Patch-VQ: ‘Patching Up’ the Video Quality Problem

@article{Ying2021PatchVQU,
  title={Patch-VQ: ‘Patching Up’ the Video Quality Problem},
  author={Zhenqiang Ying and Maniratnam Mandal and Deepti Ghadiyaram and Alan Bovik University of Texas at Austin and AI Facebook},
  journal={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2021},
  pages={14014-14024}
}
No-reference (NR) perceptual video quality assessment (VQA) is a complex, unsolved, and important problem for 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… 
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