NTIRE 2026 Challenge on Bitstream-Corrupted Video Restoration: Methods and Results
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2026
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| author | Zou, Wenbin Liu, Tianyi Wu, Kejun Zhuang, Huiping Wu, Zongwei Zhou, Zhuyun Timofte, Radu Yap, Kim-Hui Chau, Lap-Pui Wang, Yi Zhou, Shiqi Shi, Xiaodi Chen, Yuxiang Zhong, Yilian Yin, Shibo Fang, Yushun Zhu, Xilei Wang, Yahui Lu, Chen Wang, Zhitao Ha, Lifa Man, Hengyu Fan, Xiaopeng Singh, Priyansh Sidharth Dev, Krrish Kakkar, Soham Jakhetiya, Vinit Shah, Ovais Iqbal Zhou, Wei Li, Linfeng Xu, Qi Liu, Zhenyang Xu, Kepeng Qiao, Tong Tu, Jiachen Xu, Guoyi Jiang, Yaoxin Liu, Jiajia Shi, Yaokun |
| author_facet | Zou, Wenbin Liu, Tianyi Wu, Kejun Zhuang, Huiping Wu, Zongwei Zhou, Zhuyun Timofte, Radu Yap, Kim-Hui Chau, Lap-Pui Wang, Yi Zhou, Shiqi Shi, Xiaodi Chen, Yuxiang Zhong, Yilian Yin, Shibo Fang, Yushun Zhu, Xilei Wang, Yahui Lu, Chen Wang, Zhitao Ha, Lifa Man, Hengyu Fan, Xiaopeng Singh, Priyansh Sidharth Dev, Krrish Kakkar, Soham Jakhetiya, Vinit Shah, Ovais Iqbal Zhou, Wei Li, Linfeng Xu, Qi Liu, Zhenyang Xu, Kepeng Qiao, Tong Tu, Jiachen Xu, Guoyi Jiang, Yaoxin Liu, Jiajia Shi, Yaokun |
| contents | This paper reports on the NTIRE 2026 Challenge on Bitstream-Corrupted Video Restoration (BSCVR). The challenge aims to advance research on recovering visually coherent videos from corrupted bitstreams, whose decoding often produces severe spatial-temporal artifacts and content distortion. Built upon recent progress in bitstream-corrupted video recovery, the challenge provides a common benchmark for evaluating restoration methods under realistic corruption settings. We describe the dataset, evaluation protocol, and participating methods, and summarize the final results and main technical trends. The challenge highlights the difficulty of this emerging task and provides useful insights for future research on robust video restoration under practical bitstream corruption. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_06945 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | NTIRE 2026 Challenge on Bitstream-Corrupted Video Restoration: Methods and Results Zou, Wenbin Liu, Tianyi Wu, Kejun Zhuang, Huiping Wu, Zongwei Zhou, Zhuyun Timofte, Radu Yap, Kim-Hui Chau, Lap-Pui Wang, Yi Zhou, Shiqi Shi, Xiaodi Chen, Yuxiang Zhong, Yilian Yin, Shibo Fang, Yushun Zhu, Xilei Wang, Yahui Lu, Chen Wang, Zhitao Ha, Lifa Man, Hengyu Fan, Xiaopeng Singh, Priyansh Sidharth Dev, Krrish Kakkar, Soham Jakhetiya, Vinit Shah, Ovais Iqbal Zhou, Wei Li, Linfeng Xu, Qi Liu, Zhenyang Xu, Kepeng Qiao, Tong Tu, Jiachen Xu, Guoyi Jiang, Yaoxin Liu, Jiajia Shi, Yaokun Computer Vision and Pattern Recognition This paper reports on the NTIRE 2026 Challenge on Bitstream-Corrupted Video Restoration (BSCVR). The challenge aims to advance research on recovering visually coherent videos from corrupted bitstreams, whose decoding often produces severe spatial-temporal artifacts and content distortion. Built upon recent progress in bitstream-corrupted video recovery, the challenge provides a common benchmark for evaluating restoration methods under realistic corruption settings. We describe the dataset, evaluation protocol, and participating methods, and summarize the final results and main technical trends. The challenge highlights the difficulty of this emerging task and provides useful insights for future research on robust video restoration under practical bitstream corruption. |
| title | NTIRE 2026 Challenge on Bitstream-Corrupted Video Restoration: Methods and Results |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.06945 |