batch24/GopherCG-LaplacianBlendIError678,True
|
| 210 |
+
27960,ia-batch24/ANATURALBORNGAMBLER1916BertWilliams,True
|
metadata/greyscale_report/YOUTUBE_UGC_greyscale_metadata.csv
ADDED
|
@@ -0,0 +1,47 @@
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|
|
| 1 |
+
Index,vid,Is Greyscale
|
| 2 |
+
19,CoverSong_360P-1b2b,True
|
| 3 |
+
65,HowTo_360P-3aa6,True
|
| 4 |
+
91,Lecture_360P-5779,True
|
| 5 |
+
116,LyricVideo_360P-0ac7,True
|
| 6 |
+
161,Sports_360P-11b7,True
|
| 7 |
+
171,Sports_360P-3e68,True
|
| 8 |
+
211,Vlog_360P-2973,True
|
| 9 |
+
225,Animation_480P-073c,True
|
| 10 |
+
230,Animation_480P-35ee,True
|
| 11 |
+
243,CoverSong_480P-1019,True
|
| 12 |
+
247,CoverSong_480P-2d2c,True
|
| 13 |
+
352,LyricVideo_480P-0a5b,True
|
| 14 |
+
359,LyricVideo_480P-4346,True
|
| 15 |
+
367,MusicVideo_480P-1eee,True
|
| 16 |
+
371,MusicVideo_480P-41ce,True
|
| 17 |
+
379,MusicVideo_480P-6fb6,True
|
| 18 |
+
416,Sports_480P-11ec,True
|
| 19 |
+
419,Sports_480P-1d1b,True
|
| 20 |
+
427,Sports_480P-3ca4,True
|
| 21 |
+
475,Vlog_480P-34ba,True
|
| 22 |
+
489,Animation_720P-0116,True
|
| 23 |
+
494,Animation_720P-1a6d,True
|
| 24 |
+
496,Animation_720P-2abf,True
|
| 25 |
+
510,CoverSong_720P-014c,True
|
| 26 |
+
520,CoverSong_720P-6626,True
|
| 27 |
+
555,HowTo_720P-12f7,True
|
| 28 |
+
564,HowTo_720P-4d55,True
|
| 29 |
+
588,Lecture_720P-5120,True
|
| 30 |
+
601,LiveMusic_720P-3320,True
|
| 31 |
+
610,LyricVideo_720P-068d,True
|
| 32 |
+
615,LyricVideo_720P-2d24,True
|
| 33 |
+
620,LyricVideo_720P-5d3a,True
|
| 34 |
+
632,MusicVideo_720P-3c4c,True
|
| 35 |
+
640,MusicVideo_720P-7bcf,True
|
| 36 |
+
699,TelevisionClip_720P-1862,True
|
| 37 |
+
718,VerticalVideo_720P-6580,True
|
| 38 |
+
746,Animation_1080P-0c4f,True
|
| 39 |
+
748,Animation_1080P-209f,True
|
| 40 |
+
752,Animation_1080P-3d67,True
|
| 41 |
+
762,Animation_1080P-6a33,True
|
| 42 |
+
769,CoverSong_1080P-0f3a,True
|
| 43 |
+
773,CoverSong_1080P-2207,True
|
| 44 |
+
887,LiveMusic_1080P-7ead,True
|
| 45 |
+
891,LyricVideo_1080P-1b04,True
|
| 46 |
+
899,LyricVideo_1080P-63d4,True
|
| 47 |
+
911,MusicVideo_1080P-2b2b,True
|
metadata/mos_files/cvd_2014_MOS_test.csv
ADDED
|
@@ -0,0 +1,48 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
vid,framerate,MOS
|
| 2 |
+
Test1/City/Test01_City_D05,14.689766814641583,25.08
|
| 3 |
+
Test1/City/Test01_City_D06,28.600764784450337,43.78
|
| 4 |
+
Test1/Traffic/Test01_Traffic_D08,25.0,63.4
|
| 5 |
+
Test1/Traffic/Test01_Traffic_D09,25.0,60.7
|
| 6 |
+
Test2/City/Test02_City_D06,24.94904158256761,38.99
|
| 7 |
+
Test2/City/Test02_City_D10,29.41522532062596,0.21
|
| 8 |
+
Test2/Talking_head/Test02_Talking_Head_D01,30.00030000300003,64.68
|
| 9 |
+
Test2/Talking_head/Test02_Talking_Head_D02,30.00444065721727,22.64
|
| 10 |
+
Test2/Talking_head/Test02_Talking_Head_D04,14.746652509880256,16.69
|
| 11 |
+
Test2/Talking_head/Test02_Talking_Head_D10,29.79817694753435,33.8
|
| 12 |
+
Test2/Traffic/Test02_Traffic_D01,30.00030000300003,69.62
|
| 13 |
+
Test2/Traffic/Test02_Traffic_D02,29.985726794046037,25.39
|
| 14 |
+
Test2/Traffic/Test02_Traffic_D06,24.169943708201103,34.66
|
| 15 |
+
Test2/Traffic/Test02_Traffic_D09,25.0,64.3
|
| 16 |
+
Test3/City/Test03_City_D04,21.124543973906963,13.96
|
| 17 |
+
Test3/City/Test03_City_D05,29.97000002997,22.47
|
| 18 |
+
Test3/City/Test03_City_D06,14.89999865900012,12.85
|
| 19 |
+
Test3/City/Test03_City_D09,28.92003898421255,30.86
|
| 20 |
+
Test3/Newspaper/Test03_Newspaper_D07,30.059999759520004,49.85
|
| 21 |
+
Test3/Newspaper/Test03_Newspaper_D08,24.69001683859148,47.8
|
| 22 |
+
Test3/Talking_head/Test03_Talking_Head_D03,30.00003000003,67.38
|
| 23 |
+
Test3/Talking_head/Test03_Talking_Head_D04,14.09355867994092,27.86
|
| 24 |
+
Test3/Talking_head/Test03_Talking_Head_D13,29.54000307216032,32.72
|
| 25 |
+
Test4/City/Test04_City_D01,29.97000002997,61.85
|
| 26 |
+
Test4/City/Test04_City_D04,30.00003000003,69.0
|
| 27 |
+
Test4/Newspaper/Test04_Newspaper_D04,30.00003000003,82.52
|
| 28 |
+
Test4/Talking_head/Test04_Talking_Head_D11,14.269996903410672,4.5
|
| 29 |
+
Test5/City/Test05_City_D02,29.97,76.43
|
| 30 |
+
Test5/City/Test05_City_D03,25.0,68.62
|
| 31 |
+
Test5/Television/Test05_Television_D06,17.123,33.32
|
| 32 |
+
Test5/Television/Test05_Television_D08,20.379,27.84
|
| 33 |
+
Test5/Television/Test05_Television_D11,24.941,23.3
|
| 34 |
+
Test5/Television/Test05_Television_D15,29.678,36.1
|
| 35 |
+
Test5/Talking_head/Test05_Talking_Head_D05,24.0,69.78
|
| 36 |
+
Test5/Talking_head/Test05_Talking_Head_D07,19.894,73.71
|
| 37 |
+
Test5/Talking_head/Test05_Talking_Head_D09,30.0,68.07
|
| 38 |
+
Test6/City/Test06_City_D03,29.973,71.76
|
| 39 |
+
Test6/City/Test06_City_D05,28.656,61.12
|
| 40 |
+
Test6/City/Test06_City_D06,23.626,55.78
|
| 41 |
+
Test6/City/Test06_City_D09,14.914776964425274,-0.85
|
| 42 |
+
Test6/City/Test06_City_D10,27.134,23.58
|
| 43 |
+
Test6/Television/Test06_Television_D03,24.003,28.29
|
| 44 |
+
Test6/Television/Test06_Television_D07,14.828,22.59
|
| 45 |
+
Test6/Television/Test06_Television_D08,29.999,23.61
|
| 46 |
+
Test6/Television/Test06_Television_D14,29.865,31.73
|
| 47 |
+
Test6/Talking_head/Test06_Talking_head_D05,17.123,45.07
|
| 48 |
+
Test6/Talking_head/Test06_Talking_head_D16,29.918,33.02
|
metadata/mos_files/cvd_2014_MOS_train.csv
ADDED
|
@@ -0,0 +1,188 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
vid,framerate,MOS
|
| 2 |
+
Test1/City/Test01_City_D01,30.00030000300003,67.29
|
| 3 |
+
Test1/City/Test01_City_D02,15.151515151515152,24.86
|
| 4 |
+
Test1/City/Test01_City_D03,14.460897732531237,17.96
|
| 5 |
+
Test1/City/Test01_City_D04,30.003540417769297,30.23
|
| 6 |
+
Test1/City/Test01_City_D07,28.474695961433877,51.67
|
| 7 |
+
Test1/City/Test01_City_D08,25.0,63.68
|
| 8 |
+
Test1/City/Test01_City_D09,25.0,64.95
|
| 9 |
+
Test1/Talking_head/Test01_Talking_Head_D01,30.00030000300003,69.37
|
| 10 |
+
Test1/Talking_head/Test01_Talking_Head_D02,15.151515151515152,31.65
|
| 11 |
+
Test1/Talking_head/Test01_Talking_Head_D03,14.715663941325705,31.98
|
| 12 |
+
Test1/Talking_head/Test01_Talking_Head_D04,30.06334346468008,30.87
|
| 13 |
+
Test1/Talking_head/Test01_Talking_Head_D05,14.753875843184003,35.1
|
| 14 |
+
Test1/Talking_head/Test01_Talking_Head_D06,27.52697643690817,38.22
|
| 15 |
+
Test1/Talking_head/Test01_Talking_Head_D07,29.52657096120799,35.99
|
| 16 |
+
Test1/Talking_head/Test01_Talking_Head_D08,25.0,53.69
|
| 17 |
+
Test1/Talking_head/Test01_Talking_Head_D09,25.0,64.59
|
| 18 |
+
Test1/Traffic/Test01_Traffic_D01,30.00030000300003,67.74
|
| 19 |
+
Test1/Traffic/Test01_Traffic_D02,15.151515151515152,29.57
|
| 20 |
+
Test1/Traffic/Test01_Traffic_D03,14.848603637313946,27.76
|
| 21 |
+
Test1/Traffic/Test01_Traffic_D04,29.98689572656749,25.72
|
| 22 |
+
Test1/Traffic/Test01_Traffic_D05,14.86484879475806,30.21
|
| 23 |
+
Test1/Traffic/Test01_Traffic_D06,28.173934602663,43.36
|
| 24 |
+
Test1/Traffic/Test01_Traffic_D07,29.45395316232368,65.53
|
| 25 |
+
Test2/City/Test02_City_D01,30.00030000300003,53.35
|
| 26 |
+
Test2/City/Test02_City_D02,29.95599464386816,9.99
|
| 27 |
+
Test2/City/Test02_City_D03,14.96284725027756,18.83
|
| 28 |
+
Test2/City/Test02_City_D04,14.848735927110525,9.25
|
| 29 |
+
Test2/City/Test02_City_D05,29.06833092550659,45.91
|
| 30 |
+
Test2/City/Test02_City_D07,29.526832509042592,62.54
|
| 31 |
+
Test2/City/Test02_City_D08,29.73986539736921,36.88
|
| 32 |
+
Test2/City/Test02_City_D09,25.0,56.28
|
| 33 |
+
Test2/Talking_head/Test02_Talking_Head_D03,14.949627231044994,22.11
|
| 34 |
+
Test2/Talking_head/Test02_Talking_Head_D05,28.72886273923961,46.3
|
| 35 |
+
Test2/Talking_head/Test02_Talking_Head_D06,23.151149223047437,24.35
|
| 36 |
+
Test2/Talking_head/Test02_Talking_Head_D07,29.526832509042592,36.58
|
| 37 |
+
Test2/Talking_head/Test02_Talking_Head_D08,29.8686674691382,35.7
|
| 38 |
+
Test2/Talking_head/Test02_Talking_Head_D09,25.0,64.84
|
| 39 |
+
Test2/Traffic/Test02_Traffic_D03,14.96508645330444,21.78
|
| 40 |
+
Test2/Traffic/Test02_Traffic_D04,14.846928170561512,24.69
|
| 41 |
+
Test2/Traffic/Test02_Traffic_D05,28.670300149372263,30.89
|
| 42 |
+
Test2/Traffic/Test02_Traffic_D07,29.4611554665174,61.43
|
| 43 |
+
Test2/Traffic/Test02_Traffic_D08,29.87759150759339,29.35
|
| 44 |
+
Test2/Traffic/Test02_Traffic_D10,29.5801978323631,27.35
|
| 45 |
+
Test3/City/Test03_City_D01,31.57961220236216,34.75
|
| 46 |
+
Test3/City/Test03_City_D02,29.97000002997,16.48
|
| 47 |
+
Test3/City/Test03_City_D03,30.00003000003,57.62
|
| 48 |
+
Test3/City/Test03_City_D07,30.109961579689024,35.74
|
| 49 |
+
Test3/City/Test03_City_D08,24.36997521573521,30.01
|
| 50 |
+
Test3/City/Test03_City_D10,24.32001245184637,50.12
|
| 51 |
+
Test3/City/Test03_City_D11,22.229978014551744,15.47
|
| 52 |
+
Test3/City/Test03_City_D12,29.09996711703716,20.65
|
| 53 |
+
Test3/City/Test03_City_D13,29.58002283577763,32.65
|
| 54 |
+
Test3/Newspaper/Test03_Newspaper_D01,30.00030000300003,50.49
|
| 55 |
+
Test3/Newspaper/Test03_Newspaper_D02,29.97000002997,36.79
|
| 56 |
+
Test3/Newspaper/Test03_Newspaper_D03,30.00003000003,55.89
|
| 57 |
+
Test3/Newspaper/Test03_Newspaper_D04,23.572841258129685,39.98
|
| 58 |
+
Test3/Newspaper/Test03_Newspaper_D05,29.97000002997,40.1
|
| 59 |
+
Test3/Newspaper/Test03_Newspaper_D06,14.89999865900012,31.2
|
| 60 |
+
Test3/Newspaper/Test03_Newspaper_D09,29.43999246336193,47.22
|
| 61 |
+
Test3/Newspaper/Test03_Newspaper_D10,25.0,49.95
|
| 62 |
+
Test3/Newspaper/Test03_Newspaper_D11,23.91000274965032,46.51
|
| 63 |
+
Test3/Newspaper/Test03_Newspaper_D12,29.09996711703716,41.32
|
| 64 |
+
Test3/Newspaper/Test03_Newspaper_D13,29.58002283577763,38.65
|
| 65 |
+
Test3/Talking_head/Test03_Talking_Head_D01,20.0,39.03
|
| 66 |
+
Test3/Talking_head/Test03_Talking_Head_D02,29.97000002997,36.32
|
| 67 |
+
Test3/Talking_head/Test03_Talking_Head_D05,29.97000002997,36.98
|
| 68 |
+
Test3/Talking_head/Test03_Talking_Head_D06,14.89999865900012,28.2
|
| 69 |
+
Test3/Talking_head/Test03_Talking_Head_D07,30.140000301400004,35.29
|
| 70 |
+
Test3/Talking_head/Test03_Talking_Head_D08,24.649970420035498,46.1
|
| 71 |
+
Test3/Talking_head/Test03_Talking_Head_D09,29.520033770918637,37.85
|
| 72 |
+
Test3/Talking_head/Test03_Talking_Head_D10,25.0,53.33
|
| 73 |
+
Test3/Talking_head/Test03_Talking_Head_D11,23.5800116956858,38.37
|
| 74 |
+
Test3/Talking_head/Test03_Talking_Head_D12,29.09996711703716,20.86
|
| 75 |
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Test4/City/Test04_City_D02,25.0,70.56
|
| 76 |
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Test4/City/Test04_City_D03,25.0,68.6
|
| 77 |
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Test4/City/Test04_City_D05,29.97000002997,65.86
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| 78 |
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Test4/City/Test04_City_D06,10.739996498761142,-6.5
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| 79 |
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Test4/City/Test04_City_D07,14.979994217722233,16.12
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| 80 |
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Test4/City/Test04_City_D08,14.969992649733609,26.17
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| 81 |
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| 82 |
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| 83 |
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| 84 |
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| 85 |
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| 86 |
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|
| 87 |
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| 88 |
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Test4/Newspaper/Test04_Newspaper_D02,25.0,82.05
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| 89 |
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Test4/Newspaper/Test04_Newspaper_D03,25.0,81.33
|
| 90 |
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Test4/Newspaper/Test04_Newspaper_D05,29.97000002997,79.36
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| 91 |
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Test4/Newspaper/Test04_Newspaper_D06,12.910005641672466,5.08
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| 92 |
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| 93 |
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| 94 |
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| 95 |
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| 96 |
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| 97 |
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|
| 98 |
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| 99 |
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|
| 100 |
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| 101 |
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Test4/Talking_head/Test04_Talking_Head_D02,25.0,83.6
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| 102 |
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Test4/Talking_head/Test04_Talking_Head_D03,25.0,77.09
|
| 103 |
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Test4/Talking_head/Test04_Talking_Head_D04,30.00003000003,83.73
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| 104 |
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Test4/Talking_head/Test04_Talking_Head_D05,29.97000002997,93.38
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| 105 |
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|
| 106 |
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|
| 107 |
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Test4/Talking_head/Test04_Talking_Head_D08,14.950007176003444,38.31
|
| 108 |
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Test4/Talking_head/Test04_Talking_Head_D09,30.109961579689024,75.11
|
| 109 |
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Test4/Talking_head/Test04_Talking_Head_D10,25.0,61.18
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| 110 |
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Test4/Talking_head/Test04_Talking_Head_D12,29.09996711703716,17.06
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| 111 |
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Test4/Talking_head/Test04_Talking_Head_D13,29.97000002997,59.4
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| 112 |
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Test4/Talking_head/Test04_Talking_Head_D14,23.96001552609006,52.76
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| 113 |
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| 114 |
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Test5/City/Test05_City_D04,23.977,63.85
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| 115 |
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| 116 |
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Test5/City/Test05_City_D06,29.632,75.32
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| 117 |
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Test5/City/Test05_City_D07,28.196,66.67
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| 118 |
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| 119 |
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| 120 |
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Test5/City/Test05_City_D10,28.668,65.29
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| 121 |
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Test5/City/Test05_City_D11,29.985,73.55
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| 122 |
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| 123 |
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Test5/City/Test05_City_D13,25.002,62.65
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| 124 |
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Test5/City/Test05_City_D14,30.046,64.27
|
| 125 |
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Test5/City/Test05_City_D15,29.933,71.29
|
| 126 |
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Test5/City/Test05_City_D16,29.701,76.85
|
| 127 |
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Test5/Television/Test05_Television_D01,30.798,11.45
|
| 128 |
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Test5/Television/Test05_Television_D02,24.01,42.29
|
| 129 |
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Test5/Television/Test05_Television_D03,25.0,63.93
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| 130 |
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Test5/Television/Test05_Television_D04,23.977,55.0
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| 131 |
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Test5/Television/Test05_Television_D05,24.0,48.27
|
| 132 |
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Test5/Television/Test05_Television_D07,17.1,43.05
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| 133 |
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Test5/Television/Test05_Television_D09,30.0,33.59
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| 134 |
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Test5/Television/Test05_Television_D10,24.936,22.5
|
| 135 |
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Test5/Television/Test05_Television_D12,24.981,35.12
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| 136 |
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Test5/Television/Test05_Television_D13,25.001,27.91
|
| 137 |
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Test5/Television/Test05_Television_D14,24.945,18.45
|
| 138 |
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Test5/Television/Test05_Television_D16,29.692,45.88
|
| 139 |
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Test5/Talking_head/Test05_Talking_Head_D01,30.799,46.8
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| 140 |
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Test5/Talking_head/Test05_Talking_Head_D02,24.01,65.4
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Test5/Talking_head/Test05_Talking_Head_D03,25.0,83.25
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| 142 |
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Test5/Talking_head/Test05_Talking_Head_D04,23.976,80.15
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| 143 |
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Test5/Talking_head/Test05_Talking_Head_D06,29.618,71.0
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| 144 |
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Test5/Talking_head/Test05_Talking_Head_D08,24.896,61.01
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Test5/Talking_head/Test05_Talking_Head_D10,24.938,60.05
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Test5/Talking_head/Test05_Talking_Head_D11,24.943,56.29
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Test5/Talking_head/Test05_Talking_Head_D12,24.984,68.36
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Test5/Talking_head/Test05_Talking_Head_D13,25.002,61.84
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Test5/Talking_head/Test05_Talking_Head_D14,24.957,67.29
|
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Test5/Talking_head/Test05_Talking_Head_D15,29.967,63.92
|
| 151 |
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Test5/Talking_head/Test05_Talking_Head_D16,29.701,72.57
|
| 152 |
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Test6/City/Test06_City_D01,30.8,17.23
|
| 153 |
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Test6/City/Test06_City_D02,29.97,72.11
|
| 154 |
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Test6/City/Test06_City_D04,25.0,67.94
|
| 155 |
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Test6/City/Test06_City_D07,29.648,58.78
|
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Test6/City/Test06_City_D08,30.0,72.68
|
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Test6/City/Test06_City_D11,29.843,62.03
|
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Test6/City/Test06_City_D12,24.985,53.03
|
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Test6/City/Test06_City_D13,27.505,64.28
|
| 160 |
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Test6/City/Test06_City_D14,29.848,72.46
|
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Test6/City/Test06_City_D15,29.854,71.95
|
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Test6/City/Test06_City_D16,29.736,58.33
|
| 163 |
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Test6/Television/Test06_Television_D01,30.799,10.93
|
| 164 |
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Test6/Television/Test06_Television_D02,24.01,28.81
|
| 165 |
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Test6/Television/Test06_Television_D04,25.0,39.1
|
| 166 |
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Test6/Television/Test06_Television_D05,17.123,27.85
|
| 167 |
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Test6/Television/Test06_Television_D06,19.942,33.68
|
| 168 |
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Test6/Television/Test06_Television_D09,10.670741467141584,7.23
|
| 169 |
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Test6/Television/Test06_Television_D10,24.938,14.77
|
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Test6/Television/Test06_Television_D11,29.843,23.8
|
| 171 |
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Test6/Television/Test06_Television_D12,24.983,22.48
|
| 172 |
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Test6/Television/Test06_Television_D13,24.942,23.7
|
| 173 |
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Test6/Television/Test06_Television_D15,29.731,33.46
|
| 174 |
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Test6/Television/Test06_Television_D16,28.972,20.54
|
| 175 |
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Test6/Talking_head/Test06_Talking_head_D01,30.8,14.9
|
| 176 |
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Test6/Talking_head/Test06_Talking_head_D02,24.009,57.48
|
| 177 |
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Test6/Talking_head/Test06_Talking_head_D03,24.004,53.44
|
| 178 |
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Test6/Talking_head/Test06_Talking_head_D04,25.0,76.79
|
| 179 |
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Test6/Talking_head/Test06_Talking_head_D06,23.764,61.99
|
| 180 |
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Test6/Talking_head/Test06_Talking_head_D07,14.865,32.99
|
| 181 |
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Test6/Talking_head/Test06_Talking_head_D08,30.0,62.56
|
| 182 |
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Test6/Talking_head/Test06_Talking_head_D09,12.173210172908275,0.58
|
| 183 |
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Test6/Talking_head/Test06_Talking_head_D10,24.938,23.31
|
| 184 |
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Test6/Talking_head/Test06_Talking_head_D11,29.753,42.32
|
| 185 |
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Test6/Talking_head/Test06_Talking_head_D12,24.981,57.39
|
| 186 |
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Test6/Talking_head/Test06_Talking_head_D13,24.943,47.76
|
| 187 |
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Test6/Talking_head/Test06_Talking_head_D14,29.763,58.85
|
| 188 |
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Test6/Talking_head/Test06_Talking_head_D15,29.813,73.35
|
metadata/mos_files/konvid_1k_MOS_test.csv
ADDED
|
@@ -0,0 +1,241 @@
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
vid,framerate,MOS
|
| 2 |
+
8751538577,23.976,4.4
|
| 3 |
+
12262452854,24.0,4.24
|
| 4 |
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10283826203,29.97,4.14
|
| 5 |
+
10595581344,29.97,4.14
|
| 6 |
+
5893119747,25.0,4.12
|
| 7 |
+
5489207006,29.97002997,4.04
|
| 8 |
+
3863745355,23.97602398,4.02
|
| 9 |
+
6137257339,29.97002997,4.02
|
| 10 |
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12123666684,29.97002997,4.0
|
| 11 |
+
6914710581,24.0,4.0
|
| 12 |
+
11465976586,29.97,3.94
|
| 13 |
+
6949440526,29.97002997,3.92
|
| 14 |
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7832506570,29.97002997,3.92
|
| 15 |
+
8751856572,29.97,3.9
|
| 16 |
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9583034514,29.97,3.88
|
| 17 |
+
4482595368,29.97002997,3.86
|
| 18 |
+
5831253770,29.97002997,3.84
|
| 19 |
+
4029116553,23.97602398,3.82
|
| 20 |
+
6971190423,29.97002997,3.82
|
| 21 |
+
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|
| 22 |
+
8679156964,25.0,3.8
|
| 23 |
+
8995404511,29.97,3.798245614
|
| 24 |
+
3358799122,29.97002997,3.785714286
|
| 25 |
+
5770178686,23.97602398,3.78
|
| 26 |
+
7644822866,29.97002997,3.78
|
| 27 |
+
11013388203,23.97602398,3.76
|
| 28 |
+
6416725133,29.97002997,3.76
|
| 29 |
+
7274180028,23.97602398,3.76
|
| 30 |
+
7667993154,29.97002997,3.76
|
| 31 |
+
10541657773,24.0,3.74
|
| 32 |
+
7383994638,29.97002997,3.74
|
| 33 |
+
9258419063,25.0,3.74
|
| 34 |
+
9812351333,29.97,3.74
|
| 35 |
+
12859019173,29.97002997,3.72
|
| 36 |
+
4673672619,29.97002997,3.7
|
| 37 |
+
5340069287,29.97002997,3.7
|
| 38 |
+
8730797431,29.97002997,3.7
|
| 39 |
+
8931854960,29.97,3.7
|
| 40 |
+
3459410764,29.97002997,3.692946058
|
| 41 |
+
10115791113,29.97,3.68
|
| 42 |
+
12623549414,29.97,3.68
|
| 43 |
+
8088901520,29.97002997,3.68
|
| 44 |
+
3718963712,24.0,3.66
|
| 45 |
+
4227960041,29.97002997,3.66
|
| 46 |
+
5263204150,29.97002997,3.64
|
| 47 |
+
7992241767,29.97002997,3.64
|
| 48 |
+
8069880437,29.97002997,3.64
|
| 49 |
+
4484771725,29.97002997,3.62
|
| 50 |
+
6694553789,29.97002997,3.62
|
| 51 |
+
6932589551,29.97002997,3.62
|
| 52 |
+
3611217365,23.97602398,3.6
|
| 53 |
+
12382276334,25.0,3.586872587
|
| 54 |
+
5140332158,29.97002997,3.58
|
| 55 |
+
6300963347,29.97002997,3.58
|
| 56 |
+
9725289895,29.97,3.58
|
| 57 |
+
3722785424,29.97002997,3.571428571
|
| 58 |
+
11803329033,29.97,3.56
|
| 59 |
+
8229018279,29.97002997,3.56
|
| 60 |
+
8886176135,29.97,3.56
|
| 61 |
+
6716265511,24.0,3.54
|
| 62 |
+
8587203405,29.97002997,3.54
|
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metadata/mos_files/konvid_1k_MOS_train.csv
ADDED
|
@@ -0,0 +1,961 @@
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| 1 |
+
vid,framerate,MOS
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| 2 |
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| 3 |
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| 9 |
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| 11 |
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| 20 |
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| 22 |
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metadata/mos_files/live_vqc_MOS_test.csv
ADDED
|
@@ -0,0 +1,118 @@
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|
| 1 |
+
vid,framerate,MOS
|
| 2 |
+
A007,29.9750208159867,50.8675
|
| 3 |
+
A008,29.9800133244504,46.8902
|
| 4 |
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A010,30.0081749845473,31.2785
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| 5 |
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A011,29.9801797700409,87.2287
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| 6 |
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A014,29.9800133244504,69.9652
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| 7 |
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A042,30.0091389958182,76.8312
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| 8 |
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A057,29.9850074962519,66.4892
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| 9 |
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A067,29.9800796812749,77.4551
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| 10 |
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A068,29.2834711074194,39.123
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| 11 |
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A071,29.9800133244504,74.1071
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| 12 |
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A074,29.9850074962519,57.3204
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| 13 |
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A079,30.0000666668148,81.0943
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| 14 |
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A081,29.9800133244504,75.7143
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| 15 |
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A090,29.9800133244504,86.1709
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| 16 |
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A105,29.9800133244504,37.8824
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| 17 |
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B002,30.0055824339412,73.4245
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| 18 |
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B017,29.97002997003,65.3693
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B026,29.9800796812749,72.0794
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| 20 |
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B036,29.9800796812749,82.8776
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| 21 |
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B045,29.9800796812749,64.1244
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B049,29.9800796812749,78.5928
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| 23 |
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B050,29.9800796812749,77.2019
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| 24 |
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B052,29.9800796812749,58.6099
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| 26 |
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B055,29.9800796812749,64.193
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B061,29.9800796812749,63.7011
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| 28 |
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B063,29.985057280425,44.152
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| 29 |
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B065,29.9800796812749,58.7207
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| 30 |
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B070,30.0,56.4128
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| 32 |
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B082,29.9166666666667,39.3422
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| 33 |
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B083,29.97,47.1299
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B090,29.75,26.0497
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| 35 |
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B093,29.8507462686567,70.0112
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B104,30.0,71.3464
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B107,29.97002997003,76.7188
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| 38 |
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B113,30.0,73.7705
|
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B122,29.97002997003,59.2645
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| 40 |
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B126,30.0,73.5285
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B127,29.97002997003,58.382
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B128,29.97002997003,59.2601
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B130,29.97,52.7733
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B131,29.97002997003,37.062
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| 45 |
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B132,20.027,42.6774
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B145,29.97002997003,87.7442
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B151,29.9166666666667,71.6954
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B156,30.0,69.2067
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B165,29.97002997003,67.3736
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B171,29.97002997003,89.7447
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B172,29.97002997003,75.619
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| 52 |
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B183,29.97002997003,85.2941
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| 53 |
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B191,29.97002997003,82.8227
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| 54 |
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B200,29.97002997003,36.2614
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| 55 |
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B202,29.97002997003,78.8095
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B204,29.97002997003,72.3743
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B211,29.97002997003,80.995
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B214,29.97002997003,45.2158
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B224,29.97002997003,81.0055
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B227,29.97002997003,90.2448
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B228,30.0,71.1987
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B240,29.842,67.1576
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B245,30.0,82.8444
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B249,30.0,69.7869
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B253,30.0,72.1818
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B256,30.0,59.5503
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B262,29.97002997003,51.9461
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B264,30.0,78.114
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B267,29.97002997003,62.3061
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B272,24.0,49.6404
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B273,30.0,53.4568
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B276,29.97002997003,83.5027
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B279,30.0,67.9827
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B284,29.97002997003,71.3248
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B286,30.0,38.6012
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B291,30.0,71.1256
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B292,30.0,81.9686
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B294,30.0,66.3431
