NTIRE 2026 Challenge on Bitstream-Corrupted Video Restoration: Methods and Results

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Hauptverfasser: 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
Format: Preprint
Veröffentlicht: 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