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<!DOCTYPE html>
<html class="full-height" lang="en-US">
<head>
<meta charset="UTF-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
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<title>PatchVQ</title>
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<div class="container"><a class="navbar-brand" href="#"><strong>PatchVQ</strong></a>
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<li class="nav-item"><a class="nav-link active" href="#abstract">ABOUT</a></li>
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<section class="py-5 special-color-dark text-white" id="about">
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<div class="wow fadeIn">
<h2 class="h1 h1-responsive text-center my-5">Patch-VQ: ‘Patching Up’ the Video Quality Problem</h2>
<p class="lead blue-grey-text text-center w-responsive mx-auto mb-5">
<div align = "center">
<div class="authorname" style="font-size:large;line-adjust:0">
Zhenqiang Ying<sup>1*</sup>,
Maniratnam Mandal<sup>1*</sup>,
Deepti Ghadiyaram<sup>2+</sup>,
Alan Bovik<sup>1+</sup>,
</div>
<div class="authoraddress" style="line-adjust:0">
<div class="authoremail email" style="line-adjust:0">
{zqying, mmandal}@utexas.edu, deeptigp@fb.com, bovik@ece.utexas.edu
</div>
<hr>
<sup>*+</sup>Equal contribution
<br>
<sup>1</sup> University of Texas at Austin
<sup>2</sup> Facebook AI
<br/>
<!-- Funding statement: this project is funded by Facebook AI. -->
This project was supported and funded by Facebook AI.
<br/>
<a href='http://cvpr2021.thecvf.com/' ><img src='https://img.shields.io/badge/CVPR-2021-blue.svg' /> </a>
</div>
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<!-- <p class="h4 h4-responsive pt-5 pb-2 text-center"> -->
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<section class="py-5 white" id="abstract">
<div class="container">
<div class="alert alert-warning alert-dismissible fade show" role="alert">
For image quality assesment, check out <a href="https://baidut.github.io/PaQ-2-PiQ/" target="_blank">PaQ-2-PiQ.</a>
<br>
<strong> Please email yingzhenqiang at gmail dot com for any questions. Thank you!</strong>
</div>
<div class="wow fadeInUp">
<h2 class="h1 h1-responsive text-center my-5">ABSTRACT</h2>
<p class="lead blue-grey-text text-center w-responsive mx-auto mb-5">
<!-- Picture Quality Map Prediction - Generated by RoIPoolModel w/ 32x32 partition -->
</p>
No-reference (NR) perceptual video quality assessment (VQA) is a complex, unsolved, and important problem to social and streaming media applications. Efficient and accurate video quality predictors are needed to monitor and guide the processing of billions of shared, often imperfect, user-generated content (UGC). Unfortunately, current NR models are limited in their prediction capabilities on real-world, "in-the-wild" UGC video data. To advance progress on this problem, we created the largest (by far) subjective video quality dataset, containing 39, 000 real-world distorted videos and 117, 000 space-time localized video patches ("v-patches"), and 5.5M human perceptual quality annotations. Using this, we created two unique NR-VQA models: (a) a local-to-global region-based NR VQA architecture (called PVQ) that learns to predict global video quality and achieves state-of-the-art performance on 3 UGC datasets, and (b) a first-of-a-kind space-time video quality mapping engine (called PVQ Mapper) that helps localize and visualize perceptual distortions in space and time.
<br/>
<br/>
<br/>
<div class="row" align=center>
<figure class="figure">
<img style="width:80%" src="images/pvq_db.png" class="figure-img img-fluid rounded" alt="...">
<figcaption class="figure-caption">Sample video frames from our database, each resized to fit. The actual
videos are of highly diverse sizes and resolutions.</figcaption>
</figure>
<figure class="figure">
<img style="width:80%" src="images/demo.jpg" class="figure-img img-fluid rounded" alt="...">
<figcaption class="figure-caption"> <strong>A first of its kind video quality map predictor:</strong>
Space-time quality maps generated on a video using our PVQ Mapper, and sampled in time for display.
