Progressive Training of A Two-Stage Framework for Video Restoration

Zheng, Meisong; Xing, Qunliang; Qiao, Minglang; Xu, Mai; Jiang, Lai; Liu, Huaida; Chen, Ying

doi:10.1109/cvprw56347.2022.00115

Cited by 6 publications

(2 citation statements)

References 22 publications

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“…Similar to Track 1, we use the videos that are directly upscaled by the bicubic algorithm as the baseline performance in this track. The winner team NoahTerminalCV improves the PSNR by more than 2 dB over the baseline, and it successfully beats the winner method [85] in NTIRE 2022, which can be seen as the state-of-the-art method. The IVL method has the very fast running speed, and it is able to achieve the real-time super-resolution on the test videos.…”

Section: Trackmentioning

confidence: 83%

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NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results

Yang¹,

Timofte²,

Zheng³

et al. 2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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This paper reviews the Challenge on Super-Resolution of Compressed Image and Video at AIM 2022. This challenge includes two tracks. Track 1 aims at the super-resolution of compressed image, and Track 2 targets the super-resolution of compressed video. In Track 1, we use the popular dataset DIV2K as the training, validation and test sets.In Track 2, we propose the LDV 3.0 dataset, which contains 365 videos, including the LDV 2.0 dataset (335 videos) and 30 additional videos. In this challenge, there are 12 teams and 2 teams that submitted the final results to Track 1 and Track 2, respectively. The proposed methods and solutions gauge the state-of-the-art of super-resolution on compressed image and video. The proposed LDV 3.0 dataset is available at https: //github.com/RenYang-home/LDV_dataset. The homepage of this challenge is at https://github.com/RenYang-home/AIM22_CompressSR.

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Section: Trackmentioning

confidence: 83%

“…NTIRE 2022 [75] is the first challenge we organized on super-resolution of compressed video. The winner method [85] in the NTIRE 2022 challenge successfully outperforms the state-of-the-art method [11].…”

Section: Introductionmentioning

confidence: 98%