NTIRE 2020 challenge on video quality mapping: methods and results

Fuoli, Dario, Huang, Zhiwu, Danelljan, Martin and Timofte, Radu (2020) NTIRE 2020 challenge on video quality mapping: methods and results. Computer Vision and Pattern Recognition (CVPR) workshop. 13 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

This paper reviews the NTIRE 2020 challenge on videoquality mapping (VQM), which addresses the issues of quality mapping from source video domain to target video domain. The challenge includes both a supervised track (track1) 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 mapfrom more compressed videos to less compressed videos ina supervised training manner. In track 2, algorithms arerequired to learn the quality mapping from one device toanother when their quality varies substantially and weaklyaligned video pairs are available. For track 1, in total 7teams competed in the final test phase, demonstrating noveland effective solutions to the problem. For track 2, some existing methods are evaluated, showing promising solutionsto the weakly-supervised video quality mapping problem.

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Fuoli_NTIRE_2020_Challenge_on_Video_Quality_Mapping_Methods_and_Results_CVPRW_2020_paper - Other

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