Four video frames are shown at top, with spatial quality maps (blended with the original frames using magma color) immediately under, while the
bottom plots show the evolving quality of the video..</figcaption>
</figure>
</div>
<br>
</div>
</section>
<section class="py-5 text-white" style="background: black;" id="applications">
<div class="container">
<div class="wow fadeInUp">
<h2 class="h1 h1-responsive text-center my-5">EXAMPLES</h2>
<p class="lead blue-grey-text text-center w-responsive mx-auto mb-5">
Generated Space-time Quality Maps
</p>
<div class="row">
<div class="column wow fadeInLeft">
<br />
</div>
<div class="column wow fadeInLeft">
<img src="images/A037.gif" />
<img src="images/ex1.gif" />
</div>
<div class="column wow fadeInLeft">
<img src="images/A021.gif" />
<img src="images/ex2.gif" />
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<h2 class="h1 pt-5 pb-3">DOWNLOAD</h2>
<p class="px-5 mb-5 pb-3 lead">
Please submit an issue <a href='https://github.com/baidut/PatchVQ/issues'>here</a> if you encounter any difficulties.
</p>
</div>
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<div class="card card-image">
<div class="text-white text-center pricing-card d-flex align-items-center rgba-stylish-strong py-3 px-3 rounded">
<div class="card-body">
<div class="h5">Database</div>
<!-- <div class="py-5"><sup class="display-4">$</sup><span class="display-1">9</span><span class="display-4">/m</span></div> -->
<div class="py-5"><span class="display-1"><i class="fa fa-database"></i></span></div>
<ul class="list-unstyled">
<li>
<p><strong>5.5 M</strong> ratings</p>
</li>
<li>
<p><strong>6.3 k</strong> subjects</p>
</li>
<li>
<p><strong>39 k</strong> videos</p>
</li>
<li>
<p><strong>117 k</strong> patches</p>
</li>
</ul><a class="btn btn-outline-white mt-5" href='https://github.com/baidut/PatchVQ'> Download</a>
</div>
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<div class="text-white text-center pricing-card d-flex align-items-center rgba-red-strong py-3 px-3 rounded">
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<div class="h5">Code</div>
<div class="py-5"><span class="display-1"><i class="fa fa-code"></i></span></div>
<ul class="list-unstyled">
<li>
<p>Pretrained Models</p>
</li>
<li>
<p>Reproducible Results</p>
</li>
<li>
<p>Training/Testing Utils</p>
</li>
<li>
<br/>
<!-- <p><a href="https://colab.research.google.com/github/baidut/PaQ-2-PiQ/blob/master/demo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open Demo In Colab"/></a></p> -->
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<div class="h5">Paper</div>
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<br/>
<br/>
<br/>
<ul class="list-unstyled">
<li>
<p>Available on ArXiv</p>
</li>
<li>
<p>Accepted to CVPR 2021</p>
</li>
<li>
<p>
<!-- <a href='http://cvpr2020.thecvf.com/'><img style="width:260px" src="imgs/CVPR_Logo_Horz2_web.jpg" /> -->
</a></p>
</li>
<!-- <li>
<p><strong>100GB</strong> citation</p>
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<a class="btn btn-outline-white mt-5" href='https://arxiv.org/pdf/2011.13544.pdf'> Download</a>
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</section>
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<div>
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<div class="wow fadeInUp">
<h2 class="h1 h1-responsive text-center my-5">LINKS</h2>
<!-- Try it online :) -->
<!-- <p class="lead blue-grey-text text-center w-responsive mx-auto mb-5">
Powered by <a href="https://cloud.google.com/appengine">Google App Engine</a>
</p> -->
<p class="px-5 mb-5 pb-3 lead">References</p> <ol type = "1" align=left>
<li>Zhenqiang Ying, Maniratnam Mandal, Deepti Ghadiyaram, Alan Bovik. Patch-VQ: 'Patching Up' the Video Quality Problem, In CVPR 2021 </li>
<li>Zhenqiang Ying, Haoran Niu, Praful Gupta, Dhruv Mahajan, Deepti Ghadiyaram, Alan Bovik. From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality, In CVPR 2020</li>
<li>Vlad Hosu, Franz Hahn, Mohsen Jenadeleh, Hanhe Lin, Hui Men, Tamás Szirányi, Shujun Li, and Dietmar Saupe. The Konstanz natural video database(KoNViD-1k). In 2017 Ninth International Conference on Quality of Multimedia Experience (QoMEX)</li>
<li>Zeina Sinno and Alan C. Bovik. Large-scale study of percep-tual video quality. IEEE Transactions on Image Processing, vol. 28, no. 2, pp. 612-627, Feb. 2019.</li>
</ol>
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