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

Yi Xu, Minyi Zhao, Jing Liu, Xinjian Zhang, Longwen Gao, Shuigeng Zhou, Huyang Sun

CVPRW 2021 pp. 213-222

doi:10.1109/CVPRW53098.2021.00030 /cvprw/2021/xu2021cvprw-boosting/

Abstract

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.

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Cite

Text

Xu et al. "Boosting the Performance of Video Compression Artifact Reduction with Reference Frame Proposals and Frequency Domain Information." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2021. doi:10.1109/CVPRW53098.2021.00030

Markdown

[Xu et al. "Boosting the Performance of Video Compression Artifact Reduction with Reference Frame Proposals and Frequency Domain Information." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2021.](https://mlanthology.org/cvprw/2021/xu2021cvprw-boosting/) doi:10.1109/CVPRW53098.2021.00030

BibTeX

@inproceedings{xu2021cvprw-boosting,
  title     = {{Boosting the Performance of Video Compression Artifact Reduction with Reference Frame Proposals and Frequency Domain Information}},
  author    = {Xu, Yi and Zhao, Minyi and Liu, Jing and Zhang, Xinjian and Gao, Longwen and Zhou, Shuigeng and Sun, Huyang},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2021},
  pages     = {213-222},
  doi       = {10.1109/CVPRW53098.2021.00030},
  url       = {https://mlanthology.org/cvprw/2021/xu2021cvprw-boosting/}
}