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B296,29.937,63.203
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B301,30.0,49.1242
|
| 81 |
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B302,29.97002997003,72.25
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| 82 |
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B304,29.97002997003,91.7312
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| 83 |
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B307,29.8333333333333,62.3543
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| 84 |
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B311,30.0,88.8258
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B316,30.0,89.1236
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| 86 |
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G012,29.97002997003,51.3923
|
| 87 |
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G016,29.97002997003,80.5959
|
| 88 |
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G032,29.97002997003,53.278
|
| 89 |
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G038,29.97002997003,72.125
|
| 90 |
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G045,29.8507462686567,62.4847
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| 91 |
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G046,29.8507462686567,78.8212
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G048,30.0,78.9434
|
| 93 |
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G051,30.0,75.6131
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| 94 |
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G056,29.97002997003,62.0506
|
| 95 |
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G064,20.1666666666667,11.3333
|
| 96 |
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G065,24.25,68.6814
|
| 97 |
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G071,30.0,48.3714
|
| 98 |
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G072,29.97002997003,47.7432
|
| 99 |
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G073,30.0,18.6333
|
| 100 |
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G079,30.0,15.3576
|
| 101 |
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G080,29.9166666666667,26.2575
|
| 102 |
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G085,29.97002997003,76.4971
|
| 103 |
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G087,29.97002997003,71.1184
|
| 104 |
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G088,29.97002997003,56.0826
|
| 105 |
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G090,29.97002997003,75.4464
|
| 106 |
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G092,29.97002997003,67.8351
|
| 107 |
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G093,29.97002997003,66.1105
|
| 108 |
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G103,29.97002997003,81.3545
|
| 109 |
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G110,30.0,83.5217
|
| 110 |
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G111,30.0,55.0282
|
| 111 |
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G112,30.0,74.3393
|
| 112 |
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G119,29.97002997003,76.8795
|
| 113 |
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H002,29.868,69.1073
|
| 114 |
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I002,29.834,63.4225
|
| 115 |
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K001,30.0,33.0114
|
| 116 |
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P001,30.0,64.399
|
| 117 |
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P003,30.0,61.749
|
| 118 |
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P007,30.0,38.2139
|
metadata/mos_files/live_vqc_MOS_train.csv
ADDED
|
@@ -0,0 +1,469 @@
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|
| 1 |
+
vid,framerate,MOS,MOS_raw
|
| 2 |
+
A001,29.9929016799357,80.232,80.232
|
| 3 |
+
A002,29.9800796812749,57.3005,57.3005
|
| 4 |
+
A003,29.9750208159867,78.1558,78.1558
|
| 5 |
+
A005,30.0,47.1778,47.1778
|
| 6 |
+
A006,30.0078093413014,84.6517,84.6517
|
| 7 |
+
A007,29.9750208159867,50.8675,50.8675
|
| 8 |
+
A008,29.9800133244504,46.8902,46.8902
|
| 9 |
+
A009,29.9800133244504,80.7925,80.7925
|
| 10 |
+
A010,30.0081749845473,31.2785,31.2785
|
| 11 |
+
A011,29.9801797700409,87.2287,87.2287
|
| 12 |
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A012,29.9001666657438,47.7826,47.7826
|
| 13 |
+
A015,29.9882712539096,78.3957,78.3957
|
| 14 |
+
A016,29.8974421077308,37.8421,37.8421
|
| 15 |
+
A017,30.0089727825596,65.4778,65.4778
|
| 16 |
+
A018,29.9800133244504,76.0205,76.0205
|
| 17 |
+
A019,29.9800796812749,70.843,70.843
|
| 18 |
+
A020,29.9836422573907,77.7258,77.7258
|
| 19 |
+
A021,29.8500509148026,37.6011,37.6011
|
| 20 |
+
A022,30.0049842166473,38.674,38.674
|
| 21 |
+
A023,29.9800133244504,69.0588,69.0588
|
| 22 |
+
A025,29.9255850451876,42.0856,42.0856
|
| 23 |
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A027,29.9800133244504,56.3388,56.3388
|
| 24 |
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A028,29.8293706789812,41.5304,41.5304
|
| 25 |
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A029,29.9850074962519,75.3861,75.3861
|
| 26 |
+
A030,30.0055491990213,81.5278,81.5278
|
| 27 |
+
A031,29.9295989322893,62.1042,62.1042
|
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metadata/mos_files/lsvq_train_MOS_test.csv
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
metadata/mos_files/lsvq_train_MOS_train.csv
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
metadata/mos_files/youtube_ugc_MOS_test.csv
ADDED
|
@@ -0,0 +1,222 @@
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|
| 1 |
+
vid,framerate,MOS
|
| 2 |
+
Animation_360P-24d4,24.0,3.435
|
| 3 |
+
Animation_360P-47cc,30.0,3.282
|
| 4 |
+
Animation_360P-5de0,30.0,3.429
|
| 5 |
+
CoverSong_360P-0a9d,8.761000000000001,2.5380000000000003
|
| 6 |
+
CoverSong_360P-2146,15.879,2.273
|
| 7 |
+
CoverSong_360P-69ef,30.0,3.145
|
| 8 |
+
Gaming_360P-0b98,30.0,3.14
|
| 9 |
+
Gaming_360P-215f,30.0,2.869
|
| 10 |
+
Gaming_360P-21d2,25.0,2.372
|
| 11 |
+
Gaming_360P-279f,30.0,2.841
|
| 12 |
+
Gaming_360P-2f99,45.335,3.281
|
| 13 |
+
HowTo_360P-2a3b,15.0,2.589
|
| 14 |
+
HowTo_360P-5da7,30.0,3.425
|
| 15 |
+
Lecture_360P-27db,15.0,3.092
|
| 16 |
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metadata/mos_files/youtube_ugc_MOS_train.csv
ADDED
|
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|
| 1 |
+
vid,framerate,MOS
|
| 2 |
+
Animation_360P-08c9,30.0,3.652
|
| 3 |
+
Animation_360P-188f,29.97002997,3.702
|
| 4 |
+
Animation_360P-3e40,30.0,2.353
|
| 5 |
+
Animation_360P-3e52,29.97002997,3.651
|
| 6 |
+
Animation_360P-4b4c,30.0,3.003
|
| 7 |
+
Animation_360P-4edc,30.0,3.682
|
| 8 |
+
Animation_360P-5712,30.0,3.552
|
| 9 |
+
Animation_360P-631c,15.0,2.236
|
| 10 |
+
Animation_360P-69e0,29.97002997,3.382
|
| 11 |
+
Animation_360P-7856,29.97002997,2.7510000000000003
|
| 12 |
+
Animation_360P-794f,29.97002997,3.362
|
| 13 |
+
CoverSong_360P-0aa2,30.0,2.511
|
| 14 |
+
CoverSong_360P-11f9,16.522000000000002,2.79
|
| 15 |
+
CoverSong_360P-13d2,25.0,2.5780000000000003
|
| 16 |
+
CoverSong_360P-14b9,29.97002997,2.633
|
| 17 |
+
CoverSong_360P-2b4d,30.0,2.7960000000000003
|
| 18 |
+
CoverSong_360P-3218,21.0,2.427
|
| 19 |
+
CoverSong_360P-4add,29.97002997,2.855
|
| 20 |
+
CoverSong_360P-53a6,30.0,3.235
|
| 21 |
+
CoverSong_360P-59d1,30.0,2.8280000000000003
|
| 22 |
+
CoverSong_360P-5a24,29.97002997,2.789
|
| 23 |
+
CoverSong_360P-5d20,30.0,2.943
|
| 24 |
+
CoverSong_360P-67ec,29.97002997,2.404
|
| 25 |
+
CoverSong_360P-6d6a,14.758,3.273
|
| 26 |
+
CoverSong_360P-6da7,30.0,2.763
|
| 27 |
+
Gaming_360P-043e,30.0,2.582
|
| 28 |
+
Gaming_360P-187a,29.97002997,3.133
|
| 29 |
+
Gaming_360P-2164,25.0,3.245
|
| 30 |
+
Gaming_360P-2330,29.97002997,2.607
|
| 31 |
+
Gaming_360P-3794,18.506,1.475
|
| 32 |
+
Gaming_360P-3eb6,59.79099678,3.184
|
| 33 |
+
Gaming_360P-48b0,24.919,3.427
|
| 34 |
+
Gaming_360P-4f09,30.0,3.506
|
| 35 |
+
Gaming_360P-56fe,24.0,2.439
|
| 36 |
+
Gaming_360P-586d,60.04899777,3.026
|
| 37 |
+
Gaming_360P-5e0f,30.0,3.012
|
| 38 |
+
Gaming_360P-6207,30.0,3.349
|
| 39 |
+
Gaming_360P-63e6,24.453000000000003,2.636
|
| 40 |
+
Gaming_360P-73c7,23.97602398,3.247
|
| 41 |
+
Gaming_360P-7975,30.0,2.761
|
| 42 |
+
Gaming_360P-7acb,30.0,3.059
|
| 43 |
+
HowTo_360P-041c,30.0,3.214
|
| 44 |
+
HowTo_360P-0562,25.0,3.261
|
| 45 |
+
HowTo_360P-06be,25.0,3.6010000000000004
|
| 46 |
+
HowTo_360P-09f8,30.0,2.951
|
| 47 |
+
HowTo_360P-127f,25.0,3.332
|
| 48 |
+
HowTo_360P-1823,30.0,3.056
|
| 49 |
+
HowTo_360P-18e7,15.0,2.78
|
| 50 |
+
HowTo_360P-1dba,28.318,1.907
|
| 51 |
+
HowTo_360P-2074,30.0,3.536
|
| 52 |
+
HowTo_360P-2d35,29.97002997,2.847
|
| 53 |
+
HowTo_360P-2fd5,26.7,2.049
|
| 54 |
+
HowTo_360P-4276,30.0,3.448
|
| 55 |
+
HowTo_360P-4e4c,25.0,3.005
|
| 56 |
+
HowTo_360P-55e9,29.97002997,3.4960000000000004
|
| 57 |
+
HowTo_360P-6093,25.0,3.205
|
| 58 |
+
HowTo_360P-6a0e,30.0,2.104
|
| 59 |
+
HowTo_360P-7083,29.97002997,3.031
|
| 60 |
+
HowTo_360P-7dcd,24.0,3.035
|
| 61 |
+
HowTo_360P-7fb1,30.0,3.322
|
| 62 |
+
Lecture_360P-03bc,25.0,2.762
|
| 63 |
+
Lecture_360P-051a,25.0,2.64
|
| 64 |
+
Lecture_360P-114f,25.0,4.172
|
| 65 |
+
Lecture_360P-20c3,14.842,3.133
|
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| 856 |
+
Vlog_2160P-0577,29.97002997,4.27
|
| 857 |
+
Vlog_2160P-059c,29.97002997,4.441
|
| 858 |
+
Vlog_2160P-13a1,29.97002997,3.859
|
| 859 |
+
Vlog_2160P-13fe,25.0,3.992
|
| 860 |
+
Vlog_2160P-1ff6,23.97602398,3.29
|
| 861 |
+
Vlog_2160P-2953,23.97602398,3.467
|
| 862 |
+
Vlog_2160P-3019,29.97002997,4.578
|
| 863 |
+
Vlog_2160P-310b,30.0,3.857
|
| 864 |
+
Vlog_2160P-327a,30.0,3.983
|
| 865 |
+
Vlog_2160P-342a,60.0,4.321000000000001
|
| 866 |
+
Vlog_2160P-3a75,30.0,4.212
|
| 867 |
+
Vlog_2160P-408f,23.97602398,3.79
|
| 868 |
+
Vlog_2160P-416c,30.0,4.019
|
| 869 |
+
Vlog_2160P-4362,29.97002997,4.553999999999999
|
| 870 |
+
Vlog_2160P-4655,25.0,3.887
|
| 871 |
+
Vlog_2160P-4f98,30.0,3.47
|
| 872 |
+
Vlog_2160P-522f,30.0,4.324
|
| 873 |
+
Vlog_2160P-56e4,30.0,4.289
|
| 874 |
+
Vlog_2160P-5874,25.0,4.101
|
| 875 |
+
Vlog_2160P-62b2,30.0,3.39
|
| 876 |
+
Vlog_2160P-6f92,29.97002997,3.886
|
| 877 |
+
Vlog_2160P-700c,30.0,3.881
|
| 878 |
+
Vlog_2160P-70d6,30.0,3.067
|
| 879 |
+
Vlog_2160P-7324,25.0,4.194
|
| 880 |
+
Vlog_2160P-77d8,23.97602398,3.667
|
| 881 |
+
Vlog_2160P-7b5c,30.0,3.61
|
| 882 |
+
Vlog_2160P-7bfb,30.0,4.206
|
| 883 |
+
Vlog_2160P-7f05,30.0,3.111
|
metadata/test_videos.csv
ADDED
|
@@ -0,0 +1,2 @@
|
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| 1 |
+
vid,mos,width,height,pixfmt,framerate,nb_frames,bitdepth,bitrate
|
| 2 |
+
5636101558_540p,2.26,960,540,yuv420p,24,192,8,883071
|
reported_result.ipynb
ADDED
|
@@ -0,0 +1,1954 @@
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|
| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"metadata": {
|
| 5 |
+
"ExecuteTime": {
|
| 6 |
+
"end_time": "2025-03-11T16:39:14.401092Z",
|
| 7 |
+
"start_time": "2025-03-11T16:39:14.399279Z"
|
| 8 |
+
}
|
| 9 |
+
},
|
| 10 |
+
"cell_type": "code",
|
| 11 |
+
"source": [
|
| 12 |
+
"''' Extract data from file by the special keyword '''\n",
|
| 13 |
+
"import pandas as pd\n",
|
| 14 |
+
"import glob\n",
|
| 15 |
+
"import re\n",
|
| 16 |
+
"import numpy as np\n",
|
| 17 |
+
"import scipy.stats as stats\n",
|
| 18 |
+
"from scipy.stats import f_oneway"
|
| 19 |
+
],
|
| 20 |
+
"id": "3db8dc37abb935af",
|
| 21 |
+
"outputs": [],
|
| 22 |
+
"execution_count": 142
|
| 23 |
+
},
|
| 24 |
+
{
|
| 25 |
+
"metadata": {
|
| 26 |
+
"ExecuteTime": {
|
| 27 |
+
"end_time": "2025-03-11T16:39:14.420609Z",
|
| 28 |
+
"start_time": "2025-03-11T16:39:14.417506Z"
|
| 29 |
+
}
|
| 30 |
+
},
|
| 31 |
+
"cell_type": "code",
|
| 32 |
+
"source": [
|
| 33 |
+
"def read_log(file, regression_name):\n",
|
| 34 |
+
" line_list = []\n",
|
| 35 |
+
" with open(file) as f:\n",
|
| 36 |
+
" while True:\n",
|
| 37 |
+
" lines = f.readline()\n",
|
| 38 |
+
" line_list.append(lines)\n",
|
| 39 |
+
" if not lines:\n",
|
| 40 |
+
" break\n",
|
| 41 |
+
" # print(line_list)\n",
|
| 42 |
+
" if regression_name == 'mlp':\n",
|
| 43 |
+
" keyline = \"INFO - Average testing results among all repeated 80-20 holdouts:\\n\"\n",
|
| 44 |
+
" elif regression_name == 'svr':\n",
|
| 45 |
+
" keyline = \"Average testing results among all repeated 80-20 holdouts:\\n\"\n",
|
| 46 |
+
" for i in range(len(line_list)):\n",
|
| 47 |
+
" if line_list[i] == keyline:\n",
|
| 48 |
+
" key_index = i\n",
|
| 49 |
+
" srcc = line_list[key_index + 1]\n",
|
| 50 |
+
" krcc = line_list[key_index + 2]\n",
|
| 51 |
+
" plcc = line_list[key_index + 3]\n",
|
| 52 |
+
" rmse = line_list[key_index + 4]\n",
|
| 53 |
+
" return srcc, krcc, plcc, rmse\n",
|
| 54 |
+
"\n",
|
| 55 |
+
"def get_value(coef, model_name, decimals):\n",
|
| 56 |
+
" if model_name == 'mlp':\n",
|
| 57 |
+
" pattern = r\"INFO - (\\w+): (\\d+\\.\\d+) \\(std: (\\d+\\.\\d+)\\)\"\n",
|
| 58 |
+
" elif model_name == 'svr':\n",
|
| 59 |
+
" pattern = r\"(\\w+): (\\d+\\.\\d+) \\(std: (\\d+\\.\\d+)\\)\"\n",
|
| 60 |
+
"\n",
|
| 61 |
+
" matches = re.findall(pattern, coef)\n",
|
| 62 |
+
" for match in matches:\n",
|
| 63 |
+
" metric, value, std_dev = match\n",
|
| 64 |
+
" value = format(float(value), f'.{decimals}f')\n",
|
| 65 |
+
" std_dev = format(float(std_dev), f'.{decimals}f')\n",
|
| 66 |
+
" plusmius = u\"\\u00B1\"\n",
|
| 67 |
+
" coef_value = f\"{value} ({plusmius}{std_dev})\"\n",
|
| 68 |
+
" return coef_value\n"
|
| 69 |
+
],
|
| 70 |
+
"id": "initial_id",
|
| 71 |
+
"outputs": [],
|
| 72 |
+
"execution_count": 143
|
| 73 |
+
},
|
| 74 |
+
{
|
| 75 |
+
"metadata": {
|
| 76 |
+
"ExecuteTime": {
|
| 77 |
+
"end_time": "2025-03-11T16:39:14.447744Z",
|
| 78 |
+
"start_time": "2025-03-11T16:39:14.444612Z"
|
| 79 |
+
}
|
| 80 |
+
},
|
| 81 |
+
"cell_type": "code",
|
| 82 |
+
"source": [
|
| 83 |
+
"def process_log_files(data_name, model_names, regression_name, log_path, decimals):\n",
|
| 84 |
+
" df_results = pd.DataFrame(columns=['DATASET', 'MODEL', 'SRCC (±STD)', 'KRCC (±STD)', 'PLCC (±STD)', 'RMSE (±STD)'])\n",
|
| 85 |
+
" data_list, model_list, srcc_list, krcc_list, plcc_list, rmse_list = [], [], [], [], [], []\n",
|
| 86 |
+
" \n",
|
| 87 |
+
" for model in model_names:\n",
|
| 88 |
+
" log_file = f'{log_path}{data_name}_{model}_{regression_name}.log'\n",
|
| 89 |
+
" try:\n",
|
| 90 |
+
" srcc, krcc, plcc, rmse = read_log(log_file, regression_name)\n",
|
| 91 |
+
" srcc = get_value(srcc, regression_name, decimals)\n",
|
| 92 |
+
" krcc = get_value(krcc, regression_name, decimals)\n",
|
| 93 |
+
" plcc = get_value(plcc, regression_name, decimals)\n",
|
| 94 |
+
" rmse = get_value(rmse, regression_name, decimals)\n",
|
| 95 |
+
" \n",
|
| 96 |
+
" data_list.append(data_name)\n",
|
| 97 |
+
" model_list.append(model)\n",
|
| 98 |
+
" srcc_list.append(srcc)\n",
|
| 99 |
+
" krcc_list.append(krcc)\n",
|
| 100 |
+
" plcc_list.append(plcc)\n",
|
| 101 |
+
" rmse_list.append(rmse)\n",
|
| 102 |
+
" \n",
|
| 103 |
+
" except FileNotFoundError:\n",
|
| 104 |
+
" print(f\"FileNotFoundError: No such file or directory for {model}\")\n",
|
| 105 |
+
" except Exception as e:\n",
|
| 106 |
+
" print(f\"Error occurred while processing {model}: {str(e)}\")\n",
|
| 107 |
+
"\n",
|
| 108 |
+
" df_results['DATASET'] = data_list\n",
|
| 109 |
+
" df_results['MODEL'] = model_list\n",
|
| 110 |
+
" df_results['SRCC (±STD)'] = srcc_list\n",
|
| 111 |
+
" df_results['KRCC (±STD)'] = krcc_list\n",
|
| 112 |
+
" df_results['PLCC (±STD)'] = plcc_list\n",
|
| 113 |
+
" df_results['RMSE (±STD)'] = rmse_list\n",
|
| 114 |
+
" \n",
|
| 115 |
+
" return df_results"
|
| 116 |
+
],
|
| 117 |
+
"id": "7e9a39184bac25a7",
|
| 118 |
+
"outputs": [],
|
| 119 |
+
"execution_count": 144
|
| 120 |
+
},
|
| 121 |
+
{
|
| 122 |
+
"metadata": {},
|
| 123 |
+
"cell_type": "markdown",
|
| 124 |
+
"source": "Performance comparison of the evaluated NR-VQA models",
|
| 125 |
+
"id": "65b1cb6929ece5aa"
|
| 126 |
+
},
|
| 127 |
+
{
|
| 128 |
+
"metadata": {
|
| 129 |
+
"ExecuteTime": {
|
| 130 |
+
"end_time": "2025-03-11T16:39:14.454239Z",
|
| 131 |
+
"start_time": "2025-03-11T16:39:14.448711Z"
|
| 132 |
+
}
|
| 133 |
+
},
|
| 134 |
+
"cell_type": "code",
|
| 135 |
+
"source": [
|
| 136 |
+
"data_name = 'cvd_2014'\n",
|
| 137 |
+
"model_names = ['brisque', 'vbliinds', 'tlvqm', 'videval', 'rapique']\n",
|
| 138 |
+
"regression_name = 'svr'\n",
|
| 139 |
+
"log_path = './reported_results/'\n",
|
| 140 |
+
"df_results = process_log_files(data_name, model_names, regression_name, log_path, decimals=4)\n",
|
| 141 |
+
"df_results\n",
|
| 142 |
+
"# csv_name = f'./csv/{data_name}_svrlog.csv'\n",
|
| 143 |
+
"# df_results.to_csv(csv_name, index=None, encoding=\"UTF-8\")\n",
|
| 144 |
+
"# xlsx_name = f'./save_csv/{data_name}_{regression_name}_log.xlsx'\n",
|
| 145 |
+
"# df_results.to_excel(xlsx_name, index=False, encoding=\"utf-8\")"
|
| 146 |
+
],
|
| 147 |
+
"id": "2ca72decdd62d8ab",
|
| 148 |
+
"outputs": [
|
| 149 |
+
{
|
| 150 |
+
"data": {
|
| 151 |
+
"text/plain": [
|
| 152 |
+
" DATASET MODEL SRCC (±STD) KRCC (±STD) PLCC (±STD) \\\n",
|
| 153 |
+
"0 cvd_2014 brisque 0.5553 (±0.0231) 0.3895 (±0.0161) 0.5527 (±0.0627) \n",
|
| 154 |
+
"1 cvd_2014 vbliinds 0.7306 (±0.0511) 0.5393 (±0.0473) 0.7853 (±0.0414) \n",
|
| 155 |
+
"2 cvd_2014 tlvqm 0.5399 (±0.0352) 0.4006 (±0.0298) 0.5785 (±0.0498) \n",
|
| 156 |
+
"3 cvd_2014 videval 0.7663 (±0.0214) 0.5634 (±0.0221) 0.8062 (±0.0216) \n",
|
| 157 |
+
"4 cvd_2014 rapique 0.8530 (±0.0301) 0.6836 (±0.0357) 0.8766 (±0.0326) \n",
|
| 158 |
+
"\n",
|
| 159 |
+
" RMSE (±STD) \n",
|
| 160 |
+
"0 18.4752 (±1.0274) \n",
|
| 161 |
+
"1 13.7267 (±1.2130) \n",
|
| 162 |
+
"2 18.0832 (±0.7052) \n",
|
| 163 |
+
"3 13.1151 (±0.6579) \n",
|
| 164 |
+
"4 10.6670 (±1.1939) "
|
| 165 |
+
],
|
| 166 |
+
"text/html": [
|
| 167 |
+
"<div>\n",
|
| 168 |
+
"<style scoped>\n",
|
| 169 |
+
" .dataframe tbody tr th:only-of-type {\n",
|
| 170 |
+
" vertical-align: middle;\n",
|
| 171 |
+
" }\n",
|
| 172 |
+
"\n",
|
| 173 |
+
" .dataframe tbody tr th {\n",
|
| 174 |
+
" vertical-align: top;\n",
|
| 175 |
+
" }\n",
|
| 176 |
+
"\n",
|
| 177 |
+
" .dataframe thead th {\n",
|
| 178 |
+
" text-align: right;\n",
|
| 179 |
+
" }\n",
|
| 180 |
+
"</style>\n",
|
| 181 |
+
"<table border=\"1\" class=\"dataframe\">\n",
|
| 182 |
+
" <thead>\n",
|
| 183 |
+
" <tr style=\"text-align: right;\">\n",
|
| 184 |
+
" <th></th>\n",
|
| 185 |
+
" <th>DATASET</th>\n",
|
| 186 |
+
" <th>MODEL</th>\n",
|
| 187 |
+
" <th>SRCC (±STD)</th>\n",
|
| 188 |
+
" <th>KRCC (±STD)</th>\n",
|
| 189 |
+
" <th>PLCC (±STD)</th>\n",
|
| 190 |
+
" <th>RMSE (±STD)</th>\n",
|
| 191 |
+
" </tr>\n",
|
| 192 |
+
" </thead>\n",
|
| 193 |
+
" <tbody>\n",
|
| 194 |
+
" <tr>\n",
|
| 195 |
+
" <th>0</th>\n",
|
| 196 |
+
" <td>cvd_2014</td>\n",
|
| 197 |
+
" <td>brisque</td>\n",
|
| 198 |
+
" <td>0.5553 (±0.0231)</td>\n",
|
| 199 |
+
" <td>0.3895 (±0.0161)</td>\n",
|
| 200 |
+
" <td>0.5527 (±0.0627)</td>\n",
|
| 201 |
+
" <td>18.4752 (±1.0274)</td>\n",
|
| 202 |
+
" </tr>\n",
|
| 203 |
+
" <tr>\n",
|
| 204 |
+
" <th>1</th>\n",
|
| 205 |
+
" <td>cvd_2014</td>\n",
|
| 206 |
+
" <td>vbliinds</td>\n",
|
| 207 |
+
" <td>0.7306 (±0.0511)</td>\n",
|
| 208 |
+
" <td>0.5393 (±0.0473)</td>\n",
|
| 209 |
+
" <td>0.7853 (±0.0414)</td>\n",
|
| 210 |
+
" <td>13.7267 (±1.2130)</td>\n",
|
| 211 |
+
" </tr>\n",
|
| 212 |
+
" <tr>\n",
|
| 213 |
+
" <th>2</th>\n",
|
| 214 |
+
" <td>cvd_2014</td>\n",
|
| 215 |
+
" <td>tlvqm</td>\n",
|
| 216 |
+
" <td>0.5399 (±0.0352)</td>\n",
|
| 217 |
+
" <td>0.4006 (±0.0298)</td>\n",
|
| 218 |
+
" <td>0.5785 (±0.0498)</td>\n",
|
| 219 |
+
" <td>18.0832 (±0.7052)</td>\n",
|
| 220 |
+
" </tr>\n",
|
| 221 |
+
" <tr>\n",
|
| 222 |
+
" <th>3</th>\n",
|
| 223 |
+
" <td>cvd_2014</td>\n",
|
| 224 |
+
" <td>videval</td>\n",
|
| 225 |
+
" <td>0.7663 (±0.0214)</td>\n",
|
| 226 |
+
" <td>0.5634 (±0.0221)</td>\n",
|
| 227 |
+
" <td>0.8062 (±0.0216)</td>\n",
|
| 228 |
+
" <td>13.1151 (±0.6579)</td>\n",
|
| 229 |
+
" </tr>\n",
|
| 230 |
+
" <tr>\n",
|
| 231 |
+
" <th>4</th>\n",
|
| 232 |
+
" <td>cvd_2014</td>\n",
|
| 233 |
+
" <td>rapique</td>\n",
|
| 234 |
+
" <td>0.8530 (±0.0301)</td>\n",
|
| 235 |
+
" <td>0.6836 (±0.0357)</td>\n",
|
| 236 |
+
" <td>0.8766 (±0.0326)</td>\n",
|
| 237 |
+
" <td>10.6670 (±1.1939)</td>\n",
|
| 238 |
+
" </tr>\n",
|
| 239 |
+
" </tbody>\n",
|
| 240 |
+
"</table>\n",
|
| 241 |
+
"</div>"
|
| 242 |
+
]
|
| 243 |
+
},
|
| 244 |
+
"execution_count": 145,
|
| 245 |
+
"metadata": {},
|
| 246 |
+
"output_type": "execute_result"
|
| 247 |
+
}
|
| 248 |
+
],
|
| 249 |
+
"execution_count": 145
|
| 250 |
+
},
|
| 251 |
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{
|
| 252 |
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"metadata": {
|
| 253 |
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|
| 254 |
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"end_time": "2025-03-11T16:39:14.460774Z",
|
| 255 |
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"start_time": "2025-03-11T16:39:14.454807Z"
|
| 256 |
+
}
|
| 257 |
+
},
|
| 258 |
+
"cell_type": "code",
|
| 259 |
+
"source": [
|
| 260 |
+
"data_name = 'konvid_1k'\n",
|
| 261 |
+
"model_names = ['brisque', 'vbliinds', 'tlvqm', 'videval', 'rapique']\n",
|
| 262 |
+
"regression_name = 'svr'\n",
|
| 263 |
+
"log_path = './reported_results/'\n",
|
| 264 |
+
"df_results = process_log_files(data_name, model_names, regression_name, log_path, decimals=4)\n",
|
| 265 |
+
"df_results"
|
| 266 |
+
],
|
| 267 |
+
"id": "42997a595fb2bca8",
|
| 268 |
+
"outputs": [
|
| 269 |
+
{
|
| 270 |
+
"data": {
|
| 271 |
+
"text/plain": [
|
| 272 |
+
" DATASET MODEL SRCC (±STD) KRCC (±STD) PLCC (±STD) \\\n",
|
| 273 |
+
"0 konvid_1k brisque 0.6781 (±0.0083) 0.4935 (±0.0091) 0.6746 (±0.0069) \n",
|
| 274 |
+
"1 konvid_1k vbliinds 0.7258 (±0.0115) 0.5322 (±0.0109) 0.7155 (±0.0107) \n",
|
| 275 |
+
"2 konvid_1k tlvqm 0.7616 (±0.0067) 0.5635 (±0.0067) 0.7463 (±0.0065) \n",
|
| 276 |
+
"3 konvid_1k videval 0.8073 (±0.0174) 0.6036 (±0.0165) 0.7923 (±0.0169) \n",
|
| 277 |
+
"4 konvid_1k rapique 0.8219 (±0.0057) 0.6264 (±0.0074) 0.8191 (±0.0079) \n",
|
| 278 |
+
"\n",
|
| 279 |
+
" RMSE (±STD) \n",
|
| 280 |
+
"0 0.4753 (±0.0040) \n",
|
| 281 |
+
"1 0.4498 (±0.0071) \n",
|
| 282 |
+
"2 0.4285 (±0.0047) \n",
|
| 283 |
+
"3 0.3928 (±0.0133) \n",
|
| 284 |
+
"4 0.3693 (±0.0070) "
|
| 285 |
+
],
|
| 286 |
+
"text/html": [
|
| 287 |
+
"<div>\n",
|
| 288 |
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|
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|
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|
| 292 |
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|
| 293 |
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|
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|
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|
| 297 |
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|
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+
" text-align: right;\n",
|
| 299 |
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|
| 300 |
+
"</style>\n",
|
| 301 |
+
"<table border=\"1\" class=\"dataframe\">\n",
|
| 302 |
+
" <thead>\n",
|
| 303 |
+
" <tr style=\"text-align: right;\">\n",
|
| 304 |
+
" <th></th>\n",
|
| 305 |
+
" <th>DATASET</th>\n",
|
| 306 |
+
" <th>MODEL</th>\n",
|
| 307 |
+
" <th>SRCC (±STD)</th>\n",
|
| 308 |
+
" <th>KRCC (±STD)</th>\n",
|
| 309 |
+
" <th>PLCC (±STD)</th>\n",
|
| 310 |
+
" <th>RMSE (±STD)</th>\n",
|
| 311 |
+
" </tr>\n",
|
| 312 |
+
" </thead>\n",
|
| 313 |
+
" <tbody>\n",
|
| 314 |
+
" <tr>\n",
|
| 315 |
+
" <th>0</th>\n",
|
| 316 |
+
" <td>konvid_1k</td>\n",
|
| 317 |
+
" <td>brisque</td>\n",
|
| 318 |
+
" <td>0.6781 (±0.0083)</td>\n",
|
| 319 |
+
" <td>0.4935 (±0.0091)</td>\n",
|
| 320 |
+
" <td>0.6746 (±0.0069)</td>\n",
|
| 321 |
+
" <td>0.4753 (±0.0040)</td>\n",
|
| 322 |
+
" </tr>\n",
|
| 323 |
+
" <tr>\n",
|
| 324 |
+
" <th>1</th>\n",
|
| 325 |
+
" <td>konvid_1k</td>\n",
|
| 326 |
+
" <td>vbliinds</td>\n",
|
| 327 |
+
" <td>0.7258 (±0.0115)</td>\n",
|
| 328 |
+
" <td>0.5322 (±0.0109)</td>\n",
|
| 329 |
+
" <td>0.7155 (±0.0107)</td>\n",
|
| 330 |
+
" <td>0.4498 (±0.0071)</td>\n",
|
| 331 |
+
" </tr>\n",
|
| 332 |
+
" <tr>\n",
|
| 333 |
+
" <th>2</th>\n",
|
| 334 |
+
" <td>konvid_1k</td>\n",
|
| 335 |
+
" <td>tlvqm</td>\n",
|
| 336 |
+
" <td>0.7616 (±0.0067)</td>\n",
|
| 337 |
+
" <td>0.5635 (±0.0067)</td>\n",
|
| 338 |
+
" <td>0.7463 (±0.0065)</td>\n",
|
| 339 |
+
" <td>0.4285 (±0.0047)</td>\n",
|
| 340 |
+
" </tr>\n",
|
| 341 |
+
" <tr>\n",
|
| 342 |
+
" <th>3</th>\n",
|
| 343 |
+
" <td>konvid_1k</td>\n",
|
| 344 |
+
" <td>videval</td>\n",
|
| 345 |
+
" <td>0.8073 (±0.0174)</td>\n",
|
| 346 |
+
" <td>0.6036 (±0.0165)</td>\n",
|
| 347 |
+
" <td>0.7923 (±0.0169)</td>\n",
|
| 348 |
+
" <td>0.3928 (±0.0133)</td>\n",
|
| 349 |
+
" </tr>\n",
|
| 350 |
+
" <tr>\n",
|
| 351 |
+
" <th>4</th>\n",
|
| 352 |
+
" <td>konvid_1k</td>\n",
|
| 353 |
+
" <td>rapique</td>\n",
|
| 354 |
+
" <td>0.8219 (±0.0057)</td>\n",
|
| 355 |
+
" <td>0.6264 (±0.0074)</td>\n",
|
| 356 |
+
" <td>0.8191 (±0.0079)</td>\n",
|
| 357 |
+
" <td>0.3693 (±0.0070)</td>\n",
|
| 358 |
+
" </tr>\n",
|
| 359 |
+
" </tbody>\n",
|
| 360 |
+
"</table>\n",
|
| 361 |
+
"</div>"
|
| 362 |
+
]
|
| 363 |
+
},
|
| 364 |
+
"execution_count": 146,
|
| 365 |
+
"metadata": {},
|
| 366 |
+
"output_type": "execute_result"
|
| 367 |
+
}
|
| 368 |
+
],
|
| 369 |
+
"execution_count": 146
|
| 370 |
+
},
|
| 371 |
+
{
|
| 372 |
+
"metadata": {
|
| 373 |
+
"ExecuteTime": {
|
| 374 |
+
"end_time": "2025-03-11T16:39:14.466743Z",
|
| 375 |
+
"start_time": "2025-03-11T16:39:14.461700Z"
|
| 376 |
+
}
|
| 377 |
+
},
|
| 378 |
+
"cell_type": "code",
|
| 379 |
+
"source": [
|
| 380 |
+
"data_name = 'live_vqc'\n",
|
| 381 |
+
"model_names = ['brisque', 'vbliinds', 'tlvqm', 'videval', 'rapique']\n",
|
| 382 |
+
"regression_name = 'svr'\n",
|
| 383 |
+
"log_path = './reported_results/'\n",
|
| 384 |
+
"df_results = process_log_files(data_name, model_names, regression_name, log_path, decimals=4)\n",
|
| 385 |
+
"df_results"
|
| 386 |
+
],
|
| 387 |
+
"id": "ccdd1ce4bc21ed69",
|
| 388 |
+
"outputs": [
|
| 389 |
+
{
|
| 390 |
+
"data": {
|
| 391 |
+
"text/plain": [
|
| 392 |
+
" DATASET MODEL SRCC (±STD) KRCC (±STD) PLCC (±STD) \\\n",
|
| 393 |
+
"0 live_vqc brisque 0.6096 (±0.0133) 0.4420 (±0.0127) 0.6652 (±0.0176) \n",
|
| 394 |
+
"1 live_vqc vbliinds 0.6925 (±0.0103) 0.5046 (±0.0111) 0.6872 (±0.0124) \n",
|
| 395 |
+
"2 live_vqc tlvqm 0.8133 (±0.0104) 0.6231 (±0.0105) 0.7912 (±0.0148) \n",
|
| 396 |
+
"3 live_vqc videval 0.7725 (±0.0093) 0.5874 (±0.0080) 0.7752 (±0.0211) \n",
|
| 397 |
+
"4 live_vqc rapique 0.7328 (±0.0130) 0.5418 (±0.0134) 0.7520 (±0.0119) \n",
|
| 398 |
+
"\n",
|
| 399 |
+
" RMSE (±STD) \n",
|
| 400 |
+
"0 12.7480 (±0.2697) \n",
|
| 401 |
+
"1 12.4031 (±0.1948) \n",
|
| 402 |
+
"2 10.4409 (±0.3123) \n",
|
| 403 |
+
"3 10.7846 (±0.4082) \n",
|
| 404 |
+
"4 11.2538 (±0.2285) "
|
| 405 |
+
],
|
| 406 |
+
"text/html": [
|
| 407 |
+
"<div>\n",
|
| 408 |
+
"<style scoped>\n",
|
| 409 |
+
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|
| 410 |
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|
| 411 |
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|
| 412 |
+
"\n",
|
| 413 |
+
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|
| 414 |
+
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|
| 415 |
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|
| 416 |
+
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|
| 417 |
+
" .dataframe thead th {\n",
|
| 418 |
+
" text-align: right;\n",
|
| 419 |
+
" }\n",
|
| 420 |
+
"</style>\n",
|
| 421 |
+
"<table border=\"1\" class=\"dataframe\">\n",
|
| 422 |
+
" <thead>\n",
|
| 423 |
+
" <tr style=\"text-align: right;\">\n",
|
| 424 |
+
" <th></th>\n",
|
| 425 |
+
" <th>DATASET</th>\n",
|
| 426 |
+
" <th>MODEL</th>\n",
|
| 427 |
+
" <th>SRCC (±STD)</th>\n",
|
| 428 |
+
" <th>KRCC (±STD)</th>\n",
|
| 429 |
+
" <th>PLCC (±STD)</th>\n",
|
| 430 |
+
" <th>RMSE (±STD)</th>\n",
|
| 431 |
+
" </tr>\n",
|
| 432 |
+
" </thead>\n",
|
| 433 |
+
" <tbody>\n",
|
| 434 |
+
" <tr>\n",
|
| 435 |
+
" <th>0</th>\n",
|
| 436 |
+
" <td>live_vqc</td>\n",
|
| 437 |
+
" <td>brisque</td>\n",
|
| 438 |
+
" <td>0.6096 (±0.0133)</td>\n",
|
| 439 |
+
" <td>0.4420 (±0.0127)</td>\n",
|
| 440 |
+
" <td>0.6652 (±0.0176)</td>\n",
|
| 441 |
+
" <td>12.7480 (±0.2697)</td>\n",
|
| 442 |
+
" </tr>\n",
|
| 443 |
+
" <tr>\n",
|
| 444 |
+
" <th>1</th>\n",
|
| 445 |
+
" <td>live_vqc</td>\n",
|
| 446 |
+
" <td>vbliinds</td>\n",
|
| 447 |
+
" <td>0.6925 (±0.0103)</td>\n",
|
| 448 |
+
" <td>0.5046 (±0.0111)</td>\n",
|
| 449 |
+
" <td>0.6872 (±0.0124)</td>\n",
|
| 450 |
+
" <td>12.4031 (±0.1948)</td>\n",
|
| 451 |
+
" </tr>\n",
|
| 452 |
+
" <tr>\n",
|
| 453 |
+
" <th>2</th>\n",
|
| 454 |
+
" <td>live_vqc</td>\n",
|
| 455 |
+
" <td>tlvqm</td>\n",
|
| 456 |
+
" <td>0.8133 (±0.0104)</td>\n",
|
| 457 |
+
" <td>0.6231 (±0.0105)</td>\n",
|
| 458 |
+
" <td>0.7912 (±0.0148)</td>\n",
|
| 459 |
+
" <td>10.4409 (±0.3123)</td>\n",
|
| 460 |
+
" </tr>\n",
|
| 461 |
+
" <tr>\n",
|
| 462 |
+
" <th>3</th>\n",
|
| 463 |
+
" <td>live_vqc</td>\n",
|
| 464 |
+
" <td>videval</td>\n",
|
| 465 |
+
" <td>0.7725 (±0.0093)</td>\n",
|
| 466 |
+
" <td>0.5874 (±0.0080)</td>\n",
|
| 467 |
+
" <td>0.7752 (±0.0211)</td>\n",
|
| 468 |
+
" <td>10.7846 (±0.4082)</td>\n",
|
| 469 |
+
" </tr>\n",
|
| 470 |
+
" <tr>\n",
|
| 471 |
+
" <th>4</th>\n",
|
| 472 |
+
" <td>live_vqc</td>\n",
|
| 473 |
+
" <td>rapique</td>\n",
|
| 474 |
+
" <td>0.7328 (±0.0130)</td>\n",
|
| 475 |
+
" <td>0.5418 (±0.0134)</td>\n",
|
| 476 |
+
" <td>0.7520 (±0.0119)</td>\n",
|
| 477 |
+
" <td>11.2538 (±0.2285)</td>\n",
|
| 478 |
+
" </tr>\n",
|
| 479 |
+
" </tbody>\n",
|
| 480 |
+
"</table>\n",
|
| 481 |
+
"</div>"
|
| 482 |
+
]
|
| 483 |
+
},
|
| 484 |
+
"execution_count": 147,
|
| 485 |
+
"metadata": {},
|
| 486 |
+
"output_type": "execute_result"
|
| 487 |
+
}
|
| 488 |
+
],
|
| 489 |
+
"execution_count": 147
|
| 490 |
+
},
|
| 491 |
+
{
|
| 492 |
+
"metadata": {
|
| 493 |
+
"ExecuteTime": {
|
| 494 |
+
"end_time": "2025-03-11T16:39:14.472960Z",
|
| 495 |
+
"start_time": "2025-03-11T16:39:14.467320Z"
|
| 496 |
+
}
|
| 497 |
+
},
|
| 498 |
+
"cell_type": "code",
|
| 499 |
+
"source": [
|
| 500 |
+
"data_name = 'youtube_ugc'\n",
|
| 501 |
+
"model_names = ['brisque', 'vbliinds', 'tlvqm', 'videval', 'rapique']\n",
|
| 502 |
+
"regression_name = 'svr'\n",
|
| 503 |
+
"log_path = './reported_results/'\n",
|
| 504 |
+
"df_results = process_log_files(data_name, model_names, regression_name, log_path, decimals=4)\n",
|
| 505 |
+
"df_results"
|
| 506 |
+
],
|
| 507 |
+
"id": "d46b6e4cca31e163",
|
| 508 |
+
"outputs": [
|
| 509 |
+
{
|
| 510 |
+
"data": {
|
| 511 |
+
"text/plain": [
|
| 512 |
+
" DATASET MODEL SRCC (±STD) KRCC (±STD) \\\n",
|
| 513 |
+
"0 youtube_ugc brisque 0.3517 (±0.0165) 0.2416 (±0.0119) \n",
|
| 514 |
+
"1 youtube_ugc vbliinds 0.4783 (±0.0188) 0.3396 (±0.0143) \n",
|
| 515 |
+
"2 youtube_ugc tlvqm 0.6802 (±0.0183) 0.4892 (±0.0168) \n",
|
| 516 |
+
"3 youtube_ugc videval 0.7814 (±0.0049) 0.5906 (±0.0052) \n",
|
| 517 |
+
"4 youtube_ugc rapique 0.7994 (±0.0064) 0.5989 (±0.0054) \n",
|
| 518 |
+
"\n",
|
| 519 |
+
" PLCC (±STD) RMSE (±STD) \n",
|
| 520 |
+
"0 0.3768 (±0.0200) 0.6349 (±0.0056) \n",
|
| 521 |
+
"1 0.4967 (±0.0228) 0.5949 (±0.0094) \n",
|
| 522 |
+
"2 0.6876 (±0.0207) 0.4977 (±0.0132) \n",
|
| 523 |
+
"3 0.7929 (±0.0038) 0.4177 (±0.0033) \n",
|
| 524 |
+
"4 0.8300 (±0.0070) 0.3823 (±0.0068) "
|
| 525 |
+
],
|
| 526 |
+
"text/html": [
|
| 527 |
+
"<div>\n",
|
| 528 |
+
"<style scoped>\n",
|
| 529 |
+
" .dataframe tbody tr th:only-of-type {\n",
|
| 530 |
+
" vertical-align: middle;\n",
|
| 531 |
+
" }\n",
|
| 532 |
+
"\n",
|
| 533 |
+
" .dataframe tbody tr th {\n",
|
| 534 |
+
" vertical-align: top;\n",
|
| 535 |
+
" }\n",
|
| 536 |
+
"\n",
|
| 537 |
+
" .dataframe thead th {\n",
|
| 538 |
+
" text-align: right;\n",
|
| 539 |
+
" }\n",
|
| 540 |
+
"</style>\n",
|
| 541 |
+
"<table border=\"1\" class=\"dataframe\">\n",
|
| 542 |
+
" <thead>\n",
|
| 543 |
+
" <tr style=\"text-align: right;\">\n",
|
| 544 |
+
" <th></th>\n",
|
| 545 |
+
" <th>DATASET</th>\n",
|
| 546 |
+
" <th>MODEL</th>\n",
|
| 547 |
+
" <th>SRCC (±STD)</th>\n",
|
| 548 |
+
" <th>KRCC (±STD)</th>\n",
|
| 549 |
+
" <th>PLCC (±STD)</th>\n",
|
| 550 |
+
" <th>RMSE (±STD)</th>\n",
|
| 551 |
+
" </tr>\n",
|
| 552 |
+
" </thead>\n",
|
| 553 |
+
" <tbody>\n",
|
| 554 |
+
" <tr>\n",
|
| 555 |
+
" <th>0</th>\n",
|
| 556 |
+
" <td>youtube_ugc</td>\n",
|
| 557 |
+
" <td>brisque</td>\n",
|
| 558 |
+
" <td>0.3517 (±0.0165)</td>\n",
|
| 559 |
+
" <td>0.2416 (±0.0119)</td>\n",
|
| 560 |
+
" <td>0.3768 (±0.0200)</td>\n",
|
| 561 |
+
" <td>0.6349 (±0.0056)</td>\n",
|
| 562 |
+
" </tr>\n",
|
| 563 |
+
" <tr>\n",
|
| 564 |
+
" <th>1</th>\n",
|
| 565 |
+
" <td>youtube_ugc</td>\n",
|
| 566 |
+
" <td>vbliinds</td>\n",
|
| 567 |
+
" <td>0.4783 (±0.0188)</td>\n",
|
| 568 |
+
" <td>0.3396 (±0.0143)</td>\n",
|
| 569 |
+
" <td>0.4967 (±0.0228)</td>\n",
|
| 570 |
+
" <td>0.5949 (±0.0094)</td>\n",
|
| 571 |
+
" </tr>\n",
|
| 572 |
+
" <tr>\n",
|
| 573 |
+
" <th>2</th>\n",
|
| 574 |
+
" <td>youtube_ugc</td>\n",
|
| 575 |
+
" <td>tlvqm</td>\n",
|
| 576 |
+
" <td>0.6802 (±0.0183)</td>\n",
|
| 577 |
+
" <td>0.4892 (±0.0168)</td>\n",
|
| 578 |
+
" <td>0.6876 (±0.0207)</td>\n",
|
| 579 |
+
" <td>0.4977 (±0.0132)</td>\n",
|
| 580 |
+
" </tr>\n",
|
| 581 |
+
" <tr>\n",
|
| 582 |
+
" <th>3</th>\n",
|
| 583 |
+
" <td>youtube_ugc</td>\n",
|
| 584 |
+
" <td>videval</td>\n",
|
| 585 |
+
" <td>0.7814 (±0.0049)</td>\n",
|
| 586 |
+
" <td>0.5906 (±0.0052)</td>\n",
|
| 587 |
+
" <td>0.7929 (±0.0038)</td>\n",
|
| 588 |
+
" <td>0.4177 (±0.0033)</td>\n",
|
| 589 |
+
" </tr>\n",
|
| 590 |
+
" <tr>\n",
|
| 591 |
+
" <th>4</th>\n",
|
| 592 |
+
" <td>youtube_ugc</td>\n",
|
| 593 |
+
" <td>rapique</td>\n",
|
| 594 |
+
" <td>0.7994 (±0.0064)</td>\n",
|
| 595 |
+
" <td>0.5989 (±0.0054)</td>\n",
|
| 596 |
+
" <td>0.8300 (±0.0070)</td>\n",
|
| 597 |
+
" <td>0.3823 (±0.0068)</td>\n",
|
| 598 |
+
" </tr>\n",
|
| 599 |
+
" </tbody>\n",
|
| 600 |
+
"</table>\n",
|
| 601 |
+
"</div>"
|
| 602 |
+
]
|
| 603 |
+
},
|
| 604 |
+
"execution_count": 148,
|
| 605 |
+
"metadata": {},
|
| 606 |
+
"output_type": "execute_result"
|
| 607 |
+
}
|
| 608 |
+
],
|
| 609 |
+
"execution_count": 148
|
| 610 |
+
},
|
| 611 |
+
{
|
| 612 |
+
"metadata": {},
|
| 613 |
+
"cell_type": "markdown",
|
| 614 |
+
"source": "vsfa performance",
|
| 615 |
+
"id": "8489668427d0c619"
