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An Image Compression Framework With Learning-Based Filter
AbstractIn this paper, a coding framework VIP-ICT-Codec is introduced. Our method is based on the VTM (Versatile Video Coding Test Model). First, we propose a color space conversion from RGB to YUV domain by using a PCA-like operation. A method for the PCA mean calculation is proposed to de-correlate the residual components of YUV channels. In addition, the correlation of UV components are compensated considering that they share the same coding tree in VVC. We also learn a residual mapping to alleviate the over-filtered and under-filtered problem of specific images. Finally, we regard the rate control as an unconstraint Lagrangian problem to reach the target bpp. The results show that we achieve 32.625dB at the validation phase.
Related Material
[pdf][bibtex]@InProceedings{Sun_2020_CVPR_Workshops,
author = {Sun, Heming and Liu, Chao and Katto, Jiro and Fan, Yibo},
title = {An Image Compression Framework With Learning-Based Filter},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2020}
}
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