Variable Rate Image Compression With Content Adaptive Optimization

Tiansheng Guo, Jing Wang, Ze Cui, Yihui Feng, Yunying Ge, Bo Bai; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 122-123

Abstract

In this paper, we propose a variable rate image compression framework for low bit-rate image compression task. Unlike most of the variational auto-encoder (VAE) based methods, our proposal is able to achieve continuously variable rate in a single model by introducing a pair of gain units into VAE. Besides, a content adaptive optimization is applied to adapt the latent representation to the specific content while keeping the parameters of the network and the predictive model fixed. After that, due to the variable rate characteristics of our method, each image can be compressed into any quality level through a unified codec. Finally, an efficient rate control algorithm is designed to find the optimal bit allocation scheme under the constraint of the low rate challenge.

Related Material

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[bibtex]
@InProceedings{Guo_2020_CVPR_Workshops,
author = {Guo, Tiansheng and Wang, Jing and Cui, Ze and Feng, Yihui and Ge, Yunying and Bai, Bo},
title = {Variable Rate Image Compression With Content Adaptive Optimization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2020}
}