mlsanityData to 7 Oct 2026

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

arXiv 2104.10782 ↗First posted 21 Apr 2021eess.IV cs.CVcited by 9

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Abstract (arXiv)

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

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 15 of its 38 references that are in the map. First posted on arXiv 21 Apr 2021.

Builds on

15 of 38 references.

References · 15

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  12. 2017Enhancing Quality for HEVC Compressed Videoscited by 28
3 more
  1. 2020Multi-level Wavelet-based Generative Adversarial Network for Perceptual Quality Enhancement of Compressed Videocited by 26
  2. 2019Non-Local ConvLSTM for Video Compression Artifact Reductioncited by 19
  3. 2019Quality-Gated Convolutional LSTM for Enhancing Compressed Videocited by 11

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Cited by year

Citing papers per year: 2021: 1; 2022: 3; 2023: 2; 2024: 2; 2025: 1.

9 papers.