  Many deep learning based video compression artifact removal algorithms have
been proposed to recover high-quality videos from low-quality compressed
videos. Recently, methods were proposed to mine spatiotemporal information via
utilizing multiple neighboring frames as reference frames. However, these
post-processing methods take advantage of adjacent frames directly, but neglect
the information of the video itself, which can be exploited. In this paper, we
propose an effective reference frame proposal strategy to boost the performance
of the existing multi-frame approaches. Besides, we introduce a loss based on
fast Fourier transformation~(FFT) to further improve the effectiveness of
restoration. Experimental results show that our method achieves better fidelity
and perceptual performance on MFQE 2.0 dataset than the state-of-the-art
methods. And our method won Track 1 and Track 2, and was ranked the 2nd in
Track 3 of NTIRE 2021 Quality enhancement of heavily compressed videos
Challenge.
