Deep Joint Source-Channel Coding for Wireless Image Transmission with Entropy-Aware Adaptive Rate Control

TL;DR

This paper introduces a deep joint source-channel coding scheme for wireless image transmission that adaptively controls the rate based on feature map entropy, improving efficiency and image quality under limited bandwidth.

Contribution

It presents a novel entropy-aware adaptive rate control method using a single neural network supporting multiple rates for wireless image transmission.

Findings

Reduces channel bandwidth requirements

Maintains high image quality

Improves transmission efficiency

Abstract

Adaptive rate control for deep joint source and channel coding (JSCC) is considered as an effective approach to transmit sufficient information in scenarios with limited communication resources. We propose a deep JSCC scheme for wireless image transmission with entropy-aware adaptive rate control, using a single deep neural network to support multiple rates and automatically adjust the rate based on the feature maps of the input image and their entropy, as well as the channel conditions. In particular, we maximize the entropy of the feature maps to increase the average information carried by each transmitted symbol during the training. We further decide which feature maps should be activated based on their entropy, which improves the efficiency of the transmitted symbols. We also propose a pruning module to remove less important pixels in the activated feature maps in order to further improve transmission efficiency. The experimental results demonstrate that our proposed scheme learns an effective rate control strategy that reduces the required channel bandwidth while preserving the quality of the reconstructed images.