Papers › Omniscient Video Super-Resolution

Omniscient Video Super-Resolution

29 Mar 2021ICCV 2021 10arXiv:2103.15683archive 2025-07-28

Peng Yi, Zhongyuan Wang, Kui Jiang, Junjun Jiang, Tao Lu, Xin Tian, Jiayi Ma

Most recent video super-resolution (SR) methods either adopt an iterative manner to deal with low-resolution (LR) frames from a temporally sliding window, or leverage the previously estimated SR output to help reconstruct the current frame recurrently. A few studies try to combine these two structures to form a hybrid framework but have failed to give full play to it. In this paper, we propose an omniscient framework to not only utilize the preceding SR output, but also leverage the SR outputs from the present and future. The omniscient framework is more generic because the iterative, recurrent and hybrid frameworks can be regarded as its special cases. The proposed omniscient framework enables a generator to behave better than its counterparts under other frameworks. Abundant experiments on public datasets show that our method is superior to the state-of-the-art methods in objective metrics, subjective visual effects and complexity. Our code will be made public.

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Tasks

Super-ResolutionVideo Super-Resolution

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Video Super-Resolution Vid4 - 4x upscaling - BD degradation GOVSR PSNR 28.41 #5 of 18 Archive leaderboard report
Video Super-Resolution Vid4 - 4x upscaling - BD degradation GOVSR SSIM 0.8724 #5 of 18 Archive leaderboard report

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