|
| 616 |
+
},
|
| 617 |
+
{
|
| 618 |
+
"metadata": {
|
| 619 |
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"ExecuteTime": {
|
| 620 |
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"end_time": "2025-03-11T16:39:14.480216Z",
|
| 621 |
+
"start_time": "2025-03-11T16:39:14.473866Z"
|
| 622 |
+
}
|
| 623 |
+
},
|
| 624 |
+
"cell_type": "code",
|
| 625 |
+
"source": [
|
| 626 |
+
"datasets = {\n",
|
| 627 |
+
" 'cvd_2014': \"./reported_results/VSFA-CVD2014-EXP0_metrics.npy\",\n",
|
| 628 |
+
" 'konvid_1k': \"./reported_results/VSFA-KoNViD_1k-EXP0.npy\",\n",
|
| 629 |
+
" 'live_vqc': \"./reported_results/VSFA-LIVE-VQC-EXP0_metrics.npy\",\n",
|
| 630 |
+
" 'youtube_ugc': \"./reported_results/VSFA-YOUTUBE_UGC_ALL-EXP0.npy\"\n",
|
| 631 |
+
"}\n",
|
| 632 |
+
"data_list, srcc_list, krcc_list, plcc_list, rmse_list = [], [], [], [], []\n",
|
| 633 |
+
"def load_metrics_npy(file_path, indices):\n",
|
| 634 |
+
" data = np.load(file_path, allow_pickle=True)\n",
|
| 635 |
+
" return [data[index] for index in indices]\n",
|
| 636 |
+
"for dataset_name, file_path in datasets.items():\n",
|
| 637 |
+
" indices = [3, 4, 5, 6] if dataset_name in ['konvid_1k', 'youtube_ugc'] else [1, 2, 3, 4]\n",
|
| 638 |
+
" srcc, krcc, plcc, rmse = load_metrics_npy(file_path, indices)\n",
|
| 639 |
+
" data_list.append(dataset_name)\n",
|
| 640 |
+
" srcc_list.append(srcc)\n",
|
| 641 |
+
" krcc_list.append(krcc)\n",
|
| 642 |
+
" plcc_list.append(plcc)\n",
|
| 643 |
+
" rmse_list.append(rmse)\n",
|
| 644 |
+
"\n",
|
| 645 |
+
"df_vsfa = pd.DataFrame({\n",
|
| 646 |
+
" 'DATASET': data_list,\n",
|
| 647 |
+
" 'MODEL': 'VSFA',\n",
|
| 648 |
+
" 'SRCC': srcc_list,\n",
|
| 649 |
+
" 'KRCC': krcc_list,\n",
|
| 650 |
+
" 'PLCC': plcc_list,\n",
|
| 651 |
+
" 'RMSE': rmse_list\n",
|
| 652 |
+
"}).round(4)\n",
|
| 653 |
+
"df_vsfa"
|
| 654 |
+
],
|
| 655 |
+
"id": "cca8633823b8c802",
|
| 656 |
+
"outputs": [
|
| 657 |
+
{
|
| 658 |
+
"data": {
|
| 659 |
+
"text/plain": [
|
| 660 |
+
" DATASET MODEL SRCC KRCC PLCC RMSE\n",
|
| 661 |
+
"0 cvd_2014 VSFA 0.8825 0.7179 0.8784 9.8619\n",
|
| 662 |
+
"1 konvid_1k VSFA 0.8067 0.6102 0.8182 0.4056\n",
|
| 663 |
+
"2 live_vqc VSFA 0.5898 0.4120 0.5894 15.3245\n",
|
| 664 |
+
"3 youtube_ugc VSFA 0.7857 0.5814 0.7808 0.4321"
|
| 665 |
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|
| 666 |
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|
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|
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|
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|
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|
| 676 |
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"\n",
|
| 677 |
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" .dataframe thead th {\n",
|
| 678 |
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" text-align: right;\n",
|
| 679 |
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" }\n",
|
| 680 |
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"</style>\n",
|
| 681 |
+
"<table border=\"1\" class=\"dataframe\">\n",
|
| 682 |
+
" <thead>\n",
|
| 683 |
+
" <tr style=\"text-align: right;\">\n",
|
| 684 |
+
" <th></th>\n",
|
| 685 |
+
" <th>DATASET</th>\n",
|
| 686 |
+
" <th>MODEL</th>\n",
|
| 687 |
+
" <th>SRCC</th>\n",
|
| 688 |
+
" <th>KRCC</th>\n",
|
| 689 |
+
" <th>PLCC</th>\n",
|
| 690 |
+
" <th>RMSE</th>\n",
|
| 691 |
+
" </tr>\n",
|
| 692 |
+
" </thead>\n",
|
| 693 |
+
" <tbody>\n",
|
| 694 |
+
" <tr>\n",
|
| 695 |
+
" <th>0</th>\n",
|
| 696 |
+
" <td>cvd_2014</td>\n",
|
| 697 |
+
" <td>VSFA</td>\n",
|
| 698 |
+
" <td>0.8825</td>\n",
|
| 699 |
+
" <td>0.7179</td>\n",
|
| 700 |
+
" <td>0.8784</td>\n",
|
| 701 |
+
" <td>9.8619</td>\n",
|
| 702 |
+
" </tr>\n",
|
| 703 |
+
" <tr>\n",
|
| 704 |
+
" <th>1</th>\n",
|
| 705 |
+
" <td>konvid_1k</td>\n",
|
| 706 |
+
" <td>VSFA</td>\n",
|
| 707 |
+
" <td>0.8067</td>\n",
|
| 708 |
+
" <td>0.6102</td>\n",
|
| 709 |
+
" <td>0.8182</td>\n",
|
| 710 |
+
" <td>0.4056</td>\n",
|
| 711 |
+
" </tr>\n",
|
| 712 |
+
" <tr>\n",
|
| 713 |
+
" <th>2</th>\n",
|
| 714 |
+
" <td>live_vqc</td>\n",
|
| 715 |
+
" <td>VSFA</td>\n",
|
| 716 |
+
" <td>0.5898</td>\n",
|
| 717 |
+
" <td>0.4120</td>\n",
|
| 718 |
+
" <td>0.5894</td>\n",
|
| 719 |
+
" <td>15.3245</td>\n",
|
| 720 |
+
" </tr>\n",
|
| 721 |
+
" <tr>\n",
|
| 722 |
+
" <th>3</th>\n",
|
| 723 |
+
" <td>youtube_ugc</td>\n",
|
| 724 |
+
" <td>VSFA</td>\n",
|
| 725 |
+
" <td>0.7857</td>\n",
|
| 726 |
+
" <td>0.5814</td>\n",
|
| 727 |
+
" <td>0.7808</td>\n",
|
| 728 |
+
" <td>0.4321</td>\n",
|
| 729 |
+
" </tr>\n",
|
| 730 |
+
" </tbody>\n",
|
| 731 |
+
"</table>\n",
|
| 732 |
+
"</div>"
|
| 733 |
+
]
|
| 734 |
+
},
|
| 735 |
+
"execution_count": 149,
|
| 736 |
+
"metadata": {},
|
| 737 |
+
"output_type": "execute_result"
|
| 738 |
+
}
|
| 739 |
+
],
|
| 740 |
+
"execution_count": 149
|
| 741 |
+
},
|
| 742 |
+
{
|
| 743 |
+
"metadata": {},
|
| 744 |
+
"cell_type": "markdown",
|
| 745 |
+
"source": "Performance on ReLax-VQA",
|
| 746 |
+
"id": "b02ee184866c506c"
|
| 747 |
+
},
|
| 748 |
+
{
|
| 749 |
+
"metadata": {
|
| 750 |
+
"ExecuteTime": {
|
| 751 |
+
"end_time": "2025-03-11T16:39:14.486341Z",
|
| 752 |
+
"start_time": "2025-03-11T16:39:14.480691Z"
|
| 753 |
+
}
|
| 754 |
+
},
|
| 755 |
+
"cell_type": "code",
|
| 756 |
+
"source": [
|
| 757 |
+
"data_name = 'cvd_2014'\n",
|
| 758 |
+
"model_names = ['relaxvqa']\n",
|
| 759 |
+
"regression_name = 'mlp'\n",
|
| 760 |
+
"log_path = './reported_results/'\n",
|
| 761 |
+
"df_results = process_log_files(data_name, model_names, regression_name, log_path, decimals=4)\n",
|
| 762 |
+
"df_results"
|
| 763 |
+
],
|
| 764 |
+
"id": "eefdd17b16239b3a",
|
| 765 |
+
"outputs": [
|
| 766 |
+
{
|
| 767 |
+
"data": {
|
| 768 |
+
"text/plain": [
|
| 769 |
+
" DATASET MODEL SRCC (±STD) KRCC (±STD) PLCC (±STD) \\\n",
|
| 770 |
+
"0 cvd_2014 relaxvqa 0.8643 (±0.0301) 0.6960 (±0.0442) 0.8895 (±0.0255) \n",
|
| 771 |
+
"\n",
|
| 772 |
+
" RMSE (±STD) \n",
|
| 773 |
+
"0 9.8185 (±1.0969) "
|
| 774 |
+
],
|
| 775 |
+
"text/html": [
|
| 776 |
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"<div>\n",
|
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|
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|
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|
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|
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|
| 790 |
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|
| 791 |
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|
| 792 |
+
" <tr style=\"text-align: right;\">\n",
|
| 793 |
+
" <th></th>\n",
|
| 794 |
+
" <th>DATASET</th>\n",
|
| 795 |
+
" <th>MODEL</th>\n",
|
| 796 |
+
" <th>SRCC (±STD)</th>\n",
|
| 797 |
+
" <th>KRCC (±STD)</th>\n",
|
| 798 |
+
" <th>PLCC (±STD)</th>\n",
|
| 799 |
+
" <th>RMSE (±STD)</th>\n",
|
| 800 |
+
" </tr>\n",
|
| 801 |
+
" </thead>\n",
|
| 802 |
+
" <tbody>\n",
|
| 803 |
+
" <tr>\n",
|
| 804 |
+
" <th>0</th>\n",
|
| 805 |
+
" <td>cvd_2014</td>\n",
|
| 806 |
+
" <td>relaxvqa</td>\n",
|
| 807 |
+
" <td>0.8643 (±0.0301)</td>\n",
|
| 808 |
+
" <td>0.6960 (±0.0442)</td>\n",
|
| 809 |
+
" <td>0.8895 (±0.0255)</td>\n",
|
| 810 |
+
" <td>9.8185 (±1.0969)</td>\n",
|
| 811 |
+
" </tr>\n",
|
| 812 |
+
" </tbody>\n",
|
| 813 |
+
"</table>\n",
|
| 814 |
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"</div>"
|
| 815 |
+
]
|
| 816 |
+
},
|
| 817 |
+
"execution_count": 150,
|
| 818 |
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"metadata": {},
|
| 819 |
+
"output_type": "execute_result"
|
| 820 |
+
}
|
| 821 |
+
],
|
| 822 |
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"execution_count": 150
|
| 823 |
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|
| 824 |
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|
| 825 |
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"metadata": {
|
| 826 |
+
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|
| 827 |
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|
| 828 |
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|
| 829 |
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|
| 830 |
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},
|
| 831 |
+
"cell_type": "code",
|
| 832 |
+
"source": [
|
| 833 |
+
"data_name = 'konvid_1k'\n",
|
| 834 |
+
"df_results = process_log_files(data_name, model_names, regression_name, log_path, decimals=4)\n",
|
| 835 |
+
"df_results"
|
| 836 |
+
],
|
| 837 |
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|
| 838 |
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"outputs": [
|
| 839 |
+
{
|
| 840 |
+
"data": {
|
| 841 |
+
"text/plain": [
|
| 842 |
+
" DATASET MODEL SRCC (±STD) KRCC (±STD) PLCC (±STD) \\\n",
|
| 843 |
+
"0 konvid_1k relaxvqa 0.8535 (±0.0218) 0.6594 (±0.0232) 0.8473 (±0.0214) \n",
|
| 844 |
+
"\n",
|
| 845 |
+
" RMSE (±STD) \n",
|
| 846 |
+
"0 0.3370 (±0.0185) "
|
| 847 |
+
],
|
| 848 |
+
"text/html": [
|
| 849 |
+
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|
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| 859 |
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|
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|
| 862 |
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|
| 863 |
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|
| 864 |
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|
| 865 |
+
" <tr style=\"text-align: right;\">\n",
|
| 866 |
+
" <th></th>\n",
|
| 867 |
+
" <th>DATASET</th>\n",
|
| 868 |
+
" <th>MODEL</th>\n",
|
| 869 |
+
" <th>SRCC (±STD)</th>\n",
|
| 870 |
+
" <th>KRCC (±STD)</th>\n",
|
| 871 |
+
" <th>PLCC (±STD)</th>\n",
|
| 872 |
+
" <th>RMSE (±STD)</th>\n",
|
| 873 |
+
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|
| 874 |
+
" </thead>\n",
|
| 875 |
+
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|
| 876 |
+
" <tr>\n",
|
| 877 |
+
" <th>0</th>\n",
|
| 878 |
+
" <td>konvid_1k</td>\n",
|
| 879 |
+
" <td>relaxvqa</td>\n",
|
| 880 |
+
" <td>0.8535 (±0.0218)</td>\n",
|
| 881 |
+
" <td>0.6594 (±0.0232)</td>\n",
|
| 882 |
+
" <td>0.8473 (±0.0214)</td>\n",
|
| 883 |
+
" <td>0.3370 (±0.0185)</td>\n",
|
| 884 |
+
" </tr>\n",
|
| 885 |
+
" </tbody>\n",
|
| 886 |
+
"</table>\n",
|
| 887 |
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|
| 888 |
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|
| 889 |
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|
| 890 |
+
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|
| 891 |
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"metadata": {},
|
| 892 |
+
"output_type": "execute_result"
|
| 893 |
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|
| 894 |
+
],
|
| 895 |
+
"execution_count": 151
|
| 896 |
+
},
|
| 897 |
+
{
|
| 898 |
+
"metadata": {
|
| 899 |
+
"ExecuteTime": {
|
| 900 |
+
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|
| 901 |
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"start_time": "2025-03-11T16:39:14.522356Z"
|
| 902 |
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|
| 903 |
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|
| 904 |
+
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|
| 905 |
+
"source": [
|
| 906 |
+
"data_name = 'live_vqc'\n",
|
| 907 |
+
"df_results = process_log_files(data_name, model_names, regression_name, log_path, decimals=4)\n",
|
| 908 |
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"df_results"
|
| 909 |
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],
|
| 910 |
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|
| 911 |
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|
| 912 |
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{
|
| 913 |
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"data": {
|
| 914 |
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"text/plain": [
|
| 915 |
+
" DATASET MODEL SRCC (±STD) KRCC (±STD) PLCC (±STD) \\\n",
|
| 916 |
+
"0 live_vqc relaxvqa 0.7655 (±0.0378) 0.5785 (±0.0365) 0.8079 (±0.0352) \n",
|
| 917 |
+
"\n",
|
| 918 |
+
" RMSE (±STD) \n",
|
| 919 |
+
"0 9.8596 (±0.9099) "
|
| 920 |
+
],
|
| 921 |
+
"text/html": [
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| 937 |
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|
| 938 |
+
" <tr style=\"text-align: right;\">\n",
|
| 939 |
+
" <th></th>\n",
|
| 940 |
+
" <th>DATASET</th>\n",
|
| 941 |
+
" <th>MODEL</th>\n",
|
| 942 |
+
" <th>SRCC (±STD)</th>\n",
|
| 943 |
+
" <th>KRCC (±STD)</th>\n",
|
| 944 |
+
" <th>PLCC (±STD)</th>\n",
|
| 945 |
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" <th>RMSE (±STD)</th>\n",
|
| 946 |
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" </tr>\n",
|
| 947 |
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" </thead>\n",
|
| 948 |
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" <tbody>\n",
|
| 949 |
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" <tr>\n",
|
| 950 |
+
" <th>0</th>\n",
|
| 951 |
+
" <td>live_vqc</td>\n",
|
| 952 |
+
" <td>relaxvqa</td>\n",
|
| 953 |
+
" <td>0.7655 (±0.0378)</td>\n",
|
| 954 |
+
" <td>0.5785 (±0.0365)</td>\n",
|
| 955 |
+
" <td>0.8079 (±0.0352)</td>\n",
|
| 956 |
+
" <td>9.8596 (±0.9099)</td>\n",
|
| 957 |
+
" </tr>\n",
|
| 958 |
+
" </tbody>\n",
|
| 959 |
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"</table>\n",
|
| 960 |
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"</div>"
|
| 961 |
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]
|
| 962 |
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},
|
| 963 |
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"execution_count": 152,
|
| 964 |
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"metadata": {},
|
| 965 |
+
"output_type": "execute_result"
|
| 966 |
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}
|
| 967 |
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],
|
| 968 |
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"execution_count": 152
|
| 969 |
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},
|
| 970 |
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{
|
| 971 |
+
"metadata": {
|
| 972 |
+
"ExecuteTime": {
|
| 973 |
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"end_time": "2025-03-11T16:39:14.583026Z",
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| 974 |
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"start_time": "2025-03-11T16:39:14.577518Z"
|
| 975 |
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|
| 976 |
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|
| 977 |
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"cell_type": "code",
|
| 978 |
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"source": [
|
| 979 |
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"data_name = 'youtube_ugc'\n",
|
| 980 |
+
"df_results = process_log_files(data_name, model_names, regression_name, log_path, decimals=4)\n",
|
| 981 |
+
"df_results"
|
| 982 |
+
],
|
| 983 |
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"id": "fac097145800c895",
|
| 984 |
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"outputs": [
|
| 985 |
+
{
|
| 986 |
+
"data": {
|
| 987 |
+
"text/plain": [
|
| 988 |
+
" DATASET MODEL SRCC (±STD) KRCC (±STD) \\\n",
|
| 989 |
+
"0 youtube_ugc relaxvqa 0.8014 (±0.0196) 0.6167 (±0.0193) \n",
|
| 990 |
+
"\n",
|
| 991 |
+
" PLCC (±STD) RMSE (±STD) \n",
|
| 992 |
+
"0 0.8204 (±0.0186) 0.3801 (±0.0177) "
|
| 993 |
+
],
|
| 994 |
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"text/html": [
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| 995 |
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|
| 1010 |
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|
| 1011 |
+
" <tr style=\"text-align: right;\">\n",
|
| 1012 |
+
" <th></th>\n",
|
| 1013 |
+
" <th>DATASET</th>\n",
|
| 1014 |
+
" <th>MODEL</th>\n",
|
| 1015 |
+
" <th>SRCC (±STD)</th>\n",
|
| 1016 |
+
" <th>KRCC (±STD)</th>\n",
|
| 1017 |
+
" <th>PLCC (±STD)</th>\n",
|
| 1018 |
+
" <th>RMSE (±STD)</th>\n",
|
| 1019 |
+
" </tr>\n",
|
| 1020 |
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" </thead>\n",
|
| 1021 |
+
" <tbody>\n",
|
| 1022 |
+
" <tr>\n",
|
| 1023 |
+
" <th>0</th>\n",
|
| 1024 |
+
" <td>youtube_ugc</td>\n",
|
| 1025 |
+
" <td>relaxvqa</td>\n",
|
| 1026 |
+
" <td>0.8014 (±0.0196)</td>\n",
|
| 1027 |
+
" <td>0.6167 (±0.0193)</td>\n",
|
| 1028 |
+
" <td>0.8204 (±0.0186)</td>\n",
|
| 1029 |
+
" <td>0.3801 (±0.0177)</td>\n",
|
| 1030 |
+
" </tr>\n",
|
| 1031 |
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" </tbody>\n",
|
| 1032 |
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"</table>\n",
|
| 1033 |
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"</div>"
|
| 1034 |
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]
|
| 1035 |
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},
|
| 1036 |
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"execution_count": 153,
|
| 1037 |
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"metadata": {},
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| 1038 |
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"output_type": "execute_result"
|
| 1039 |
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}
|
| 1040 |
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],
|
| 1041 |
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"execution_count": 153
|
| 1042 |
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},
|
| 1043 |
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{
|
| 1044 |
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"metadata": {},
|
| 1045 |
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"cell_type": "markdown",
|
| 1046 |
+
"source": "Ablation study on fragments",
|
| 1047 |
+
"id": "ba6cedaad3ce5f1"
|
| 1048 |
+
},
|
| 1049 |
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{
|
| 1050 |
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"metadata": {
|
| 1051 |
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|
| 1052 |
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|
| 1053 |
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"start_time": "2025-03-11T16:39:14.628197Z"
|
| 1054 |
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}
|
| 1055 |
+
},
|
| 1056 |
+
"cell_type": "code",
|
| 1057 |
+
"source": [
|
| 1058 |
+
"data_name = 'konvid_1k'\n",
|
| 1059 |
+
"model_names = ['frame_diff_resnet50', 'optical_flow_resnet50', 'frame_diff_vit', 'optical_flow_vit', 'RF_frame_diff_resnet50', 'RF_optical_flow_resnet50', 'RF_frame_diff_vit', 'RF_optical_flow_vit',\n",
|
| 1060 |
+
" 'MF_resnet50', 'MF_vit', 'MF_resnet50_vit']\n",
|
| 1061 |
+
"regression_name = 'mlp'\n",
|
| 1062 |
+
"log_path = './reported_results/ablation_study/fragmentation/'\n",
|
| 1063 |
+
"df_results = process_log_files(data_name, model_names, regression_name, log_path, decimals=4)\n",
|
| 1064 |
+
"df_results"
|
| 1065 |
+
],
|
| 1066 |
+
"id": "f4b41d3d64b5b4c",
|
| 1067 |
+
"outputs": [
|
| 1068 |
+
{
|
| 1069 |
+
"data": {
|
| 1070 |
+
"text/plain": [
|
| 1071 |
+
" DATASET MODEL SRCC (±STD) KRCC (±STD) \\\n",
|
| 1072 |
+
"0 konvid_1k frame_diff_resnet50 0.6526 (±0.0221) 0.4654 (±0.0170) \n",
|
| 1073 |
+
"1 konvid_1k optical_flow_resnet50 0.5793 (±0.0392) 0.4088 (±0.0298) \n",
|
| 1074 |
+
"2 konvid_1k frame_diff_vit 0.6384 (±0.0333) 0.4633 (±0.0245) \n",
|
| 1075 |
+
"3 konvid_1k optical_flow_vit 0.5692 (±0.0376) 0.4075 (±0.0293) \n",
|
| 1076 |
+
"4 konvid_1k RF_frame_diff_resnet50 0.7082 (±0.0345) 0.5168 (±0.0301) \n",
|
| 1077 |
+
"5 konvid_1k RF_optical_flow_resnet50 0.4844 (±0.0504) 0.3356 (±0.0369) \n",
|
| 1078 |
+
"6 konvid_1k RF_frame_diff_vit 0.7145 (±0.0297) 0.5221 (±0.0265) \n",
|
| 1079 |
+
"7 konvid_1k RF_optical_flow_vit 0.5032 (±0.0495) 0.3508 (±0.0360) \n",
|
| 1080 |
+
"8 konvid_1k MF_resnet50 0.6792 (±0.0424) 0.4852 (±0.0342) \n",
|
| 1081 |
+
"9 konvid_1k MF_vit 0.7062 (±0.0298) 0.5130 (±0.0270) \n",
|
| 1082 |
+
"10 konvid_1k MF_resnet50_vit 0.7324 (±0.0340) 0.5337 (±0.0298) \n",
|
| 1083 |
+
"\n",
|
| 1084 |
+
" PLCC (±STD) RMSE (±STD) \n",
|
| 1085 |
+
"0 0.6525 (±0.0202) 0.4889 (±0.0153) \n",
|
| 1086 |
+
"1 0.5848 (±0.0423) 0.5356 (±0.0233) \n",
|
| 1087 |
+
"2 0.6454 (±0.0298) 0.4890 (±0.0162) \n",
|
| 1088 |
+
"3 0.5844 (±0.0385) 0.5243 (±0.0191) \n",
|
| 1089 |
+
"4 0.7073 (±0.0345) 0.4547 (±0.0185) \n",
|
| 1090 |
+
"5 0.4958 (±0.0518) 0.5634 (±0.0213) \n",
|
| 1091 |
+
"6 0.7226 (±0.0281) 0.4487 (±0.0153) \n",
|
| 1092 |
+
"7 0.5335 (±0.0582) 0.5504 (±0.0224) \n",
|
| 1093 |
+
"8 0.6788 (±0.0440) 0.4761 (±0.0254) \n",
|
| 1094 |
+
"9 0.7057 (±0.0296) 0.4582 (±0.0175) \n",
|
| 1095 |
+
"10 0.7291 (±0.0335) 0.4408 (±0.0216) "
|
| 1096 |
+
],
|
| 1097 |
+
"text/html": [
|
| 1098 |
+
"<div>\n",
|
| 1099 |
+
"<style scoped>\n",
|
| 1100 |
+
" .dataframe tbody tr th:only-of-type {\n",
|
| 1101 |
+
" vertical-align: middle;\n",
|
| 1102 |
+
" }\n",
|
| 1103 |
+
"\n",
|
| 1104 |
+
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|
| 1105 |
+
" vertical-align: top;\n",
|
| 1106 |
+
" }\n",
|
| 1107 |
+
"\n",
|
| 1108 |
+
" .dataframe thead th {\n",
|
| 1109 |
+
" text-align: right;\n",
|
| 1110 |
+
" }\n",
|
| 1111 |
+
"</style>\n",
|
| 1112 |
+
"<table border=\"1\" class=\"dataframe\">\n",
|
| 1113 |
+
" <thead>\n",
|
| 1114 |
+
" <tr style=\"text-align: right;\">\n",
|
| 1115 |
+
" <th></th>\n",
|
| 1116 |
+
" <th>DATASET</th>\n",
|
| 1117 |
+
" <th>MODEL</th>\n",
|
| 1118 |
+
" <th>SRCC (±STD)</th>\n",
|
| 1119 |
+
" <th>KRCC (±STD)</th>\n",
|
| 1120 |
+
" <th>PLCC (±STD)</th>\n",
|
| 1121 |
+
" <th>RMSE (±STD)</th>\n",
|
| 1122 |
+
" </tr>\n",
|
| 1123 |
+
" </thead>\n",
|
| 1124 |
+
" <tbody>\n",
|
| 1125 |
+
" <tr>\n",
|
| 1126 |
+
" <th>0</th>\n",
|
| 1127 |
+
" <td>konvid_1k</td>\n",
|
| 1128 |
+
" <td>frame_diff_resnet50</td>\n",
|
| 1129 |
+
" <td>0.6526 (±0.0221)</td>\n",
|
| 1130 |
+
" <td>0.4654 (±0.0170)</td>\n",
|
| 1131 |
+
" <td>0.6525 (±0.0202)</td>\n",
|
| 1132 |
+
" <td>0.4889 (±0.0153)</td>\n",
|
| 1133 |
+
" </tr>\n",
|
| 1134 |
+
" <tr>\n",
|
| 1135 |
+
" <th>1</th>\n",
|
| 1136 |
+
" <td>konvid_1k</td>\n",
|
| 1137 |
+
" <td>optical_flow_resnet50</td>\n",
|
| 1138 |
+
" <td>0.5793 (±0.0392)</td>\n",
|
| 1139 |
+
" <td>0.4088 (±0.0298)</td>\n",
|
| 1140 |
+
" <td>0.5848 (±0.0423)</td>\n",
|
| 1141 |
+
" <td>0.5356 (±0.0233)</td>\n",
|
| 1142 |
+
" </tr>\n",
|
| 1143 |
+
" <tr>\n",
|
| 1144 |
+
" <th>2</th>\n",
|
| 1145 |
+
" <td>konvid_1k</td>\n",
|
| 1146 |
+
" <td>frame_diff_vit</td>\n",
|
| 1147 |
+
" <td>0.6384 (±0.0333)</td>\n",
|
| 1148 |
+
" <td>0.4633 (±0.0245)</td>\n",
|
| 1149 |
+
" <td>0.6454 (±0.0298)</td>\n",
|
| 1150 |
+
" <td>0.4890 (±0.0162)</td>\n",
|
| 1151 |
+
" </tr>\n",
|
| 1152 |
+
" <tr>\n",
|
| 1153 |
+
" <th>3</th>\n",
|
| 1154 |
+
" <td>konvid_1k</td>\n",
|
| 1155 |
+
" <td>optical_flow_vit</td>\n",
|
| 1156 |
+
" <td>0.5692 (±0.0376)</td>\n",
|
| 1157 |
+
" <td>0.4075 (±0.0293)</td>\n",
|
| 1158 |
+
" <td>0.5844 (±0.0385)</td>\n",
|
| 1159 |
+
" <td>0.5243 (±0.0191)</td>\n",
|
| 1160 |
+
" </tr>\n",
|
| 1161 |
+
" <tr>\n",
|
| 1162 |
+
" <th>4</th>\n",
|
| 1163 |
+
" <td>konvid_1k</td>\n",
|
| 1164 |
+
" <td>RF_frame_diff_resnet50</td>\n",
|
| 1165 |
+
" <td>0.7082 (±0.0345)</td>\n",
|
| 1166 |
+
" <td>0.5168 (±0.0301)</td>\n",
|
| 1167 |
+
" <td>0.7073 (±0.0345)</td>\n",
|
| 1168 |
+
" <td>0.4547 (±0.0185)</td>\n",
|
| 1169 |
+
" </tr>\n",
|
| 1170 |
+
" <tr>\n",
|
| 1171 |
+
" <th>5</th>\n",
|
| 1172 |
+
" <td>konvid_1k</td>\n",
|
| 1173 |
+
" <td>RF_optical_flow_resnet50</td>\n",
|
| 1174 |
+
" <td>0.4844 (±0.0504)</td>\n",
|
| 1175 |
+
" <td>0.3356 (±0.0369)</td>\n",
|
| 1176 |
+
" <td>0.4958 (±0.0518)</td>\n",
|
| 1177 |
+
" <td>0.5634 (±0.0213)</td>\n",
|
| 1178 |
+
" </tr>\n",
|
| 1179 |
+
" <tr>\n",
|
| 1180 |
+
" <th>6</th>\n",
|
| 1181 |
+
" <td>konvid_1k</td>\n",
|
| 1182 |
+
" <td>RF_frame_diff_vit</td>\n",
|
| 1183 |
+
" <td>0.7145 (±0.0297)</td>\n",
|
| 1184 |
+
" <td>0.5221 (±0.0265)</td>\n",
|
| 1185 |
+
" <td>0.7226 (±0.0281)</td>\n",
|
| 1186 |
+
" <td>0.4487 (±0.0153)</td>\n",
|
| 1187 |
+
" </tr>\n",
|
| 1188 |
+
" <tr>\n",
|
| 1189 |
+
" <th>7</th>\n",
|
| 1190 |
+
" <td>konvid_1k</td>\n",
|
| 1191 |
+
" <td>RF_optical_flow_vit</td>\n",
|
| 1192 |
+
" <td>0.5032 (±0.0495)</td>\n",
|
| 1193 |
+
" <td>0.3508 (±0.0360)</td>\n",
|
| 1194 |
+
" <td>0.5335 (±0.0582)</td>\n",
|
| 1195 |
+
" <td>0.5504 (±0.0224)</td>\n",
|
| 1196 |
+
" </tr>\n",
|
| 1197 |
+
" <tr>\n",
|
| 1198 |
+
" <th>8</th>\n",
|
| 1199 |
+
" <td>konvid_1k</td>\n",
|
| 1200 |
+
" <td>MF_resnet50</td>\n",
|
| 1201 |
+
" <td>0.6792 (±0.0424)</td>\n",
|
| 1202 |
+
" <td>0.4852 (±0.0342)</td>\n",
|
| 1203 |
+
" <td>0.6788 (±0.0440)</td>\n",
|
| 1204 |
+
" <td>0.4761 (±0.0254)</td>\n",
|
| 1205 |
+
" </tr>\n",
|
| 1206 |
+
" <tr>\n",
|
| 1207 |
+
" <th>9</th>\n",
|
| 1208 |
+
" <td>konvid_1k</td>\n",
|
| 1209 |
+
" <td>MF_vit</td>\n",
|
| 1210 |
+
" <td>0.7062 (±0.0298)</td>\n",
|
| 1211 |
+
" <td>0.5130 (±0.0270)</td>\n",
|
| 1212 |
+
" <td>0.7057 (±0.0296)</td>\n",
|
| 1213 |
+
" <td>0.4582 (±0.0175)</td>\n",
|
| 1214 |
+
" </tr>\n",
|
| 1215 |
+
" <tr>\n",
|
| 1216 |
+
" <th>10</th>\n",
|
| 1217 |
+
" <td>konvid_1k</td>\n",
|
| 1218 |
+
" <td>MF_resnet50_vit</td>\n",
|
| 1219 |
+
" <td>0.7324 (±0.0340)</td>\n",
|
| 1220 |
+
" <td>0.5337 (±0.0298)</td>\n",
|
| 1221 |
+
" <td>0.7291 (±0.0335)</td>\n",
|
| 1222 |
+
" <td>0.4408 (±0.0216)</td>\n",
|
| 1223 |
+
" </tr>\n",
|
| 1224 |
+
" </tbody>\n",
|
| 1225 |
+
"</table>\n",
|
| 1226 |
+
"</div>"
|
| 1227 |
+
]
|
| 1228 |
+
},
|
| 1229 |
+
"execution_count": 154,
|
| 1230 |
+
"metadata": {},
|
| 1231 |
+
"output_type": "execute_result"
|
| 1232 |
+
}
|
| 1233 |
+
],
|
| 1234 |
+
"execution_count": 154
|
| 1235 |
+
},
|
| 1236 |
+
{
|
| 1237 |
+
"metadata": {},
|
| 1238 |
+
"cell_type": "markdown",
|
| 1239 |
+
"source": "Ablation study on layer stack",
|
| 1240 |
+
"id": "df44ade0d921b4b5"
|
| 1241 |
+
},
|
| 1242 |
+
{
|
| 1243 |
+
"metadata": {
|
| 1244 |
+
"ExecuteTime": {
|
| 1245 |
+
"end_time": "2025-03-11T16:39:14.741037Z",
|
| 1246 |
+
"start_time": "2025-03-11T16:39:14.722921Z"
|
| 1247 |
+
}
|
| 1248 |
+
},
|
| 1249 |
+
"cell_type": "code",
|
| 1250 |
+
"source": [
|
| 1251 |
+
"data_name = 'konvid_1k'\n",
|
| 1252 |
+
"model_names = ['pool_vgg16', 'pool_resnet50', 'pool_vit', 'layer_stack_vgg16', 'layer_stack_resnet50', 'pool_pool_vgg16_vit', 'pool_pool_resnet50_vit', 'layer_stack_pool_vgg16_vit', 'layer_stack_pool_resnet50_vit']\n",
|
| 1253 |
+
"regression_name = 'mlp'\n",
|
| 1254 |
+
"log_path = './reported_results/ablation_study/layer_stack/'\n",
|
| 1255 |
+
"df_results = process_log_files(data_name, model_names, regression_name, log_path, decimals=4)\n",
|
| 1256 |
+
"df_results"
|
| 1257 |
+
],
|
| 1258 |
+
"id": "49f7b819608adda7",
|
| 1259 |
+
"outputs": [
|
| 1260 |
+
{
|
| 1261 |
+
"data": {
|
| 1262 |
+
"text/plain": [
|
| 1263 |
+
" DATASET MODEL SRCC (±STD) \\\n",
|
| 1264 |
+
"0 konvid_1k pool_vgg16 0.6665 (±0.0370) \n",
|
| 1265 |
+
"1 konvid_1k pool_resnet50 0.6751 (±0.0313) \n",
|
| 1266 |
+
"2 konvid_1k pool_vit 0.6661 (±0.0396) \n",
|
| 1267 |
+
"3 konvid_1k layer_stack_vgg16 0.7513 (±0.0250) \n",
|
| 1268 |
+
"4 konvid_1k layer_stack_resnet50 0.7630 (±0.0239) \n",
|
| 1269 |
+
"5 konvid_1k pool_pool_vgg16_vit 0.7100 (±0.0273) \n",
|
| 1270 |
+
"6 konvid_1k pool_pool_resnet50_vit 0.7253 (±0.0259) \n",
|
| 1271 |
+
"7 konvid_1k layer_stack_pool_vgg16_vit 0.7650 (±0.0245) \n",
|
| 1272 |
+
"8 konvid_1k layer_stack_pool_resnet50_vit 0.7697 (±0.0230) \n",
|
| 1273 |
+
"\n",
|
| 1274 |
+
" KRCC (±STD) PLCC (±STD) RMSE (±STD) \n",
|
| 1275 |
+
"0 0.4878 (±0.0313) 0.6880 (±0.0328) 0.4629 (±0.0188) \n",
|
| 1276 |
+
"1 0.4881 (±0.0257) 0.7047 (±0.0292) 0.4550 (±0.0162) \n",
|
| 1277 |
+
"2 0.4906 (±0.0332) 0.7082 (±0.0356) 0.4515 (±0.0199) \n",
|
| 1278 |
+
"3 0.5626 (±0.0228) 0.7700 (±0.0219) 0.4108 (±0.0186) \n",
|
| 1279 |
+
"4 0.5699 (±0.0219) 0.7828 (±0.0224) 0.3990 (±0.0162) \n",
|
| 1280 |
+
"5 0.5210 (±0.0235) 0.7364 (±0.0247) 0.4370 (±0.0153) \n",
|
| 1281 |
+
"6 0.5308 (±0.0223) 0.7469 (±0.0263) 0.4277 (±0.0151) \n",
|
| 1282 |
+
"7 0.5742 (±0.0236) 0.7827 (±0.0213) 0.4042 (±0.0177) \n",
|
| 1283 |
+
"8 0.5779 (±0.0213) 0.7897 (±0.0209) 0.3948 (±0.0152) "
|
| 1284 |
+
],
|
| 1285 |
+
"text/html": [
|
| 1286 |
+
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|
| 1287 |
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|
| 1288 |
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|
| 1289 |
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|
| 1290 |
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|
| 1291 |
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|
| 1292 |
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|
| 1293 |
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|
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|
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|
| 1296 |
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|
| 1297 |
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|
| 1298 |
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" }\n",
|
| 1299 |
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"</style>\n",
|
| 1300 |
+
"<table border=\"1\" class=\"dataframe\">\n",
|
| 1301 |
+
" <thead>\n",
|
| 1302 |
+
" <tr style=\"text-align: right;\">\n",
|
| 1303 |
+
" <th></th>\n",
|
| 1304 |
+
" <th>DATASET</th>\n",
|
| 1305 |
+
" <th>MODEL</th>\n",
|
| 1306 |
+
" <th>SRCC (±STD)</th>\n",
|
| 1307 |
+
" <th>KRCC (±STD)</th>\n",
|
| 1308 |
+
" <th>PLCC (±STD)</th>\n",
|
| 1309 |
+
" <th>RMSE (±STD)</th>\n",
|
| 1310 |
+
" </tr>\n",
|
| 1311 |
+
" </thead>\n",
|
| 1312 |
+
" <tbody>\n",
|
| 1313 |
+
" <tr>\n",
|
| 1314 |
+
" <th>0</th>\n",
|
| 1315 |
+
" <td>konvid_1k</td>\n",
|
| 1316 |
+
" <td>pool_vgg16</td>\n",
|
| 1317 |
+
" <td>0.6665 (±0.0370)</td>\n",
|
| 1318 |
+
" <td>0.4878 (±0.0313)</td>\n",
|
| 1319 |
+
" <td>0.6880 (±0.0328)</td>\n",
|
| 1320 |
+
" <td>0.4629 (±0.0188)</td>\n",
|
| 1321 |
+
" </tr>\n",
|
| 1322 |
+
" <tr>\n",
|
| 1323 |
+
" <th>1</th>\n",
|
| 1324 |
+
" <td>konvid_1k</td>\n",
|
| 1325 |
+
" <td>pool_resnet50</td>\n",
|
| 1326 |
+
" <td>0.6751 (±0.0313)</td>\n",
|
| 1327 |
+
" <td>0.4881 (±0.0257)</td>\n",
|
| 1328 |
+
" <td>0.7047 (±0.0292)</td>\n",
|
| 1329 |
+
" <td>0.4550 (±0.0162)</td>\n",
|
| 1330 |
+
" </tr>\n",
|
| 1331 |
+
" <tr>\n",
|
| 1332 |
+
" <th>2</th>\n",
|
| 1333 |
+
" <td>konvid_1k</td>\n",
|
| 1334 |
+
" <td>pool_vit</td>\n",
|
| 1335 |
+
" <td>0.6661 (±0.0396)</td>\n",
|
| 1336 |
+
" <td>0.4906 (±0.0332)</td>\n",
|
| 1337 |
+
" <td>0.7082 (±0.0356)</td>\n",
|
| 1338 |
+
" <td>0.4515 (±0.0199)</td>\n",
|
| 1339 |
+
" </tr>\n",
|
| 1340 |
+
" <tr>\n",
|
| 1341 |
+
" <th>3</th>\n",
|
| 1342 |
+
" <td>konvid_1k</td>\n",
|
| 1343 |
+
" <td>layer_stack_vgg16</td>\n",
|
| 1344 |
+
" <td>0.7513 (±0.0250)</td>\n",
|
| 1345 |
+
" <td>0.5626 (±0.0228)</td>\n",
|
| 1346 |
+
" <td>0.7700 (±0.0219)</td>\n",
|
| 1347 |
+
" <td>0.4108 (±0.0186)</td>\n",
|
| 1348 |
+
" </tr>\n",
|
| 1349 |
+
" <tr>\n",
|
| 1350 |
+
" <th>4</th>\n",
|
| 1351 |
+
" <td>konvid_1k</td>\n",
|
| 1352 |
+
" <td>layer_stack_resnet50</td>\n",
|
| 1353 |
+
" <td>0.7630 (±0.0239)</td>\n",
|
| 1354 |
+
" <td>0.5699 (±0.0219)</td>\n",
|
| 1355 |
+
" <td>0.7828 (±0.0224)</td>\n",
|
| 1356 |
+
" <td>0.3990 (±0.0162)</td>\n",
|
| 1357 |
+
" </tr>\n",
|
| 1358 |
+
" <tr>\n",
|
| 1359 |
+
" <th>5</th>\n",
|
| 1360 |
+
" <td>konvid_1k</td>\n",
|
| 1361 |
+
" <td>pool_pool_vgg16_vit</td>\n",
|
| 1362 |
+
" <td>0.7100 (±0.0273)</td>\n",
|
| 1363 |
+
" <td>0.5210 (±0.0235)</td>\n",
|
| 1364 |
+
" <td>0.7364 (±0.0247)</td>\n",
|
| 1365 |
+
" <td>0.4370 (±0.0153)</td>\n",
|
| 1366 |
+
" </tr>\n",
|
| 1367 |
+
" <tr>\n",
|
| 1368 |
+
" <th>6</th>\n",
|
| 1369 |
+
" <td>konvid_1k</td>\n",
|
| 1370 |
+
" <td>pool_pool_resnet50_vit</td>\n",
|
| 1371 |
+
" <td>0.7253 (±0.0259)</td>\n",
|
| 1372 |
+
" <td>0.5308 (±0.0223)</td>\n",
|
| 1373 |
+
" <td>0.7469 (±0.0263)</td>\n",
|
| 1374 |
+
" <td>0.4277 (±0.0151)</td>\n",
|
| 1375 |
+
" </tr>\n",
|
| 1376 |
+
" <tr>\n",
|
| 1377 |
+
" <th>7</th>\n",
|
| 1378 |
+
" <td>konvid_1k</td>\n",
|
| 1379 |
+
" <td>layer_stack_pool_vgg16_vit</td>\n",
|
| 1380 |
+
" <td>0.7650 (±0.0245)</td>\n",
|
| 1381 |
+
" <td>0.5742 (±0.0236)</td>\n",
|
| 1382 |
+
" <td>0.7827 (±0.0213)</td>\n",
|
| 1383 |
+
" <td>0.4042 (±0.0177)</td>\n",
|
| 1384 |
+
" </tr>\n",
|
| 1385 |
+
" <tr>\n",
|
| 1386 |
+
" <th>8</th>\n",
|
| 1387 |
+
" <td>konvid_1k</td>\n",
|
| 1388 |
+
" <td>layer_stack_pool_resnet50_vit</td>\n",
|
| 1389 |
+
" <td>0.7697 (±0.0230)</td>\n",
|
| 1390 |
+
" <td>0.5779 (±0.0213)</td>\n",
|
| 1391 |
+
" <td>0.7897 (±0.0209)</td>\n",
|
| 1392 |
+
" <td>0.3948 (±0.0152)</td>\n",
|
| 1393 |
+
" </tr>\n",
|
| 1394 |
+
" </tbody>\n",
|
| 1395 |
+
"</table>\n",
|
| 1396 |
+
"</div>"
|
| 1397 |
+
]
|
| 1398 |
+
},
|
| 1399 |
+
"execution_count": 155,
|
| 1400 |
+
"metadata": {},
|
| 1401 |
+
"output_type": "execute_result"
|
| 1402 |
+
}
|
| 1403 |
+
],
|
| 1404 |
+
"execution_count": 155
|
| 1405 |
+
},
|
| 1406 |
+
{
|
| 1407 |
+
"metadata": {
|
| 1408 |
+
"ExecuteTime": {
|
| 1409 |
+
"end_time": "2025-03-11T16:39:14.762824Z",
|
| 1410 |
+
"start_time": "2025-03-11T16:39:14.759557Z"
|
| 1411 |
+
}
|
| 1412 |
+
},
|
| 1413 |
+
"cell_type": "code",
|
| 1414 |
+
"source": [
|
| 1415 |
+
"def process_finetune_csv(pattern, select_criteria, is_finetune):\n",
|
| 1416 |
+
" csv_files = glob.glob(pattern)\n",
|
| 1417 |
+
" combined_df = pd.DataFrame()\n",
|
| 1418 |
+
" if is_finetune == True:\n",
|
| 1419 |
+
" for file in csv_files:\n",
|
| 1420 |
+
" df = pd.read_csv(file)\n",
|
| 1421 |
+
" df['LAYER_NAME'] = 'layer_stack+pool'\n",
|
| 1422 |
+
" combined_df = pd.concat([combined_df, df], ignore_index=True)\n",
|
| 1423 |
+
" df.to_csv(file, index=False)\n",
|
| 1424 |
+
" combined_df = combined_df.sort_values(by='DATASET')\n",
|
| 1425 |
+
" combined_df = combined_df.round(4)\n",
|
| 1426 |
+
" df_byrmse = combined_df[combined_df['SELECT_CRITERIA'] == 'byrmse']\n",
|
| 1427 |
+
" df_bykrcc = combined_df[combined_df['SELECT_CRITERIA'] == 'bykrcc']\n",
|
| 1428 |
+
" if len(df_byrmse) == len(df_bykrcc):\n",
|
| 1429 |
+
" combined_horizontal = pd.concat([df_byrmse.reset_index(drop=True), df_bykrcc.reset_index(drop=True)], axis=1)\n",
|
| 1430 |
+
" print(f\"Performance with SELECT_CRITERIA = {select_criteria} on LSVQ trained model\")\n",
|
| 1431 |
+
" return combined_horizontal\n",
|
| 1432 |
+
" else:\n",
|
| 1433 |
+
" for file in csv_files:\n",
|
| 1434 |
+
" df = pd.read_csv(file)\n",
|
| 1435 |
+
" df['LAYER_NAME'] = 'layer_stack+pool'\n",
|
| 1436 |
+
" combined_df = pd.concat([combined_df, df], ignore_index=True)\n",
