This paper reviews the NTIRE 2020 challenge on video quality mapping (VQM), which addresses the issues of quality mapping from source video domain to target video domain. The challenge includes both a supervised track (track 1) and a weakly-supervised track (track 2) for two benchmark datasets. In particular, track 1 offers a new Internet video benchmark, requiring algorithms to learn the map from more compressed videos to less compressed videos in a supervised training manner. In track 2, algorithms are required to learn the quality mapping from one device to another when their quality varies substantially and weakly-aligned video pairs are available. For track 1, in total 7 teams competed in the final test phase, demonstrating novel and effective solutions to the problem. For track 2, some existing methods are evaluated, showing promising solutions to the weakly-supervised video quality mapping problem.

本文回顾了NTIRE 2020挑战赛在视频质量映射方面的研究成果，该挑战赛分为有监督和弱监督两个轨道，对两个基准数据集进行源视频领域到目标视频领域的质量映射，其中track 1要求算法以有监督的方式从更压缩的视频向更少压缩的视频学习映射，track 2要求算法在弱对准的视频对上学习如何在一个设备质量变化明显的情况下将质量映射到另一个设备上，7组团队参加了track 1，展示了有效的解决方案，而对于track 2，评估了一些现有的方法，展示出弱监督下视频质量映射方面的有希望的解决方案。

NTIRE 2020 视频质量映射挑战：方法与结果