| # UVQ: Universal Video Quality Model | |
| This repository contains checkpointed models of Google's Universal Video Quality (UVQ) model. | |
| UVQ is a no-reference perceptual video quality assessment model that is designed to work | |
| well on user-generated content, where there is no pristine reference. | |
| Read this blog post for an overview of UVQ: | |
| "[UVQ: Measuring YouTube's Perceptual Video Quality](https://ai.googleblog.com/2022/08/uvq-measuring-youtubes-perceptual-video.html)", Google AI Blog 2022 | |
| More details are available in our paper: | |
| Yilin Wang, Junjie Ke, Hossein Talebi, Joong Gon Yim, Neil Birkbeck, Balu Adsumilli, Peyman Milanfar, Feng Yang, "[Rich features for perceptual quality assessment of UGC videos](https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Rich_Features_for_Perceptual_Quality_Assessment_of_UGC_Videos_CVPR_2021_paper.html)", CVPR 2021. | |
| The corresponding data from the paper is available for download from: [YouTube UGC Dataset](https://media.withyoutube.com) | |
| ## Running the code | |
| ### Dependencies | |
| You must have [FFmpeg](http://www.ffmpeg.org/) installed and available on your path. | |
| The models and code require Python 3.6 (or greater) and [Tensorflow](https://www.tensorflow.org/install). | |
| With virtualenv, you can install the requirements to a virtual environment: | |
| ``` | |
| virtualenv venv | |
| source venv/bin/activate | |
| pip3 install -r requirements.txt | |
| ``` | |
| ### Predict Quality | |
| You can grab some examples videos from the [YouTube UGC Dataset](https://media.withyoutube.com). For example, you can get Gaming_1080P-0ce6_orig.mp4 using curl: | |
| ``` | |
| curl -o Gaming_1080P-0ce6_orig.mp4 https://storage.googleapis.com/ugc-dataset/vp9_compressed_videos/Gaming_1080P-0ce6_orig.mp4 | |
| ``` | |
| You can then run the example: | |
| ```bash | |
| mkdir -p results | |
| python3 uvq_main.py --input_files="Gaming_1080P-0ce6_orig,20,Gaming_1080P-0ce6_orig.mp4" --output_dir results --model_dir models | |
| ``` | |
| #### Input file formatting | |
| The input files format is a line with the following fields: | |
| `id,video_length,filepath` | |
| #### Results | |
| The `output_dir` will contain a csv file with the results for each model. For example, | |
| ```bash | |
| cat results/mos_ytugc20s_0_Gaming_1080P-0ce6_orig.mp4_orig.csv | |
| ``` | |
| Gives: | |
| ```bash | |
| Gaming_1080P-0ce6,compression,3.927867603302002 | |
| Gaming_1080P-0ce6,content,3.945391607284546 | |
| Gaming_1080P-0ce6,distortion,4.267196607589722 | |
| Gaming_1080P-0ce6,compression_content,3.9505696296691895 | |
| Gaming_1080P-0ce6,compression_distortion,4.062019920349121 | |
| Gaming_1080P-0ce6,content_distortion,4.067790699005127 | |
| Gaming_1080P-0ce6,compression_content_distortion,4.058663845062256 | |
| ``` | |
| We provide multiple predcited scores, using different combinations of UVQ features. | |
| `compression_content_distortion` (combining three features) is our default score for Mean Opinion Score (MOS) prediction. | |
| The output features folder includes UVQ labels and raw features: | |
| ```bash | |
| Gaming_1080P-0ce6_orig_feature_compression.binary | |
| Gaming_1080P-0ce6_orig_feature_content.binary | |
| Gaming_1080P-0ce6_orig_feature_distortion.binary | |
| Gaming_1080P-0ce6_orig_label_compression.csv | |
| Gaming_1080P-0ce6_orig_label_content.csv | |
| Gaming_1080P-0ce6_orig_label_distortion.csv | |
| ``` | |
| UVQ labels (.csv, each row corresponding to 1s chunk):<br /> | |
| compression: 16 compression levels per row, corresponding to 4x4 subregions of the entire frame.<br /> | |
| distortion: 26 distortion types defined in [KADID-10k](http://database.mmsp-kn.de/kadid-10k-database.html) for 2x2 subregions. The first element is the undefined type. <br /> | |
| content: 3862 content labels defined in [YouTube-8M](https://research.google.com/youtube8m/).<br /> | |
| UVQ raw features (in binary):<br /> | |
| 25600 float numbers per 1s chunk. | |
| ## Contributors | |
| [//]: contributor-faces | |
| <a href="https://github.com/yilinwang01"><img src="https://avatars.githubusercontent.com/u/30224449?v=4" title="yilinwang01" width="80" height="80"></a> | |
| <a href="https://github.com/nbirkbeck"><img src="https://avatars.githubusercontent.com/u/6225937?v=4" title="nbirkbeck" width="80" height="80"></a> | |
| <a href="https://github.com/megabalu"><img src="https://avatars.githubusercontent.com/u/99928166?v=4" title="megabalu" width="80" height="80"></a> | |
| [//]: contributor-faces | |