|
| 1437 |
+
" df.to_csv(file, index=False)\n",
|
| 1438 |
+
" combined_df = combined_df.sort_values(by='DATASET')\n",
|
| 1439 |
+
" combined_df = combined_df.round(4)\n",
|
| 1440 |
+
" return combined_df"
|
| 1441 |
+
],
|
| 1442 |
+
"id": "8ca6931697279736",
|
| 1443 |
+
"outputs": [],
|
| 1444 |
+
"execution_count": 156
|
| 1445 |
+
},
|
| 1446 |
+
{
|
| 1447 |
+
"metadata": {},
|
| 1448 |
+
"cell_type": "markdown",
|
| 1449 |
+
"source": "with Fine-tuning on an LSVQ-trained model",
|
| 1450 |
+
"id": "8d249d7514f827ad"
|
| 1451 |
+
},
|
| 1452 |
+
{
|
| 1453 |
+
"metadata": {
|
| 1454 |
+
"ExecuteTime": {
|
| 1455 |
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"end_time": "2025-03-11T16:39:14.828569Z",
|
| 1456 |
+
"start_time": "2025-03-11T16:39:14.809573Z"
|
| 1457 |
+
}
|
| 1458 |
+
},
|
| 1459 |
+
"cell_type": "code",
|
| 1460 |
+
"source": [
|
| 1461 |
+
"log_path = './reported_results/fine_tune/'\n",
|
| 1462 |
+
"pattern_rmse = log_path + \"/*_relaxvqa_byrmse_finetune.csv\"\n",
|
| 1463 |
+
"result = process_finetune_csv(pattern_rmse, 'byrmse', True)\n",
|
| 1464 |
+
"result"
|
| 1465 |
+
],
|
| 1466 |
+
"id": "fbbf12f1692e1070",
|
| 1467 |
+
"outputs": [
|
| 1468 |
+
{
|
| 1469 |
+
"name": "stdout",
|
| 1470 |
+
"output_type": "stream",
|
| 1471 |
+
"text": [
|
| 1472 |
+
"Performance with SELECT_CRITERIA = byrmse on LSVQ trained model\n"
|
| 1473 |
+
]
|
| 1474 |
+
},
|
| 1475 |
+
{
|
| 1476 |
+
"data": {
|
| 1477 |
+
"text/plain": [
|
| 1478 |
+
" DATASET MODEL LAYER_NAME SRCC KRCC PLCC RMSE \\\n",
|
| 1479 |
+
"0 cvd_2014 relaxvqa layer_stack+pool 0.8974 0.7299 0.9294 8.1812 \n",
|
| 1480 |
+
"1 konvid_1k relaxvqa layer_stack+pool 0.8720 0.6881 0.8668 0.3211 \n",
|
| 1481 |
+
"2 live_vqc relaxvqa layer_stack+pool 0.8468 0.6649 0.8876 7.9869 \n",
|
| 1482 |
+
"3 youtube_ugc relaxvqa layer_stack+pool 0.8469 0.6623 0.8652 0.3437 \n",
|
| 1483 |
+
"\n",
|
| 1484 |
+
" SELECT_CRITERIA DATASET MODEL LAYER_NAME SRCC KRCC \\\n",
|
| 1485 |
+
"0 byrmse cvd_2014 relaxvqa layer_stack+pool 0.8899 0.7169 \n",
|
| 1486 |
+
"1 byrmse konvid_1k relaxvqa layer_stack+pool 0.8694 0.6834 \n",
|
| 1487 |
+
"2 byrmse live_vqc relaxvqa layer_stack+pool 0.8367 0.6531 \n",
|
| 1488 |
+
"3 byrmse youtube_ugc relaxvqa layer_stack+pool 0.8456 0.6562 \n",
|
| 1489 |
+
"\n",
|
| 1490 |
+
" PLCC RMSE SELECT_CRITERIA \n",
|
| 1491 |
+
"0 0.9124 9.0719 bykrcc \n",
|
| 1492 |
+
"1 0.8620 0.3263 bykrcc \n",
|
| 1493 |
+
"2 0.8726 8.4696 bykrcc \n",
|
| 1494 |
+
"3 0.8631 0.3462 bykrcc "
|
| 1495 |
+
],
|
| 1496 |
+
"text/html": [
|
| 1497 |
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"<div>\n",
|
| 1498 |
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| 1499 |
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|
| 1500 |
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|
| 1501 |
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|
| 1502 |
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|
| 1503 |
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|
| 1504 |
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|
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| 1506 |
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|
| 1507 |
+
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|
| 1508 |
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|
| 1509 |
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|
| 1510 |
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|
| 1511 |
+
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|
| 1512 |
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|
| 1513 |
+
" <tr style=\"text-align: right;\">\n",
|
| 1514 |
+
" <th></th>\n",
|
| 1515 |
+
" <th>DATASET</th>\n",
|
| 1516 |
+
" <th>MODEL</th>\n",
|
| 1517 |
+
" <th>LAYER_NAME</th>\n",
|
| 1518 |
+
" <th>SRCC</th>\n",
|
| 1519 |
+
" <th>KRCC</th>\n",
|
| 1520 |
+
" <th>PLCC</th>\n",
|
| 1521 |
+
" <th>RMSE</th>\n",
|
| 1522 |
+
" <th>SELECT_CRITERIA</th>\n",
|
| 1523 |
+
" <th>DATASET</th>\n",
|
| 1524 |
+
" <th>MODEL</th>\n",
|
| 1525 |
+
" <th>LAYER_NAME</th>\n",
|
| 1526 |
+
" <th>SRCC</th>\n",
|
| 1527 |
+
" <th>KRCC</th>\n",
|
| 1528 |
+
" <th>PLCC</th>\n",
|
| 1529 |
+
" <th>RMSE</th>\n",
|
| 1530 |
+
" <th>SELECT_CRITERIA</th>\n",
|
| 1531 |
+
" </tr>\n",
|
| 1532 |
+
" </thead>\n",
|
| 1533 |
+
" <tbody>\n",
|
| 1534 |
+
" <tr>\n",
|
| 1535 |
+
" <th>0</th>\n",
|
| 1536 |
+
" <td>cvd_2014</td>\n",
|
| 1537 |
+
" <td>relaxvqa</td>\n",
|
| 1538 |
+
" <td>layer_stack+pool</td>\n",
|
| 1539 |
+
" <td>0.8974</td>\n",
|
| 1540 |
+
" <td>0.7299</td>\n",
|
| 1541 |
+
" <td>0.9294</td>\n",
|
| 1542 |
+
" <td>8.1812</td>\n",
|
| 1543 |
+
" <td>byrmse</td>\n",
|
| 1544 |
+
" <td>cvd_2014</td>\n",
|
| 1545 |
+
" <td>relaxvqa</td>\n",
|
| 1546 |
+
" <td>layer_stack+pool</td>\n",
|
| 1547 |
+
" <td>0.8899</td>\n",
|
| 1548 |
+
" <td>0.7169</td>\n",
|
| 1549 |
+
" <td>0.9124</td>\n",
|
| 1550 |
+
" <td>9.0719</td>\n",
|
| 1551 |
+
" <td>bykrcc</td>\n",
|
| 1552 |
+
" </tr>\n",
|
| 1553 |
+
" <tr>\n",
|
| 1554 |
+
" <th>1</th>\n",
|
| 1555 |
+
" <td>konvid_1k</td>\n",
|
| 1556 |
+
" <td>relaxvqa</td>\n",
|
| 1557 |
+
" <td>layer_stack+pool</td>\n",
|
| 1558 |
+
" <td>0.8720</td>\n",
|
| 1559 |
+
" <td>0.6881</td>\n",
|
| 1560 |
+
" <td>0.8668</td>\n",
|
| 1561 |
+
" <td>0.3211</td>\n",
|
| 1562 |
+
" <td>byrmse</td>\n",
|
| 1563 |
+
" <td>konvid_1k</td>\n",
|
| 1564 |
+
" <td>relaxvqa</td>\n",
|
| 1565 |
+
" <td>layer_stack+pool</td>\n",
|
| 1566 |
+
" <td>0.8694</td>\n",
|
| 1567 |
+
" <td>0.6834</td>\n",
|
| 1568 |
+
" <td>0.8620</td>\n",
|
| 1569 |
+
" <td>0.3263</td>\n",
|
| 1570 |
+
" <td>bykrcc</td>\n",
|
| 1571 |
+
" </tr>\n",
|
| 1572 |
+
" <tr>\n",
|
| 1573 |
+
" <th>2</th>\n",
|
| 1574 |
+
" <td>live_vqc</td>\n",
|
| 1575 |
+
" <td>relaxvqa</td>\n",
|
| 1576 |
+
" <td>layer_stack+pool</td>\n",
|
| 1577 |
+
" <td>0.8468</td>\n",
|
| 1578 |
+
" <td>0.6649</td>\n",
|
| 1579 |
+
" <td>0.8876</td>\n",
|
| 1580 |
+
" <td>7.9869</td>\n",
|
| 1581 |
+
" <td>byrmse</td>\n",
|
| 1582 |
+
" <td>live_vqc</td>\n",
|
| 1583 |
+
" <td>relaxvqa</td>\n",
|
| 1584 |
+
" <td>layer_stack+pool</td>\n",
|
| 1585 |
+
" <td>0.8367</td>\n",
|
| 1586 |
+
" <td>0.6531</td>\n",
|
| 1587 |
+
" <td>0.8726</td>\n",
|
| 1588 |
+
" <td>8.4696</td>\n",
|
| 1589 |
+
" <td>bykrcc</td>\n",
|
| 1590 |
+
" </tr>\n",
|
| 1591 |
+
" <tr>\n",
|
| 1592 |
+
" <th>3</th>\n",
|
| 1593 |
+
" <td>youtube_ugc</td>\n",
|
| 1594 |
+
" <td>relaxvqa</td>\n",
|
| 1595 |
+
" <td>layer_stack+pool</td>\n",
|
| 1596 |
+
" <td>0.8469</td>\n",
|
| 1597 |
+
" <td>0.6623</td>\n",
|
| 1598 |
+
" <td>0.8652</td>\n",
|
| 1599 |
+
" <td>0.3437</td>\n",
|
| 1600 |
+
" <td>byrmse</td>\n",
|
| 1601 |
+
" <td>youtube_ugc</td>\n",
|
| 1602 |
+
" <td>relaxvqa</td>\n",
|
| 1603 |
+
" <td>layer_stack+pool</td>\n",
|
| 1604 |
+
" <td>0.8456</td>\n",
|
| 1605 |
+
" <td>0.6562</td>\n",
|
| 1606 |
+
" <td>0.8631</td>\n",
|
| 1607 |
+
" <td>0.3462</td>\n",
|
| 1608 |
+
" <td>bykrcc</td>\n",
|
| 1609 |
+
" </tr>\n",
|
| 1610 |
+
" </tbody>\n",
|
| 1611 |
+
"</table>\n",
|
| 1612 |
+
"</div>"
|
| 1613 |
+
]
|
| 1614 |
+
},
|
| 1615 |
+
"execution_count": 157,
|
| 1616 |
+
"metadata": {},
|
| 1617 |
+
"output_type": "execute_result"
|
| 1618 |
+
}
|
| 1619 |
+
],
|
| 1620 |
+
"execution_count": 157
|
| 1621 |
+
},
|
| 1622 |
+
{
|
| 1623 |
+
"metadata": {
|
| 1624 |
+
"ExecuteTime": {
|
| 1625 |
+
"end_time": "2025-03-11T16:39:14.844063Z",
|
| 1626 |
+
"start_time": "2025-03-11T16:39:14.842635Z"
|
| 1627 |
+
}
|
| 1628 |
+
},
|
| 1629 |
+
"cell_type": "code",
|
| 1630 |
+
"source": [
|
| 1631 |
+
"# pattern_krcc = \"./reported_results/old/*_relaxvqa_bykrcc_finetune.csv\"\n",
|
| 1632 |
+
"# result = process_finetune_csv(pattern_krcc, 'bykrcc', True)\n",
|
| 1633 |
+
"# result"
|
| 1634 |
+
],
|
| 1635 |
+
"id": "a3e5697b434fb2e5",
|
| 1636 |
+
"outputs": [],
|
| 1637 |
+
"execution_count": 158
|
| 1638 |
+
},
|
| 1639 |
+
{
|
| 1640 |
+
"metadata": {},
|
| 1641 |
+
"cell_type": "markdown",
|
| 1642 |
+
"source": "without Fine-tuning on an LSVQ-trained model",
|
| 1643 |
+
"id": "3862e848cc4a698c"
|
| 1644 |
+
},
|
| 1645 |
+
{
|
| 1646 |
+
"metadata": {
|
| 1647 |
+
"ExecuteTime": {
|
| 1648 |
+
"end_time": "2025-03-11T16:39:14.924048Z",
|
| 1649 |
+
"start_time": "2025-03-11T16:39:14.910223Z"
|
| 1650 |
+
}
|
| 1651 |
+
},
|
| 1652 |
+
"cell_type": "code",
|
| 1653 |
+
"source": [
|
| 1654 |
+
"pattern_rmse_wo = log_path + \"/*_relaxvqa_byrmse*_wo_finetune.csv\"\n",
|
| 1655 |
+
"result_wo_finetune = process_finetune_csv(pattern_rmse_wo, 'byrmse', False)\n",
|
| 1656 |
+
"result_wo_finetune"
|
| 1657 |
+
],
|
| 1658 |
+
"id": "84a42c874461ba03",
|
| 1659 |
+
"outputs": [
|
| 1660 |
+
{
|
| 1661 |
+
"data": {
|
| 1662 |
+
"text/plain": [
|
| 1663 |
+
" DATASET MODEL LAYER_NAME SRCC KRCC PLCC RMSE \\\n",
|
| 1664 |
+
"1 cvd_2014 relaxvqa layer_stack+pool 0.7845 0.5930 0.8336 12.2445 \n",
|
| 1665 |
+
"0 konvid_1k relaxvqa layer_stack+pool 0.8312 0.6418 0.8427 0.3466 \n",
|
| 1666 |
+
"2 live_vqc relaxvqa layer_stack+pool 0.7664 0.5812 0.8242 9.8201 \n",
|
| 1667 |
+
"3 youtube_ugc relaxvqa layer_stack+pool 0.8104 0.6131 0.8354 0.3768 \n",
|
| 1668 |
+
"\n",
|
| 1669 |
+
" SELECT_CRITERIA \n",
|
| 1670 |
+
"1 byrmse \n",
|
| 1671 |
+
"0 byrmse \n",
|
| 1672 |
+
"2 byrmse \n",
|
| 1673 |
+
"3 byrmse "
|
| 1674 |
+
],
|
| 1675 |
+
"text/html": [
|
| 1676 |
+
"<div>\n",
|
| 1677 |
+
"<style scoped>\n",
|
| 1678 |
+
" .dataframe tbody tr th:only-of-type {\n",
|
| 1679 |
+
" vertical-align: middle;\n",
|
| 1680 |
+
" }\n",
|
| 1681 |
+
"\n",
|
| 1682 |
+
" .dataframe tbody tr th {\n",
|
| 1683 |
+
" vertical-align: top;\n",
|
| 1684 |
+
" }\n",
|
| 1685 |
+
"\n",
|
| 1686 |
+
" .dataframe thead th {\n",
|
| 1687 |
+
" text-align: right;\n",
|
| 1688 |
+
" }\n",
|
| 1689 |
+
"</style>\n",
|
| 1690 |
+
"<table border=\"1\" class=\"dataframe\">\n",
|
| 1691 |
+
" <thead>\n",
|
| 1692 |
+
" <tr style=\"text-align: right;\">\n",
|
| 1693 |
+
" <th></th>\n",
|
| 1694 |
+
" <th>DATASET</th>\n",
|
| 1695 |
+
" <th>MODEL</th>\n",
|
| 1696 |
+
" <th>LAYER_NAME</th>\n",
|
| 1697 |
+
" <th>SRCC</th>\n",
|
| 1698 |
+
" <th>KRCC</th>\n",
|
| 1699 |
+
" <th>PLCC</th>\n",
|
| 1700 |
+
" <th>RMSE</th>\n",
|
| 1701 |
+
" <th>SELECT_CRITERIA</th>\n",
|
| 1702 |
+
" </tr>\n",
|
| 1703 |
+
" </thead>\n",
|
| 1704 |
+
" <tbody>\n",
|
| 1705 |
+
" <tr>\n",
|
| 1706 |
+
" <th>1</th>\n",
|
| 1707 |
+
" <td>cvd_2014</td>\n",
|
| 1708 |
+
" <td>relaxvqa</td>\n",
|
| 1709 |
+
" <td>layer_stack+pool</td>\n",
|
| 1710 |
+
" <td>0.7845</td>\n",
|
| 1711 |
+
" <td>0.5930</td>\n",
|
| 1712 |
+
" <td>0.8336</td>\n",
|
| 1713 |
+
" <td>12.2445</td>\n",
|
| 1714 |
+
" <td>byrmse</td>\n",
|
| 1715 |
+
" </tr>\n",
|
| 1716 |
+
" <tr>\n",
|
| 1717 |
+
" <th>0</th>\n",
|
| 1718 |
+
" <td>konvid_1k</td>\n",
|
| 1719 |
+
" <td>relaxvqa</td>\n",
|
| 1720 |
+
" <td>layer_stack+pool</td>\n",
|
| 1721 |
+
" <td>0.8312</td>\n",
|
| 1722 |
+
" <td>0.6418</td>\n",
|
| 1723 |
+
" <td>0.8427</td>\n",
|
| 1724 |
+
" <td>0.3466</td>\n",
|
| 1725 |
+
" <td>byrmse</td>\n",
|
| 1726 |
+
" </tr>\n",
|
| 1727 |
+
" <tr>\n",
|
| 1728 |
+
" <th>2</th>\n",
|
| 1729 |
+
" <td>live_vqc</td>\n",
|
| 1730 |
+
" <td>relaxvqa</td>\n",
|
| 1731 |
+
" <td>layer_stack+pool</td>\n",
|
| 1732 |
+
" <td>0.7664</td>\n",
|
| 1733 |
+
" <td>0.5812</td>\n",
|
| 1734 |
+
" <td>0.8242</td>\n",
|
| 1735 |
+
" <td>9.8201</td>\n",
|
| 1736 |
+
" <td>byrmse</td>\n",
|
| 1737 |
+
" </tr>\n",
|
| 1738 |
+
" <tr>\n",
|
| 1739 |
+
" <th>3</th>\n",
|
| 1740 |
+
" <td>youtube_ugc</td>\n",
|
| 1741 |
+
" <td>relaxvqa</td>\n",
|
| 1742 |
+
" <td>layer_stack+pool</td>\n",
|
| 1743 |
+
" <td>0.8104</td>\n",
|
| 1744 |
+
" <td>0.6131</td>\n",
|
| 1745 |
+
" <td>0.8354</td>\n",
|
| 1746 |
+
" <td>0.3768</td>\n",
|
| 1747 |
+
" <td>byrmse</td>\n",
|
| 1748 |
+
" </tr>\n",
|
| 1749 |
+
" </tbody>\n",
|
| 1750 |
+
"</table>\n",
|
| 1751 |
+
"</div>"
|
| 1752 |
+
]
|
| 1753 |
+
},
|
| 1754 |
+
"execution_count": 159,
|
| 1755 |
+
"metadata": {},
|
| 1756 |
+
"output_type": "execute_result"
|
| 1757 |
+
}
|
| 1758 |
+
],
|
| 1759 |
+
"execution_count": 159
|
| 1760 |
+
},
|
| 1761 |
+
{
|
| 1762 |
+
"metadata": {
|
| 1763 |
+
"ExecuteTime": {
|
| 1764 |
+
"end_time": "2025-03-11T16:39:14.926433Z",
|
| 1765 |
+
"start_time": "2025-03-11T16:39:14.924907Z"
|
| 1766 |
+
}
|
| 1767 |
+
},
|
| 1768 |
+
"cell_type": "code",
|
| 1769 |
+
"source": [
|
| 1770 |
+
"# pattern_krcc_wo = \"./reported_results/old/*_relaxvqa_bykrcc*_wo_finetune.csv\"\n",
|
| 1771 |
+
"# result = process_finetune_csv(pattern_krcc_wo, 'bykrcc', False)\n",
|
| 1772 |
+
"# result_wo_finetune"
|
| 1773 |
+
],
|
| 1774 |
+
"id": "8dfee368cceec0e1",
|
| 1775 |
+
"outputs": [],
|
| 1776 |
+
"execution_count": 160
|
| 1777 |
+
},
|
| 1778 |
+
{
|
| 1779 |
+
"metadata": {},
|
| 1780 |
+
"cell_type": "markdown",
|
| 1781 |
+
"source": "overall performance",
|
| 1782 |
+
"id": "6c02e6c294f8adf7"
|
| 1783 |
+
},
|
| 1784 |
+
{
|
| 1785 |
+
"metadata": {
|
| 1786 |
+
"ExecuteTime": {
|
| 1787 |
+
"end_time": "2025-03-11T16:39:14.984802Z",
|
| 1788 |
+
"start_time": "2025-03-11T16:39:14.977867Z"
|
| 1789 |
+
}
|
| 1790 |
+
},
|
| 1791 |
+
"cell_type": "code",
|
| 1792 |
+
"source": [
|
| 1793 |
+
"# new\n",
|
| 1794 |
+
"data = {\n",
|
| 1795 |
+
" 'BRISQUE': {'SRCC': [0.5553, 0.6781, 0.6096, 0.3517], 'PLCC': [0.5527, 0.6746, 0.6652, 0.3768]},\n",
|
| 1796 |
+
" 'V-BLIINDS': {'SRCC': [0.7306, 0.7258, 0.6925, 0.4783], 'PLCC': [0.7853, 0.7155, 0.6872, 0.4967]},\n",
|
| 1797 |
+
" 'TLVQM': {'SRCC': [0.5399, 0.7616, 0.8133, 0.6802], 'PLCC': [0.5785, 0.7463, 0.7912, 0.6876]},\n",
|
| 1798 |
+
" 'VIDEVAL': {'SRCC': [0.7663, 0.8073, 0.7725, 0.7814], 'PLCC': [0.8062, 0.7923, 0.7752, 0.7929]},\n",
|
| 1799 |
+
" 'RAPIQUE': {'SRCC': [0.8530, 0.8219, 0.7328, 0.7994], 'PLCC': [0.8766, 0.8191, 0.7520, 0.8300]},\n",
|
| 1800 |
+
" 'VSFA': {'SRCC': [0.8825, 0.8067, 0.5898, 0.7857], 'PLCC': [0.8784, 0.8182, 0.5894, 0.7808]},\n",
|
| 1801 |
+
" 'Fast-VQA-B (w/o FT)': {'SRCC': [0.8124, 0.8581, 0.8261, 0.7241], 'PLCC': [0.8221, 0.8551, 0.8466, 0.7364]},\n",
|
| 1802 |
+
" 'Fast-VQA-B (w/ FT)': {'SRCC': [0.8530, 0.8869, 0.8484, 0.8385], 'PLCC': [0.8456, 0.8776, 0.8703, 0.8302]},\n",
|
| 1803 |
+
" 'DOVER (Technical Branch)': {'SRCC': [0.8571, 0.8736, 0.7898, 0.8181], 'PLCC': [0.8588, 0.8852, 0.8573, 0.8197]},\n",
|
| 1804 |
+
" 'ReLaX-VQA (ours)': {'SRCC': [0.8643, 0.8535, 0.7655, 0.8014], 'PLCC': [0.8895, 0.8473, 0.8079, 0.8204]},\n",
|
| 1805 |
+
" 'ReLaX-VQA (w/o FT)': {'SRCC': [0.7845, 0.8312, 0.7664, 0.8104], 'PLCC': [0.8336, 0.8427, 0.8242, 0.8354]},\n",
|
| 1806 |
+
" 'ReLaX-VQA (w/ FT)': {'SRCC': [0.8974, 0.8720, 0.8468, 0.8469], 'PLCC': [0.9294, 0.8668, 0.8876, 0.8652]},\n",
|
| 1807 |
+
"}\n",
|
| 1808 |
+
"# avg performance of each model for each dataset\n",
|
| 1809 |
+
"avg_performance = {}\n",
|
| 1810 |
+
"for model, metrics in data.items():\n",
|
| 1811 |
+
" avg_srcc = np.mean(metrics['SRCC'])\n",
|
| 1812 |
+
" avg_plcc = np.mean(metrics['PLCC'])\n",
|
| 1813 |
+
" avg_performance[model] = {'SRCC': avg_srcc, 'PLCC': avg_plcc}\n",
|
| 1814 |
+
"df_avg_performance = pd.DataFrame.from_dict(avg_performance, orient='index').round(4)\n",
|
| 1815 |
+
"df_avg_performance"
|
| 1816 |
+
],
|
| 1817 |
+
"id": "ce48ed6f16d15be6",
|
| 1818 |
+
"outputs": [
|
| 1819 |
+
{
|
| 1820 |
+
"data": {
|
| 1821 |
+
"text/plain": [
|
| 1822 |
+
" SRCC PLCC\n",
|
| 1823 |
+
"BRISQUE 0.5487 0.5673\n",
|
| 1824 |
+
"V-BLIINDS 0.6568 0.6712\n",
|
| 1825 |
+
"TLVQM 0.6988 0.7009\n",
|
| 1826 |
+
"VIDEVAL 0.7819 0.7916\n",
|
| 1827 |
+
"RAPIQUE 0.8018 0.8194\n",
|
| 1828 |
+
"VSFA 0.7662 0.7667\n",
|
| 1829 |
+
"Fast-VQA-B (w/o FT) 0.8052 0.8151\n",
|
| 1830 |
+
"Fast-VQA-B (w/ FT) 0.8567 0.8559\n",
|
| 1831 |
+
"DOVER (Technical Branch) 0.8347 0.8552\n",
|
| 1832 |
+
"ReLaX-VQA (ours) 0.8212 0.8413\n",
|
| 1833 |
+
"ReLaX-VQA (w/o FT) 0.7981 0.8340\n",
|
| 1834 |
+
"ReLaX-VQA (w/ FT) 0.8658 0.8872"
|
| 1835 |
+
],
|
| 1836 |
+
"text/html": [
|
| 1837 |
+
"<div>\n",
|
| 1838 |
+
"<style scoped>\n",
|
| 1839 |
+
" .dataframe tbody tr th:only-of-type {\n",
|
| 1840 |
+
" vertical-align: middle;\n",
|
| 1841 |
+
" }\n",
|
| 1842 |
+
"\n",
|
| 1843 |
+
" .dataframe tbody tr th {\n",
|
| 1844 |
+
" vertical-align: top;\n",
|
| 1845 |
+
" }\n",
|
| 1846 |
+
"\n",
|
| 1847 |
+
" .dataframe thead th {\n",
|
| 1848 |
+
" text-align: right;\n",
|
| 1849 |
+
" }\n",
|
| 1850 |
+
"</style>\n",
|
| 1851 |
+
"<table border=\"1\" class=\"dataframe\">\n",
|
| 1852 |
+
" <thead>\n",
|
| 1853 |
+
" <tr style=\"text-align: right;\">\n",
|
| 1854 |
+
" <th></th>\n",
|
| 1855 |
+
" <th>SRCC</th>\n",
|
| 1856 |
+
" <th>PLCC</th>\n",
|
| 1857 |
+
" </tr>\n",
|
| 1858 |
+
" </thead>\n",
|
| 1859 |
+
" <tbody>\n",
|
| 1860 |
+
" <tr>\n",
|
| 1861 |
+
" <th>BRISQUE</th>\n",
|
| 1862 |
+
" <td>0.5487</td>\n",
|
| 1863 |
+
" <td>0.5673</td>\n",
|
| 1864 |
+
" </tr>\n",
|
| 1865 |
+
" <tr>\n",
|
| 1866 |
+
" <th>V-BLIINDS</th>\n",
|
| 1867 |
+
" <td>0.6568</td>\n",
|
| 1868 |
+
" <td>0.6712</td>\n",
|
| 1869 |
+
" </tr>\n",
|
| 1870 |
+
" <tr>\n",
|
| 1871 |
+
" <th>TLVQM</th>\n",
|
| 1872 |
+
" <td>0.6988</td>\n",
|
| 1873 |
+
" <td>0.7009</td>\n",
|
| 1874 |
+
" </tr>\n",
|
| 1875 |
+
" <tr>\n",
|
| 1876 |
+
" <th>VIDEVAL</th>\n",
|
| 1877 |
+
" <td>0.7819</td>\n",
|
| 1878 |
+
" <td>0.7916</td>\n",
|
| 1879 |
+
" </tr>\n",
|
| 1880 |
+
" <tr>\n",
|
| 1881 |
+
" <th>RAPIQUE</th>\n",
|
| 1882 |
+
" <td>0.8018</td>\n",
|
| 1883 |
+
" <td>0.8194</td>\n",
|
| 1884 |
+
" </tr>\n",
|
| 1885 |
+
" <tr>\n",
|
| 1886 |
+
" <th>VSFA</th>\n",
|
| 1887 |
+
" <td>0.7662</td>\n",
|
| 1888 |
+
" <td>0.7667</td>\n",
|
| 1889 |
+
" </tr>\n",
|
| 1890 |
+
" <tr>\n",
|
| 1891 |
+
" <th>Fast-VQA-B (w/o FT)</th>\n",
|
| 1892 |
+
" <td>0.8052</td>\n",
|
| 1893 |
+
" <td>0.8151</td>\n",
|
| 1894 |
+
" </tr>\n",
|
| 1895 |
+
" <tr>\n",
|
| 1896 |
+
" <th>Fast-VQA-B (w/ FT)</th>\n",
|
| 1897 |
+
" <td>0.8567</td>\n",
|
| 1898 |
+
" <td>0.8559</td>\n",
|
| 1899 |
+
" </tr>\n",
|
| 1900 |
+
" <tr>\n",
|
| 1901 |
+
" <th>DOVER (Technical Branch)</th>\n",
|
| 1902 |
+
" <td>0.8347</td>\n",
|
| 1903 |
+
" <td>0.8552</td>\n",
|
| 1904 |
+
" </tr>\n",
|
| 1905 |
+
" <tr>\n",
|
| 1906 |
+
" <th>ReLaX-VQA (ours)</th>\n",
|
| 1907 |
+
" <td>0.8212</td>\n",
|
| 1908 |
+
" <td>0.8413</td>\n",
|
| 1909 |
+
" </tr>\n",
|
| 1910 |
+
" <tr>\n",
|
| 1911 |
+
" <th>ReLaX-VQA (w/o FT)</th>\n",
|
| 1912 |
+
" <td>0.7981</td>\n",
|
| 1913 |
+
" <td>0.8340</td>\n",
|
| 1914 |
+
" </tr>\n",
|
| 1915 |
+
" <tr>\n",
|
| 1916 |
+
" <th>ReLaX-VQA (w/ FT)</th>\n",
|
| 1917 |
+
" <td>0.8658</td>\n",
|
| 1918 |
+
" <td>0.8872</td>\n",
|
| 1919 |
+
" </tr>\n",
|
| 1920 |
+
" </tbody>\n",
|
| 1921 |
+
"</table>\n",
|
| 1922 |
+
"</div>"
|
| 1923 |
+
]
|
| 1924 |
+
},
|
| 1925 |
+
"execution_count": 161,
|
| 1926 |
+
"metadata": {},
|
| 1927 |
+
"output_type": "execute_result"
|
| 1928 |
+
}
|
| 1929 |
+
],
|
| 1930 |
+
"execution_count": 161
|
| 1931 |
+
}
|
| 1932 |
+
],
|
| 1933 |
+
"metadata": {
|
| 1934 |
+
"kernelspec": {
|
| 1935 |
+
"display_name": "Python 3",
|
| 1936 |
+
"language": "python",
|
| 1937 |
+
"name": "python3"
|
| 1938 |
+
},
|
| 1939 |
+
"language_info": {
|
| 1940 |
+
"codemirror_mode": {
|
| 1941 |
+
"name": "ipython",
|
| 1942 |
+
"version": 2
|
| 1943 |
+
},
|
| 1944 |
+
"file_extension": ".py",
|
| 1945 |
+
"mimetype": "text/x-python",
|
| 1946 |
+
"name": "python",
|
| 1947 |
+
"nbconvert_exporter": "python",
|
| 1948 |
+
"pygments_lexer": "ipython2",
|
| 1949 |
+
"version": "2.7.6"
|
| 1950 |
+
}
|
| 1951 |
+
},
|
| 1952 |
+
"nbformat": 4,
|
| 1953 |
+
"nbformat_minor": 5
|
| 1954 |
+
}
|
requirements.txt
ADDED
|
@@ -0,0 +1,132 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
anyio==4.3.0
|
| 2 |
+
argon2-cffi==23.1.0
|
| 3 |
+
argon2-cffi-bindings==21.2.0
|
| 4 |
+
arrow==1.3.0
|
| 5 |
+
asttokens==2.4.1
|
| 6 |
+
async-lru==2.0.4
|
| 7 |
+
attrs==23.2.0
|
| 8 |
+
Babel==2.14.0
|
| 9 |
+
backcall==0.2.0
|
| 10 |
+
beautifulsoup4==4.12.3
|
| 11 |
+
bleach==6.1.0
|
| 12 |
+
certifi==2024.2.2
|
| 13 |
+
cffi==1.16.0
|
| 14 |
+
charset-normalizer==3.3.2
|
| 15 |
+
colorama==0.4.6
|
| 16 |
+
comm==0.2.2
|
| 17 |
+
contourpy==1.1.1
|
| 18 |
+
cycler==0.12.1
|
| 19 |
+
debugpy==1.8.1
|
| 20 |
+
decorator==5.1.1
|
| 21 |
+
defusedxml==0.7.1
|
| 22 |
+
exceptiongroup==1.2.1
|
| 23 |
+
executing==2.0.1
|
| 24 |
+
fastjsonschema==2.19.1
|
| 25 |
+
filelock==3.9.0
|
| 26 |
+
fonttools==4.51.0
|
| 27 |
+
fqdn==1.5.1
|
| 28 |
+
fsspec==2023.4.0
|
| 29 |
+
h11==0.14.0
|
| 30 |
+
httpcore==1.0.5
|
| 31 |
+
httpx==0.27.0
|
| 32 |
+
idna==3.7
|
| 33 |
+
imageio==2.34.0
|
| 34 |
+
importlib_metadata==7.1.0
|
| 35 |
+
importlib_resources==6.4.0
|
| 36 |
+
ipykernel==6.29.4
|
| 37 |
+
ipython==8.12.3
|
| 38 |
+
ipywidgets==8.1.2
|
| 39 |
+
isoduration==20.11.0
|
| 40 |
+
jedi==0.19.1
|
| 41 |
+
Jinja2==3.1.2
|
| 42 |
+
joblib==1.4.0
|
| 43 |
+
json5==0.9.25
|
| 44 |
+
jsonpointer==2.4
|
| 45 |
+
jsonschema==4.21.1
|
| 46 |
+
jsonschema-specifications==2023.12.1
|
| 47 |
+
jupyter==1.0.0
|
| 48 |
+
jupyter-console==6.6.3
|
| 49 |
+
jupyter-events==0.10.0
|
| 50 |
+
jupyter-lsp==2.2.5
|
| 51 |
+
jupyter_client==8.6.1
|
| 52 |
+
jupyter_core==5.7.2
|
| 53 |
+
jupyter_server==2.14.0
|
| 54 |
+
jupyter_server_terminals==0.5.3
|
| 55 |
+
jupyterlab==4.1.6
|
| 56 |
+
jupyterlab_pygments==0.3.0
|
| 57 |
+
jupyterlab_server==2.27.1
|
| 58 |
+
jupyterlab_widgets==3.0.10
|
| 59 |
+
kiwisolver==1.4.5
|
| 60 |
+
MarkupSafe==2.1.3
|
| 61 |
+
matplotlib==3.7.5
|
| 62 |
+
matplotlib-inline==0.1.6
|
| 63 |
+
mistune==3.0.2
|
| 64 |
+
mpmath==1.3.0
|
| 65 |
+
nbclient==0.10.0
|
| 66 |
+
nbconvert==7.16.3
|
| 67 |
+
nbformat==5.10.4
|
| 68 |
+
nest-asyncio==1.6.0
|
| 69 |
+
networkx==3.0
|
| 70 |
+
notebook==7.1.3
|
| 71 |
+
notebook_shim==0.2.4
|
| 72 |
+
numpy==1.24.1
|
| 73 |
+
opencv-python==4.9.0.80
|
| 74 |
+
overrides==7.7.0
|
| 75 |
+
packaging==24.0
|
| 76 |
+
pandas==2.0.3
|
| 77 |
+
pandocfilters==1.5.1
|
| 78 |
+
parso==0.8.4
|
| 79 |
+
pickleshare==0.7.5
|
| 80 |
+
pillow==10.2.0
|
| 81 |
+
pkgutil_resolve_name==1.3.10
|
| 82 |
+
platformdirs==4.2.0
|
| 83 |
+
prometheus_client==0.20.0
|
| 84 |
+
prompt-toolkit==3.0.43
|
| 85 |
+
psutil==5.9.8
|
| 86 |
+
pure-eval==0.2.2
|
| 87 |
+
pycparser==2.22
|
| 88 |
+
Pygments==2.17.2
|
| 89 |
+
pyparsing==3.1.2
|
| 90 |
+
python-dateutil==2.9.0.post0
|
| 91 |
+
python-json-logger==2.0.7
|
| 92 |
+
pytz==2024.1
|
| 93 |
+
pywin32==306
|
| 94 |
+
pywinpty==2.0.13
|
| 95 |
+
PyYAML==6.0.1
|
| 96 |
+
pyzmq==26.0.2
|
| 97 |
+
qtconsole==5.5.1
|
| 98 |
+
QtPy==2.4.1
|
| 99 |
+
referencing==0.34.0
|
| 100 |
+
requests==2.31.0
|
| 101 |
+
rfc3339-validator==0.1.4
|
| 102 |
+
rfc3986-validator==0.1.1
|
| 103 |
+
rpds-py==0.18.0
|
| 104 |
+
scikit-learn==1.3.2
|
| 105 |
+
scipy==1.10.1
|
| 106 |
+
seaborn==0.13.2
|
| 107 |
+
Send2Trash==1.8.3
|
| 108 |
+
six==1.16.0
|
| 109 |
+
sniffio==1.3.1
|
| 110 |
+
soupsieve==2.5
|
| 111 |
+
stack-data==0.6.3
|
| 112 |
+
sympy==1.12
|
| 113 |
+
terminado==0.18.1
|
| 114 |
+
threadpoolctl==3.4.0
|
| 115 |
+
tinycss2==1.3.0
|
| 116 |
+
tomli==2.0.1
|
| 117 |
+
torch==2.2.2+cu118
|
| 118 |
+
torchaudio==2.2.2+cu118
|
| 119 |
+
torchvision==0.17.2+cu118
|
| 120 |
+
tornado==6.4
|
| 121 |
+
traitlets==5.14.2
|
| 122 |
+
types-python-dateutil==2.9.0.20240316
|
| 123 |
+
typing_extensions==4.8.0
|
| 124 |
+
tzdata==2024.1
|
| 125 |
+
uri-template==1.3.0
|
| 126 |
+
urllib3==2.2.1
|
| 127 |
+
wcwidth==0.2.13
|
| 128 |
+
webcolors==1.13
|
| 129 |
+
webencodings==0.5.1
|
| 130 |
+
websocket-client==1.7.0
|
| 131 |
+
widgetsnbextension==4.0.10
|
| 132 |
+
zipp==3.18.1
|
src/data_processing/__init__.py
ADDED
|
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# __init__.py
|
| 2 |
+
print("Data processing")
|
src/data_processing/check_greyscale.py
ADDED
|
@@ -0,0 +1,102 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import subprocess
|
| 2 |
+
from pathlib import Path
|
| 3 |
+
import cv2
|
| 4 |
+
import pandas as pd
|
| 5 |
+
import os
|
| 6 |
+
import numpy as np
|
| 7 |
+
|
| 8 |
+
def convert_to_mp4(input_path, output_path, data_name):
|
| 9 |
+
os.environ['PATH'] += os.pathsep + r"C:\Users\um20242\ffmpeg\bin"
|
| 10 |
+
if data_name == 'live_qualcomm':
|
| 11 |
+
command = ['ffmpeg.exe', '-hide_banner', '-loglevel', 'error',
|
| 12 |
+
'-s', f'1920x1080', '-pix_fmt', 'yuv420p', '-framerate', '25.0',
|
| 13 |
+
'-i', str(input_path), '-c:v', 'libx264', '-preset', 'fast', '-c:a', 'aac', '-y',
|
| 14 |
+
str(output_path)]
|
| 15 |
+
else:
|
| 16 |
+
command = ['ffmpeg.exe', '-i', str(input_path), '-c:v', 'libx264', '-preset', 'fast', '-c:a', 'aac', '-y', str(output_path)]
|
| 17 |
+
try:
|
| 18 |
+
subprocess.run(command, check=True)
|
| 19 |
+
print(f"success: {output_path}")
|
| 20 |
+
return True
|
| 21 |
+
except subprocess.CalledProcessError as e:
|
| 22 |
+
print(f"error: {e}")
|
| 23 |
+
return False
|
| 24 |
+
|
| 25 |
+
def is_greyscale_image(image):
|
| 26 |
+
if len(image.shape) == 2:
|
| 27 |
+
return True
|
| 28 |
+
elif len(image.shape) == 3 and image.shape[2] == 3:
|
| 29 |
+
b, g, r = image[:, :, 0], image[:, :, 1], image[:, :, 2]
|
| 30 |
+
color_threshold = 3
|
| 31 |
+
if (cv2.norm(b - g, cv2.NORM_INF) <= color_threshold and
|
| 32 |
+
cv2.norm(b - r, cv2.NORM_INF) <= color_threshold and
|
| 33 |
+
cv2.norm(g - r, cv2.NORM_INF) <= color_threshold):
|
| 34 |
+
return True
|
| 35 |
+
return False
|
| 36 |
+
|
| 37 |
+
def check_video_greyscale(video_path):
|
| 38 |
+
cap = cv2.VideoCapture(str(video_path))
|
| 39 |
+
if not cap.isOpened():
|
| 40 |
+
print(f"Cannot open video file: {video_path}")
|
| 41 |
+
return False, False
|
| 42 |
+
|
| 43 |
+
frame_read = False
|
| 44 |
+
while True:
|
| 45 |
+
ret, frame = cap.read()
|
| 46 |
+
if not ret:
|
| 47 |
+
break
|
| 48 |
+
frame_read = True
|
| 49 |
+
if not is_greyscale_image(frame):
|
| 50 |
+
cap.release()
|
| 51 |
+
return False, True
|
| 52 |
+
|
| 53 |
+
cap.release()
|
| 54 |
+
# 如果循环结束后没有发现彩色帧，且至少读取到一帧，认为视频是灰度的
|
| 55 |
+
return True if frame_read else False, frame_read
|
| 56 |
+
|
| 57 |
+
def process_videos_from_csv(metadata_path, output_csv, data_name):
|
| 58 |
+
df = pd.read_csv(metadata_path)
|
| 59 |
+
results = []
|
| 60 |
+
for i, row in df.iterrows():
|
| 61 |
+
frame_read = True
|
| 62 |
+
if data_name == 'youtube_ugc':
|
| 63 |
+
video_path = Path("D:/video_dataset/ugc-dataset/youtube_ugc/original_videos") / f"{row['resolution']}P" / f"{row['vid']}.mkv"
|
| 64 |
+
elif data_name == 'konvid_1k':
|
| 65 |
+
video_path = Path("D:/video_dataset/KoNViD_1k/KoNViD_1k_videos") / f"{row['vid']}.mp4"
|
| 66 |
+
elif data_name == 'lsvq_train' or data_name == 'lsvq_test' or data_name == 'lsvq_test_1080P':
|
| 67 |
+
video_path = Path("D:/video_dataset/LSVQ") / f"{row['vid']}.mp4"
|
| 68 |
+
elif data_name == 'live_vqc':
|
| 69 |
+
video_path = Path("D:/video_dataset/LIVE-VQC/video") / f"{row['vid']}.mp4"
|
| 70 |
+
elif data_name == 'live_qualcomm':
|
| 71 |
+
video_path = Path("D:/video_dataset/LIVE-Qualcomm") / f"{row['vid']}.yuv"
|
| 72 |
+
elif data_name == 'cvd_2014':
|
| 73 |
+
video_path = Path("D:/video_dataset/CVD2014") / f"{row['vid']}.avi"
|
| 74 |
+
|
| 75 |
+
converted_path = video_path.with_suffix('.mp4')
|
| 76 |
+
need_conversion = data_name == 'live_qualcomm' or not frame_read
|
| 77 |
+
|
| 78 |
+
if need_conversion:
|
| 79 |
+
convert_to_mp4(video_path, converted_path, data_name)
|
| 80 |
+
is_greyscale, frame_read = check_video_greyscale(converted_path)
|
| 81 |
+
os.remove(converted_path)
|
| 82 |
+
else:
|
| 83 |
+
is_greyscale, frame_read = check_video_greyscale(video_path) # 直接检查原视频
|
| 84 |
+
|
| 85 |
+
|
| 86 |
+
if is_greyscale:
|
| 87 |
+
print(video_path)
|
| 88 |
+
results.append({'Index': i, 'vid': row['vid'], 'Is Greyscale': is_greyscale})
|
| 89 |
+
|
| 90 |
+
if len(results) != 0:
|
| 91 |
+
result_df = pd.DataFrame(results)
|
| 92 |
+
result_df.to_csv(output_csv, index=False)
|
| 93 |
+
print(f"results saved to {output_csv}")
|
| 94 |
+
else:
|
| 95 |
+
print('no greyscale video')
|
| 96 |
+
|
| 97 |
+
if __name__ == '__main__':
|
| 98 |
+
data_name = 'live_qualcomm'
|
| 99 |
+
metadata_path = f'../../metadata/{data_name.upper()}_metadata.csv'
|
| 100 |
+
greyscale_path = f'../../metadata/greyscale_report/{data_name.upper()}_greyscale_metadata.csv'
|
| 101 |
+
|
| 102 |
+
process_videos_from_csv(metadata_path, greyscale_path, data_name)
|
src/data_processing/extract_metadata_NR.py
ADDED
|
@@ -0,0 +1,194 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import pandas as pd
|
| 2 |
+
import subprocess
|
| 3 |
+
import json
|
| 4 |
+
import os
|
| 5 |
+
from scipy.io import loadmat
|
| 6 |
+
|
| 7 |
+
def parse_framerate(framerate_str):
|
| 8 |
+
num, den = framerate_str.split('/')
|
| 9 |
+
framerate = float(num)/float(den)
|
| 10 |
+
return framerate
|
| 11 |
+
|
| 12 |
+
def delete_file(file_path):
|
| 13 |
+
try:
|
| 14 |
+
os.remove(file_path)
|
| 15 |
+
except OSError as e:
|
| 16 |
+
print(f"error: {e}")
|
| 17 |
+
|
| 18 |
+
def convert_yuv_to_mp4(yuv_file, output_mp4_file, resolution, pixel_format):
|
| 19 |
+
ffmpeg_path = "C://Users//um20242//ffmpeg//bin//ffmpeg.exe"
|
| 20 |
+
cmd = f"{ffmpeg_path} -y -s {resolution} -pix_fmt {pixel_format} -i {yuv_file} -c:v libx264 {output_mp4_file}"
|
| 21 |
+
try:
|
| 22 |
+
subprocess.run(cmd, shell=True, check=True)
|
| 23 |
+
except subprocess.CalledProcessError as e:
|
| 24 |
+
print(f"error: {e}")
|
| 25 |
+
return False
|
| 26 |
+
return True
|
| 27 |
+
|
| 28 |
+
def get_video_metadata(video_file):
|
| 29 |
+
print(video_file)
|
| 30 |
+
ffprobe_path = "C://Users//um20242//ffmpeg//bin//ffprobe.exe"
|
| 31 |
+
cmd = f'{ffprobe_path} -v error -select_streams v:0 -show_entries stream=width,height,nb_frames,r_frame_rate,bit_rate,bits_per_raw_sample,pix_fmt -of json {video_file}'
|
| 32 |
+
|
| 33 |
+
try:
|
| 34 |
+
result = subprocess.run(cmd, shell=True, capture_output=True, check=True)
|
| 35 |
+
info = json.loads(result.stdout)
|
| 36 |
+
except Exception as e:
|
| 37 |
+
print(f"Error processing file {video_file}: {e}")
|
| 38 |
+
return {}
|
| 39 |
+
|
| 40 |
+
# get metadata using ffmpeg
|
| 41 |
+
width = info['streams'][0]['width']
|
| 42 |
+
height = info['streams'][0]['height']
|
| 43 |
+
nb_frames = info['streams'][0].get('nb_frames', 'N/A') # Number of frames might not be available for some formats
|
| 44 |
+
pixfmt = info['streams'][0]['pix_fmt']
|
| 45 |
+
framerate = info['streams'][0]['r_frame_rate']
|
| 46 |
+
framerate = parse_framerate(framerate)
|
| 47 |
+
bitdepth = info['streams'][0].get('bits_per_raw_sample', 'N/A')
|
| 48 |
+
bitrate = info['streams'][0].get('bit_rate', 'N/A')
|
| 49 |
+
print(framerate)
|
| 50 |
+
|
| 51 |
+
return width, height, nb_frames, pixfmt, framerate, bitdepth, bitrate
|
| 52 |
+
|
| 53 |
+
def create_dataframe(vid_list, mos_list, width_list, height_list, pixfmt_list, framerate_list, nb_frames_list, bitdepth_list, bitrate_list):
|
| 54 |
+
data = {
|
| 55 |
+
'vid': vid_list,
|
| 56 |
+
'mos': mos_list,
|
| 57 |
+
'width': width_list,
|
| 58 |
+
'height': height_list,
|
| 59 |
+
'pixfmt': pixfmt_list,
|
| 60 |
+
'framerate': framerate_list,
|
| 61 |
+
'nb_frames': nb_frames_list,
|
| 62 |
+
'bitdepth': bitdepth_list,
|
| 63 |
+
'bitrate': bitrate_list
|
| 64 |
+
}
|
| 65 |
+
df_new = pd.DataFrame(data)
|
| 66 |
+
return df_new
|
| 67 |
+
|
| 68 |
+
def extract_csv2metadata(df, video_type):
|
| 69 |
+
vid_list = []
|
| 70 |
+
mos_list = []
|
| 71 |
+
width_list = []
|
| 72 |
+
height_list = []
|
| 73 |
+
nb_frames_list = []
|
| 74 |
+
pixfmt_list = []
|
| 75 |
+
framerate_list = []
|
| 76 |
+
bitdepth_list = []
|
| 77 |
+
bitrate_list = []
|
| 78 |
+
if video_type == 'lsvq':
|
| 79 |
+
for i in range(len(df)):
|
| 80 |
+
video_path = f"D://video_dataset//LSVQ//{df['name'][i]}.mp4"
|
| 81 |
+
if os.path.exists(video_path):
|
| 82 |
+
vid_list.append(df['name'][i])
|
| 83 |
+
mos_list.append(df['mos'][i])
|
| 84 |
+
width_list.append(df['width'][i])
|
| 85 |
+
height_list.append(df['height'][i])
|
| 86 |
+
nb_frames_list.append(df['frame_number'][i])
|
| 87 |
+
|
| 88 |
+
_, _, _, pixfmt, framerate, bitdepth, bitrate = get_video_metadata(video_path)
|
| 89 |
+
pixfmt_list.append(pixfmt)
|
| 90 |
+
framerate_list.append(framerate)
|
| 91 |
+
bitdepth_list.append(bitdepth)
|
| 92 |
+
bitrate_list.append(bitrate)
|
| 93 |
+
else:
|
| 94 |
+
pass
|
| 95 |
+
|
| 96 |
+
elif video_type == 'live_vqc':
|
| 97 |
+
vid_list = df['vid'].tolist()
|
| 98 |
+
vid_list = [vid.replace('.mp4', '') for vid in vid_list]
|
| 99 |
+
mos_list = df['mos'].tolist()
|
| 100 |
+
width_list = df['width'].tolist()
|
| 101 |
+
height_list = df['height'].tolist()
|
| 102 |
+
pixfmt_list = df['pixfmt'].tolist()
|
| 103 |
+
framerate_list = df['framerate'].tolist()
|
| 104 |
+
nb_frames_list = df['nb_frames'].tolist()
|
| 105 |
+
bitdepth_list = df['bitdepth'].tolist()
|
| 106 |
+
bitrate_list = df['bitrate'].tolist()
|
| 107 |
+
|
| 108 |
+
df_new = create_dataframe(vid_list, mos_list, width_list, height_list, pixfmt_list, framerate_list, nb_frames_list, bitdepth_list, bitrate_list)
|
| 109 |
+
return df_new
|
| 110 |
+
|
| 111 |
+
def extract_mat2metadata(mat_file, video_type):
|
| 112 |
+
data = loadmat(mat_file)
|
| 113 |
+
all_variables = {}
|
| 114 |
+
for key, value in data.items():
|
| 115 |
+
# ignore '__'
|
| 116 |
+
if not key.startswith('__') and not key.endswith('__'):
|
| 117 |
+
all_variables[key] = value
|
| 118 |
+
|
| 119 |
+
vid_list = []
|
| 120 |
+
mos_list = []
|
| 121 |
+
width_list = []
|
| 122 |
+
height_list = []
|
| 123 |
+
nb_frames_list = []
|
| 124 |
+
pixfmt_list = []
|
| 125 |
+
framerate_list = []
|
| 126 |
+
bitdepth_list = []
|
| 127 |
+
bitrate_list = []
|
| 128 |
+
for i in range(len(all_variables['video_names'])):
|
| 129 |
+
vid = all_variables['video_names'][i].flatten()[0].item()
|
| 130 |
+
mos = all_variables['scores'][i].flatten()[0].item()
|
| 131 |
+
|
| 132 |
+
if video_type == 'cvd_2014':
|
| 133 |
+
video_name = vid.replace('.avi', '')
|
| 134 |
+
video_path = f"D://video_dataset//CVD2014//{video_name}.avi"
|
| 135 |
+
elif video_type == 'live_qualcomm':
|
| 136 |
+
video_name = vid.replace('.yuv', '')
|
| 137 |
+
tmp_yuv_file = f"D://video_dataset//LIVE-Qualcomm//{video_name}.yuv"
|
| 138 |
+
video_path = f"D://video_dataset//LIVE-Qualcomm//{video_name}.mp4"
|
| 139 |
+
convert_yuv_to_mp4(tmp_yuv_file, video_path, "1920x1080", "yuv420p")
|
| 140 |
+
|
| 141 |
+
width, height, nb_frames, pixfmt, framerate, bitdepth, bitrate = get_video_metadata(video_path)
