Adaptive Information Bottleneck Guided Joint Source and Channel Coding for Image Transmission

TL;DR

This paper introduces an adaptive joint source and channel coding method guided by information bottleneck principles, which reduces transmission data and enhances image reconstruction quality in image transmission tasks.

Contribution

It proposes a novel IB-based loss function and an adaptive algorithm to dynamically balance compression and reconstruction quality in JSCC.

Findings

Significantly reduces transmitted data volume.

Improves image reconstruction quality.

Enhances downstream task accuracy.

Abstract

Joint source and channel coding (JSCC) for image transmission has attracted increasing attention due to its robustness and high efficiency. However, the existing deep JSCC research mainly focuses on minimizing the distortion between the transmitted and received information under a fixed number of available channels. Therefore, the transmitted rate may be far more than its required minimum value. In this paper, an adaptive information bottleneck (IB) guided joint source and channel coding (AIB-JSCC) method is proposed for image transmission. The goal of AIB-JSCC is to reduce the transmission rate while improving the image reconstruction quality. In particular, a new IB objective for image transmission is proposed so as to minimize the distortion and the transmission rate. A mathematically tractable lower bound on the proposed objective is derived, and then, adopted as the loss function of AIB-JSCC. To trade off compression and reconstruction quality, an adaptive algorithm is proposed to adjust the hyperparameter of the proposed loss function dynamically according to the distortion during the training. Experimental results show that AIB-JSCC can significantly reduce the required amount of transmitted data and improve the reconstruction quality and downstream task accuracy.