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CBANet: Towards Complexity and Bitrate Adaptive Deep Image Compression using a Single Network by Guo, Jinyang; Xu, Dong; Lu, Guo is a scholarly article available to read on EtoBox.

What is CBANet: Towards Complexity and Bitrate Adaptive Deep Image Compression using a Single Network about?

In this paper, we propose a new deep image compression framework called Complexity and Bitrate Adaptive Network (CBANet), which aims to learn one single network to support variable bitrate coding under different computational complexity constraints. In contrast to the existing state-of-the-art learning based image compression frameworks that only consider the rate-distortion trade-off without introducing any constraint related to the computational complexity, our CBANet considers the trade-off between the rate and distortion under dynamic computational complexity constraints. Specifically, to decode the images with one single decoder under various computational complexity constraints, we propose a new multi-branch complexity adaptive module, in which each branch only takes a small portion of the computational budget of the decoder. The reconstructed images with different visual qualities can be readily generated by using different numbers of branches. Furthermore, to achieve variable bitrate decoding with one single decoder, we propose a bitrate adaptive module to project the representation from a base bitrate to the expected representation at a target bitrate for transmission. The

Author

Guo, Jinyang; Xu, Dong; Lu, Guo

Published

2021

Language

EN