CBANet: Towards Complexity and Bitrate Adaptive Deep Image Compression using a Single Network

TL;DR

CBANet introduces a unified deep image compression network that adaptively manages bitrate and computational complexity, enabling flexible decoding with a single model across various constraints, reducing storage needs and maintaining high quality.

Contribution

The paper presents a novel single-network framework with complexity and bitrate adaptive modules, allowing variable bitrate decoding under different computational constraints.

Findings

Supports multiple bitrate decoding with one network

Reduces storage requirements for deployment

Achieves effective compression across benchmarks

Abstract

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. Then it will project the transmitted representation at the target bitrate back to that at the base bitrate for the decoding process. The proposed bit adaptive module can significantly reduce the storage requirement for deployment platforms. As a result, our CBANet enables one single codec to support multiple bitrate decoding under various computational complexity constraints. Comprehensive experiments on two benchmark datasets demonstrate the effectiveness of our CBANet for deep image compression.