Paper Info
Title
NTIRE 2020 Challenge on Video Quality Mapping: Methods and Results
Abstract
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.
Year
DOI
Venue
2020
10.1109/CVPRW50498.2020.00246
CVPR Workshops
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
21
Name
Order
Citations
PageRank
Dario Fuoli101.01
Zhiwu Huang225215.26
Danelljan Martin3134449.35
Radu Timofte41880118.45
Hua Wang5109077.62
Longcun Jin600.34
Dewei Su700.34
Jing Liu825027.30
Jaehoon Lee94012.37
Michal Kudelski1000.34
Lukasz Bala1100.34
Dmitry Hrybov1200.34
Marcin Mozejko1300.34
Muchen Li1421.04
Siyao Li1523.09
Bo Pang16154.55
Cewu Lu1799362.08
Chao Li1831.74
He, D.193313.67
Fu Li2010.69
Shilei Wen217913.59