NTIRE 2020 Challenge on Video Quality Mapping: Methods and Results
- URL: http://arxiv.org/abs/2005.02291v3
- Date: Mon, 15 Jun 2020 22:12:40 GMT
- Title: NTIRE 2020 Challenge on Video Quality Mapping: Methods and Results
- Authors: Dario Fuoli, Zhiwu Huang, Martin Danelljan, Radu Timofte, Hua Wang,
Longcun Jin, Dewei Su, Jing Liu, Jaehoon Lee, Michal Kudelski, Lukasz Bala,
Dmitry Hrybov, Marcin Mozejko, Muchen Li, Siyao Li, Bo Pang, Cewu Lu, Chao
Li, Dongliang He, Fu Li, Shilei Wen
- Abstract summary: This paper reviews the NTIRE 2020 challenge on video quality mapping (VQM)
The challenge includes both a supervised track (track 1) and a weakly-supervised track (track 2) for two benchmark datasets.
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.
- Score: 131.05847851975236
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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