|
| 142 |
+
vid_list.append(video_name)
|
| 143 |
+
mos_list.append(mos)
|
| 144 |
+
width_list.append(width)
|
| 145 |
+
height_list.append(height)
|
| 146 |
+
nb_frames_list.append(nb_frames)
|
| 147 |
+
pixfmt_list.append(pixfmt)
|
| 148 |
+
framerate_list.append(framerate)
|
| 149 |
+
bitdepth_list.append(bitdepth)
|
| 150 |
+
bitrate_list.append(bitrate)
|
| 151 |
+
if video_type == 'live_qualcomm':
|
| 152 |
+
delete_file(video_path)
|
| 153 |
+
|
| 154 |
+
df_new = create_dataframe(vid_list, mos_list, width_list, height_list, pixfmt_list, framerate_list, nb_frames_list, bitdepth_list, bitrate_list)
|
| 155 |
+
return df_new
|
| 156 |
+
|
| 157 |
+
def save_to_csv(dataframe, output_path):
|
| 158 |
+
dataframe.to_csv(output_path, index=False)
|
| 159 |
+
|
| 160 |
+
if __name__ == '__main__':
|
| 161 |
+
|
| 162 |
+
video_type = 'live_vqc'
|
| 163 |
+
print(video_type)
|
| 164 |
+
|
| 165 |
+
# LSVQ
|
| 166 |
+
if video_type == 'lsvq':
|
| 167 |
+
set_name = 'train' #train, test, test_1080P
|
| 168 |
+
df = pd.read_csv(f"D://video_dataset//LSVQ//LSVQ_whole_{set_name}.csv")
|
| 169 |
+
df_new = extract_csv2metadata(df, video_type)
|
| 170 |
+
print(df_new)
|
| 171 |
+
video_type = f'LSVQ_{set_name.upper()}'
|
| 172 |
+
|
| 173 |
+
# LIVE_VQC
|
| 174 |
+
elif video_type == 'live_vqc':
|
| 175 |
+
df = pd.read_csv(f"D://video_dataset//LIVE-VQC//LIVE_VQC_metadata.csv")
|
| 176 |
+
df_new = extract_csv2metadata(df, video_type)
|
| 177 |
+
print(df_new)
|
| 178 |
+
|
| 179 |
+
# CVD2014
|
| 180 |
+
elif video_type == 'cvd_2014':
|
| 181 |
+
mat_file = "D://video_dataset//CVD2014//CVD2014info.mat"
|
| 182 |
+
df_new = extract_mat2metadata(mat_file, video_type)
|
| 183 |
+
print(df_new)
|
| 184 |
+
|
| 185 |
+
# LIVE-Qualcomm
|
| 186 |
+
elif video_type == 'live_qualcomm':
|
| 187 |
+
mat_file = "D://video_dataset//LIVE-Qualcomm//LIVE-Qualcomminfo.mat"
|
| 188 |
+
df_new = extract_mat2metadata(mat_file, video_type)
|
| 189 |
+
print(df_new)
|
| 190 |
+
|
| 191 |
+
output_csv_path = f'../../metadata/{video_type.upper()}_metadata.csv'
|
| 192 |
+
save_to_csv(df_new, output_csv_path)
|
| 193 |
+
|
| 194 |
+
|
src/data_processing/recover_median_train_test.py
ADDED
|
@@ -0,0 +1,76 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import pandas as pd
|
| 2 |
+
from scipy.io import loadmat, savemat
|
| 3 |
+
import numpy as np
|
| 4 |
+
|
| 5 |
+
def split_features(data_name, df, result_file, feature_path, layer_name):
|
| 6 |
+
data = loadmat(result_file)
|
| 7 |
+
all_variables = {}
|
| 8 |
+
for key, value in data.items():
|
| 9 |
+
# ignore '__'
|
| 10 |
+
if not key.startswith('__') and not key.endswith('__'):
|
| 11 |
+
all_variables[key] = value
|
| 12 |
+
|
| 13 |
+
if data_name == 'konvid_1k':
|
| 14 |
+
for i in range(len(all_variables['Test_videos_Median_model'])):
|
| 15 |
+
test_vids = all_variables['Test_videos_Median_model'][i]
|
| 16 |
+
test_vids = test_vids.tolist()
|
| 17 |
+
else:
|
| 18 |
+
test_vids = []
|
| 19 |
+
for i in range(len(all_variables['Test_videos_Median_model'])):
|
| 20 |
+
vid = all_variables['Test_videos_Median_model'][i].strip()
|
| 21 |
+
test_vids.append(vid)
|
| 22 |
+
|
| 23 |
+
# drop greyscale videos
|
| 24 |
+
if data_name == 'youtube_ugc':
|
| 25 |
+
grey_df = pd.read_csv(f'{metadata_path}/greyscale_report/{data_name.upper()}_greyscale_metadata.csv')
|
| 26 |
+
grey_indices = grey_df.iloc[:, 0].tolist()
|
| 27 |
+
df = df.drop(index=grey_indices).reset_index(drop=True)
|
| 28 |
+
|
| 29 |
+
all_vids = df.iloc[:, 0].tolist()
|
| 30 |
+
print(all_vids)
|
| 31 |
+
print(test_vids)
|
| 32 |
+
train_vids = list(set(all_vids) - set(test_vids))
|
| 33 |
+
print(len(test_vids))
|
| 34 |
+
print(len(train_vids))
|
| 35 |
+
|
| 36 |
+
# split all_data into train and test based on vids
|
| 37 |
+
train_df = df[df.iloc[:, 0].isin(train_vids)]
|
| 38 |
+
test_df = df[df.iloc[:, 0].isin(test_vids)]
|
| 39 |
+
print(len(test_df))
|
| 40 |
+
|
| 41 |
+
# reorder columns
|
| 42 |
+
sorted_train_df = pd.DataFrame({'vid': train_df.iloc[:, 0], 'framerate': train_df['framerate'], 'MOS': train_df['mos']})
|
| 43 |
+
sorted_test_df = pd.DataFrame({'vid': test_df.iloc[:, 0], 'framerate': test_df['framerate'], 'MOS': test_df['mos']})
|
| 44 |
+
|
| 45 |
+
# use indices from the train and test DataFrames to split features
|
| 46 |
+
data = loadmat(f'{feature_path}{layer_name}/original_features/{network_name}_{data_name}_original_features.mat')
|
| 47 |
+
features = data[f'{data_name}']
|
| 48 |
+
|
| 49 |
+
if data_name == 'youtube_ugc':
|
| 50 |
+
features = np.delete(features, grey_indices, axis=0)
|
| 51 |
+
|
| 52 |
+
train_features = features[train_df.index]
|
| 53 |
+
test_features = features[test_df.index]
|
| 54 |
+
|
| 55 |
+
# recover the median train and test files
|
| 56 |
+
sorted_train_df.to_csv(f'{metadata_path}mos_files/{data_name}_MOS_train.csv', index=False)
|
| 57 |
+
sorted_test_df.to_csv(f'{metadata_path}mos_files/{data_name}_MOS_test.csv', index=False)
|
| 58 |
+
savemat(f'{feature_path}{layer_name}/relaxvqa_{data_name}_original_train_features.mat', {f'{data_name}_train_features': train_features})
|
| 59 |
+
savemat(f'{feature_path}{layer_name}/relaxvqa_{data_name}_original_test_features.mat', {f'{data_name}_test_features': test_features})
|
| 60 |
+
|
| 61 |
+
return train_features, test_features, test_vids
|
| 62 |
+
|
| 63 |
+
if __name__ == '__main__':
|
| 64 |
+
metadata_path = '../../metadata/'
|
| 65 |
+
feature_path = '../../features_merged_frag/'
|
| 66 |
+
result_path = f'../../log/result/'
|
| 67 |
+
|
| 68 |
+
data_name = 'cvd_2014'
|
| 69 |
+
network_name = 'relaxvqa'
|
| 70 |
+
layer_name = 'pool'
|
| 71 |
+
model_name = 'Mlp'
|
| 72 |
+
select_criteria = 'byrmse'
|
| 73 |
+
|
| 74 |
+
df = pd.read_csv(f'{metadata_path}/{data_name.upper()}_metadata.csv')
|
| 75 |
+
result_file = f'{result_path}{data_name}_{network_name}_{select_criteria}.mat'
|
| 76 |
+
train_features, test_features, test_vids = split_features(data_name, df, result_file, feature_path, layer_name)
|
src/data_processing/split_train_test.py
ADDED
|
@@ -0,0 +1,180 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
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|
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|
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|
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|
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|
|
|
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|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import pandas as pd
|
| 2 |
+
import numpy as np
|
| 3 |
+
import os
|
| 4 |
+
from scipy.io import loadmat, savemat
|
| 5 |
+
from sklearn.model_selection import train_test_split
|
| 6 |
+
import logging
|
| 7 |
+
|
| 8 |
+
# cross_dataset
|
| 9 |
+
def process_cross_dataset(train_data_name, test_data_name, metadata_path, feature_path, network_name):
|
| 10 |
+
metadata_name1 = f"{train_data_name.replace('_all', '').upper()}_metadata.csv"
|
| 11 |
+
metadata_name2 = f"{test_data_name.replace('_all', '').upper()}_metadata.csv"
|
| 12 |
+
# load CSV data
|
| 13 |
+
train_df = pd.read_csv(f'{metadata_path}/{metadata_name1}')
|
| 14 |
+
test_df = pd.read_csv(f'{metadata_path}/{metadata_name2}')
|
| 15 |
+
|
| 16 |
+
# grayscale videos, do not consider them for fair comparison
|
| 17 |
+
grey_df_train = pd.read_csv(f"{metadata_path}/greyscale_report/{train_data_name.replace('_all', '').upper()}_greyscale_metadata.csv")
|
| 18 |
+
grey_df_test = pd.read_csv(f"{metadata_path}/greyscale_report/{test_data_name.replace('_all', '').upper()}_greyscale_metadata.csv")
|
| 19 |
+
grey_indices_train = grey_df_train.iloc[:, 0].tolist()
|
| 20 |
+
grey_indices_test = grey_df_test.iloc[:, 0].tolist()
|
| 21 |
+
train_df = train_df.drop(index=grey_indices_train).reset_index(drop=True)
|
| 22 |
+
test_df = test_df.drop(index=grey_indices_test).reset_index(drop=True)
|
| 23 |
+
|
| 24 |
+
# split videonames into train and test sets
|
| 25 |
+
train_vids = train_df.iloc[:, 0]
|
| 26 |
+
test_vids = test_df.iloc[:, 0]
|
| 27 |
+
|
| 28 |
+
# scores (1-100) map to 1-5
|
| 29 |
+
train_scores = train_df['mos'].tolist()
|
| 30 |
+
test_scores = test_df['mos'].tolist()
|
| 31 |
+
if train_data_name == 'konvid_1k_all' or train_data_name == 'youtube_ugc_all':
|
| 32 |
+
train_mos_list = ((np.array(train_scores) - 1) * (99/4) + 1.0).tolist()
|
| 33 |
+
else:
|
| 34 |
+
train_mos_list = train_scores
|
| 35 |
+
if test_data_name == 'konvid_1k_all' or test_data_name == 'youtube_ugc_all':
|
| 36 |
+
test_mos_list = ((np.array(test_scores) - 1) * (99/4) + 1.0).tolist()
|
| 37 |
+
else:
|
| 38 |
+
test_mos_list = test_scores
|
| 39 |
+
|
| 40 |
+
# reorder columns
|
| 41 |
+
sorted_train_df = pd.DataFrame({'vid': train_df['vid'], 'framerate': train_df['framerate'], 'MOS': train_mos_list, 'MOS_raw': train_df['mos']})
|
| 42 |
+
sorted_test_df = pd.DataFrame({'vid': test_df['vid'], 'framerate': test_df['framerate'], 'MOS': test_mos_list, 'MOS_raw': test_df['mos']})
|
| 43 |
+
|
| 44 |
+
# use indices from the train and test DataFrames to split features
|
| 45 |
+
train_data = loadmat(f"{feature_path}/{train_data_name.replace('_all', '')}_{network_name}_feats.mat")
|
| 46 |
+
test_data = loadmat(f"{feature_path}/{test_data_name.replace('_all', '')}_{network_name}_feats.mat")
|
| 47 |
+
train_features = train_data[f"{train_data_name.replace('_all', '')}"]
|
| 48 |
+
test_features = test_data[f"{test_data_name.replace('_all', '')}"]
|
| 49 |
+
train_features = np.delete(train_features, grey_indices_train, axis=0)
|
| 50 |
+
test_features = np.delete(test_features, grey_indices_test, axis=0)
|
| 51 |
+
|
| 52 |
+
# save the files
|
| 53 |
+
sorted_train_df.to_csv(f'{metadata_path}mos_files/{train_data_name}_MOS_train.csv', index=False)
|
| 54 |
+
sorted_test_df.to_csv(f'{metadata_path}mos_files/{test_data_name}_MOS_test.csv', index=False)
|
| 55 |
+
os.makedirs(os.path.join(feature_path, "split_train_test"), exist_ok=True)
|
| 56 |
+
savemat(f'{feature_path}/split_train_test/{train_data_name}_{network_name}_cross_train_features.mat', {f'{train_data_name}_train_features': train_features})
|
| 57 |
+
savemat(f'{feature_path}/split_train_test/{test_data_name}_{network_name}_cross_test_features.mat', {f'{test_data_name}_test_features': test_features})
|
| 58 |
+
|
| 59 |
+
return train_features, test_features, test_vids
|
| 60 |
+
|
| 61 |
+
#NR: original
|
| 62 |
+
def process_lsvq(train_data_name, test_data_name, metadata_path, feature_path, network_name):
|
| 63 |
+
train_df = pd.read_csv(f'{metadata_path}/{train_data_name.upper()}_metadata.csv')
|
| 64 |
+
test_df = pd.read_csv(f'{metadata_path}/{test_data_name.upper()}_metadata.csv')
|
| 65 |
+
|
| 66 |
+
# grayscale videos, do not consider them for fair comparison
|
| 67 |
+
grey_df_train = pd.read_csv(f'{metadata_path}/greyscale_report/{train_data_name.upper()}_greyscale_metadata.csv')
|
| 68 |
+
grey_df_test = pd.read_csv(f'{metadata_path}/greyscale_report/{test_data_name.upper()}_greyscale_metadata.csv')
|
| 69 |
+
grey_indices_train = grey_df_train.iloc[:, 0].tolist()
|
| 70 |
+
grey_indices_test = grey_df_test.iloc[:, 0].tolist()
|
| 71 |
+
train_df = train_df.drop(index=grey_indices_train).reset_index(drop=True)
|
| 72 |
+
test_df = test_df.drop(index=grey_indices_test).reset_index(drop=True)
|
| 73 |
+
test_vids = test_df['vid']
|
| 74 |
+
|
| 75 |
+
# mos scores
|
| 76 |
+
train_scores = train_df['mos'].tolist()
|
| 77 |
+
test_scores = test_df['mos'].tolist()
|
| 78 |
+
train_mos_list = train_scores
|
| 79 |
+
test_mos_list = test_scores
|
| 80 |
+
|
| 81 |
+
# reorder columns
|
| 82 |
+
sorted_train_df = pd.DataFrame({'vid': train_df['vid'], 'framerate': train_df['framerate'], 'MOS': train_mos_list, 'MOS_raw': train_df['mos']})
|
| 83 |
+
sorted_test_df = pd.DataFrame({'vid': test_df['vid'], 'framerate': test_df['framerate'], 'MOS': test_mos_list, 'MOS_raw': test_df['mos']})
|
| 84 |
+
|
| 85 |
+
# use indices from the train and test DataFrames to split features
|
| 86 |
+
train_data_chunk_1 = loadmat(f'{feature_path}/{train_data_name}_{network_name}_feats_chunk_1.mat')[f'{train_data_name}']
|
| 87 |
+
train_data_chunk_2 = loadmat(f'{feature_path}/{train_data_name}_{network_name}_feats_chunk_2.mat')[f'{train_data_name}']
|
| 88 |
+
train_data_chunk_3 = loadmat(f'{feature_path}/{train_data_name}_{network_name}_feats_chunk_3.mat')[f'{train_data_name}']
|
| 89 |
+
merged_train_data = np.vstack((train_data_chunk_1, train_data_chunk_2, train_data_chunk_3))
|
| 90 |
+
print(f"loaded {train_data_name}: dimensions are {merged_train_data.shape}")
|
| 91 |
+
train_features = merged_train_data
|
| 92 |
+
|
| 93 |
+
test_data = loadmat(f'{feature_path}/{test_data_name}_{network_name}_feats.mat')
|
| 94 |
+
test_features = test_data[f'{test_data_name}']
|
| 95 |
+
train_features = np.delete(train_features, grey_indices_train, axis=0)
|
| 96 |
+
test_features = np.delete(test_features, grey_indices_test, axis=0)
|
| 97 |
+
print(len(train_features))
|
| 98 |
+
print(len(test_features))
|
| 99 |
+
|
| 100 |
+
# save the files
|
| 101 |
+
sorted_train_df.to_csv(f'{metadata_path}mos_files/{train_data_name}_MOS_train.csv', index=False)
|
| 102 |
+
sorted_test_df.to_csv(f'{metadata_path}mos_files/{train_data_name}_MOS_test.csv', index=False)
|
| 103 |
+
os.makedirs(os.path.join(feature_path, "split_train_test"), exist_ok=True)
|
| 104 |
+
# savemat(f'{feature_path}/split_train_test/{train_data_name}_{network_name}_train_features.mat', {f'{train_data_name}_train_features': train_features})
|
| 105 |
+
# savemat(f'{feature_path}/split_train_test/{train_data_name}_{network_name}_test_features.mat', {f'{train_data_name}_test_features': test_features})
|
| 106 |
+
|
| 107 |
+
return train_features, test_features, test_vids
|
| 108 |
+
|
| 109 |
+
def process_other(data_name, test_size, random_state, metadata_path, feature_path, network_name):
|
| 110 |
+
metadata_name = f'{data_name.upper()}_metadata.csv'
|
| 111 |
+
if data_name == 'test':
|
| 112 |
+
metadata_name = f'{data_name}_videos.csv'
|
| 113 |
+
# load CSV data
|
| 114 |
+
df = pd.read_csv(f'{metadata_path}/{metadata_name}')
|
| 115 |
+
|
| 116 |
+
if data_name == 'youtube_ugc':
|
| 117 |
+
# grayscale videos, do not consider them for fair comparison
|
| 118 |
+
grey_df = pd.read_csv(f'{metadata_path}/greyscale_report/{data_name.upper()}_greyscale_metadata.csv')
|
| 119 |
+
grey_indices = grey_df.iloc[:, 0].tolist()
|
| 120 |
+
df = df.drop(index=grey_indices).reset_index(drop=True)
|
| 121 |
+
|
| 122 |
+
# get unique vids
|
| 123 |
+
unique_vids = df['vid'].unique()
|
| 124 |
+
|
| 125 |
+
# split videonames into train and test sets
|
| 126 |
+
train_vids, test_vids = train_test_split(unique_vids, test_size=test_size, random_state=random_state)
|
| 127 |
+
|
| 128 |
+
# split all_dfs into train and test based on vids
|
| 129 |
+
train_df = df[df['vid'].isin(train_vids)]
|
| 130 |
+
test_df = df[df['vid'].isin(test_vids)]
|
| 131 |
+
|
| 132 |
+
# mos scores
|
| 133 |
+
train_scores = train_df['mos'].tolist()
|
| 134 |
+
test_scores = test_df['mos'].tolist()
|
| 135 |
+
train_mos_list = train_scores
|
| 136 |
+
test_mos_list = test_scores
|
| 137 |
+
|
| 138 |
+
# reorder columns
|
| 139 |
+
sorted_train_df = pd.DataFrame({'vid': train_df['vid'], 'framerate': train_df['framerate'], 'MOS': train_mos_list, 'MOS_raw': train_df['mos']})
|
| 140 |
+
sorted_test_df = pd.DataFrame({'vid': test_df['vid'], 'framerate': test_df['framerate'], 'MOS': test_mos_list, 'MOS_raw': test_df['mos']})
|
| 141 |
+
|
| 142 |
+
# use indices from the train and test DataFrames to split features
|
| 143 |
+
data = loadmat(f'{feature_path}/{data_name}_{network_name}_feats.mat')
|
| 144 |
+
features = data[f'{data_name}']
|
| 145 |
+
if data_name == 'youtube_ugc':
|
| 146 |
+
features = np.delete(features, grey_indices, axis=0)
|
| 147 |
+
train_features = features[train_df.index]
|
| 148 |
+
test_features = features[test_df.index]
|
| 149 |
+
|
| 150 |
+
# save the files
|
| 151 |
+
sorted_train_df.to_csv(f'{metadata_path}mos_files/{data_name}_MOS_train.csv', index=False)
|
| 152 |
+
sorted_test_df.to_csv(f'{metadata_path}mos_files/{data_name}_MOS_test.csv', index=False)
|
| 153 |
+
os.makedirs(os.path.join(feature_path, "split_train_test"), exist_ok=True)
|
| 154 |
+
savemat(f'{feature_path}/split_train_test/{data_name}_{network_name}_train_features.mat', {f'{data_name}_train_features': train_features})
|
| 155 |
+
savemat(f'{feature_path}/split_train_test/{data_name}_{network_name}_test_features.mat', {f'{data_name}_test_features': test_features})
|
| 156 |
+
|
| 157 |
+
return train_features, test_features, test_vids
|
| 158 |
+
|
| 159 |
+
|
| 160 |
+
if __name__ == '__main__':
|
| 161 |
+
network_name = 'relaxvqa'
|
| 162 |
+
data_name = "test"
|
| 163 |
+
metadata_path = '../../metadata/'
|
| 164 |
+
feature_path = '../../features/'
|
| 165 |
+
|
| 166 |
+
# train test split
|
| 167 |
+
test_size = 0.2
|
| 168 |
+
random_state = None
|
| 169 |
+
|
| 170 |
+
if data_name == 'lsvq_train':
|
| 171 |
+
test_data_name = 'lsvq_test'
|
| 172 |
+
process_lsvq(data_name, test_data_name, metadata_path, feature_path, network_name)
|
| 173 |
+
|
| 174 |
+
elif data_name == 'cross_dataset':
|
| 175 |
+
train_data_name = 'youtube_ugc_all'
|
| 176 |
+
test_data_name = 'cvd_2014_all'
|
| 177 |
+
_, _, test_vids = process_cross_dataset(train_data_name, test_data_name, metadata_path, feature_path, network_name)
|
| 178 |
+
|
| 179 |
+
else:
|
| 180 |
+
process_other(data_name, test_size, random_state, metadata_path, feature_path, network_name)
|
src/demo_test_gpu.py
ADDED
|
@@ -0,0 +1,247 @@
|
|
|
|
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|
|
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|
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|
|
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|
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|
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|
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|
|
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|
|
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|
|
|
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|
|
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|
|
|
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|
|
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|
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|
|
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|
|
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|
|
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|
|
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|
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|
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|
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|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import argparse
|
| 2 |
+
import time
|
| 3 |
+
import math
|
| 4 |
+
import os
|
| 5 |
+
import shutil
|
| 6 |
+
from joblib import load
|
| 7 |
+
import cv2
|
| 8 |
+
import torch
|
| 9 |
+
import torch.nn as nn
|
| 10 |
+
from torch.utils.data import DataLoader, Dataset
|
| 11 |
+
from thop import profile
|
| 12 |
+
from torchvision import models, transforms
|
| 13 |
+
|
| 14 |
+
from extractor.visualise_vit_layer import VitGenerator
|
| 15 |
+
from relax_vqa import get_deep_feature, process_video_feature, process_patches, get_frame_patches, flow_to_rgb, merge_fragments, concatenate_features
|
| 16 |
+
from extractor.vf_extract import process_video_residual
|
| 17 |
+
from model_regression import Mlp, preprocess_data
|
| 18 |
+
|
| 19 |
+
def fix_state_dict(state_dict):
|
| 20 |
+
new_state_dict = {}
|
| 21 |
+
for k, v in state_dict.items():
|
| 22 |
+
if k.startswith('module.'):
|
| 23 |
+
name = k[7:]
|
| 24 |
+
elif k == 'n_averaged':
|
| 25 |
+
continue
|
| 26 |
+
else:
|
| 27 |
+
name = k
|
| 28 |
+
new_state_dict[name] = v
|
| 29 |
+
return new_state_dict
|
| 30 |
+
|
| 31 |
+
def preprocess_data(X, y=None, imp=None, scaler=None):
|
| 32 |
+
if not isinstance(X, torch.Tensor):
|
| 33 |
+
X = torch.tensor(X, device='cuda' if torch.cuda.is_available() else 'cpu')
|
| 34 |
+
X = torch.where(torch.isnan(X) | torch.isinf(X), torch.tensor(0.0, device=X.device), X)
|
| 35 |
+
|
| 36 |
+
if imp is not None or scaler is not None:
|
| 37 |
+
X_np = X.cpu().numpy()
|
| 38 |
+
if imp is not None:
|
| 39 |
+
X_np = imp.transform(X_np)
|
| 40 |
+
if scaler is not None:
|
| 41 |
+
X_np = scaler.transform(X_np)
|
| 42 |
+
X = torch.from_numpy(X_np).to(X.device)
|
| 43 |
+
|
| 44 |
+
if y is not None and y.size > 0:
|
| 45 |
+
if not isinstance(y, torch.Tensor):
|
| 46 |
+
y = torch.tensor(y, device=X.device)
|
| 47 |
+
y = y.reshape(-1).squeeze()
|
| 48 |
+
else:
|
| 49 |
+
y = None
|
| 50 |
+
|
| 51 |
+
return X, y, imp, scaler
|
| 52 |
+
|
| 53 |
+
def load_model(config, device, input_features=35203):
|
| 54 |
+
network_name = 'relaxvqa'
|
| 55 |
+
# input_features = X_test_processed.shape[1]
|
| 56 |
+
model = Mlp(input_features=input_features, out_features=1, drop_rate=0.2, act_layer=nn.GELU).to(device)
|
| 57 |
+
if config['is_finetune']:
|
| 58 |
+
model_path = os.path.join(config['save_path'], f"fine_tune_model/{config['video_type']}_{network_name}_{config['select_criteria']}_fine_tuned_model.pth")
|
| 59 |
+
else:
|
| 60 |
+
model_path = os.path.join(config['save_path'], f"{config['train_data_name']}_{network_name}_{config['select_criteria']}_trained_median_model_param_onLSVQ_TEST.pth")
|
| 61 |
+
# print("Loading model from:", model_path)
|
| 62 |
+
state_dict = torch.load(model_path, map_location=device)
|
| 63 |
+
fixed_state_dict = fix_state_dict(state_dict)
|
| 64 |
+
try:
|
| 65 |
+
model.load_state_dict(fixed_state_dict)
|
| 66 |
+
except RuntimeError as e:
|
| 67 |
+
print(e)
|
| 68 |
+
return model
|
| 69 |
+
|
| 70 |
+
def evaluate_video_quality(config, resnet50, vit, model_mlp):
|
| 71 |
+
is_finetune = config['is_finetune']
|
| 72 |
+
save_path = config['save_path']
|
| 73 |
+
video_type = config['video_type']
|
| 74 |
+
video_name = config['video_name']
|
| 75 |
+
framerate = config['framerate']
|
| 76 |
+
sampled_fragment_path = os.path.join("../video_sampled_frame/sampled_frame/", "test_sampled_fragment")
|
| 77 |
+
|
| 78 |
+
if video_type == 'youtube_ugc':
|
| 79 |
+
video_path = f'../ugc_original_videos/{video_name}.mkv'
|
| 80 |
+
else:
|
| 81 |
+
video_path = f'../ugc_original_videos/{video_name}.mp4'
|
| 82 |
+
target_size = 224
|
| 83 |
+
patch_size = 16
|
| 84 |
+
top_n = int((target_size / patch_size) * (target_size / patch_size))
|
| 85 |
+
|
| 86 |
+
# sampled video frames
|
| 87 |
+
start_time = time.time()
|
| 88 |
+
frames, frames_next = process_video_residual(video_type, video_name, framerate, video_path, sampled_fragment_path)
|
| 89 |
+
|
| 90 |
+
# get ResNet50 layer-stack features and ViT pooling features
|
| 91 |
+
all_frame_activations_resnet = []
|
| 92 |
+
all_frame_activations_vit = []
|
| 93 |
+
# get fragments ResNet50 features and ViT features
|
| 94 |
+
all_frame_activations_sampled_resnet = []
|
| 95 |
+
all_frame_activations_merged_resnet = []
|
| 96 |
+
all_frame_activations_sampled_vit = []
|
| 97 |
+
all_frame_activations_merged_vit = []
|
| 98 |
+
|
| 99 |
+
batch_size = 64 # Define the number of frames to process in parallel
|
| 100 |
+
for i in range(0, len(frames_next), batch_size):
|
| 101 |
+
batch_frames = frames[i:i + batch_size]
|
| 102 |
+
batch_rgb_frames = [cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) for frame in batch_frames]
|
| 103 |
+
batch_frames_next = frames_next[i:i + batch_size]
|
| 104 |
+
batch_tensors = torch.stack([transforms.ToTensor()(frame) for frame in batch_frames]).to(device)
|
| 105 |
+
batch_rgb_tensors = torch.stack([transforms.ToTensor()(frame_rgb) for frame_rgb in batch_rgb_frames]).to(device)
|
| 106 |
+
batch_tensors_next = torch.stack([transforms.ToTensor()(frame_next) for frame_next in batch_frames_next]).to(device)
|
| 107 |
+
|
| 108 |
+
# compute residuals
|
| 109 |
+
residuals = torch.abs(batch_tensors_next - batch_tensors)
|
| 110 |
+
|
| 111 |
+
# calculate optical flows
|
| 112 |
+
batch_gray_frames = [cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) for frame in batch_frames]
|
| 113 |
+
batch_gray_frames_next = [cv2.cvtColor(frame_next, cv2.COLOR_BGR2GRAY) for frame_next in batch_frames_next]
|
| 114 |
+
batch_gray_frames = [frame.cpu().numpy() if isinstance(frame, torch.Tensor) else frame for frame in batch_gray_frames]
|
| 115 |
+
batch_gray_frames_next = [frame.cpu().numpy() if isinstance(frame, torch.Tensor) else frame for frame in batch_gray_frames_next]
|
| 116 |
+
flows = [cv2.calcOpticalFlowFarneback(batch_gray_frames[j], batch_gray_frames_next[j], None, 0.5, 3, 15, 3, 5, 1.2,0) for j in range(len(batch_gray_frames))]
|
| 117 |
+
|
| 118 |
+
for j in range(batch_tensors.size(0)):
|
| 119 |
+
'''sampled video frames'''
|
| 120 |
+
frame_tensor = batch_tensors[j].unsqueeze(0)
|
| 121 |
+
frame_rgb_tensor = batch_rgb_tensors[j].unsqueeze(0)
|
| 122 |
+
# frame_next_tensor = batch_tensors_next[j].unsqueeze(0)
|
| 123 |
+
frame_number = i + j + 1
|
| 124 |
+
|
| 125 |
+
# ResNet50 layer-stack features
|
| 126 |
+
activations_dict_resnet, _, _ = get_deep_feature('resnet50', video_name, frame_rgb_tensor, frame_number, resnet50, device, 'layerstack')
|
| 127 |
+
all_frame_activations_resnet.append(activations_dict_resnet)
|
| 128 |
+
# ViT pooling features
|
| 129 |
+
activations_dict_vit, _, _ = get_deep_feature('vit', video_name, frame_rgb_tensor, frame_number, vit, device, 'pool')
|
| 130 |
+
all_frame_activations_vit.append(activations_dict_vit)
|
| 131 |
+
|
| 132 |
+
|
| 133 |
+
'''residual video frames'''
|
| 134 |
+
residual = residuals[j].unsqueeze(0)
|
| 135 |
+
flow = flows[j]
|
| 136 |
+
original_path = os.path.join(sampled_fragment_path, f'{video_name}_{frame_number}.png')
|
| 137 |
+
|
| 138 |
+
# Frame Differencing
|
| 139 |
+
residual_frag_path, diff_frag, positions = process_patches(original_path, 'frame_diff', residual, patch_size, target_size, top_n)
|
| 140 |
+
# Frame fragment
|
| 141 |
+
frame_patches = get_frame_patches(frame_tensor, positions, patch_size, target_size)
|
| 142 |
+
# Optical Flow
|
| 143 |
+
opticalflow_rgb = flow_to_rgb(flow)
|
| 144 |
+
opticalflow_rgb_tensor = transforms.ToTensor()(opticalflow_rgb).unsqueeze(0).to(device)
|
| 145 |
+
opticalflow_frag_path, flow_frag, _ = process_patches(original_path, 'optical_flow', opticalflow_rgb_tensor, patch_size, target_size, top_n)
|
| 146 |
+
|
| 147 |
+
merged_frag = merge_fragments(diff_frag, flow_frag)
|
| 148 |
+
|
| 149 |
+
# fragments ResNet50 features
|
| 150 |
+
sampled_frag_activations_resnet, _, _ = get_deep_feature('resnet50', video_name, frame_patches, frame_number, resnet50, device, 'layerstack')
|
| 151 |
+
merged_frag_activations_resnet, _, _ = get_deep_feature('resnet50', video_name, merged_frag, frame_number, resnet50, device, 'pool')
|
| 152 |
+
all_frame_activations_sampled_resnet.append(sampled_frag_activations_resnet)
|
| 153 |
+
all_frame_activations_merged_resnet.append(merged_frag_activations_resnet)
|
| 154 |
+
# fragments ViT features
|
| 155 |
+
sampled_frag_activations_vit,_, _ = get_deep_feature('vit', video_name, frame_patches, frame_number, vit, device, 'pool')
|
| 156 |
+
merged_frag_activations_vit, _, _ = get_deep_feature('vit', video_name, merged_frag, frame_number, vit, device, 'pool')
|
| 157 |
+
all_frame_activations_sampled_vit.append(sampled_frag_activations_vit)
|
| 158 |
+
all_frame_activations_merged_vit.append(merged_frag_activations_vit)
|
| 159 |
+
|
| 160 |
+
print(f'video frame number: {len(all_frame_activations_resnet)}')
|
| 161 |
+
averaged_frames_resnet = process_video_feature(all_frame_activations_resnet, 'resnet50', 'layerstack')
|
| 162 |
+
averaged_frames_vit = process_video_feature(all_frame_activations_vit, 'vit', 'pool')
|
| 163 |
+
# print("ResNet50 layer-stacking feature shape:", averaged_frames_resnet.shape)
|
| 164 |
+
# print("ViT pooling feature shape:", averaged_frames_vit.shape)
|
| 165 |
+
averaged_frames_sampled_resnet = process_video_feature(all_frame_activations_sampled_resnet, 'resnet50', 'layerstack')
|
| 166 |
+
averaged_frames_merged_resnet = process_video_feature(all_frame_activations_merged_resnet, 'resnet50', 'pool')
|
| 167 |
+
averaged_combined_feature_resnet = concatenate_features(averaged_frames_sampled_resnet, averaged_frames_merged_resnet)
|
| 168 |
+
# print("Sampled fragments ResNet50 features shape:", averaged_frames_sampled_resnet.shape)
|
| 169 |
+
# print("Merged fragments ResNet50 features shape:", averaged_frames_merged_resnet.shape)
|
| 170 |
+
averaged_frames_sampled_vit = process_video_feature(all_frame_activations_sampled_vit, 'vit', 'pool')
|
| 171 |
+
averaged_frames_merged_vit = process_video_feature(all_frame_activations_merged_vit, 'vit', 'pool')
|
| 172 |
+
averaged_combined_feature_vit = concatenate_features(averaged_frames_sampled_vit, averaged_frames_merged_vit)
|
| 173 |
+
# print("Sampled fragments ViT features shape:", averaged_frames_sampled_vit.shape)
|
| 174 |
+
# print("Merged fragments ResNet50 features shape:", averaged_frames_merged_vit.shape)
|
| 175 |
+
|
| 176 |
+
# remove tmp folders
|
| 177 |
+
shutil.rmtree(sampled_fragment_path)
|
| 178 |
+
|
| 179 |
+
# concatenate features
|
| 180 |
+
combined_features = torch.cat([torch.mean(averaged_frames_resnet, dim=0), torch.mean(averaged_frames_vit, dim=0),
|
| 181 |
+
torch.mean(averaged_combined_feature_resnet, dim=0), torch.mean(averaged_combined_feature_vit, dim=0)], dim=0).view(1, -1)
|
| 182 |
+
imputer = load(f'{save_path}/scaler/{video_type}_imputer.pkl')
|
| 183 |
+
scaler = load(f'{save_path}/scaler/{video_type}_scaler.pkl')
|
| 184 |
+
X_test_processed, _, _, _ = preprocess_data(combined_features, None, imp=imputer, scaler=scaler)
|
| 185 |
+
feature_tensor = X_test_processed
|
| 186 |
+
|
| 187 |
+
# evaluation for test video
|
| 188 |
+
model_mlp.eval()
|
| 189 |
+
|
| 190 |
+
with torch.no_grad():
|
| 191 |
+
with torch.cuda.amp.autocast():
|
| 192 |
+
prediction = model_mlp(feature_tensor)
|
| 193 |
+
predicted_score = prediction.item()
|
| 194 |
+
# print(f"Raw Predicted Quality Score: {predicted_score}")
|
| 195 |
+
run_time = time.time() - start_time
|
| 196 |
+
|
| 197 |
+
if not is_finetune:
|
| 198 |
+
if video_type in ['konvid_1k', 'youtube_ugc']:
|
| 199 |
+
scaled_prediction = ((predicted_score - 1) / (99 / 4)) + 1.0
|
| 200 |
+
# print(f"Scaled Predicted Quality Score (1-5): {scaled_prediction}")
|
| 201 |
+
return scaled_prediction, run_time
|
| 202 |
+
else:
|
| 203 |
+
scaled_prediction = predicted_score
|
| 204 |
+
return scaled_prediction, run_time
|
| 205 |
+
else:
|
| 206 |
+
return predicted_score, run_time
|
| 207 |
+
|
| 208 |
+
def parse_arguments():
|
| 209 |
+
parser = argparse.ArgumentParser()
|
| 210 |
+
parser.add_argument('-device', type=str, default='gpu', help='cpu or gpu')
|
| 211 |
+
parser.add_argument('-model_name', type=str, default='Mlp', help='Name of the regression model')
|
| 212 |
+
parser.add_argument('-select_criteria', type=str, default='byrmse', help='Selection criteria')
|
| 213 |
+
parser.add_argument('-train_data_name', type=str, default='lsvq_train', help='Name of the training data')
|
| 214 |
+
parser.add_argument('-is_finetune', type=bool, default=True, help='With or without finetune')
|
| 215 |
+
parser.add_argument('-save_path', type=str, default='../model/', help='Path to save models')
|
| 216 |
+
parser.add_argument('-video_type', type=str, default='konvid_1k', help='Type of video')
|
| 217 |
+
parser.add_argument('-video_name', type=str, default='5636101558_540p', help='Name of the video')
|
| 218 |
+
parser.add_argument('-framerate', type=float, default=24, help='Frame rate of the video')
|
| 219 |
+
|
| 220 |
+
args = parser.parse_args()
|
| 221 |
+
return args
|
| 222 |
+
|
| 223 |
+
if __name__ == '__main__':
|
| 224 |
+
args = parse_arguments()
|
| 225 |
+
config = vars(args)
|
| 226 |
+
if config['device'] == "gpu":
|
| 227 |
+
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
| 228 |
+
else:
|
| 229 |
+
device = torch.device("cpu")
|
| 230 |
+
print(f"Running on {'GPU' if device.type == 'cuda' else 'CPU'}")
|
| 231 |
+
|
| 232 |
+
# load models to device
|
| 233 |
+
resnet50 = models.resnet50(pretrained=True).to(device)
|
| 234 |
+
vit = VitGenerator('vit_base', 16, device, evaluate=True, random=False, verbose=True)
|
| 235 |
+
model_mlp = load_model(config, device)
|
| 236 |
+
|
| 237 |
+
total_time = 0
|
| 238 |
+
num_runs = 10
|
| 239 |
+
for i in range(num_runs):
|
| 240 |
+
quality_prediction, run_time = evaluate_video_quality(config, resnet50, vit, model_mlp)
|
| 241 |
+
print(f"Run {i + 1} - Time taken: {run_time:.4f} seconds")
|
| 242 |
+
|
| 243 |
+
total_time += run_time
|
| 244 |
+
average_time = total_time / num_runs
|
| 245 |
+
|
| 246 |
+
print(f"Average running time over {num_runs} runs: {average_time:.4f} seconds")
|
| 247 |
+
print("Predicted Quality Score:", quality_prediction)
|
src/extractor/__init__.py
ADDED
|
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# __init__.py
|
| 2 |
+
print("Initializing extractor")
|
src/extractor/vf_extract.py
ADDED
|
@@ -0,0 +1,72 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import math
|
| 2 |
+
import pandas as pd
|
| 3 |
+
import cv2
|
| 4 |
+
import os
|
| 5 |
+
|
| 6 |
+
def extract_frames(video_path, sampled_path, frame_interval, residual=False):
|
| 7 |
+
try:
|
| 8 |
+
video_name = os.path.splitext(os.path.basename(video_path))[0]
|
| 9 |
+
cap = cv2.VideoCapture(video_path)
|
| 10 |
+
frames = []
|
| 11 |
+
|
| 12 |
+
if not cap.isOpened():
|
| 13 |
+
print(f"Error: Could not open video file {video_path}")
|
| 14 |
+
return frames
|
| 15 |
+
|
| 16 |
+
frame_count = 0
|
| 17 |
+
saved_frame_count = 0
|
| 18 |
+
while True:
|
| 19 |
+
ret, frame = cap.read()
|
| 20 |
+
if not ret:
|
| 21 |
+
break
|
| 22 |
+
if (frame_count % frame_interval == 0 and not residual) or (
|
| 23 |
+
(frame_count - 1) % frame_interval == 0 and residual):
|
| 24 |
+
# suffix = '_next' if residual else ''
|
| 25 |
+
# output_filename = os.path.join(sampled_path, f'{video_name}_{saved_frame_count + 1}{suffix}.png')
|
| 26 |
+
# cv2.imwrite(output_filename, frame)
|
| 27 |
+
frames.append(frame)
|
| 28 |
+
saved_frame_count += 1
|
| 29 |
+
|
| 30 |
+
frame_count += 1
|
| 31 |
+
cap.release()
|
| 32 |
+
frame_type = 'next frames' if residual else 'sampled frames'
|
| 33 |
+
print(f'Extraction of {frame_type} for {video_name} completed!')
