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+uvq/models/compressionnet_baseline/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
+uvq/models/contentnet_baseline/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
+uvq/models/distortionnet_baseline/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text

uvq/LICENSE

@@ -0,0 +1,202 @@
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uvq/README.md

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+# 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
+

uvq/docs/contributing.md

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+# How to Contribute
+
+We'd love to accept your patches and contributions to this project. There are
+just a few small guidelines you need to follow.
+
+## Contributor License Agreement
+
+Contributions to this project must be accompanied by a Contributor License
+Agreement. You (or your employer) retain the copyright to your contribution;
+this simply gives us permission to use and redistribute your contributions as
+part of the project. Head over to <https://cla.developers.google.com/> to see
+your current agreements on file or to sign a new one.
+
+You generally only need to submit a CLA once, so if you've already submitted one
+(even if it was for a different project), you probably don't need to do it
+again.
+
+## Code reviews
+
+All submissions, including submissions by project members, require review. We
+use GitHub pull requests for this purpose. Consult
+[GitHub Help](https://help.github.com/articles/about-pull-requests/) for more
+information on using pull requests.
+
+## Community Guidelines
+
+This project follows [Google's Open Source Community
+Guidelines](https://opensource.google/conduct/).

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