NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Dataset and Study
This work addresses video quality enhancement for compressed videos, providing a new dataset and benchmark, but it is incremental as it builds on existing challenge frameworks.
The paper introduced the Large-scale Diverse Video (LDV) dataset for video enhancement and analyzed methods from the NTIRE 2021 challenge, finding that it advanced the state-of-the-art for quality enhancement of compressed video.
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 compressed 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 proposed methods of the challenge and several methods in previous works 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 proposed LDV dataset is publicly available at the homepage of the challenge: https://github.com/RenYang-home/NTIRE21_VEnh