|
| 34 |
+
return frames
|
| 35 |
+
except Exception as e:
|
| 36 |
+
print(f"An unexpected error occurred: {e}")
|
| 37 |
+
|
| 38 |
+
|
| 39 |
+
def process_video_residual(video_type, video_name, framerate, video_path, sampled_path):
|
| 40 |
+
if not os.path.exists(sampled_path):
|
| 41 |
+
os.makedirs(sampled_path)
|
| 42 |
+
# cap = cv2.VideoCapture(video_path)
|
| 43 |
+
# framerate = cap.get(cv2.CAP_PROP_FPS)
|
| 44 |
+
# print(f'framerate: {framerate}')
|
| 45 |
+
frame_interval = math.ceil(framerate / 2) if framerate < 2 else int(framerate / 2)
|
| 46 |
+
# print(f'Frame interval: {frame_interval}')
|
| 47 |
+
|
| 48 |
+
frames = extract_frames(video_path, sampled_path, frame_interval, residual=False)
|
| 49 |
+
frames_next = extract_frames(video_path, sampled_path, frame_interval, residual=True)
|
| 50 |
+
return frames, frames_next
|
| 51 |
+
|
| 52 |
+
|
| 53 |
+
if __name__ == '__main__':
|
| 54 |
+
video_type = 'test'
|
| 55 |
+
|
| 56 |
+
if video_type == 'test':
|
| 57 |
+
ugcdata = pd.read_csv("../../metadata/test_videos.csv")
|
| 58 |
+
|
| 59 |
+
for i in range(len(ugcdata)):
|
| 60 |
+
video_name = ugcdata['vid'][i]
|
| 61 |
+
framerate = ugcdata['framerate'][i]
|
| 62 |
+
print(f'Processing video: {video_name}, framerate: {framerate}')
|
| 63 |
+
|
| 64 |
+
video_path = f"../../ugc_original_videos/{video_name}.mp4"
|
| 65 |
+
sampled_path = f'../../video_sampled_frame/original_sampled_frame/{video_name}/'
|
| 66 |
+
|
| 67 |
+
if not os.path.exists(sampled_path):
|
| 68 |
+
os.makedirs(sampled_path)
|
| 69 |
+
|
| 70 |
+
print(f'{video_name}')
|
| 71 |
+
frames, frames_next = process_video_residual(video_type, video_name, framerate, video_path, sampled_path)
|
| 72 |
+
print(f'Extracted {len(frames)} frames and {len(frames_next)} residual frames for video: {video_name}')
|
src/extractor/visualise_resnet.py
ADDED
|
@@ -0,0 +1,187 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import warnings
|
| 2 |
+
warnings.filterwarnings("ignore")
|
| 3 |
+
import os
|
| 4 |
+
import glob
|
| 5 |
+
import numpy as np
|
| 6 |
+
import pandas as pd
|
| 7 |
+
import matplotlib.pyplot as plt
|
| 8 |
+
import torch
|
| 9 |
+
from torchvision import models, transforms
|
| 10 |
+
from thop import profile
|
| 11 |
+
is_flop_cal = False
|
| 12 |
+
|
| 13 |
+
# get the activation
|
| 14 |
+
def get_activation(model, layer, input_img_data):
|
| 15 |
+
model.eval()
|
| 16 |
+
activations = []
|
| 17 |
+
inputs = []
|
| 18 |
+
|
| 19 |
+
def hook(module, input, output):
|
| 20 |
+
activations.append(output)
|
| 21 |
+
inputs.append(input[0])
|
| 22 |
+
|
| 23 |
+
hook_handle = layer.register_forward_hook(hook)
|
| 24 |
+
with torch.no_grad():
|
| 25 |
+
model(input_img_data)
|
| 26 |
+
hook_handle.remove()
|
| 27 |
+
return activations, inputs
|
| 28 |
+
|
| 29 |
+
def get_activation_map(frame, layer_name, resnet50, device):
|
| 30 |
+
# image pre-processing
|
| 31 |
+
transform = transforms.Compose([
|
| 32 |
+
transforms.Resize((224, 224)),
|
| 33 |
+
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
|
| 34 |
+
])
|
| 35 |
+
|
| 36 |
+
# Apply the transformations (resize and normalize)
|
| 37 |
+
frame_tensor = transform(frame)
|
| 38 |
+
|
| 39 |
+
# adding index 0 changes the original [C, H, W] shape to [1, C, H, W]
|
| 40 |
+
if frame_tensor.dim() == 3:
|
| 41 |
+
frame_tensor = frame_tensor.unsqueeze(0)
|
| 42 |
+
# print(f'Image dimension: {frame_tensor.shape}')
|
| 43 |
+
|
| 44 |
+
# getting the activation of a given layer
|
| 45 |
+
layer_obj = eval(layer_name)
|
| 46 |
+
activations, inputs = get_activation(resnet50, layer_obj, frame_tensor)
|
| 47 |
+
activated_img = activations[0][0]
|
| 48 |
+
activation_array = activated_img.cpu().numpy()
|
| 49 |
+
|
| 50 |
+
# calculate FLOPs for layer
|
| 51 |
+
if is_flop_cal == True:
|
| 52 |
+
flops, params = profile(layer_obj, inputs=(inputs[0],), verbose=False)
|
| 53 |
+
if params == 0 and isinstance(layer_obj, torch.nn.Conv2d):
|
| 54 |
+
params = layer_obj.in_channels * layer_obj.out_channels * layer_obj.kernel_size[0] * layer_obj.kernel_size[1]
|
| 55 |
+
if layer_obj.bias is not None:
|
| 56 |
+
params += layer_obj.out_channels
|
| 57 |
+
# print(f"FLOPs for {layer_name}: {flops}, Params: {params}")
|
| 58 |
+
else:
|
| 59 |
+
flops, params = None, None
|
| 60 |
+
return activated_img, activation_array, flops, params
|
| 61 |
+
|
| 62 |
+
def process_video_frame(video_name, frame, frame_number, all_layers, resnet50, device):
|
| 63 |
+
# create a dictionary to store activation arrays for each layer
|
| 64 |
+
activations_dict = {}
|
| 65 |
+
total_flops = 0
|
| 66 |
+
total_params = 0
|
| 67 |
+
for layer_name in all_layers:
|
| 68 |
+
fig_name = f"resnet50_feature_map_layer_{layer_name}"
|
| 69 |
+
combined_name = f"resnet50_feature_map"
|
| 70 |
+
|
| 71 |
+
activated_img, activation_array, flops, params = get_activation_map(frame, layer_name, resnet50, device)
|
| 72 |
+
if is_flop_cal == True:
|
| 73 |
+
total_flops += flops
|
| 74 |
+
total_params += params
|
| 75 |
+
|
| 76 |
+
# save activation maps as png
|
| 77 |
+
# png_path = f'../visualisation/resnet50/{video_name}/frame_{frame_number}/'
|
| 78 |
+
# npy_path = f'../features/resnet50/{video_name}/frame_{frame_number}/'
|
| 79 |
+
# os.makedirs(png_path, exist_ok=True)
|
| 80 |
+
# os.makedirs(npy_path, exist_ok=True)
|
| 81 |
+
# get_activation_png(png_path, fig_name, activated_img)
|
| 82 |
+
# save activation features as npy
|
| 83 |
+
# get_activation_npy(npy_path, fig_name, activation_array)
|
| 84 |
+
# save to the dictionary
|
| 85 |
+
activations_dict[layer_name] = activated_img
|
| 86 |
+
|
| 87 |
+
# print(f"total FLOPs for Resnet50 layerstack: {total_flops}, Params: {total_params}")
|
| 88 |
+
frame_npy_path = f'../features/resnet50/{video_name}/frame_{frame_number}_{combined_name}.npy'
|
| 89 |
+
return activations_dict, frame_npy_path, total_flops, total_params
|
| 90 |
+
|
| 91 |
+
def get_activation_png(png_path, fig_name, activated_img, n=8):
|
| 92 |
+
fig = plt.figure(figsize=(10, 10))
|
| 93 |
+
|
| 94 |
+
# visualise activation map for 64 channels
|
| 95 |
+
for i in range(n):
|
| 96 |
+
for j in range(n):
|
| 97 |
+
idx = (n * i) + j
|
| 98 |
+
if idx >= activated_img.shape[0]:
|
| 99 |
+
break
|
| 100 |
+
ax = fig.add_subplot(n, n, idx + 1)
|
| 101 |
+
ax.imshow(activated_img[idx].cpu().numpy(), cmap='viridis')
|
| 102 |
+
ax.axis('off')
|
| 103 |
+
|
| 104 |
+
# save figures
|
| 105 |
+
fig_path = f'{png_path}{fig_name}.png'
|
| 106 |
+
print(fig_path)
|
| 107 |
+
print("----------------" + '\n')
|
| 108 |
+
plt.savefig(fig_path)
|
| 109 |
+
plt.close()
|
| 110 |
+
|
| 111 |
+
def get_activation_npy(npy_path, fig_name, activation_array):
|
| 112 |
+
np.save(f'{npy_path}{fig_name}.npy', activation_array)
|
| 113 |
+
|
| 114 |
+
if __name__ == '__main__':
|
| 115 |
+
device_name = "gpu"
|
| 116 |
+
if device_name == "gpu":
|
| 117 |
+
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
| 118 |
+
else:
|
| 119 |
+
device = torch.device("cpu")
|
| 120 |
+
print(f"Running on {'GPU' if device.type == 'cuda' else 'CPU'}")
|
| 121 |
+
# pre-trained ResNet-50 model to device
|
| 122 |
+
resnet50 = models.resnet50(pretrained=True).to(device)
|
| 123 |
+
|
| 124 |
+
all_layers = ['resnet50.conv1',
|
| 125 |
+
'resnet50.layer1[0]', 'resnet50.layer1[1]', 'resnet50.layer1[2]',
|
| 126 |
+
'resnet50.layer2[0]', 'resnet50.layer2[1]', 'resnet50.layer2[2]', 'resnet50.layer2[3]',
|
| 127 |
+
'resnet50.layer3[0]', 'resnet50.layer3[1]', 'resnet50.layer3[2]', 'resnet50.layer3[3]',
|
| 128 |
+
'resnet50.layer4[0]', 'resnet50.layer4[1]', 'resnet50.layer4[2]']
|
| 129 |
+
|
| 130 |
+
video_type = 'test'
|
| 131 |
+
# Test
|
| 132 |
+
if video_type == 'test':
|
| 133 |
+
metadata_path = "../../metadata/test_videos.csv"
|
| 134 |
+
# NR:
|
| 135 |
+
elif video_type == 'resolution_ugc':
|
| 136 |
+
resolution = '360P'
|
| 137 |
+
metadata_path = f"../../metadata/YOUTUBE_UGC_{resolution}_metadata.csv"
|
| 138 |
+
else:
|
| 139 |
+
metadata_path = f'../../metadata/{video_type.upper()}_metadata.csv'
|
| 140 |
+
|
| 141 |
+
ugcdata = pd.read_csv(metadata_path)
|
| 142 |
+
for i in range(len(ugcdata)):
|
| 143 |
+
video_name = ugcdata['vid'][i]
|
| 144 |
+
sampled_frame_path = os.path.join('../..', 'video_sampled_frame', 'sampled_frame', f'{video_name}')
|
| 145 |
+
|
| 146 |
+
print(f"Processing video: {video_name}")
|
| 147 |
+
image_paths = glob.glob(os.path.join(sampled_frame_path, f'{video_name}_*.png'))
|
| 148 |
+
frame_number = 0
|
| 149 |
+
for image in image_paths:
|
| 150 |
+
print(f"{image}")
|
| 151 |
+
frame_number += 1
|
| 152 |
+
process_video_frame(video_name, image, frame_number, all_layers, resnet50, device)
|
| 153 |
+
|
| 154 |
+
# # ResNet-50 layers to visualize
|
| 155 |
+
# layers_to_visualize_resnet50 = {
|
| 156 |
+
# 'conv1': 0,
|
| 157 |
+
# 'layer1.0.conv1': 2,
|
| 158 |
+
# 'layer1.0.conv2': 3,
|
| 159 |
+
# 'layer1.1.conv1': 5,
|
| 160 |
+
# 'layer1.1.conv2': 6,
|
| 161 |
+
# 'layer1.2.conv1': 8,
|
| 162 |
+
# 'layer1.2.conv2': 9,
|
| 163 |
+
# 'layer2.0.conv1': 11,
|
| 164 |
+
# 'layer2.0.conv2': 12,
|
| 165 |
+
# 'layer2.1.conv1': 14,
|
| 166 |
+
# 'layer2.1.conv2': 15,
|
| 167 |
+
# 'layer2.2.conv1': 17,
|
| 168 |
+
# 'layer2.2.conv2': 18,
|
| 169 |
+
# 'layer2.3.conv1': 20,
|
| 170 |
+
# 'layer2.3.conv2': 21,
|
| 171 |
+
# 'layer3.0.conv1': 23,
|
| 172 |
+
# 'layer3.0.conv2': 24,
|
| 173 |
+
# 'layer3.0.downsample.0': 25,
|
| 174 |
+
# 'layer3.1.conv1': 27,
|
| 175 |
+
# 'layer3.1.conv2': 28,
|
| 176 |
+
# 'layer3.2.conv1': 30,
|
| 177 |
+
# 'layer3.2.conv2': 31,
|
| 178 |
+
# 'layer3.3.conv1': 33,
|
| 179 |
+
# 'layer3.3.conv2': 34,
|
| 180 |
+
# 'layer4.0.conv1': 36,
|
| 181 |
+
# 'layer4.0.conv2': 37,
|
| 182 |
+
# 'layer4.0.downsample.0': 38,
|
| 183 |
+
# 'layer4.1.conv1': 40,
|
| 184 |
+
# 'layer4.1.conv2': 41,
|
| 185 |
+
# 'layer4.2.conv1': 43,
|
| 186 |
+
# 'layer4.2.conv2': 44,
|
| 187 |
+
# }
|
src/extractor/visualise_resnet_layer.py
ADDED
|
@@ -0,0 +1,194 @@
|
|
|
|
|
|
|
|
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|
|
|
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|
|
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|
|
|
|
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|
|
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|
|
|
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|
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|
|
|
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|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import warnings
|
| 2 |
+
warnings.filterwarnings("ignore")
|
| 3 |
+
import os
|
| 4 |
+
import glob
|
| 5 |
+
import numpy as np
|
| 6 |
+
import pandas as pd
|
| 7 |
+
import matplotlib.pyplot as plt
|
| 8 |
+
import torch
|
| 9 |
+
from torchvision import models, transforms
|
| 10 |
+
from thop import profile
|
| 11 |
+
is_flop_cal = False
|
| 12 |
+
|
| 13 |
+
# get the activation
|
| 14 |
+
def get_activation(model, layer, input_img_data):
|
| 15 |
+
model.eval()
|
| 16 |
+
activations = []
|
| 17 |
+
inputs = []
|
| 18 |
+
|
| 19 |
+
def hook(module, input, output):
|
| 20 |
+
activations.append(output)
|
| 21 |
+
inputs.append(input[0])
|
| 22 |
+
|
| 23 |
+
hook_handle = layer.register_forward_hook(hook)
|
| 24 |
+
with torch.no_grad():
|
| 25 |
+
model(input_img_data)
|
| 26 |
+
hook_handle.remove()
|
| 27 |
+
return activations, inputs
|
| 28 |
+
|
| 29 |
+
def get_activation_map(frame, layer_name, resnet50, device):
|
| 30 |
+
# image pre-processing
|
| 31 |
+
transform = transforms.Compose([
|
| 32 |
+
transforms.Resize((224, 224)),
|
| 33 |
+
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
|
| 34 |
+
])
|
| 35 |
+
|
| 36 |
+
# Apply the transformations (resize and normalize)
|
| 37 |
+
frame_tensor = transform(frame)
|
| 38 |
+
|
| 39 |
+
# adding index 0 changes the original [C, H, W] shape to [1, C, H, W]
|
| 40 |
+
if frame_tensor.dim() == 3:
|
| 41 |
+
frame_tensor = frame_tensor.unsqueeze(0)
|
| 42 |
+
# print(f'Image dimension: {frame_tensor.shape}')
|
| 43 |
+
|
| 44 |
+
# getting the activation of a given layer
|
| 45 |
+
conv_idx = layer_name
|
| 46 |
+
layer_obj = eval(conv_idx)
|
| 47 |
+
activations, inputs = get_activation(resnet50, layer_obj, frame_tensor)
|
| 48 |
+
activated_img = activations[0][0]
|
| 49 |
+
activation_array = activated_img.cpu().numpy()
|
| 50 |
+
|
| 51 |
+
# calculate FLOPs for layer
|
| 52 |
+
if is_flop_cal == True:
|
| 53 |
+
flops, params = profile(layer_obj, inputs=(inputs[0],), verbose=False)
|
| 54 |
+
if params == 0 and isinstance(layer_obj, torch.nn.Conv2d):
|
| 55 |
+
params = layer_obj.in_channels * layer_obj.out_channels * layer_obj.kernel_size[0] * layer_obj.kernel_size[1]
|
| 56 |
+
if layer_obj.bias is not None:
|
| 57 |
+
params += layer_obj.out_channels
|
| 58 |
+
# print(f"FLOPs for {layer_name}: {flops}, Params: {params}")
|
| 59 |
+
else:
|
| 60 |
+
flops, params = None, None
|
| 61 |
+
return activated_img, activation_array, flops, params
|
| 62 |
+
|
| 63 |
+
def process_video_frame(video_name, frame, frame_number, layer_name, resnet50, device):
|
| 64 |
+
# create a dictionary to store activation arrays for each layer
|
| 65 |
+
activations_dict = {}
|
| 66 |
+
total_flops = 0
|
| 67 |
+
total_params = 0
|
| 68 |
+
fig_name = f"resnet50_feature_map_layer_{layer_name}"
|
| 69 |
+
combined_name = f"resnet50_feature_map"
|
| 70 |
+
|
| 71 |
+
activated_img, activation_array, flops, params = get_activation_map(frame, layer_name, resnet50, device)
|
| 72 |
+
if is_flop_cal == True:
|
| 73 |
+
total_flops += flops
|
| 74 |
+
total_params += params
|
| 75 |
+
|
| 76 |
+
# save activation maps as png
|
| 77 |
+
# png_path = f'../visualisation/resnet50/{video_name}/frame_{frame_number}/'
|
| 78 |
+
# npy_path = f'../features/resnet50/{video_name}/frame_{frame_number}/'
|
| 79 |
+
# os.makedirs(png_path, exist_ok=True)
|
| 80 |
+
# os.makedirs(npy_path, exist_ok=True)
|
| 81 |
+
# get_activation_png(png_path, fig_name, activated_img)
|
| 82 |
+
# save activation features as pny
|
| 83 |
+
# get_activation_npy(npy_path, fig_name, activation_array)
|
| 84 |
+
|
| 85 |
+
# print(f"total FLOPs for Resnet50 layerstack: {total_flops}, Params: {total_params}")
|
| 86 |
+
frame_npy_path = f'../features/resnet50/{video_name}/frame_{frame_number}_{combined_name}.npy'
|
| 87 |
+
return activated_img, frame_npy_path, total_flops, total_params
|
| 88 |
+
|
| 89 |
+
def get_activation_png(png_path, fig_name, activated_img, n=8):
|
| 90 |
+
fig = plt.figure(figsize=(10, 10))
|
| 91 |
+
|
| 92 |
+
# visualise activation map for 64 channels
|
| 93 |
+
for i in range(n):
|
| 94 |
+
for j in range(n):
|
| 95 |
+
idx = (n * i) + j
|
| 96 |
+
if idx >= activated_img.shape[0]:
|
| 97 |
+
break
|
| 98 |
+
ax = fig.add_subplot(n, n, idx + 1)
|
| 99 |
+
ax.imshow(activated_img[idx].cpu().numpy(), cmap='viridis')
|
| 100 |
+
ax.axis('off')
|
| 101 |
+
|
| 102 |
+
# save figures
|
| 103 |
+
fig_path = f'{png_path}{fig_name}.png'
|
| 104 |
+
print(fig_path)
|
| 105 |
+
print("----------------" + '\n')
|
| 106 |
+
plt.savefig(fig_path)
|
| 107 |
+
plt.close()
|
| 108 |
+
|
| 109 |
+
def get_activation_npy(npy_path, fig_name, activation_array):
|
| 110 |
+
np.save(f'{npy_path}{fig_name}.npy', activation_array)
|
| 111 |
+
|
| 112 |
+
if __name__ == '__main__':
|
| 113 |
+
device_name = "gpu"
|
| 114 |
+
if device_name == "gpu":
|
| 115 |
+
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
| 116 |
+
else:
|
| 117 |
+
device = torch.device("cpu")
|
| 118 |
+
print(f"Running on {'GPU' if device.type == 'cuda' else 'CPU'}")
|
| 119 |
+
# pre-trained ResNet-50 model to device
|
| 120 |
+
resnet50 = models.resnet50(pretrained=True).to(device)
|
| 121 |
+
|
| 122 |
+
for idx, (name, layer) in enumerate(resnet50.named_children()):
|
| 123 |
+
print(f"Index: {idx}, Layer Name: {name}, Layer Type: {type(layer)}")
|
| 124 |
+
layer_name = 'layer4.2.conv2'
|
| 125 |
+
|
| 126 |
+
video_type = 'test'
|
| 127 |
+
# Test
|
| 128 |
+
if video_type == 'test':
|
| 129 |
+
metadata_path = "../../metadata/test_videos.csv"
|
| 130 |
+
# NR:
|
| 131 |
+
elif video_type == 'resolution_ugc':
|
| 132 |
+
resolution = '360P'
|
| 133 |
+
metadata_path = f"../../metadata/YOUTUBE_UGC_{resolution}_metadata.csv"
|
| 134 |
+
else:
|
| 135 |
+
metadata_path = f'../../metadata/{video_type.upper()}_metadata.csv'
|
| 136 |
+
|
| 137 |
+
ugcdata = pd.read_csv(metadata_path)
|
| 138 |
+
for i in range(len(ugcdata)):
|
| 139 |
+
video_name = ugcdata['vid'][i]
|
| 140 |
+
sampled_frame_path = os.path.join('../..', 'video_sampled_frame', 'sampled_frame', f'{video_name}')
|
| 141 |
+
|
| 142 |
+
print(f"Processing video: {video_name}")
|
| 143 |
+
image_paths = glob.glob(os.path.join(sampled_frame_path, f'{video_name}_*.png'))
|
| 144 |
+
frame_number = 0
|
| 145 |
+
for image in image_paths:
|
| 146 |
+
print(f"{image}")
|
| 147 |
+
frame_number += 1
|
| 148 |
+
process_video_frame(video_name, image, frame_number, layer_name, resnet50, device)
|
| 149 |
+
|
| 150 |
+
# # ResNet-50 layers to visualize
|
| 151 |
+
# layers_to_visualize_resnet50 = {
|
| 152 |
+
# 'conv1': 0,
|
| 153 |
+
# 'layer1.0.conv1': 2,
|
| 154 |
+
# 'layer1.0.conv2': 3,
|
| 155 |
+
# 'layer1.1.conv1': 5,
|
| 156 |
+
# 'layer1.1.conv2': 6,
|
| 157 |
+
# 'layer1.2.conv1': 8,
|
| 158 |
+
# 'layer1.2.conv2': 9,
|
| 159 |
+
# 'layer2.0.conv1': 11,
|
| 160 |
+
# 'layer2.0.conv2': 12,
|
| 161 |
+
# 'layer2.1.conv1': 14,
|
| 162 |
+
# 'layer2.1.conv2': 15,
|
| 163 |
+
# 'layer2.2.conv1': 17,
|
| 164 |
+
# 'layer2.2.conv2': 18,
|
| 165 |
+
# 'layer2.3.conv1': 20,
|
| 166 |
+
# 'layer2.3.conv2': 21,
|
| 167 |
+
# 'layer3.0.conv1': 23,
|
| 168 |
+
# 'layer3.0.conv2': 24,
|
| 169 |
+
# 'layer3.0.downsample.0': 25,
|
| 170 |
+
# 'layer3.1.conv1': 27,
|
| 171 |
+
# 'layer3.1.conv2': 28,
|
| 172 |
+
# 'layer3.2.conv1': 30,
|
| 173 |
+
# 'layer3.2.conv2': 31,
|
| 174 |
+
# 'layer3.3.conv1': 33,
|
| 175 |
+
# 'layer3.3.conv2': 34,
|
| 176 |
+
# 'layer4.0.conv1': 36,
|
| 177 |
+
# 'layer4.0.conv2': 37,
|
| 178 |
+
# 'layer4.0.downsample.0': 38,
|
| 179 |
+
# 'layer4.1.conv1': 40,
|
| 180 |
+
# 'layer4.1.conv2': 41,
|
| 181 |
+
# 'layer4.2.conv1': 43,
|
| 182 |
+
# 'layer4.2.conv2': 44,
|
| 183 |
+
# }
|
| 184 |
+
|
| 185 |
+
# Index: 0, Layer Name: conv1, Layer Type: <class 'torch.nn.modules.conv.Conv2d'>
|
| 186 |
+
# Index: 1, Layer Name: bn1, Layer Type: <class 'torch.nn.modules.batchnorm.BatchNorm2d'>
|
| 187 |
+
# Index: 2, Layer Name: relu, Layer Type: <class 'torch.nn.modules.activation.ReLU'>
|
| 188 |
+
# Index: 3, Layer Name: maxpool, Layer Type: <class 'torch.nn.modules.pooling.MaxPool2d'>
|
| 189 |
+
# Index: 4, Layer Name: layer1, Layer Type: <class 'torch.nn.modules.container.Sequential'>
|
| 190 |
+
# Index: 5, Layer Name: layer2, Layer Type: <class 'torch.nn.modules.container.Sequential'>
|
| 191 |
+
# Index: 6, Layer Name: layer3, Layer Type: <class 'torch.nn.modules.container.Sequential'>
|
| 192 |
+
# Index: 7, Layer Name: layer4, Layer Type: <class 'torch.nn.modules.container.Sequential'>
|
| 193 |
+
# Index: 8, Layer Name: avgpool, Layer Type: <class 'torch.nn.modules.pooling.AdaptiveAvgPool2d'>
|
| 194 |
+
# Index: 9, Layer Name: fc, Layer Type: <class 'torch.nn.modules.linear.Linear'>
|
src/extractor/visualise_vgg.py
ADDED
|
@@ -0,0 +1,159 @@
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|
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|
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|
|
|
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|
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|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import warnings
|
| 2 |
+
warnings.filterwarnings("ignore")
|
| 3 |
+
import os
|
| 4 |
+
import glob
|
| 5 |
+
import numpy as np
|
| 6 |
+
import pandas as pd
|
| 7 |
+
import matplotlib.pyplot as plt
|
| 8 |
+
import torch
|
| 9 |
+
from torchvision import models, transforms
|
| 10 |
+
from thop import profile
|
| 11 |
+
|
| 12 |
+
|
| 13 |
+
# get the activation
|
| 14 |
+
def get_activation(model, layer, input_img_data):
|
| 15 |
+
model.eval()
|
| 16 |
+
activations = []
|
| 17 |
+
inputs = []
|
| 18 |
+
|
| 19 |
+
def hook(module, input, output):
|
| 20 |
+
activations.append(output)
|
| 21 |
+
inputs.append(input[0])
|
| 22 |
+
|
| 23 |
+
hook_handle = layer.register_forward_hook(hook)
|
| 24 |
+
with torch.no_grad():
|
| 25 |
+
model(input_img_data)
|
| 26 |
+
hook_handle.remove()
|
| 27 |
+
return activations, inputs
|
| 28 |
+
|
| 29 |
+
def get_activation_map(frame, layer_name, vgg16, device):
|
| 30 |
+
# image pre-processing
|
| 31 |
+
transform = transforms.Compose([
|
| 32 |
+
transforms.Resize((224, 224)),
|
| 33 |
+
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
|
| 34 |
+
])
|
| 35 |
+
|
| 36 |
+
# Apply the transformations (resize and normalize)
|
| 37 |
+
frame_tensor = transform(frame)
|
| 38 |
+
|
| 39 |
+
# adding index 0 changes the original [C, H, W] shape to [1, C, H, W]
|
| 40 |
+
if frame_tensor.dim() == 3:
|
| 41 |
+
frame_tensor = frame_tensor.unsqueeze(0)
|
| 42 |
+
# print(f'Image dimension: {frame_tensor.shape}')
|
| 43 |
+
|
| 44 |
+
# getting the activation of a given layer
|
| 45 |
+
layer_obj = vgg16.features[layer_name]
|
| 46 |
+
activations, inputs = get_activation(vgg16, layer_obj, frame_tensor)
|
| 47 |
+
activated_img = activations[0][0]
|
| 48 |
+
activation_array = activated_img.cpu().numpy()
|
| 49 |
+
|
| 50 |
+
# calculate FLOPs for layer
|
| 51 |
+
flops, params = profile(layer_obj, inputs=(inputs[0],), verbose=False)
|
| 52 |
+
if params == 0 and isinstance(layer_obj, torch.nn.Conv2d):
|
| 53 |
+
params = layer_obj.in_channels * layer_obj.out_channels * layer_obj.kernel_size[0] * layer_obj.kernel_size[1]
|
| 54 |
+
if layer_obj.bias is not None:
|
| 55 |
+
params += layer_obj.out_channels
|
| 56 |
+
# print(f"FLOPs for {layer_name}: {flops}, Params: {params}")
|
| 57 |
+
return activated_img, activation_array, flops, params
|
| 58 |
+
|
| 59 |
+
def process_video_frame(video_name, frame, frame_number, all_layers, vgg16, device):
|
| 60 |
+
# create a dictionary to store activation arrays for each layer
|
| 61 |
+
activations_dict = {}
|
| 62 |
+
total_flops = 0
|
| 63 |
+
total_params = 0
|
| 64 |
+
for layer_name in all_layers:
|
| 65 |
+
fig_name = f"vgg16_feature_map_layer_{layer_name}"
|
| 66 |
+
combined_name = f"vgg16_feature_map"
|
| 67 |
+
|
| 68 |
+
activated_img, activation_array, flops, params = get_activation_map(frame, layer_name, vgg16, device)
|
| 69 |
+
total_flops += flops
|
| 70 |
+
total_params += params
|
| 71 |
+
|
| 72 |
+
# save activation maps as png
|
| 73 |
+
# png_path = f'../visualisation/vgg16/{video_name}/frame_{frame_number}/'
|
| 74 |
+
# npy_path = f'../features/vgg16/{video_name}/frame_{frame_number}/'
|
| 75 |
+
# os.makedirs(png_path, exist_ok=True)
|
| 76 |
+
# os.makedirs(npy_path, exist_ok=True)
|
| 77 |
+
# get_activation_png(png_path, fig_name, activated_img)
|
| 78 |
+
# save activation features as npy
|
| 79 |
+
# get_activation_npy(npy_path, fig_name, activation_array)
|
| 80 |
+
# save to the dictionary
|
| 81 |
+
activations_dict[layer_name] = activated_img
|
| 82 |
+
|
| 83 |
+
# print(f"total FLOPs for Resnet50 layerstack: {total_flops}, Params: {total_params}")
|
| 84 |
+
frame_npy_path = f'../features/vgg16/{video_name}/frame_{frame_number}_{combined_name}.npy'
|
| 85 |
+
return activations_dict, frame_npy_path, total_flops, total_params
|
| 86 |
+
|
| 87 |
+
def get_activation_png(png_path, fig_name, activated_img, n=8):
|
| 88 |
+
fig = plt.figure(figsize=(10, 10))
|
| 89 |
+
|
| 90 |
+
# visualise activation map for 64 channels
|
| 91 |
+
for i in range(n):
|
| 92 |
+
for j in range(n):
|
| 93 |
+
idx = (n * i) + j
|
| 94 |
+
if idx >= activated_img.shape[0]:
|
| 95 |
+
break
|
| 96 |
+
ax = fig.add_subplot(n, n, idx + 1)
|
| 97 |
+
ax.imshow(activated_img[idx].cpu().numpy(), cmap='viridis')
|
| 98 |
+
ax.axis('off')
|
| 99 |
+
|
| 100 |
+
# save figures
|
| 101 |
+
fig_path = f'{png_path}{fig_name}.png'
|
| 102 |
+
print(fig_path)
|
| 103 |
+
print("----------------" + '\n')
|
| 104 |
+
plt.savefig(fig_path)
|
| 105 |
+
plt.close()
|
| 106 |
+
|
| 107 |
+
def get_activation_npy(npy_path, fig_name, activation_array):
|
| 108 |
+
np.save(f'{npy_path}{fig_name}.npy', activation_array)
|
| 109 |
+
|
| 110 |
+
if __name__ == '__main__':
|
| 111 |
+
device_name = "gpu"
|
| 112 |
+
if device_name == "gpu":
|
| 113 |
+
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
| 114 |
+
else:
|
| 115 |
+
device = torch.device("cpu")
|
| 116 |
+
print(f"Running on {'GPU' if device.type == 'cuda' else 'CPU'}")
|
| 117 |
+
# pre-trained VGG16 model to device
|
| 118 |
+
vgg16 = models.vgg16(pretrained=True).to(device)
|
| 119 |
+
all_layers = [0, 2, 5, 7, 10, 12, 14, 17, 19, 21, 24, 26, 28]
|
| 120 |
+
|
| 121 |
+
video_type = 'test'
|
| 122 |
+
# Test
|
| 123 |
+
if video_type == 'test':
|
| 124 |
+
metadata_path = "../../metadata/test_videos.csv"
|
| 125 |
+
# NR:
|
| 126 |
+
elif video_type == 'resolution_ugc':
|
| 127 |
+
resolution = '360P'
|
| 128 |
+
metadata_path = f"../../metadata/YOUTUBE_UGC_{resolution}_metadata.csv"
|
| 129 |
+
else:
|
| 130 |
+
metadata_path = f'../../metadata/{video_type.upper()}_metadata.csv'
|
| 131 |
+
|
| 132 |
+
ugcdata = pd.read_csv(metadata_path)
|
| 133 |
+
for i in range(len(ugcdata)):
|
| 134 |
+
video_name = ugcdata['vid'][i]
|
| 135 |
+
sampled_frame_path = os.path.join('../..', 'video_sampled_frame', 'sampled_frame', f'{video_name}')
|
| 136 |
+
|
| 137 |
+
print(f"Processing video: {video_name}")
|
| 138 |
+
image_paths = glob.glob(os.path.join(sampled_frame_path, f'{video_name}_*.png'))
|
| 139 |
+
frame_number = 0
|
| 140 |
+
for image in image_paths:
|
| 141 |
+
print(f"{image}")
|
| 142 |
+
frame_number += 1
|
| 143 |
+
process_video_frame(video_name, image, frame_number, all_layers, vgg16, device)
|
| 144 |
+
|
| 145 |
+
# layers_to_visualize = {
|
| 146 |
+
# 'conv1_1': 0,
|
| 147 |
+
# 'conv1_2': 2,
|
| 148 |
+
# 'conv2_1': 5,
|
| 149 |
+
# 'conv2_2': 7,
|
| 150 |
+
# 'conv3_1': 10,
|
| 151 |
+
# 'conv3_2': 12,
|
| 152 |
+
# 'conv3_3': 14,
|
| 153 |
+
# 'conv4_1': 17,
|
| 154 |
+
# 'conv4_2': 19,
|
| 155 |
+
# 'conv4_3': 21,
|
| 156 |
+
# 'conv5_1': 24,
|
| 157 |
+
# 'conv5_2': 26,
|
| 158 |
+
# 'conv5_3': 28,
|
| 159 |
+
# }
|
src/extractor/visualise_vgg_layer.py
ADDED
|
@@ -0,0 +1,211 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
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|
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|
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|
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|
|
|
|
|
|
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|
|
|
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|
|
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|
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|
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|
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|
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|
|
|
|
|
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|
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|
|
|
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|
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|
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|
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|
|
|
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|
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|
|
|
|
|
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|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import warnings
|
| 2 |
+
warnings.filterwarnings("ignore")
|
| 3 |
+
import os
|
| 4 |
+
import glob
|
| 5 |
+
import numpy as np
|
| 6 |
+
import pandas as pd
|
| 7 |
+
import matplotlib.pyplot as plt
|
| 8 |
+
import torch
|
| 9 |
+
from torchvision import models, transforms
|
| 10 |
+
from thop import profile
|
| 11 |
+
|
| 12 |
+
|
| 13 |
+
# get the activation
|
| 14 |
+
def get_activation(model, layer, input_img_data):
|
| 15 |
+
model.eval()
|
| 16 |
+
activations = []
|
| 17 |
+
inputs = []
|
| 18 |
+
|
| 19 |
+
def hook(module, input, output):
|
| 20 |
+
activations.append(output)
|
| 21 |
+
inputs.append(input[0])
|
| 22 |
+
|
| 23 |
+
hook_handle = layer.register_forward_hook(hook)
|
| 24 |
+
with torch.no_grad():
|
| 25 |
+
model(input_img_data)
|
| 26 |
+
hook_handle.remove()
|
| 27 |
+
return activations, inputs
|
| 28 |
+
|
| 29 |
+
def get_activation_map(frame, layer_name, vgg16, device):
|
| 30 |
+
# image pre-processing
|
| 31 |
+
transform = transforms.Compose([
|
| 32 |
+
transforms.Resize((224, 224)),
|
| 33 |
+
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
|
| 34 |
+
])
|
| 35 |
+
|
| 36 |
+
# Apply the transformations (resize and normalize)
|
| 37 |
+
frame_tensor = transform(frame)
|
| 38 |
+
|
| 39 |
+
# adding index 0 changes the original [C, H, W] shape to [1, C, H, W]
|
| 40 |
+
if frame_tensor.dim() == 3:
|
| 41 |
+
frame_tensor = frame_tensor.unsqueeze(0)
|
| 42 |
+
# print(f'Image dimension: {frame_tensor.shape}')
|
| 43 |
+
|
| 44 |
+
# getting the activation of a given layer
|
| 45 |
+
if layer_name == 'fc2' or layer_name == 'fc1':
|
| 46 |
+
fc_idx = layer_name.replace('fc', '')
|
| 47 |
+
if fc_idx == '2':
|
| 48 |
+
fc_idx = int(fc_idx) + 1
|
| 49 |
+
else:
|
| 50 |
+
fc_idx = int(fc_idx) - 1
|
| 51 |
+
layer_obj = vgg16.classifier[fc_idx]
|
| 52 |
+
else:
|
| 53 |
+
conv_idx = layer_name
|
| 54 |
+
layer_obj = vgg16.features[conv_idx]
|
| 55 |
+
activations, inputs = get_activation(vgg16, layer_obj, frame_tensor)
|
| 56 |
+
activated_img = activations[0][0]
|
| 57 |
+
activation_array = activated_img.cpu().numpy()
|
| 58 |
+
|
| 59 |
+
# calculate FLOPs for layer
|
| 60 |
+
flops, params = profile(layer_obj, inputs=(inputs[0],), verbose=False)
|
| 61 |
+
if params == 0 and isinstance(layer_obj, torch.nn.Conv2d):
|
| 62 |
+
params = layer_obj.in_channels * layer_obj.out_channels * layer_obj.kernel_size[0] * layer_obj.kernel_size[1]
|
| 63 |
+
if layer_obj.bias is not None:
|
| 64 |
+
params += layer_obj.out_channels
|
| 65 |
+
# print(f"FLOPs for {layer_name}: {flops}, Params: {params}")
|
| 66 |
+
return activated_img, activation_array, flops, params
|
| 67 |
+
|
| 68 |
+
def process_video_frame(video_name, frame, frame_number, layer_name, vgg16, device):
|
| 69 |
+
# create a dictionary to store activation arrays for each layer
|
| 70 |
+
activations_dict = {}
|
| 71 |
+
total_flops = 0
|
| 72 |
+
total_params = 0
|
| 73 |
+
fig_name = f"vgg16_feature_map_layer_{layer_name}"
|
| 74 |
+
combined_name = f"vgg16_feature_map"
|
| 75 |
+
|
| 76 |
+
activated_img, activation_array, flops, params = get_activation_map(frame, layer_name, vgg16, device)
|
| 77 |
+
total_flops += flops
|
| 78 |
+
total_params += params
|
| 79 |
+
|
| 80 |
+
# save activation maps as png
|
| 81 |
+
# png_path = f'../visualisation/vgg16/{video_name}/frame_{frame_number}/'
|
| 82 |
+
# npy_path = f'../features/vgg16/{video_name}/frame_{frame_number}/'
|
| 83 |
+
# os.makedirs(png_path, exist_ok=True)
|
| 84 |
+
# os.makedirs(npy_path, exist_ok=True)
|
| 85 |
+
# get_activation_png(png_path, fig_name, activated_img)
|
| 86 |
+
# save activation features as pny
|
| 87 |
+
# get_activation_npy(npy_path, fig_name, activation_array)
|
| 88 |
+
|
| 89 |
+
# print(f"total FLOPs for Resnet50 layerstack: {total_flops}, Params: {total_params}")
|
| 90 |
+
frame_npy_path = f'../features/vgg16/{video_name}/frame_{frame_number}_{combined_name}.npy'
|
| 91 |
+
return activated_img, frame_npy_path, total_flops, total_params
|
| 92 |
+
|
| 93 |
+
def get_activation_png(png_path, fig_name, activated_img, n=8):
|
| 94 |
+
fig = plt.figure(figsize=(10, 10))
|
| 95 |
+
|
| 96 |
+
# visualise activation map for 64 channels
|
| 97 |
+
for i in range(n):
|
| 98 |
+
for j in range(n):
|
| 99 |
+
idx = (n * i) + j
|
| 100 |
+
if idx >= activated_img.shape[0]:
|
| 101 |
+
break
|
| 102 |
+
ax = fig.add_subplot(n, n, idx + 1)
|
| 103 |
+
ax.imshow(activated_img[idx].cpu().numpy(), cmap='viridis')
|
| 104 |
+
ax.axis('off')
|
| 105 |
+
|
| 106 |
+
# save figures
|
| 107 |
+
fig_path = f'{png_path}{fig_name}.png'
|
| 108 |
+
print(fig_path)
|
| 109 |
+
print("----------------" + '\n')
|
| 110 |
+
plt.savefig(fig_path)
|
| 111 |
+
plt.close()
|
| 112 |
+
|
| 113 |
+
def get_activation_npy(npy_path, fig_name, activation_array):
|
| 114 |
+
np.save(f'{npy_path}{fig_name}.npy', activation_array)
|
| 115 |
+
|
| 116 |
+
if __name__ == '__main__':
|
| 117 |
+
device_name = "gpu"
|
| 118 |
+
if device_name == "gpu":
|
| 119 |
+
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
| 120 |
+
else:
|
| 121 |
+
device = torch.device("cpu")
|
| 122 |
+
print(f"Running on {'GPU' if device.type == 'cuda' else 'CPU'}")
|
| 123 |
+
# pre-trained VGG16 model to device
|
| 124 |
+
vgg16 = models.vgg16(pretrained=True).to(device)
|
| 125 |
+
|
| 126 |
+
for idx, layer in enumerate(vgg16.features):
|
| 127 |
+
print(f"Index: {idx}, Layer Type: {type(layer)}")
|
| 128 |
+
layer_name = 'fc2'
|
| 129 |
+
|
| 130 |
+
video_type = 'test'
|
| 131 |
+
# Test
|
| 132 |
+
if video_type == 'test':
|
| 133 |
+
metadata_path = "../../metadata/test_videos.csv"
|
| 134 |
+
# NR:
|
| 135 |
+
elif video_type == 'resolution_ugc':
|
| 136 |
+
resolution = '360P'
|
| 137 |
+
metadata_path = f"../../metadata/YOUTUBE_UGC_{resolution}_metadata.csv"
|
| 138 |
+
else:
|
| 139 |
+
metadata_path = f'../../metadata/{video_type.upper()}_metadata.csv'
|
| 140 |
+
|
| 141 |
+
ugcdata = pd.read_csv(metadata_path)
|
| 142 |
+
for i in range(len(ugcdata)):
|
| 143 |
+
video_name = ugcdata['vid'][i]
|
| 144 |
+
sampled_frame_path = os.path.join('../..', 'video_sampled_frame', 'sampled_frame', f'{video_name}')
|
| 145 |
+
|
| 146 |
+
print(f"Processing video: {video_name}")
|
| 147 |
+
image_paths = glob.glob(os.path.join(sampled_frame_path, f'{video_name}_*.png'))
|
| 148 |
+
frame_number = 0
|
| 149 |
+
for image in image_paths:
|
| 150 |
+
print(f"{image}")
|
| 151 |
+
frame_number += 1
|
| 152 |
+
process_video_frame(video_name, image, frame_number, layer_name, vgg16, device)
|
| 153 |
+
|
| 154 |
+
# layers_to_visualize = {
|
| 155 |
+
# 'conv1_1': 0,
|
| 156 |
+
# 'conv1_2': 2,
|
| 157 |
+
# 'conv2_1': 5,
|
| 158 |
+
# 'conv2_2': 7,
|
| 159 |
+
# 'conv3_1': 10,
|
| 160 |
+
# 'conv3_2': 12,
|
| 161 |
+
# 'conv3_3': 14,
|
| 162 |
+
# 'conv4_1': 17,
|
| 163 |
+
# 'conv4_2': 19,
|
| 164 |
+
# 'conv4_3': 21,
|
| 165 |
+
# 'conv5_1': 24,
|
| 166 |
+
# 'conv5_2': 26,
|
| 167 |
+
# 'conv5_3': 28,
|
| 168 |
+
# }
|
| 169 |
+
|
| 170 |
+
# Sequential(
|
| 171 |
+
# (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
| 172 |
+
# (1): ReLU(inplace=True)
|
| 173 |
+
# (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
| 174 |
+
# (3): ReLU(inplace=True)
|
| 175 |
+
# (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
|
| 176 |
+
# (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
| 177 |
+
# (6): ReLU(inplace=True)
|
| 178 |
+
# (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
| 179 |
+
# (8): ReLU(inplace=True)
|
| 180 |
+
# (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
|
| 181 |
+
# (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
| 182 |
+
# (11): ReLU(inplace=True)
|
| 183 |
+
# (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
| 184 |
+
# (13): ReLU(inplace=True)
|
| 185 |
+
# (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
| 186 |
+
# (15): ReLU(inplace=True)
|
| 187 |
+
# (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
|
| 188 |
+
# (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
| 189 |
+
# (18): ReLU(inplace=True)
|
| 190 |
+
# (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
| 191 |
+
# (20): ReLU(inplace=True)
|
| 192 |
+
# (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
| 193 |
+
# (22): ReLU(inplace=True)
|
| 194 |
+
# (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
|
| 195 |
+
# (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
| 196 |
+
# (25): ReLU(inplace=True)
|
| 197 |
+
# (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
| 198 |
+
# (27): ReLU(inplace=True)
|
| 199 |
+
# (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
| 200 |
+
# (29): ReLU(inplace=True)
|
| 201 |
+
# (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
|
| 202 |
+
# )
|
| 203 |
+
# Sequential(
|
| 204 |
+
# (0): Linear(in_features=25088, out_features=4096, bias=True)
|
| 205 |
+
# (1): ReLU(inplace=True)
|
| 206 |
+
# (2): Dropout(p=0.5, inplace=False)
|
| 207 |
+
# (3): Linear(in_features=4096, out_features=4096, bias=True)
|
| 208 |
+
# (4): ReLU(inplace=True)
|
| 209 |
+
# (5): Dropout(p=0.5, inplace=False)
|
| 210 |
+
# (6): Linear(in_features=4096, out_features=1000, bias=True)
|
| 211 |
+
# )
|
src/extractor/visualise_vit_layer.py
ADDED
|
@@ -0,0 +1,516 @@
|
|
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|
|
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|
| 1 |
+
import os
|
| 2 |
+
import glob
|
| 3 |
+
import math
|
| 4 |
+
from functools import partial
|
| 5 |
+
import torch
|
| 6 |
+
|
| 7 |
+
import ipywidgets as widgets
|
| 8 |
+
import io
|
| 9 |
+
from PIL import Image
|
| 10 |
+
from torchvision import transforms
|
| 11 |
+
import matplotlib.pyplot as plt
|
| 12 |
+
import numpy as np
|
| 13 |
+
import pandas as pd
|
| 14 |
+
from torch import nn
|
| 15 |
+
from thop import profile
|
| 16 |
+
is_flop_cal = False
|
| 17 |
+
|
| 18 |
+
import warnings
|
| 19 |
+
warnings.filterwarnings("ignore")
|
| 20 |
+
|
| 21 |
+
# Step 2: Creating a Vision Transformer
|
| 22 |
+
# normalise the torch
|
| 23 |
+
def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
|
| 24 |
+
# type: (Tensor, float, float, float, float) -> Tensor
|
| 25 |
+
return _no_grad_trunc_normal_(tensor, mean, std, a, b)
|
| 26 |
+
|
| 27 |
+
#用于执行无梯度截断正态分布初始化。这两个函数在模型初始化中使用，确保权重被适当地初始化。
|
| 28 |
+
def _no_grad_trunc_normal_(tensor, mean, std, a, b):
|
| 29 |
+
def norm_cdf(x):
|
| 30 |
+
# computes standard normal cumulative distribution function
|
| 31 |
+
return (1. + math.erf(x / math.sqrt(2.))) / 2.
