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 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 proposed LDV dataset is publicly available at the homepage of the challenge: https://github.com/RenYang-home/NTIRE21_VEnh

翻译：本文介绍了用于加强视频的新数据集,并研究了2021年国家电视网在提高压缩视频质量方面最先进的方法,这是国家电视网在提高压缩视频质量方面的第一个挑战,它有三个竞赛、几百名参与者和数十个拟议解决方案,我们新收集的大型多样化视频数据集用于应对挑战。我们的研究分析了挑战的解决方案和以往文献中关于拟议中的LDV数据集的若干代表性方法。我们发现,2021年国家电视网在提高压缩视频质量方面提出了最先进的挑战。提议的LDV数据集可在挑战的主页上公开查阅:https://github.com/RenYang-home/NTERE21_Venh。