ML Anthology

Learned Image Restoration for VVC Intra Coding

Ming Lu, Tong Chen, Haojie Liu, Zhan Ma
CVPRW 2019
/cvprw/2019/lu2019cvprw-learned/

Abstract

We propose a learned image restoration network as the post-processing module for emerging Versatile Video Coding (VVC) Intra Profile (https://jvet.hhi.fraunhofer.de) based image coding to further improve the reconstructed image quality. The image restoration network is designed using multi-scale spatial priors to effectively alleviate compression artifacts in the decoded images induced by the quantization based lossy compression algorithms. Experimental results demonstrate the performance gains of our proposed post-porcessing network with VVC Intra coding, offering about 6.5% Bjontegaard-Delta Rate (BD-Rate) reduction for YUV 4:4:4 and 12.2% for YUV 4:2:0, against the VVC Intra without our restoration network on the Test Dataset P/M released by the Computer Vision Lab of ETH Zurich, where the distortion is Peak Signal to Noise Ratio (PSNR).

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Cite

Text

Lu et al. "Learned Image Restoration for VVC Intra Coding." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.

Markdown

[Lu et al. "Learned Image Restoration for VVC Intra Coding." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.](https://mlanthology.org/cvprw/2019/lu2019cvprw-learned/)

BibTeX

@inproceedings{lu2019cvprw-learned,
  title     = {{Learned Image Restoration for VVC Intra Coding}},
  author    = {Lu, Ming and Chen, Tong and Liu, Haojie and Ma, Zhan},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2019},
  url       = {https://mlanthology.org/cvprw/2019/lu2019cvprw-learned/}
}