CBANet: Toward Complexity and Bitrate Adaptive Deep Image Compression Using a Single Network
Jinyang Guo, Dong Xu, Guo Lu
Abstract
In this work, we propose a new deep image compression framework called Complexity and Bitrate Adaptive Network (CBANet) that aims to learn one single network to support variable bitrate coding under various computational complexity levels. 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 complex rate-distortion-complexity trade-off when learning a single network to support multiple computational complexity levels and variable bitrates. Since it is a non-trivial task to solve such a rate-distortion-complexity related optimization problem, we propose a two-step approach to decouple this complex optimization task into a complexity-distortion optimization sub-task and a rate-distortion optimization sub-task, and additionally propose a new network design strategy by introducing a Complexity Adaptive Module (CAM) and a Bitrate Adaptive Module (BAM) to respectively achieve the complexity-distortion and rate-distortion trade-offs. As a general approach, our network design strategy can be readily incorporated into different deep image compression methods to achieve complexity and bitrate adaptive image compression by using a single network. Comprehensive experiments on two benchmark datasets demonstrate the effectiveness of our CBANet for deep image compression. Code is released at https://github.com/JinyangGuo/CBANet-release.