|
| 32 |
+
|
| 33 |
+
#对输入进行随机丢弃一部分元素，实现随机深度（Stochastic Depth）。
|
| 34 |
+
def drop_path(x, drop_prob: float = 0., training: bool = False):
|
| 35 |
+
if drop_prob == 0. or not training:
|
| 36 |
+
return x
|
| 37 |
+
keep_prob = 1 - drop_prob
|
| 38 |
+
# work with diff dim tensors, not just 2D ConvNets
|
| 39 |
+
shape = (x.shape[0],) + (1,) * (x.ndim - 1)
|
| 40 |
+
random_tensor = keep_prob + \
|
| 41 |
+
torch.rand(shape, dtype=x.dtype, device=x.device)
|
| 42 |
+
random_tensor.floor_() # binarize
|
| 43 |
+
output = x.div(keep_prob) * random_tensor
|
| 44 |
+
return output
|
| 45 |
+
|
| 46 |
+
#用于在残差块的主路径上应用 drop_path 函数。
|
| 47 |
+
class DropPath(nn.Module):
|
| 48 |
+
"""
|
| 49 |
+
Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
|
| 50 |
+
"""
|
| 51 |
+
|
| 52 |
+
def __init__(self, drop_prob=None):
|
| 53 |
+
super(DropPath, self).__init__()
|
| 54 |
+
self.drop_prob = drop_prob
|
| 55 |
+
|
| 56 |
+
def forward(self, x):
|
| 57 |
+
return drop_path(x, self.drop_prob, self.training)
|
| 58 |
+
|
| 59 |
+
#一个多层感知机（MLP）类，包含两个线性层和一个激活函数，用于在残差块中对特征进行非线性映射。
|
| 60 |
+
class Mlp(nn.Module):
|
| 61 |
+
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
|
| 62 |
+
super().__init__()
|
| 63 |
+
out_features = out_features or in_features
|
| 64 |
+
hidden_features = hidden_features or in_features
|
| 65 |
+
self.fc1 = nn.Linear(in_features, hidden_features)
|
| 66 |
+
self.act = act_layer()
|
| 67 |
+
self.fc2 = nn.Linear(hidden_features, out_features)
|
| 68 |
+
self.drop = nn.Dropout(drop)
|
| 69 |
+
|
| 70 |
+
def forward(self, x):
|
| 71 |
+
x = self.fc1(x)
|
| 72 |
+
x = self.act(x)
|
| 73 |
+
x = self.drop(x)
|
| 74 |
+
x = self.fc2(x)
|
| 75 |
+
x = self.drop(x)
|
| 76 |
+
return x
|
| 77 |
+
|
| 78 |
+
# 自注意力机制类，用于在残差块中计算注意力权重并应用它们。
|
| 79 |
+
class Attention(nn.Module):
|
| 80 |
+
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
|
| 81 |
+
super().__init__()
|
| 82 |
+
self.num_heads = num_heads
|
| 83 |
+
head_dim = dim // num_heads
|
| 84 |
+
self.scale = qk_scale or head_dim ** -0.5
|
| 85 |
+
|
| 86 |
+
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
|
| 87 |
+
self.attn_drop = nn.Dropout(attn_drop)
|
| 88 |
+
self.proj = nn.Linear(dim, dim)
|
| 89 |
+
self.proj_drop = nn.Dropout(proj_drop)
|
| 90 |
+
|
| 91 |
+
def forward(self, x):
|
| 92 |
+
B, N, C = x.shape
|
| 93 |
+
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C //
|
| 94 |
+
self.num_heads).permute(2, 0, 3, 1, 4)
|
| 95 |
+
q, k, v = qkv[0], qkv[1], qkv[2]
|
| 96 |
+
|
| 97 |
+
attn = (q @ k.transpose(-2, -1)) * self.scale
|
| 98 |
+
attn = attn.softmax(dim=-1)
|
| 99 |
+
attn = self.attn_drop(attn)
|
| 100 |
+
|
| 101 |
+
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
|
| 102 |
+
x = self.proj(x)
|
| 103 |
+
x = self.proj_drop(x)
|
| 104 |
+
return x, attn
|
| 105 |
+
|
| 106 |
+
# 一个残差块类，包含一个自注意力模块和一个MLP模块。
|
| 107 |
+
class Block(nn.Module):
|
| 108 |
+
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
|
| 109 |
+
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
|
| 110 |
+
super().__init__()
|
| 111 |
+
self.norm1 = norm_layer(dim)
|
| 112 |
+
self.attn = Attention(
|
| 113 |
+
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
|
| 114 |
+
self.drop_path = DropPath(
|
| 115 |
+
drop_path) if drop_path > 0. else nn.Identity()
|
| 116 |
+
self.norm2 = norm_layer(dim)
|
| 117 |
+
mlp_hidden_dim = int(dim * mlp_ratio)
|
| 118 |
+
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim,
|
| 119 |
+
act_layer=act_layer, drop=drop)
|
| 120 |
+
|
| 121 |
+
def forward(self, x, return_attention=False):
|
| 122 |
+
y, attn = self.attn(self.norm1(x))
|
| 123 |
+
if return_attention:
|
| 124 |
+
return attn
|
| 125 |
+
x = x + self.drop_path(y)
|
| 126 |
+
x = x + self.drop_path(self.mlp(self.norm2(x)))
|
| 127 |
+
return x
|
| 128 |
+
|
| 129 |
+
# 图像到块嵌入类，将输入图像分割成块并将它们映射到嵌入空间
|
| 130 |
+
class PatchEmbed(nn.Module):
|
| 131 |
+
"""
|
| 132 |
+
Image to Patch Embedding
|
| 133 |
+
"""
|
| 134 |
+
|
| 135 |
+
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
|
| 136 |
+
super().__init__()
|
| 137 |
+
num_patches = (img_size // patch_size) * (img_size // patch_size)
|
| 138 |
+
self.img_size = img_size
|
| 139 |
+
self.patch_size = patch_size
|
| 140 |
+
self.num_patches = num_patches
|
| 141 |
+
self.proj = nn.Conv2d(in_chans, embed_dim,
|
| 142 |
+
kernel_size=patch_size, stride=patch_size)
|
| 143 |
+
|
| 144 |
+
def forward(self, x):
|
| 145 |
+
B, C, H, W = x.shape
|
| 146 |
+
x = self.proj(x).flatten(2).transpose(1, 2)
|
| 147 |
+
return x
|
| 148 |
+
|
| 149 |
+
# Vision Transformer模型的主要实现。包含多个残差块、嵌入层等。（还需要学里面每一步代码具体在做什么）
|
| 150 |
+
class VisionTransformer(nn.Module):
|
| 151 |
+
"""
|
| 152 |
+
Vision Transformer
|
| 153 |
+
"""
|
| 154 |
+
def __init__(self, img_size=[224], patch_size=16, in_chans=3, num_classes=0, embed_dim=768, depth=12,
|
| 155 |
+
num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
|
| 156 |
+
drop_path_rate=0., norm_layer=nn.LayerNorm, **kwargs):
|
| 157 |
+
super().__init__()
|
| 158 |
+
self.num_features = self.embed_dim = embed_dim
|
| 159 |
+
|
| 160 |
+
self.patch_embed = PatchEmbed(
|
| 161 |
+
img_size=img_size[0], patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
|
| 162 |
+
num_patches = self.patch_embed.num_patches
|
| 163 |
+
|
| 164 |
+
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
|
| 165 |
+
self.pos_embed = nn.Parameter(
|
| 166 |
+
torch.zeros(1, num_patches + 1, embed_dim))
|
| 167 |
+
self.pos_drop = nn.Dropout(p=drop_rate)
|
| 168 |
+
|
| 169 |
+
# stochastic depth decay rule
|
| 170 |
+
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]
|
| 171 |
+
self.blocks = nn.ModuleList([
|
| 172 |
+
Block(
|
| 173 |
+
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
|
| 174 |
+
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
|
| 175 |
+
for i in range(depth)])
|
| 176 |
+
self.norm = norm_layer(embed_dim)
|
| 177 |
+
|
| 178 |
+
# classifier head
|
| 179 |
+
self.head = nn.Linear(
|
| 180 |
+
embed_dim, num_classes) if num_classes > 0 else nn.Identity()
|
| 181 |
+
|
| 182 |
+
trunc_normal_(self.pos_embed, std=.02)
|
| 183 |
+
trunc_normal_(self.cls_token, std=.02)
|
| 184 |
+
self.apply(self._init_weights)
|
| 185 |
+
|
| 186 |
+
def _init_weights(self, m):
|
| 187 |
+
if isinstance(m, nn.Linear):
|
| 188 |
+
trunc_normal_(m.weight, std=.02)
|
| 189 |
+
if isinstance(m, nn.Linear) and m.bias is not None:
|
| 190 |
+
nn.init.constant_(m.bias, 0)
|
| 191 |
+
elif isinstance(m, nn.LayerNorm):
|
| 192 |
+
nn.init.constant_(m.bias, 0)
|
| 193 |
+
nn.init.constant_(m.weight, 1.0)
|
| 194 |
+
|
| 195 |
+
def interpolate_pos_encoding(self, x, w, h):
|
| 196 |
+
npatch = x.shape[1] - 1
|
| 197 |
+
N = self.pos_embed.shape[1] - 1
|
| 198 |
+
if npatch == N and w == h:
|
| 199 |
+
return self.pos_embed
|
| 200 |
+
class_pos_embed = self.pos_embed[:, 0]
|
| 201 |
+
patch_pos_embed = self.pos_embed[:, 1:]
|
| 202 |
+
dim = x.shape[-1]
|
| 203 |
+
w0 = w // self.patch_embed.patch_size
|
| 204 |
+
h0 = h // self.patch_embed.patch_size
|
| 205 |
+
|
| 206 |
+
# see discussion at https://github.com/facebookresearch/dino/issues/8
|
| 207 |
+
w0, h0 = w0 + 0.1, h0 + 0.1
|
| 208 |
+
patch_pos_embed = nn.functional.interpolate(
|
| 209 |
+
patch_pos_embed.reshape(1, int(math.sqrt(N)), int(
|
| 210 |
+
math.sqrt(N)), dim).permute(0, 3, 1, 2),
|
| 211 |
+
scale_factor=(w0 / math.sqrt(N), h0 / math.sqrt(N)),
|
| 212 |
+
mode='bicubic',
|
| 213 |
+
)
|
| 214 |
+
assert int(
|
| 215 |
+
w0) == patch_pos_embed.shape[-2] and int(h0) == patch_pos_embed.shape[-1]
|
| 216 |
+
patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
|
| 217 |
+
return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1)
|
| 218 |
+
|
| 219 |
+
def prepare_tokens(self, x):
|
| 220 |
+
B, nc, w, h = x.shape
|
| 221 |
+
x = self.patch_embed(x) # patch linear embedding
|
| 222 |
+
|
| 223 |
+
# add the [CLS] token to the embed patch tokens
|
| 224 |
+
cls_tokens = self.cls_token.expand(B, -1, -1)
|
| 225 |
+
x = torch.cat((cls_tokens, x), dim=1)
|
| 226 |
+
|
| 227 |
+
# add positional encoding to each token
|
| 228 |
+
x = x + self.interpolate_pos_encoding(x, w, h)
|
| 229 |
+
|
| 230 |
+
return self.pos_drop(x)
|
| 231 |
+
|
| 232 |
+
def forward(self, x):
|
| 233 |
+
x = self.prepare_tokens(x)
|
| 234 |
+
for blk in self.blocks:
|
| 235 |
+
x = blk(x)
|
| 236 |
+
x = self.norm(x)
|
| 237 |
+
return x[:, 0], x[:, 1:] # return CLS token and attention_features maps
|
| 238 |
+
|
| 239 |
+
def get_last_selfattention(self, x):
|
| 240 |
+
x = self.prepare_tokens(x)
|
| 241 |
+
for i, blk in enumerate(self.blocks):
|
| 242 |
+
if i < len(self.blocks) - 1:
|
| 243 |
+
x = blk(x)
|
| 244 |
+
else:
|
| 245 |
+
# return attention of the last block
|
| 246 |
+
# print(f"return attention of the last block: {x.shape}")
|
| 247 |
+
# print(blk(x, return_attention=True).shape)
|
| 248 |
+
return blk(x, return_attention=True)
|
| 249 |
+
|
| 250 |
+
def get_intermediate_layers(self, x, n=1):
|
| 251 |
+
x = self.prepare_tokens(x)
|
| 252 |
+
|
| 253 |
+
output = []
|
| 254 |
+
for i, blk in enumerate(self.blocks):
|
| 255 |
+
x = blk(x)
|
| 256 |
+
if len(self.blocks) - i <= n:
|
| 257 |
+
output.append(self.norm(x))
|
| 258 |
+
return output
|
| 259 |
+
|
| 260 |
+
# Vision Transformer 模型的生成器类，用于实例化和配置特定模型。
|
| 261 |
+
class VitGenerator(object):
|
| 262 |
+
def __init__(self, name_model, patch_size, device, evaluate=True, random=False, verbose=False):
|
| 263 |
+
self.name_model = name_model
|
| 264 |
+
self.patch_size = patch_size
|
| 265 |
+
self.evaluate = evaluate
|
| 266 |
+
self.device = device
|
| 267 |
+
self.verbose = verbose
|
| 268 |
+
self.model = self._getModel()
|
| 269 |
+
self._initializeModel()
|
| 270 |
+
if not random:
|
| 271 |
+
self._loadPretrainedWeights()
|
| 272 |
+
|
| 273 |
+
def _getModel(self):
|
| 274 |
+
if self.verbose:
|
| 275 |
+
pass
|
| 276 |
+
# print((f"[INFO] Initializing {self.name_model} with patch size of {self.patch_size}"))
|
| 277 |
+
if self.name_model == 'vit_tiny':
|
| 278 |
+
model = VisionTransformer(patch_size=self.patch_size, embed_dim=192, depth=12, num_heads=3, mlp_ratio=4,
|
| 279 |
+
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6))
|
| 280 |
+
|
| 281 |
+
elif self.name_model == 'vit_small':
|
| 282 |
+
model = VisionTransformer(patch_size=self.patch_size, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4,
|
| 283 |
+
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6))
|
| 284 |
+
|
| 285 |
+
elif self.name_model == 'vit_base':
|
| 286 |
+
model = VisionTransformer(patch_size=self.patch_size, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4,
|
| 287 |
+
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6))
|
| 288 |
+
else:
|
| 289 |
+
raise f"No model found with {self.name_model}"
|
| 290 |
+
|
| 291 |
+
return model
|
| 292 |
+
|
| 293 |
+
def _initializeModel(self):
|
| 294 |
+
if self.evaluate:
|
| 295 |
+
for p in self.model.parameters():
|
| 296 |
+
p.requires_grad = False
|
| 297 |
+
|
| 298 |
+
self.model.eval()
|
| 299 |
+
|
| 300 |
+
self.model.to(self.device)
|
| 301 |
+
|
| 302 |
+
def _loadPretrainedWeights(self):
|
| 303 |
+
if self.verbose:
|
| 304 |
+
pass
|
| 305 |
+
# print(("[INFO] Loading weights"))
|
| 306 |
+
url = None
|
| 307 |
+
if self.name_model == 'vit_small' and self.patch_size == 16:
|
| 308 |
+
url = "dino_deitsmall16_pretrain/dino_deitsmall16_pretrain.pth"
|
| 309 |
+
|
| 310 |
+
elif self.name_model == 'vit_small' and self.patch_size == 8:
|
| 311 |
+
url = "dino_deitsmall8_300ep_pretrain/dino_deitsmall8_300ep_pretrain.pth"
|
| 312 |
+
|
| 313 |
+
elif self.name_model == 'vit_base' and self.patch_size == 16:
|
| 314 |
+
url = "dino_vitbase16_pretrain/dino_vitbase16_pretrain.pth"
|
| 315 |
+
|
| 316 |
+
elif self.name_model == 'vit_base' and self.patch_size == 8:
|
| 317 |
+
url = "dino_vitbase8_pretrain/dino_vitbase8_pretrain.pth"
|
| 318 |
+
|
| 319 |
+
if url is None:
|
| 320 |
+
pass
|
| 321 |
+
# print((f"Since no pretrained weights have been found with name {self.name_model} and patch size {self.patch_size}, random weights will be used"))
|
| 322 |
+
|
| 323 |
+
else:
|
| 324 |
+
state_dict = torch.hub.load_state_dict_from_url(
|
| 325 |
+
url="https://dl.fbaipublicfiles.com/dino/" + url)
|
| 326 |
+
self.model.load_state_dict(state_dict, strict=True)
|
| 327 |
+
# print(url)
|
| 328 |
+
|
| 329 |
+
def get_last_selfattention(self, img):
|
| 330 |
+
return self.model.get_last_selfattention(img.to(self.device))
|
| 331 |
+
|
| 332 |
+
def __call__(self, x):
|
| 333 |
+
return self.model(x)
|
| 334 |
+
|
| 335 |
+
# Step 3: Creating Visualization Functions
|
| 336 |
+
def transform(img, img_size):
|
| 337 |
+
img = transforms.Resize(img_size)(img)
|
| 338 |
+
img = transforms.ToTensor()(img)
|
| 339 |
+
return img
|
| 340 |
+
|
| 341 |
+
def visualize_predict(model, img_tensor, patch_size, device, video_name, frame_number, fig_name, combined_name):
|
| 342 |
+
if img_tensor.dim() == 3:
|
| 343 |
+
img_tensor = img_tensor.unsqueeze(0)
|
| 344 |
+
attention = visualize_attention(model, img_tensor, patch_size, device)
|
| 345 |
+
# save activation maps as png
|
| 346 |
+
# png_path = f'../visualisation/resnet50/{video_name}/frame_{frame_number}/'
|
| 347 |
+
# os.makedirs(png_path, exist_ok=True)
|
| 348 |
+
# get_activation_png(img, png_path, fig_name, attention)
|
| 349 |
+
# save activation features as npy
|
| 350 |
+
activations_dict, frame_npy_path = get_activation_npy(video_name, frame_number, fig_name, combined_name, attention)
|
| 351 |
+
return activations_dict, frame_npy_path
|
| 352 |
+
|
| 353 |
+
def visualize_attention(model, img_tensor, patch_size, device):
|
| 354 |
+
# img_tensor: format [1, C, H, W]
|
| 355 |
+
# Adjust the image dimensions to be divisible by the patch size
|
| 356 |
+
w, h = img_tensor.shape[2] - img_tensor.shape[2] % patch_size, img_tensor.shape[3] - img_tensor.shape[3] % patch_size
|
| 357 |
+
img_tensor = img_tensor[:, :, :w, :h]
|
| 358 |
+
|
| 359 |
+
w_featmap = img_tensor.shape[-2] // patch_size
|
| 360 |
+
h_featmap = img_tensor.shape[-1] // patch_size
|
| 361 |
+
|
| 362 |
+
attentions = model.get_last_selfattention(img_tensor.to(device))
|
| 363 |
+
nh = attentions.shape[1] # number of heads
|
| 364 |
+
|
| 365 |
+
# keep only the output patch attention
|
| 366 |
+
attentions = attentions[0, :, 0, 1:].reshape(nh, -1)
|
| 367 |
+
attentions = attentions.reshape(nh, w_featmap, h_featmap)
|
| 368 |
+
attentions = nn.functional.interpolate(attentions.unsqueeze(0), scale_factor=patch_size, mode="nearest")[0].cpu().numpy()
|
| 369 |
+
|
| 370 |
+
return attentions
|
| 371 |
+
|
| 372 |
+
def get_activation_png(img, png_path, fig_name, attention):
|
| 373 |
+
n_heads = attention.shape[0]
|
| 374 |
+
|
| 375 |
+
# attention maps
|
| 376 |
+
for i in range(n_heads):
|
| 377 |
+
plt.imshow(attention[i], cmap='viridis') #cmap='viridis', cmap='inferno'
|
| 378 |
+
plt.title(f"Head n: {i + 1}")
|
| 379 |
+
plt.axis('off') # Turn off axis ticks and labels
|
| 380 |
+
|
| 381 |
+
# Save figures
|
| 382 |
+
fig_path = f'{png_path}{fig_name}_head_{i + 1}.png'
|
| 383 |
+
print(fig_path)
|
| 384 |
+
plt.savefig(fig_path)
|
| 385 |
+
plt.close()
|
| 386 |
+
|
| 387 |
+
# head mean map
|
| 388 |
+
plt.figure(figsize=(10, 10))
|
| 389 |
+
image_name = fig_name.replace('vit_feature_map_', '')
|
| 390 |
+
text = [f"{image_name}", "Head Mean"]
|
| 391 |
+
for i, fig in enumerate([img, np.mean(attention, 0)]):
|
| 392 |
+
plt.subplot(1, 2, i+1)
|
| 393 |
+
plt.imshow(fig, cmap='viridis')
|
| 394 |
+
plt.title(text[i])
|
| 395 |
+
plt.axis('off') # Turn off axis ticks and labels
|
| 396 |
+
fig_path1 = f'{png_path}{fig_name}_head_mean.png'
|
| 397 |
+
print(fig_path1)
|
| 398 |
+
print("----------------" + '\n')
|
| 399 |
+
plt.savefig(fig_path1)
|
| 400 |
+
plt.close()
|
| 401 |
+
|
| 402 |
+
# combine
|
| 403 |
+
# plt.figure(figsize=(20, 20))
|
| 404 |
+
# for i in range(n_heads):
|
| 405 |
+
# plt.subplot(n_heads//3, 3, i+1)
|
| 406 |
+
# plt.imshow(attention[i], cmap='inferno')
|
| 407 |
+
# plt.title(f"Head n: {i+1}")
|
| 408 |
+
# plt.tight_layout()
|
| 409 |
+
# fig_path2 = png_path + fig_name + '_heads.png'
|
| 410 |
+
# print(fig_path2 + '\n')
|
| 411 |
+
# plt.savefig(fig_path2)
|
| 412 |
+
# plt.close()
|
| 413 |
+
|
| 414 |
+
def get_activation_npy(video_name, frame_number, fig_name, combined_name, attention):
|
| 415 |
+
# save activation features as pny
|
| 416 |
+
# npy_path = f'../features/vit/{video_name}/frame_{frame_number}/'
|
| 417 |
+
# os.makedirs(npy_path, exist_ok=True)
|
| 418 |
+
|
| 419 |
+
mean_attention = attention.mean(axis=0)
|
| 420 |
+
frame_npy_path = f'../features/vit/{video_name}/frame_{frame_number}_{combined_name}.npy'
|
| 421 |
+
|
| 422 |
+
return mean_attention, frame_npy_path
|
| 423 |
+
|
| 424 |
+
|
| 425 |
+
class Loader(object):
|
| 426 |
+
def __init__(self):
|
| 427 |
+
self.uploader = widgets.FileUpload(accept='image/*', multiple=False)
|
| 428 |
+
self._start()
|
| 429 |
+
|
| 430 |
+
def _start(self):
|
| 431 |
+
display(self.uploader)
|
| 432 |
+
|
| 433 |
+
def getLastImage(self):
|
| 434 |
+
try:
|
| 435 |
+
for uploaded_filename in self.uploader.value:
|
| 436 |
+
uploaded_filename = uploaded_filename
|
| 437 |
+
img = Image.open(io.BytesIO(
|
| 438 |
+
bytes(self.uploader.value[uploaded_filename]['content'])))
|
| 439 |
+
|
| 440 |
+
return img
|
| 441 |
+
except:
|
| 442 |
+
return None
|
| 443 |
+
|
| 444 |
+
def saveImage(self, path):
|
| 445 |
+
with open(path, 'wb') as output_file:
|
| 446 |
+
for uploaded_filename in self.uploader.value:
|
| 447 |
+
content = self.uploader.value[uploaded_filename]['content']
|
| 448 |
+
output_file.write(content)
|
| 449 |
+
|
| 450 |
+
def process_video_frame(video_name, frame, frame_number, model, patch_size, device):
|
| 451 |
+
# resize image
|
| 452 |
+
if frame.dim() == 3:
|
| 453 |
+
frame = frame.unsqueeze(0)
|
| 454 |
+
if frame.shape[2:] != (224, 224):
|
| 455 |
+
frame_tensor = torch.nn.functional.interpolate(frame, size=(224, 224), mode='bicubic', align_corners=False)
|
| 456 |
+
else:
|
| 457 |
+
frame_tensor = frame
|
| 458 |
+
|
| 459 |
+
# Calculate FLOPs and Params
|
| 460 |
+
if is_flop_cal == True:
|
| 461 |
+
total_flops, total_params = profile(model.model, inputs=(frame_tensor,), verbose=False)
|
| 462 |
+
print(f"total FLOPs for ViT layerstack: {total_flops}, Params: {total_params}")
|
| 463 |
+
else:
|
| 464 |
+
total_flops, total_params = None, None
|
| 465 |
+
|
| 466 |
+
fig_name = f"vit_feature_map"
|
| 467 |
+
combined_name = f"vit_feature_map"
|
| 468 |
+
|
| 469 |
+
# activations_dict, frame_npy_path = visualize_predict(model, frame_tensor, patch_size, device, video_name, frame_number, fig_name, combined_name)
|
| 470 |
+
attention_features, frame_feature_npy_path = extract_features(model, frame_tensor, video_name, frame_number, combined_name)
|
| 471 |
+
return attention_features, frame_feature_npy_path, total_flops, total_params
|
| 472 |
+
|
| 473 |
+
def extract_features(model, img_tensor, video_name, frame_number, combined_name):
|
| 474 |
+
if img_tensor.dim() == 3:
|
| 475 |
+
img_tensor = img_tensor.unsqueeze(0)
|
| 476 |
+
cls_token, attention_features = model(img_tensor)
|
| 477 |
+
|
| 478 |
+
attention_features = attention_features.squeeze(0)
|
| 479 |
+
frame_feature_npy_path = f'../features/vit/{video_name}/frame_attention_{frame_number}_{combined_name}.npy'
|
| 480 |
+
return attention_features, frame_feature_npy_path
|
| 481 |
+
|
| 482 |
+
if __name__ == '__main__':
|
| 483 |
+
# Step 4: Visualizing Images
|
| 484 |
+
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
|
| 485 |
+
if device.type == "cuda":
|
| 486 |
+
torch.cuda.set_device(0)
|
| 487 |
+
|
| 488 |
+
name_model = 'vit_base'
|
| 489 |
+
patch_size = 16
|
| 490 |
+
|
| 491 |
+
model = VitGenerator(name_model, patch_size,
|
| 492 |
+
device, evaluate=True, random=False, verbose=True)
|
| 493 |
+
|
| 494 |
+
video_type = 'test'
|
| 495 |
+
# Test
|
| 496 |
+
if video_type == 'test':
|
| 497 |
+
metadata_path = "../../metadata/test_videos.csv"
|
| 498 |
+
# NR:
|
| 499 |
+
elif video_type == 'resolution_ugc':
|
| 500 |
+
resolution = '360P'
|
| 501 |
+
metadata_path = f"../../metadata/YOUTUBE_UGC_{resolution}_metadata.csv"
|
| 502 |
+
else:
|
| 503 |
+
metadata_path = f'../../metadata/{video_type.upper()}_metadata.csv'
|
| 504 |
+
|
| 505 |
+
ugcdata = pd.read_csv(metadata_path)
|
| 506 |
+
for i in range(len(ugcdata)):
|
| 507 |
+
video_name = ugcdata['vid'][i]
|
| 508 |
+
sampled_frame_path = os.path.join('../..', 'video_sampled_frame', 'sampled_frame', f'{video_name}')
|
| 509 |
+
|
| 510 |
+
print(f"Processing video: {video_name}")
|
| 511 |
+
image_paths = glob.glob(os.path.join(sampled_frame_path, f'{video_name}_*.png'))
|
| 512 |
+
frame_number = 0
|
| 513 |
+
for image in image_paths:
|
| 514 |
+
print(f"{image}")
|
| 515 |
+
frame_number += 1
|
| 516 |
+
process_video_frame(video_name, image, frame_number, model, patch_size, device)
|
src/feature_fragment_layerstack.py
ADDED
|
@@ -0,0 +1,318 @@
|
|
|
|
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|
|
|
|
|
|
|
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|
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|
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|
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|
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|
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|
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|
|
|
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|
|
|
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|
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|
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|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
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|
| 1 |
+
from PIL import Image
|
| 2 |
+
import pandas as pd
|
| 3 |
+
import numpy as np
|
| 4 |
+
import os
|
| 5 |
+
from pathlib import Path
|
| 6 |
+
import scipy.io
|
| 7 |
+
import shutil
|
| 8 |
+
import torch
|
| 9 |
+
import time
|
| 10 |
+
import cv2
|
| 11 |
+
from torchvision import models, transforms
|
| 12 |
+
|
| 13 |
+
from utils.logger_setup import logger
|
| 14 |
+
from extractor import visualise_vgg, visualise_resnet, vf_extract
|
| 15 |
+
|
| 16 |
+
|
| 17 |
+
def load_metadata(video_type):
|
| 18 |
+
print(f'video_type: {video_type}\n')
|
| 19 |
+
# Test
|
| 20 |
+
if video_type == 'test':
|
| 21 |
+
return pd.read_csv("../metadata/test_videos.csv")
|
| 22 |
+
# NR:
|
| 23 |
+
elif video_type == 'resolution_ugc':
|
| 24 |
+
resolution = '360P'
|
| 25 |
+
return pd.read_csv(f"../metadata/YOUTUBE_UGC_{resolution}_metadata.csv")
|
| 26 |
+
else:
|
| 27 |
+
return pd.read_csv(f'../metadata/{video_type.upper()}_metadata.csv')
|
| 28 |
+
|
| 29 |
+
def get_video_paths(network_name, video_type, videodata, i):
|
| 30 |
+
video_name = videodata['vid'][i]
|
| 31 |
+
video_width = videodata['width'][i]
|
| 32 |
+
video_height = videodata['height'][i]
|
| 33 |
+
pixfmt = videodata['pixfmt'][i]
|
| 34 |
+
framerate = videodata['framerate'][i]
|
| 35 |
+
common_path = os.path.join('..', 'video_sampled_frame')
|
| 36 |
+
|
| 37 |
+
# Test
|
| 38 |
+
if video_type == 'test':
|
| 39 |
+
video_path = f"../ugc_original_videos/{video_name}.mp4"
|
| 40 |
+
|
| 41 |
+
# NR:
|
| 42 |
+
elif video_type == 'konvid_1k':
|
| 43 |
+
video_path = Path("D:/video_dataset/KoNViD_1k/KoNViD_1k_videos") / f"{video_name}.mp4"
|
| 44 |
+
elif video_type == 'lsvq_train' or video_type == 'lsvq_test' or video_type == 'lsvq_test_1080P':
|
| 45 |
+
print(f'video_name: {video_name}')
|
| 46 |
+
video_path = Path("D:/video_dataset/LSVQ") / f"{video_name}.mp4"
|
| 47 |
+
print(f'video_path: {video_path}')
|
| 48 |
+
video_name = os.path.splitext(os.path.basename(video_path))[0]
|
| 49 |
+
elif video_type == 'live_vqc':
|
| 50 |
+
video_path = Path("D:/video_dataset/LIVE-VQC/video") / f"{video_name}.mp4"
|
| 51 |
+
elif video_type == 'live_qualcomm':
|
| 52 |
+
video_path = Path("D:/video_dataset/LIVE-Qualcomm") / f"{video_name}.yuv"
|
| 53 |
+
video_name = os.path.splitext(os.path.basename(video_path))[0]
|
| 54 |
+
elif video_type == 'cvd_2014':
|
| 55 |
+
video_path = Path("D:/video_dataset/CVD2014") / f"{video_name}.avi"
|
| 56 |
+
video_name = os.path.splitext(os.path.basename(video_path))[0]
|
| 57 |
+
elif video_type == 'youtube_ugc':
|
| 58 |
+
video_path = Path("D:/video_dataset/ugc-dataset/youtube_ugc/") / f"{video_name}.mkv"
|
| 59 |
+
video_name = os.path.splitext(os.path.basename(video_path))[0]
|
| 60 |
+
sampled_frame_path = os.path.join(common_path, f'fragment_layerstack', f'video_{str(i + 1)}')
|
| 61 |
+
feature_name = f"{network_name}_feature_map"
|
| 62 |
+
|
| 63 |
+
if video_type == 'resolution_ugc':
|
| 64 |
+
resolution = '360P'
|
| 65 |
+
# video_path = f'/user/work/um20242/dataset/ugc-dataset/{resolution}/{video_name}.mkv'
|
| 66 |
+
video_path = Path(f"D:/video_dataset/ugc-dataset/youtube_ugc/original_videos/{resolution}") / f"{video_name}.mkv"
|
| 67 |
+
sampled_frame_path = os.path.join(common_path, f'ytugc_sampled_frame_{resolution}', f'video_{str(i + 1)}')
|
| 68 |
+
feature_name = f"{network_name}_feature_map_{resolution}"
|
| 69 |
+
|
| 70 |
+
return video_name, video_path, sampled_frame_path, feature_name, video_width, video_height, pixfmt, framerate
|
| 71 |
+
|
| 72 |
+
def get_deep_feature(network_name, video_name, frame, frame_number, model, device, layer_name):
|
| 73 |
+
png_path = f'../visualisation/{network_name}_{layer_name}/{video_name}/'
|
| 74 |
+
os.makedirs(png_path, exist_ok=True)
|
| 75 |
+
|
| 76 |
+
if network_name == 'resnet50':
|
| 77 |
+
if layer_name == 'layerstack':
|
| 78 |
+
all_layers = ['resnet50.conv1',
|
| 79 |
+
'resnet50.layer1[0]', 'resnet50.layer1[1]', 'resnet50.layer1[2]',
|
| 80 |
+
'resnet50.layer2[0]', 'resnet50.layer2[1]', 'resnet50.layer2[2]', 'resnet50.layer2[3]',
|
| 81 |
+
'resnet50.layer3[0]', 'resnet50.layer3[1]', 'resnet50.layer3[2]', 'resnet50.layer3[3]',
|
| 82 |
+
'resnet50.layer4[0]', 'resnet50.layer4[1]', 'resnet50.layer4[2]']
|
| 83 |
+
resnet50 = model
|
| 84 |
+
activations_dict, _, total_flops, total_params = visualise_resnet.process_video_frame(video_name, frame, frame_number, all_layers, resnet50, device)
|
| 85 |
+
|
| 86 |
+
elif network_name == 'vgg16':
|
| 87 |
+
if layer_name == 'layerstack':
|
| 88 |
+
all_layers = [0, 2, 5, 7, 10, 12, 14, 17, 19, 21, 24, 26, 28]
|
| 89 |
+
vgg16 = model
|
| 90 |
+
activations_dict, _, total_flops, total_params = visualise_vgg.process_video_frame(video_name, frame, frame_number, all_layers, vgg16, device)
|
| 91 |
+
|
| 92 |
+
return png_path, activations_dict, total_flops, total_params
|
| 93 |
+
|
| 94 |
+
def process_video_feature(video_feature, network_name, layer_name):
|
| 95 |
+
# print(f'video frame number: {len(video_feature)}')
|
| 96 |
+
|
| 97 |
+
# initialize an empty list to store processed frames
|
| 98 |
+
averaged_frames = []
|
| 99 |
+
# iterate through each frame in the video_feature
|
| 100 |
+
for frame in video_feature:
|
| 101 |
+
frame_features = []
|
| 102 |
+
|
| 103 |
+
if layer_name == 'layerstack':
|
| 104 |
+
# iterate through each layer in the current framex
|
| 105 |
+
for layer_array in frame.values():
|
| 106 |
+
# calculate the mean along the specified axes (1 and 2) for each layer
|
| 107 |
+
layer_mean = torch.mean(layer_array, dim=(1, 2))
|
| 108 |
+
# append the calculated mean to the list for the current frame
|
| 109 |
+
frame_features.append(layer_mean)
|
| 110 |
+
|
| 111 |
+
# concatenate the layer means horizontally to form the processed frame
|
| 112 |
+
processed_frame = torch.hstack(frame_features)
|
| 113 |
+
averaged_frames.append(processed_frame)
|
| 114 |
+
averaged_frames = torch.stack(averaged_frames)
|
| 115 |
+
|
| 116 |
+
# output the shape of the resulting feature vector
|
| 117 |
+
logger.debug(f"Shape of feature vector after global pooling: {averaged_frames.shape}")
|
| 118 |
+
return averaged_frames
|
| 119 |
+
|
| 120 |
+
def flow_to_rgb(flow):
|
| 121 |
+
mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1])
|
| 122 |
+
mag = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)
|
| 123 |
+
# convert angle to hue
|
| 124 |
+
hue = ang * 180 / np.pi / 2
|
| 125 |
+
|
| 126 |
+
# create HSV
|
| 127 |
+
hsv = np.zeros((flow.shape[0], flow.shape[1], 3), dtype=np.uint8)
|
| 128 |
+
hsv[..., 0] = hue
|
| 129 |
+
hsv[..., 1] = 255
|
| 130 |
+
hsv[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)
|
| 131 |
+
# convert HSV to RGB
|
| 132 |
+
rgb = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
|
| 133 |
+
return rgb
|
| 134 |
+
|
| 135 |
+
def get_patch_diff(residual_frame, patch_size):
|
| 136 |
+
h, w = residual_frame.shape[2:] # Assuming (1, C, H, W) shape
|
| 137 |
+
h_adj = (h // patch_size) * patch_size
|
| 138 |
+
w_adj = (w // patch_size) * patch_size
|
| 139 |
+
residual_frame_adj = residual_frame[:, :, :h_adj, :w_adj]
|
| 140 |
+
# calculate absolute patch difference
|
| 141 |
+
diff = torch.zeros((h_adj // patch_size, w_adj // patch_size), device=residual_frame.device)
|
| 142 |
+
for i in range(0, h_adj, patch_size):
|
| 143 |
+
for j in range(0, w_adj, patch_size):
|
| 144 |
+
patch = residual_frame_adj[:, :, i:i + patch_size, j:j + patch_size]
|
| 145 |
+
# absolute sum
|
| 146 |
+
diff[i // patch_size, j // patch_size] = torch.sum(torch.abs(patch))
|
| 147 |
+
return diff
|
| 148 |
+
|
| 149 |
+
def extract_important_patches(residual_frame, diff, patch_size=16, target_size=224, top_n=196):
|
| 150 |
+
# find top n patches indices
|
| 151 |
+
patch_idx = torch.argsort(-diff.view(-1))
|
| 152 |
+
top_patches = [(idx // diff.shape[1], idx % diff.shape[1]) for idx in patch_idx[:top_n]]
|
| 153 |
+
sorted_idx = sorted(top_patches, key=lambda x: (x[0], x[1]))
|
| 154 |
+
|
| 155 |
+
imp_patches_img = torch.zeros((residual_frame.shape[1], target_size, target_size), dtype=residual_frame.dtype, device=residual_frame.device)
|
| 156 |
+
patches_per_row = target_size // patch_size # 14
|
| 157 |
+
# order the patch in the original location relation
|
| 158 |
+
positions = []
|
| 159 |
+
for idx, (y, x) in enumerate(sorted_idx):
|
| 160 |
+
patch = residual_frame[:, :, y * patch_size:(y + 1) * patch_size, x * patch_size:(x + 1) * patch_size]
|
| 161 |
+
# new patch location
|
| 162 |
+
row_idx = idx // patches_per_row
|
| 163 |
+
col_idx = idx % patches_per_row
|
| 164 |
+
start_y = row_idx * patch_size
|
| 165 |
+
start_x = col_idx * patch_size
|
| 166 |
+
imp_patches_img[:, start_y:start_y + patch_size, start_x:start_x + patch_size] = patch
|
| 167 |
+
positions.append((y.item(), x.item()))
|
| 168 |
+
return imp_patches_img, positions
|
| 169 |
+
|
| 170 |
+
def get_frame_patches(frame, positions, patch_size, target_size):
|
| 171 |
+
imp_patches_img = torch.zeros((frame.shape[1], target_size, target_size), dtype=frame.dtype, device=frame.device)
|
| 172 |
+
patches_per_row = target_size // patch_size
|
| 173 |
+
|
| 174 |
+
for idx, (y, x) in enumerate(positions):
|
| 175 |
+
start_y = y * patch_size
|
| 176 |
+
start_x = x * patch_size
|
| 177 |
+
end_y = start_y + patch_size
|
| 178 |
+
end_x = start_x + patch_size
|
| 179 |
+
|
| 180 |
+
patch = frame[:, :, start_y:end_y, start_x:end_x]
|
| 181 |
+
row_idx = idx // patches_per_row
|
| 182 |
+
col_idx = idx % patches_per_row
|
| 183 |
+
target_start_y = row_idx * patch_size
|
| 184 |
+
target_start_x = col_idx * patch_size
|
| 185 |
+
|
| 186 |
+
imp_patches_img[:, target_start_y:target_start_y + patch_size,
|
| 187 |
+
target_start_x:target_start_x + patch_size] = patch.squeeze(0)
|
| 188 |
+
return imp_patches_img
|
| 189 |
+
|
| 190 |
+
def process_patches(original_path, frag_name, residual, patch_size, target_size, top_n):
|
| 191 |
+
diff = get_patch_diff(residual, patch_size)
|
| 192 |
+
imp_patches, positions = extract_important_patches(residual, diff, patch_size, target_size, top_n)
|
| 193 |
+
if frag_name == 'frame_diff':
|
| 194 |
+
frag_path = original_path.replace('.png', '_residual_imp.png')
|
| 195 |
+
elif frag_name == 'optical_flow':
|
| 196 |
+
frag_path = original_path.replace('.png', '_residual_of_imp.png')
|
| 197 |
+
# cv2.imwrite(frag_path, imp_patches)
|
| 198 |
+
return frag_path, imp_patches, positions
|
| 199 |
+
|
| 200 |
+
# Frame Differencing
|
| 201 |
+
def compute_frame_difference(frame_tensor, frame_next_tensor, frame_path, patch_size, target_size, top_n):
|
| 202 |
+
residual = torch.abs(frame_next_tensor - frame_tensor)
|
| 203 |
+
return process_patches(frame_path, 'frame_diff', residual, patch_size, target_size, top_n)
|
| 204 |
+
|
| 205 |
+
# Optical Flow
|
| 206 |
+
def compute_optical_flow(frame, frame_next, frame_path, patch_size, target_size, top_n, device):
|
| 207 |
+
flow = cv2.calcOpticalFlowFarneback(cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY),
|
| 208 |
+
cv2.cvtColor(frame_next, cv2.COLOR_BGR2GRAY),
|
| 209 |
+
None, 0.5, 3, 15, 3, 5, 1.2, 0)
|
| 210 |
+
opticalflow_rgb = flow_to_rgb(flow)
|
| 211 |
+
opticalflow_rgb_tensor = transforms.ToTensor()(opticalflow_rgb).unsqueeze(0).to(device)
|
| 212 |
+
return process_patches(frame_path, 'optical_flow', opticalflow_rgb_tensor, patch_size, target_size, top_n)
|
| 213 |
+
|
| 214 |
+
def merge_fragments(diff_fragment, flow_fragment):
|
| 215 |
+
alpha = 0.5
|
| 216 |
+
merged_fragment = diff_fragment * alpha + flow_fragment * (1 - alpha)
|
| 217 |
+
return merged_fragment
|
| 218 |
+
|
| 219 |
+
def concatenate_features(frame_feature, residual_feature):
|
| 220 |
+
return torch.cat((frame_feature, residual_feature), dim=-1)
|
| 221 |
+
|
| 222 |
+
|
| 223 |
+
if __name__ == '__main__':
|
| 224 |
+
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
|
| 225 |
+
if device.type == "cuda":
|
| 226 |
+
torch.cuda.set_device(0)
|
| 227 |
+
# device = torch.device("cpu")
|
| 228 |
+
|
| 229 |
+
video_type = 'test' # test
|
| 230 |
+
# resolution_ugc/konvid_1k/live_vqc/cvd_2014/live_qualcomm
|
| 231 |
+
# lsvq_train/lsvq_test/lsvq_test_1080P/
|
| 232 |
+
frag_name = 'framediff_frag' # framediff_frag, opticalflow_frag, sampled_frag, merged_frag
|
| 233 |
+
network_name = 'resnet50'
|
| 234 |
+
layer_name = 'layerstack'
|
| 235 |
+
if network_name == 'resnet50':
|
| 236 |
+
model = models.resnet50(pretrained=True).to(device)
|
| 237 |
+
else:
|
| 238 |
+
model = models.vgg16(pretrained=True).to(device)
|
| 239 |
+
|
| 240 |
+
logger.info(f"video type: {video_type}, frag name: {frag_name}, network name: {network_name}, layer name: {layer_name}")
|
| 241 |
+
logger.info(f"torch cuda: {torch.cuda.is_available()}")
|
| 242 |
+
|
| 243 |
+
videodata = load_metadata(video_type)
|
| 244 |
+
valid_video_types = ['test',
|
| 245 |
+
'resolution_ugc', 'konvid_1k', 'live_vqc', 'cvd_2014', 'live_qualcomm',
|
| 246 |
+
'lsvq_train', 'lsvq_test', 'lsvq_test_1080P']
|
| 247 |
+
target_size = 224
|
| 248 |
+
patch_size = 16
|
| 249 |
+
top_n = int((target_size / patch_size) * (target_size / patch_size))
|
| 250 |
+
|
| 251 |
+
begin_time = time.time()
|
| 252 |
+
if video_type in valid_video_types:
|
| 253 |
+
for i in range(len(videodata)):
|
| 254 |
+
start_time = time.time()
|
| 255 |
+
|
| 256 |
+
video_name, video_path, sampled_frame_path, feature_name, video_width, video_height, pixfmt, framerate = get_video_paths(network_name, video_type, videodata, i)
|
| 257 |
+
frames, frames_next = vf_extract.process_video_residual(video_type, video_name, framerate, video_path, sampled_frame_path)
|
| 258 |
+
|
| 259 |
+
logger.info(f'{video_name}')
|
| 260 |
+
all_frame_activations_feats = []
|
| 261 |
+
for j, (frame, frame_next) in enumerate(zip(frames, frames_next)):
|
| 262 |
+
frame_number = j + 1
|
| 263 |
+
frame_path = os.path.join(sampled_frame_path, f'{video_name}_{frame_number}.png')
|
| 264 |
+
# compute residual
|
| 265 |
+
frame_tensor = transforms.ToTensor()(frame).unsqueeze(0).to(device)
|
