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Many deep learning based video compression artifact removal algorithms havebeen proposed to recover high-quality videos from low-quality compressedvideos. Recently, methods were proposed to mine spatiotemporal information viautilizing multiple neighboring frames as reference frames. However, thesepost-processing methods take advantage of adjacent frames directly, but neglectthe information of the video itself, which can be exploited. In this paper, wepropose an effective reference frame proposal strategy to boost the performanceof the existing multi-frame approaches. Besides, we introduce a loss based onfast Fourier transformation~(FFT) to further improve the effectiveness ofrestoration. Experimental results show that our method achieves better fidelityand perceptual performance on MFQE 2.0 dataset than the state-of-the-artmethods. And our method won Track 1 and Track 2, and was ranked the 2nd inTrack 3 of NTIRE 2021 Quality enhancement of heavily compressed videosChallenge.

Boosting the Performance of Video Compression Artifact Reduction with Reference Frame Proposals and Frequency Domain Information