ML Anthology

NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Dataset and Study

Ren Yang, Radu Timofte
CVPRW 2021 pp. 667-676
doi:10.1109/CVPRW53098.2021.00076 /cvprw/2021/yang2021cvprw-ntire-a/

Abstract

This paper introduces a novel dataset for video enhancement and studies the state-of-the-art methods of the NTIRE 2021 challenge on quality enhancement of com-pressed video. The challenge is the first NTIRE challenge in this direction, with three competitions, hundreds of participants and tens of proposed solutions. Our newly collected Large-scale Diverse Video (LDV) dataset is employed in the challenge. In our study, we analyze the solutions of the challenges and several representative methods from previous literature on the proposed LDV dataset. We find that the NTIRE 2021 challenge advances the state-of-the-art of quality enhancement on compressed video. The pro-posed LDV dataset is publicly available at the homepage of the challenge: https://github.com/RenYang-home/NTIRE21_VEnh

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Cite

Text

Yang and Timofte. "NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Dataset and Study." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2021. doi:10.1109/CVPRW53098.2021.00076

Markdown

[Yang and Timofte. "NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Dataset and Study." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2021.](https://mlanthology.org/cvprw/2021/yang2021cvprw-ntire-a/) doi:10.1109/CVPRW53098.2021.00076

BibTeX

@inproceedings{yang2021cvprw-ntire-a,
  title     = {{NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Dataset and Study}},
  author    = {Yang, Ren and Timofte, Radu},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2021},
  pages     = {667-676},
  doi       = {10.1109/CVPRW53098.2021.00076},
  url       = {https://mlanthology.org/cvprw/2021/yang2021cvprw-ntire-a/}
}