| 266 |
+
frame_next_tensor = transforms.ToTensor()(frame_next).unsqueeze(0).to(device)
|
| 267 |
+
|
| 268 |
+
# DNN feature extraction
|
| 269 |
+
if frag_name in ['framediff_frag', 'sampled_frag', 'merged_frag']:
|
| 270 |
+
residual_frag_path, diff_frag, positions = compute_frame_difference(frame_tensor, frame_next_tensor, frame_path, patch_size, target_size, top_n)
|
| 271 |
+
png_path, frag_activations, total_flops, total_params = get_deep_feature(network_name, video_name, diff_frag, frame_number, model, device, layer_name)
|
| 272 |
+
|
| 273 |
+
if frag_name == 'sampled_frag':
|
| 274 |
+
frame_patches = get_frame_patches(frame_tensor, positions, patch_size, target_size)
|
| 275 |
+
png_path, frag_activations, total_flops, total_params = get_deep_feature(network_name, video_name, frame_patches, frame_number, model, device, layer_name)
|
| 276 |
+
|
| 277 |
+
elif frag_name == 'merged_frag':
|
| 278 |
+
of_frag_path, flow_frag, _ = compute_optical_flow(frame, frame_next, frame_path, patch_size, target_size, top_n, device)
|
| 279 |
+
merged_frag = merge_fragments(diff_frag, flow_frag)
|
| 280 |
+
png_path, frag_activations, total_flops, total_params = get_deep_feature(network_name, video_name, merged_frag, frame_number, model, device, layer_name)
|
| 281 |
+
|
| 282 |
+
elif frag_name == 'opticalflow_frag':
|
| 283 |
+
of_frag_path, flow_frag, _ = compute_optical_flow(frame, frame_next, frame_path, patch_size, target_size, top_n, device)
|
| 284 |
+
png_path, frag_activations, total_flops, total_params = get_deep_feature(network_name, video_name, flow_frag, frame_number, model, device, layer_name)
|
| 285 |
+
|
| 286 |
+
# feature combined
|
| 287 |
+
all_frame_activations_feats.append(frag_activations)
|
| 288 |
+
|
| 289 |
+
averaged_frames_feats = process_video_feature(all_frame_activations_feats, network_name, layer_name)
|
| 290 |
+
print("Features shape:", averaged_frames_feats.shape)
|
| 291 |
+
# remove tmp folders
|
| 292 |
+
shutil.rmtree(png_path)
|
| 293 |
+
shutil.rmtree(sampled_frame_path)
|
| 294 |
+
|
| 295 |
+
averaged_npy = averaged_frames_feats.cpu().numpy()
|
| 296 |
+
# save the processed data as numpy file
|
| 297 |
+
output_npy_path = f'../features/{video_type}/{frag_name}_{network_name}_{layer_name}/'
|
| 298 |
+
os.makedirs(output_npy_path, exist_ok=True)
|
| 299 |
+
# output_npy_name = f'{output_npy_path}video_{str(i + 1)}_{feature_name}.npy'
|
| 300 |
+
# np.save(output_npy_name, averaged_npy)
|
| 301 |
+
# print(f'Processed file saved to: {output_npy_name}')
|
| 302 |
+
|
| 303 |
+
run_time = time.time() - start_time
|
| 304 |
+
print(f"Execution time for {video_name} feature extraction: {run_time:.4f} seconds\n")
|
| 305 |
+
|
| 306 |
+
# save feature mat file
|
| 307 |
+
average_data = np.mean(averaged_npy, axis=0)
|
| 308 |
+
if i == 0:
|
| 309 |
+
feats_matrix = np.zeros((len(videodata),) + average_data.shape)
|
| 310 |
+
feats_matrix[i] = average_data
|
| 311 |
+
|
| 312 |
+
print((f'All features shape: {feats_matrix.shape}'))
|
| 313 |
+
logger.debug(f'\n All features shape: {feats_matrix.shape}')
|
| 314 |
+
mat_file_path = f'../features/{video_type}/'
|
| 315 |
+
mat_file_name = f'{mat_file_path}{video_type}_{frag_name}_{network_name}_{layer_name}_feats.mat'
|
| 316 |
+
scipy.io.savemat(mat_file_name, {video_type: feats_matrix})
|
| 317 |
+
logger.debug(f'Successfully created {mat_file_name}')
|
| 318 |
+
logger.debug(f"Execution time for all feature extraction: {time.time() - begin_time:.4f} seconds\n")
|
src/feature_fragment_pool.py
ADDED
|
@@ -0,0 +1,333 @@
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|
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|
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|
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|
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|
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|
|
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|
|
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|
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|
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|
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|
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|
|
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|
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|
|
|
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|
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|
|
|
|
|
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|
|
|
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|
|
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|
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|
|
|
|
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|
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|
|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
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|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
from PIL import Image
|
| 2 |
+
import pandas as pd
|
| 3 |
+
import numpy as np
|
| 4 |
+
import os
|
| 5 |
+
from pathlib import Path
|
| 6 |
+
import scipy.io
|
| 7 |
+
import shutil
|
| 8 |
+
import torch
|
| 9 |
+
import time
|
| 10 |
+
import cv2
|
| 11 |
+
from torchvision import models, transforms
|
| 12 |
+
|
| 13 |
+
from utils.logger_setup import logger
|
| 14 |
+
from extractor import visualise_vgg_layer, visualise_resnet_layer, visualise_vit_layer, vf_extract
|
| 15 |
+
|
| 16 |
+
|
| 17 |
+
def load_metadata(video_type):
|
| 18 |
+
print(f'video_type: {video_type}\n')
|
| 19 |
+
# Test
|
| 20 |
+
if video_type == 'test':
|
| 21 |
+
return pd.read_csv("../metadata/test_videos.csv")
|
| 22 |
+
# NR:
|
| 23 |
+
elif video_type == 'resolution_ugc':
|
| 24 |
+
resolution = '360P'
|
| 25 |
+
return pd.read_csv(f"../metadata/YOUTUBE_UGC_{resolution}_metadata.csv")
|
| 26 |
+
else:
|
| 27 |
+
return pd.read_csv(f'../metadata/{video_type.upper()}_metadata.csv')
|
| 28 |
+
|
| 29 |
+
def get_video_paths(network_name, video_type, videodata, i):
|
| 30 |
+
video_name = videodata['vid'][i]
|
| 31 |
+
video_width = videodata['width'][i]
|
| 32 |
+
video_height = videodata['height'][i]
|
| 33 |
+
pixfmt = videodata['pixfmt'][i]
|
| 34 |
+
framerate = videodata['framerate'][i]
|
| 35 |
+
common_path = os.path.join('..', 'video_sampled_frame')
|
| 36 |
+
|
| 37 |
+
# Test
|
| 38 |
+
if video_type == 'test':
|
| 39 |
+
video_path = f"../ugc_original_videos/{video_name}.mp4"
|
| 40 |
+
|
| 41 |
+
# NR:
|
| 42 |
+
elif video_type == 'konvid_1k':
|
| 43 |
+
video_path = Path("D:/video_dataset/KoNViD_1k/KoNViD_1k_videos") / f"{video_name}.mp4"
|
| 44 |
+
elif video_type == 'lsvq_train' or video_type == 'lsvq_test' or video_type == 'lsvq_test_1080P':
|
| 45 |
+
print(f'video_name: {video_name}')
|
| 46 |
+
video_path = Path("D:/video_dataset/LSVQ") / f"{video_name}.mp4"
|
| 47 |
+
print(f'video_path: {video_path}')
|
| 48 |
+
video_name = os.path.splitext(os.path.basename(video_path))[0]
|
| 49 |
+
elif video_type == 'live_vqc':
|
| 50 |
+
video_path = Path("D:/video_dataset/LIVE-VQC/video") / f"{video_name}.mp4"
|
| 51 |
+
elif video_type == 'live_qualcomm':
|
| 52 |
+
video_path = Path("D:/video_dataset/LIVE-Qualcomm") / f"{video_name}.yuv"
|
| 53 |
+
video_name = os.path.splitext(os.path.basename(video_path))[0]
|
| 54 |
+
elif video_type == 'cvd_2014':
|
| 55 |
+
video_path = Path("D:/video_dataset/CVD2014") / f"{video_name}.avi"
|
| 56 |
+
video_name = os.path.splitext(os.path.basename(video_path))[0]
|
| 57 |
+
elif video_type == 'youtube_ugc':
|
| 58 |
+
video_path = Path("D:/video_dataset/ugc-dataset/youtube_ugc/") / f"{video_name}.mkv"
|
| 59 |
+
video_name = os.path.splitext(os.path.basename(video_path))[0]
|
| 60 |
+
sampled_frame_path = os.path.join(common_path, f'fragment_layerstack', f'video_{str(i + 1)}')
|
| 61 |
+
feature_name = f"{network_name}_feature_map"
|
| 62 |
+
|
| 63 |
+
if video_type == 'resolution_ugc':
|
| 64 |
+
resolution = '360P'
|
| 65 |
+
# video_path = f'/user/work/um20242/dataset/ugc-dataset/{resolution}/{video_name}.mkv'
|
| 66 |
+
video_path = Path(f"D:/video_dataset/ugc-dataset/youtube_ugc/original_videos/{resolution}") / f"{video_name}.mkv"
|
| 67 |
+
sampled_frame_path = os.path.join(common_path, f'ytugc_sampled_frame_{resolution}', f'video_{str(i + 1)}')
|
| 68 |
+
feature_name = f"{network_name}_feature_map_{resolution}"
|
| 69 |
+
|
| 70 |
+
return video_name, video_path, sampled_frame_path, feature_name, video_width, video_height, pixfmt, framerate
|
| 71 |
+
|
| 72 |
+
def get_deep_feature(network_name, video_name, frame, frame_number, model, device, layer_name):
|
| 73 |
+
png_path = f'../visualisation/{network_name}_{layer_name}/{video_name}/'
|
| 74 |
+
os.makedirs(png_path, exist_ok=True)
|
| 75 |
+
|
| 76 |
+
if network_name == 'resnet50':
|
| 77 |
+
if layer_name == 'pool':
|
| 78 |
+
visual_layer = 'resnet50.avgpool' # before avg_pool
|
| 79 |
+
resnet50 = model
|
| 80 |
+
activations_dict, _, total_flops, total_params = visualise_resnet_layer.process_video_frame(video_name, frame, frame_number, visual_layer, resnet50, device)
|
| 81 |
+
|
| 82 |
+
elif network_name == 'vgg16':
|
| 83 |
+
if layer_name == 'pool':
|
| 84 |
+
# visual_layer = 'fc1'
|
| 85 |
+
visual_layer = 'fc2' # fc1 = vgg16.classifier[0], fc2 = vgg16.classifier[3]
|
| 86 |
+
vgg16 = model
|
| 87 |
+
activations_dict, _, total_flops, total_params = visualise_vgg_layer.process_video_frame(video_name, frame, frame_number, visual_layer, vgg16, device)
|
| 88 |
+
|
| 89 |
+
elif network_name == 'vit':
|
| 90 |
+
patch_size = 16
|
| 91 |
+
activations_dict, _, total_flops, total_params = visualise_vit_layer.process_video_frame(video_name, frame, frame_number, model, patch_size, device)
|
| 92 |
+
|
| 93 |
+
return png_path, activations_dict, total_flops, total_params
|
| 94 |
+
|
| 95 |
+
def process_video_feature(video_feature, network_name, layer_name):
|
| 96 |
+
# print(f'video frame number: {len(video_feature)}')
|
| 97 |
+
|
| 98 |
+
# initialize an empty list to store processed frames
|
| 99 |
+
averaged_frames = []
|
| 100 |
+
# iterate through each frame in the video_feature
|
| 101 |
+
for frame in video_feature:
|
| 102 |
+
frame_features = []
|
| 103 |
+
|
| 104 |
+
if layer_name == 'pool':
|
| 105 |
+
if network_name == 'vit':
|
| 106 |
+
# global mean and std
|
| 107 |
+
global_mean = torch.mean(frame, dim=0)
|
| 108 |
+
global_max = torch.max(frame, dim=0)[0]
|
| 109 |
+
global_std = torch.std(frame, dim=0)
|
| 110 |
+
# concatenate all pooling
|
| 111 |
+
combined_features = torch.hstack([global_mean, global_max, global_std])
|
| 112 |
+
frame_features.append(combined_features)
|
| 113 |
+
|
| 114 |
+
elif network_name == 'resnet50':
|
| 115 |
+
frame = torch.squeeze(torch.tensor(frame))
|
| 116 |
+
# global mean and std
|
| 117 |
+
global_mean = torch.mean(frame, dim=0)
|
| 118 |
+
global_max = torch.max(frame, dim=0)[0]
|
| 119 |
+
global_std = torch.std(frame, dim=0)
|
| 120 |
+
# concatenate all pooling
|
| 121 |
+
combined_features = torch.hstack([frame, global_mean, global_max, global_std])
|
| 122 |
+
frame_features.append(combined_features)
|
| 123 |
+
|
| 124 |
+
# concatenate the layer means horizontally to form the processed frame
|
| 125 |
+
processed_frame = torch.hstack(frame_features)
|
| 126 |
+
averaged_frames.append(processed_frame)
|
| 127 |
+
averaged_frames = torch.stack(averaged_frames)
|
| 128 |
+
|
| 129 |
+
# output the shape of the resulting feature vector
|
| 130 |
+
logger.debug(f"Shape of feature vector after global pooling: {averaged_frames.shape}")
|
| 131 |
+
return averaged_frames
|
| 132 |
+
|
| 133 |
+
def flow_to_rgb(flow):
|
| 134 |
+
mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1])
|
| 135 |
+
mag = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)
|
| 136 |
+
# convert angle to hue
|
| 137 |
+
hue = ang * 180 / np.pi / 2
|
| 138 |
+
|
| 139 |
+
# create HSV
|
| 140 |
+
hsv = np.zeros((flow.shape[0], flow.shape[1], 3), dtype=np.uint8)
|
| 141 |
+
hsv[..., 0] = hue
|
| 142 |
+
hsv[..., 1] = 255
|
| 143 |
+
hsv[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)
|
| 144 |
+
# convert HSV to RGB
|
| 145 |
+
rgb = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
|
| 146 |
+
return rgb
|
| 147 |
+
|
| 148 |
+
def get_patch_diff(residual_frame, patch_size):
|
| 149 |
+
h, w = residual_frame.shape[2:] # Assuming (1, C, H, W) shape
|
| 150 |
+
h_adj = (h // patch_size) * patch_size
|
| 151 |
+
w_adj = (w // patch_size) * patch_size
|
| 152 |
+
residual_frame_adj = residual_frame[:, :, :h_adj, :w_adj]
|
| 153 |
+
# calculate absolute patch difference
|
| 154 |
+
diff = torch.zeros((h_adj // patch_size, w_adj // patch_size), device=residual_frame.device)
|
| 155 |
+
for i in range(0, h_adj, patch_size):
|
| 156 |
+
for j in range(0, w_adj, patch_size):
|
| 157 |
+
patch = residual_frame_adj[:, :, i:i + patch_size, j:j + patch_size]
|
| 158 |
+
# absolute sum
|
| 159 |
+
diff[i // patch_size, j // patch_size] = torch.sum(torch.abs(patch))
|
| 160 |
+
return diff
|
| 161 |
+
|
| 162 |
+
def extract_important_patches(residual_frame, diff, patch_size=16, target_size=224, top_n=196):
|
| 163 |
+
# find top n patches indices
|
| 164 |
+
patch_idx = torch.argsort(-diff.view(-1))
|
| 165 |
+
top_patches = [(idx // diff.shape[1], idx % diff.shape[1]) for idx in patch_idx[:top_n]]
|
| 166 |
+
sorted_idx = sorted(top_patches, key=lambda x: (x[0], x[1]))
|
| 167 |
+
|
| 168 |
+
imp_patches_img = torch.zeros((residual_frame.shape[1], target_size, target_size), dtype=residual_frame.dtype, device=residual_frame.device)
|
| 169 |
+
patches_per_row = target_size // patch_size # 14
|
| 170 |
+
# order the patch in the original location relation
|
| 171 |
+
positions = []
|
| 172 |
+
for idx, (y, x) in enumerate(sorted_idx):
|
| 173 |
+
patch = residual_frame[:, :, y * patch_size:(y + 1) * patch_size, x * patch_size:(x + 1) * patch_size]
|
| 174 |
+
# new patch location
|
| 175 |
+
row_idx = idx // patches_per_row
|
| 176 |
+
col_idx = idx % patches_per_row
|
| 177 |
+
start_y = row_idx * patch_size
|
| 178 |
+
start_x = col_idx * patch_size
|
| 179 |
+
imp_patches_img[:, start_y:start_y + patch_size, start_x:start_x + patch_size] = patch
|
| 180 |
+
positions.append((y.item(), x.item()))
|
| 181 |
+
return imp_patches_img, positions
|
| 182 |
+
|
| 183 |
+
def get_frame_patches(frame, positions, patch_size, target_size):
|
| 184 |
+
imp_patches_img = torch.zeros((frame.shape[1], target_size, target_size), dtype=frame.dtype, device=frame.device)
|
| 185 |
+
patches_per_row = target_size // patch_size
|
| 186 |
+
|
| 187 |
+
for idx, (y, x) in enumerate(positions):
|
| 188 |
+
start_y = y * patch_size
|
| 189 |
+
start_x = x * patch_size
|
| 190 |
+
end_y = start_y + patch_size
|
| 191 |
+
end_x = start_x + patch_size
|
| 192 |
+
|
| 193 |
+
patch = frame[:, :, start_y:end_y, start_x:end_x]
|
| 194 |
+
row_idx = idx // patches_per_row
|
| 195 |
+
col_idx = idx % patches_per_row
|
| 196 |
+
target_start_y = row_idx * patch_size
|
| 197 |
+
target_start_x = col_idx * patch_size
|
| 198 |
+
|
| 199 |
+
imp_patches_img[:, target_start_y:target_start_y + patch_size,
|
| 200 |
+
target_start_x:target_start_x + patch_size] = patch.squeeze(0)
|
| 201 |
+
return imp_patches_img
|
| 202 |
+
|
| 203 |
+
def process_patches(original_path, frag_name, residual, patch_size, target_size, top_n):
|
| 204 |
+
diff = get_patch_diff(residual, patch_size)
|
| 205 |
+
imp_patches, positions = extract_important_patches(residual, diff, patch_size, target_size, top_n)
|
| 206 |
+
if frag_name == 'frame_diff':
|
| 207 |
+
frag_path = original_path.replace('.png', '_residual_imp.png')
|
| 208 |
+
elif frag_name == 'optical_flow':
|
| 209 |
+
frag_path = original_path.replace('.png', '_residual_of_imp.png')
|
| 210 |
+
# cv2.imwrite(frag_path, imp_patches)
|
| 211 |
+
return frag_path, imp_patches, positions
|
| 212 |
+
|
| 213 |
+
# Frame Differencing
|
| 214 |
+
def compute_frame_difference(frame_tensor, frame_next_tensor, frame_path, patch_size, target_size, top_n):
|
| 215 |
+
residual = torch.abs(frame_next_tensor - frame_tensor)
|
| 216 |
+
return process_patches(frame_path, 'frame_diff', residual, patch_size, target_size, top_n)
|
| 217 |
+
|
| 218 |
+
# Optical Flow
|
| 219 |
+
def compute_optical_flow(frame, frame_next, frame_path, patch_size, target_size, top_n, device):
|
| 220 |
+
flow = cv2.calcOpticalFlowFarneback(cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY),
|
| 221 |
+
cv2.cvtColor(frame_next, cv2.COLOR_BGR2GRAY),
|
| 222 |
+
None, 0.5, 3, 15, 3, 5, 1.2, 0)
|
| 223 |
+
opticalflow_rgb = flow_to_rgb(flow)
|
| 224 |
+
opticalflow_rgb_tensor = transforms.ToTensor()(opticalflow_rgb).unsqueeze(0).to(device)
|
| 225 |
+
return process_patches(frame_path, 'optical_flow', opticalflow_rgb_tensor, patch_size, target_size, top_n)
|
| 226 |
+
|
| 227 |
+
def merge_fragments(diff_fragment, flow_fragment):
|
| 228 |
+
alpha = 0.5
|
| 229 |
+
merged_fragment = diff_fragment * alpha + flow_fragment * (1 - alpha)
|
| 230 |
+
return merged_fragment
|
| 231 |
+
|
| 232 |
+
def concatenate_features(frame_feature, residual_feature):
|
| 233 |
+
return torch.cat((frame_feature, residual_feature), dim=-1)
|
| 234 |
+
|
| 235 |
+
|
| 236 |
+
if __name__ == '__main__':
|
| 237 |
+
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
|
| 238 |
+
if device.type == "cuda":
|
| 239 |
+
torch.cuda.set_device(0)
|
| 240 |
+
# device = torch.device("cpu")
|
| 241 |
+
|
| 242 |
+
video_type = 'test' # test
|
| 243 |
+
# resolution_ugc/konvid_1k/live_vqc/cvd_2014/live_qualcomm
|
| 244 |
+
# lsvq_train/lsvq_test/lsvq_test_1080P/
|
| 245 |
+
frag_name = 'framediff_frag' # framediff_frag, opticalflow_frag, sampled_frag, merged_frag
|
| 246 |
+
network_name = 'vit'
|
| 247 |
+
layer_name = 'pool'
|
| 248 |
+
if network_name == 'vit':
|
| 249 |
+
model = visualise_vit_layer.VitGenerator('vit_base', 16, device, evaluate=True, random=False, verbose=True)
|
| 250 |
+
elif network_name == 'resnet50':
|
| 251 |
+
model = models.resnet50(pretrained=True).to(device)
|
| 252 |
+
else:
|
| 253 |
+
model = models.vgg16(pretrained=True).to(device)
|
| 254 |
+
|
| 255 |
+
logger.info(f"video type: {video_type}, frag name: {frag_name}, network name: {network_name}, layer name: {layer_name}")
|
| 256 |
+
logger.info(f"torch cuda: {torch.cuda.is_available()}")
|
| 257 |
+
|
| 258 |
+
videodata = load_metadata(video_type)
|
| 259 |
+
valid_video_types = ['test',
|
| 260 |
+
'resolution_ugc', 'konvid_1k', 'live_vqc', 'cvd_2014', 'live_qualcomm',
|
| 261 |
+
'lsvq_train', 'lsvq_test', 'lsvq_test_1080P']
|
| 262 |
+
target_size = 224
|
| 263 |
+
patch_size = 16
|
| 264 |
+
top_n = int((target_size / patch_size) * (target_size / patch_size))
|
| 265 |
+
|
| 266 |
+
begin_time = time.time()
|
| 267 |
+
if video_type in valid_video_types:
|
| 268 |
+
for i in range(len(videodata)):
|
| 269 |
+
start_time = time.time()
|
| 270 |
+
|
| 271 |
+
video_name, video_path, sampled_frame_path, feature_name, video_width, video_height, pixfmt, framerate = get_video_paths(network_name, video_type, videodata, i)
|
| 272 |
+
frames, frames_next = vf_extract.process_video_residual(video_type, video_name, framerate, video_path, sampled_frame_path)
|
| 273 |
+
|
| 274 |
+
logger.info(f'{video_name}')
|
| 275 |
+
all_frame_activations_feats = []
|
| 276 |
+
for j, (frame, frame_next) in enumerate(zip(frames, frames_next)):
|
| 277 |
+
frame_number = j + 1
|
| 278 |
+
frame_path = os.path.join(sampled_frame_path, f'{video_name}_{frame_number}.png')
|
| 279 |
+
# compute residual
|
| 280 |
+
frame_tensor = transforms.ToTensor()(frame).unsqueeze(0).to(device)
|
| 281 |
+
frame_next_tensor = transforms.ToTensor()(frame_next).unsqueeze(0).to(device)
|
| 282 |
+
|
| 283 |
+
# DNN feature extraction
|
| 284 |
+
if frag_name in ['framediff_frag', 'sampled_frag', 'merged_frag']:
|
| 285 |
+
residual_frag_path, diff_frag, positions = compute_frame_difference(frame_tensor, frame_next_tensor, frame_path, patch_size, target_size, top_n)
|
| 286 |
+
png_path, frag_activations, total_flops, total_params = get_deep_feature(network_name, video_name, diff_frag, frame_number, model, device, layer_name)
|
| 287 |
+
|
| 288 |
+
if frag_name == 'sampled_frag':
|
| 289 |
+
frame_patches = get_frame_patches(frame_tensor, positions, patch_size, target_size)
|
| 290 |
+
png_path, frag_activations, total_flops, total_params = get_deep_feature(network_name, video_name, frame_patches, frame_number, model, device, layer_name)
|
| 291 |
+
|
| 292 |
+
elif frag_name == 'merged_frag':
|
| 293 |
+
of_frag_path, flow_frag, _ = compute_optical_flow(frame, frame_next, frame_path, patch_size, target_size, top_n, device)
|
| 294 |
+
merged_frag = merge_fragments(diff_frag, flow_frag)
|
| 295 |
+
png_path, frag_activations, total_flops, total_params = get_deep_feature(network_name, video_name, merged_frag, frame_number, model, device, layer_name)
|
| 296 |
+
|
| 297 |
+
elif frag_name == 'opticalflow_frag':
|
| 298 |
+
of_frag_path, flow_frag, _ = compute_optical_flow(frame, frame_next, frame_path, patch_size, target_size, top_n, device)
|
| 299 |
+
png_path, frag_activations, total_flops, total_params = get_deep_feature(network_name, video_name, flow_frag, frame_number, model, device, layer_name)
|
| 300 |
+
|
| 301 |
+
# feature combined
|
| 302 |
+
all_frame_activations_feats.append(frag_activations)
|
| 303 |
+
|
| 304 |
+
averaged_frames_feats = process_video_feature(all_frame_activations_feats, network_name, layer_name)
|
| 305 |
+
print("Features shape:", averaged_frames_feats.shape)
|
| 306 |
+
# remove tmp folders
|
| 307 |
+
shutil.rmtree(png_path)
|
| 308 |
+
shutil.rmtree(sampled_frame_path)
|
| 309 |
+
|
| 310 |
+
averaged_npy = averaged_frames_feats.cpu().numpy()
|
| 311 |
+
# save the processed data as numpy file
|
| 312 |
+
output_npy_path = f'../features/{video_type}/{frag_name}_{network_name}_{layer_name}/'
|
| 313 |
+
os.makedirs(output_npy_path, exist_ok=True)
|
| 314 |
+
# output_npy_name = f'{output_npy_path}video_{str(i + 1)}_{feature_name}.npy'
|
| 315 |
+
# np.save(output_npy_name, averaged_npy)
|
| 316 |
+
# print(f'Processed file saved to: {output_npy_name}')
|
| 317 |
+
|
| 318 |
+
run_time = time.time() - start_time
|
| 319 |
+
print(f"Execution time for {video_name} feature extraction: {run_time:.4f} seconds\n")
|
| 320 |
+
|
| 321 |
+
# save feature mat file
|
| 322 |
+
average_data = np.mean(averaged_npy, axis=0)
|
| 323 |
+
if i == 0:
|
| 324 |
+
feats_matrix = np.zeros((len(videodata),) + average_data.shape)
|
| 325 |
+
feats_matrix[i] = average_data
|
| 326 |
+
|
| 327 |
+
print((f'All features shape: {feats_matrix.shape}'))
|
| 328 |
+
logger.debug(f'\n All features shape: {feats_matrix.shape}')
|
| 329 |
+
mat_file_path = f'../features/{video_type}/'
|
| 330 |
+
mat_file_name = f'{mat_file_path}{video_type}_{frag_name}_{network_name}_{layer_name}_feats.mat'
|
| 331 |
+
scipy.io.savemat(mat_file_name, {video_type: feats_matrix})
|
| 332 |
+
logger.debug(f'Successfully created {mat_file_name}')
|
| 333 |
+
logger.debug(f"Execution time for all feature extraction: {time.time() - begin_time:.4f} seconds\n")
|
src/feature_layerstack.py
ADDED
|
@@ -0,0 +1,239 @@
|
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| 1 |
+
from PIL import Image
|
| 2 |
+
import pandas as pd
|
| 3 |
+
import numpy as np
|
| 4 |
+
import os
|
| 5 |
+
from pathlib import Path
|
| 6 |
+
import scipy.io
|
| 7 |
+
import shutil
|
| 8 |
+
import torch
|
| 9 |
+
import time
|
| 10 |
+
import cv2
|
| 11 |
+
from torchvision import models, transforms
|
| 12 |
+
|
| 13 |
+
from utils.logger_setup import logger
|
| 14 |
+
from extractor import visualise_vgg, visualise_resnet, vf_extract
|
| 15 |
+
|
| 16 |
+
|
| 17 |
+
def load_metadata(video_type):
|
| 18 |
+
print(f'video_type: {video_type}\n')
|
| 19 |
+
# Test
|
| 20 |
+
if video_type == 'test':
|
| 21 |
+
return pd.read_csv("../metadata/test_videos.csv")
|
| 22 |
+
# NR:
|
| 23 |
+
elif video_type == 'resolution_ugc':
|
| 24 |
+
resolution = '360P'
|
| 25 |
+
return pd.read_csv(f"../metadata/YOUTUBE_UGC_{resolution}_metadata.csv")
|
| 26 |
+
else:
|
| 27 |
+
return pd.read_csv(f'../metadata/{video_type.upper()}_metadata.csv')
|
| 28 |
+
|
| 29 |
+
def get_video_paths(network_name, video_type, videodata, i):
|
| 30 |
+
video_name = videodata['vid'][i]
|
| 31 |
+
video_width = videodata['width'][i]
|
| 32 |
+
video_height = videodata['height'][i]
|
| 33 |
+
pixfmt = videodata['pixfmt'][i]
|
| 34 |
+
framerate = videodata['framerate'][i]
|
| 35 |
+
common_path = os.path.join('..', 'video_sampled_frame')
|
| 36 |
+
|
| 37 |
+
# Test
|
| 38 |
+
if video_type == 'test':
|
| 39 |
+
video_path = f"../ugc_original_videos/{video_name}.mp4"
|
| 40 |
+
|
| 41 |
+
# NR:
|
| 42 |
+
elif video_type == 'konvid_1k':
|
| 43 |
+
video_path = Path("D:/video_dataset/KoNViD_1k/KoNViD_1k_videos") / f"{video_name}.mp4"
|
| 44 |
+
elif video_type == 'lsvq_train' or video_type == 'lsvq_test' or video_type == 'lsvq_test_1080P':
|
| 45 |
+
print(f'video_name: {video_name}')
|
| 46 |
+
video_path = Path("D:/video_dataset/LSVQ") / f"{video_name}.mp4"
|
| 47 |
+
print(f'video_path: {video_path}')
|
| 48 |
+
video_name = os.path.splitext(os.path.basename(video_path))[0]
|
| 49 |
+
elif video_type == 'live_vqc':
|
| 50 |
+
video_path = Path("D:/video_dataset/LIVE-VQC/video") / f"{video_name}.mp4"
|
| 51 |
+
elif video_type == 'live_qualcomm':
|
| 52 |
+
video_path = Path("D:/video_dataset/LIVE-Qualcomm") / f"{video_name}.yuv"
|
| 53 |
+
video_name = os.path.splitext(os.path.basename(video_path))[0]
|
| 54 |
+
elif video_type == 'cvd_2014':
|
| 55 |
+
video_path = Path("D:/video_dataset/CVD2014") / f"{video_name}.avi"
|
| 56 |
+
video_name = os.path.splitext(os.path.basename(video_path))[0]
|
| 57 |
+
elif video_type == 'youtube_ugc':
|
| 58 |
+
video_path = Path("D:/video_dataset/ugc-dataset/youtube_ugc/") / f"{video_name}.mkv"
|
| 59 |
+
video_name = os.path.splitext(os.path.basename(video_path))[0]
|
| 60 |
+
sampled_frame_path = os.path.join(common_path, f'layerstack', f'video_{str(i + 1)}')
|
| 61 |
+
feature_name = f"{network_name}_feature_map"
|
| 62 |
+
|
| 63 |
+
if video_type == 'resolution_ugc':
|
| 64 |
+
resolution = '360P'
|
| 65 |
+
# video_path = f'/user/work/um20242/dataset/ugc-dataset/{resolution}/{video_name}.mkv'
|
| 66 |
+
video_path = Path(f"D:/video_dataset/ugc-dataset/youtube_ugc/original_videos/{resolution}") / f"{video_name}.mkv"
|
| 67 |
+
sampled_frame_path = os.path.join(common_path, f'ytugc_sampled_frame_{resolution}', f'video_{str(i + 1)}')
|
| 68 |
+
feature_name = f"{network_name}_feature_map_{resolution}"
|
| 69 |
+
|
| 70 |
+
return video_name, video_path, sampled_frame_path, feature_name, video_width, video_height, pixfmt, framerate
|
| 71 |
+
|
| 72 |
+
def get_deep_feature(network_name, video_name, frame, frame_number, model, device, layer_name):
|
| 73 |
+
png_path = f'../visualisation/{network_name}_{layer_name}/{video_name}/'
|
| 74 |
+
os.makedirs(png_path, exist_ok=True)
|
| 75 |
+
|
| 76 |
+
if network_name == 'resnet50':
|
| 77 |
+
if layer_name == 'layerstack':
|
| 78 |
+
all_layers = ['resnet50.conv1',
|
| 79 |
+
'resnet50.layer1[0]', 'resnet50.layer1[1]', 'resnet50.layer1[2]',
|
| 80 |
+
'resnet50.layer2[0]', 'resnet50.layer2[1]', 'resnet50.layer2[2]', 'resnet50.layer2[3]',
|
| 81 |
+
'resnet50.layer3[0]', 'resnet50.layer3[1]', 'resnet50.layer3[2]', 'resnet50.layer3[3]',
|
| 82 |
+
'resnet50.layer4[0]', 'resnet50.layer4[1]', 'resnet50.layer4[2]']
|
| 83 |
+
resnet50 = model
|
| 84 |
+
activations_dict, _, total_flops, total_params = visualise_resnet.process_video_frame(video_name, frame, frame_number, all_layers, resnet50, device)
|
| 85 |
+
|
| 86 |
+
elif network_name == 'vgg16':
|
| 87 |
+
if layer_name == 'layerstack':
|
| 88 |
+
all_layers = [0, 2, 5, 7, 10, 12, 14, 17, 19, 21, 24, 26, 28]
|
| 89 |
+
vgg16 = model
|
| 90 |
+
activations_dict, _, total_flops, total_params = visualise_vgg.process_video_frame(video_name, frame, frame_number, all_layers, vgg16, device)
|
| 91 |
+
|
| 92 |
+
return png_path, activations_dict, total_flops, total_params
|
| 93 |
+
|
| 94 |
+
def process_video_feature(video_feature, network_name, layer_name):
|
| 95 |
+
# print(f'video frame number: {len(video_feature)}')
|
| 96 |
+
|
| 97 |
+
# initialize an empty list to store processed frames
|
| 98 |
+
averaged_frames = []
|
| 99 |
+
# iterate through each frame in the video_feature
|
| 100 |
+
for frame in video_feature:
|
| 101 |
+
frame_features = []
|
| 102 |
+
|
| 103 |
+
if layer_name == 'layerstack':
|
| 104 |
+
# iterate through each layer in the current framex
|
| 105 |
+
for layer_array in frame.values():
|
| 106 |
+
# calculate the mean along the specified axes (1 and 2) for each layer
|
| 107 |
+
layer_mean = torch.mean(layer_array, dim=(1, 2))
|
| 108 |
+
# append the calculated mean to the list for the current frame
|
| 109 |
+
frame_features.append(layer_mean)
|
| 110 |
+
|
| 111 |
+
# concatenate the layer means horizontally to form the processed frame
|
| 112 |
+
processed_frame = torch.hstack(frame_features)
|
| 113 |
+
averaged_frames.append(processed_frame)
|
| 114 |
+
averaged_frames = torch.stack(averaged_frames)
|
| 115 |
+
|
| 116 |
+
# output the shape of the resulting feature vector
|
| 117 |
+
logger.debug(f"Shape of feature vector after global pooling: {averaged_frames.shape}")
|
| 118 |
+
return averaged_frames
|
| 119 |
+
|
| 120 |
+
def flow_to_rgb(flow):
|
| 121 |
+
mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1])
|
| 122 |
+
mag = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)
|
| 123 |
+
# convert angle to hue
|
| 124 |
+
hue = ang * 180 / np.pi / 2
|
| 125 |
+
|
| 126 |
+
# create HSV
|
| 127 |
+
hsv = np.zeros((flow.shape[0], flow.shape[1], 3), dtype=np.uint8)
|
| 128 |
+
hsv[..., 0] = hue
|
| 129 |
+
hsv[..., 1] = 255
|
| 130 |
+
hsv[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)
|
| 131 |
+
# convert HSV to RGB
|
| 132 |
+
rgb = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
|
| 133 |
+
return rgb
|
| 134 |
+
|
| 135 |
+
# Frame Differencing
|
| 136 |
+
def compute_frame_difference(frame_tensor, frame_next_tensor):
|
| 137 |
+
residual = torch.abs(frame_next_tensor - frame_tensor)
|
| 138 |
+
return residual
|
| 139 |
+
|
| 140 |
+
# Optical Flow
|
| 141 |
+
def compute_optical_flow(frame, frame_next, device):
|
| 142 |
+
flow = cv2.calcOpticalFlowFarneback(cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY),
|
| 143 |
+
cv2.cvtColor(frame_next, cv2.COLOR_BGR2GRAY),
|
| 144 |
+
None, 0.5, 3, 15, 3, 5, 1.2, 0)
|
| 145 |
+
opticalflow_rgb = flow_to_rgb(flow)
|
| 146 |
+
opticalflow_rgb_tensor = transforms.ToTensor()(opticalflow_rgb).unsqueeze(0).to(device)
|
| 147 |
+
return opticalflow_rgb_tensor
|
| 148 |
+
|
| 149 |
+
|
| 150 |
+
if __name__ == '__main__':
|
| 151 |
+
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
|
| 152 |
+
if device.type == "cuda":
|
| 153 |
+
torch.cuda.set_device(0)
|
| 154 |
+
# device = torch.device("cpu")
|
| 155 |
+
|
| 156 |
+
video_type = 'test' # test
|
| 157 |
+
# resolution_ugc/konvid_1k/live_vqc/cvd_2014/live_qualcomm
|
| 158 |
+
# lsvq_train/lsvq_test/lsvq_test_1080P/
|
| 159 |
+
frame_name = 'sampled_frame' # sampled_frame, frame_diff, optical_flow
|
| 160 |
+
network_name = 'resnet50'
|
| 161 |
+
layer_name = 'layerstack'
|
| 162 |
+
if network_name == 'resnet50':
|
| 163 |
+
model = models.resnet50(pretrained=True).to(device)
|
| 164 |
+
else:
|
| 165 |
+
model = models.vgg16(pretrained=True).to(device)
|
| 166 |
+
|
| 167 |
+
logger.info(f"video type: {video_type}, frame name: {frame_name}, network name: {network_name}, layer name: {layer_name}")
|
| 168 |
+
logger.info(f"torch cuda: {torch.cuda.is_available()}")
|
| 169 |
+
|
| 170 |
+
videodata = load_metadata(video_type)
|
| 171 |
+
valid_video_types = ['test',
|
| 172 |
+
'resolution_ugc', 'konvid_1k', 'live_vqc', 'cvd_2014', 'live_qualcomm',
|
| 173 |
+
'lsvq_train', 'lsvq_test', 'lsvq_test_1080P']
|
| 174 |
+
|
| 175 |
+
begin_time = time.time()
|
| 176 |
+
if video_type in valid_video_types:
|
| 177 |
+
for i in range(len(videodata)):
|
| 178 |
+
start_time = time.time()
|
| 179 |
+
|
| 180 |
+
video_name, video_path, sampled_frame_path, feature_name, video_width, video_height, pixfmt, framerate = get_video_paths(network_name, video_type, videodata, i)
|
| 181 |
+
frames, frames_next = vf_extract.process_video_residual(video_type, video_name, framerate, video_path, sampled_frame_path)
|
| 182 |
+
|
| 183 |
+
logger.info(f'{video_name}')
|
| 184 |
+
all_frame_activations_feats = []
|
| 185 |
+
for j, (frame, frame_next) in enumerate(zip(frames, frames_next)):
|
| 186 |
+
frame_number = j + 1
|
| 187 |
+
|
| 188 |
+
# DNN feature extraction
|
| 189 |
+
if frame_name in ['frame_diff', 'optical_flow']:
|
| 190 |
+
# compute residual
|
| 191 |
+
frame_tensor = transforms.ToTensor()(frame).unsqueeze(0).to(device)
|
| 192 |
+
frame_next_tensor = transforms.ToTensor()(frame_next).unsqueeze(0).to(device)
|
| 193 |
+
|
| 194 |
+
if frame_name == 'frame_diff':
|
| 195 |
+
residual = compute_frame_difference(frame_tensor, frame_next_tensor)
|
| 196 |
+
png_path, frag_activations, total_flops, total_params = get_deep_feature(network_name, video_name, residual, frame_number, model, device, layer_name)
|
| 197 |
+
|
| 198 |
+
elif frame_name == 'optical_flow':
|
| 199 |
+
opticalflow_rgb_tensor = compute_optical_flow(frame, frame_next, device)
|
| 200 |
+
png_path, frag_activations, total_flops, total_params = get_deep_feature(network_name, video_name, opticalflow_rgb_tensor, frame_number, model, device, layer_name)
|
| 201 |
+
|
| 202 |
+
elif frame_name == 'sampled_frame':
|
| 203 |
+
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
|
| 204 |
+
frame_rgb_tensor = transforms.ToTensor()(frame_rgb).unsqueeze(0).to(device)
|
| 205 |
+
png_path, frag_activations, total_flops, total_params = get_deep_feature(network_name, video_name, frame_rgb_tensor, frame_number, model, device, layer_name)
|
| 206 |
+
|
| 207 |
+
# feature combined
|
| 208 |
+
all_frame_activations_feats.append(frag_activations)
|
| 209 |
+
|
| 210 |
+
averaged_frames_feats = process_video_feature(all_frame_activations_feats, network_name, layer_name)
|
| 211 |
+
print("Features shape:", averaged_frames_feats.shape)
|
| 212 |
+
# remove tmp folders
|
| 213 |
+
shutil.rmtree(png_path)
|
| 214 |
+
shutil.rmtree(sampled_frame_path)
|
| 215 |
+
|
| 216 |
+
averaged_npy = averaged_frames_feats.cpu().numpy()
|
| 217 |
+
# save the processed data as numpy file
|
| 218 |
+
output_npy_path = f'../features/{video_type}/{frame_name}_{network_name}_{layer_name}/'
|
| 219 |
+
os.makedirs(output_npy_path, exist_ok=True)
|
| 220 |
+
# output_npy_name = f'{output_npy_path}video_{str(i + 1)}_{feature_name}.npy'
|
| 221 |
+
# np.save(output_npy_name, averaged_npy)
|
| 222 |
+
# print(f'Processed file saved to: {output_npy_name}')
|
| 223 |
+
|
| 224 |
+
run_time = time.time() - start_time
|
| 225 |
+
print(f"Execution time for {video_name} feature extraction: {run_time:.4f} seconds\n")
|
| 226 |
+
|
| 227 |
+
# save feature mat file
|
| 228 |
+
average_data = np.mean(averaged_npy, axis=0)
|
| 229 |
+
if i == 0:
|
| 230 |
+
feats_matrix = np.zeros((len(videodata),) + average_data.shape)
|
| 231 |
+
feats_matrix[i] = average_data
|
| 232 |
+
|
| 233 |
+
print((f'All features shape: {feats_matrix.shape}'))
|
| 234 |
+
logger.debug(f'\n All features shape: {feats_matrix.shape}')
|
| 235 |
+
mat_file_path = f'../features/{video_type}/'
|
| 236 |
+
mat_file_name = f'{mat_file_path}{video_type}_{frame_name}_{network_name}_{layer_name}_feats.mat'
|
| 237 |
+
scipy.io.savemat(mat_file_name, {video_type: feats_matrix})
|
| 238 |
+
logger.debug(f'Successfully created {mat_file_name}')
|
| 239 |
+
logger.debug(f"Execution time for all feature extraction: {time.time() - begin_time:.4f} seconds\n")
|