U-Net-Based Generative Joint Source-Channel Coding for Wireless Image Transmission

TL;DR

This paper introduces two deep learning-based joint source-channel coding methods using U-Net architectures for wireless image transmission, improving perceptual quality and fidelity over existing approaches.

Contribution

The paper proposes G-UNet-JSCC and cGAN-JSCC, novel deep generative JSCC schemes that enhance image reconstruction quality and robustness in wireless transmission.

Findings

cGAN-JSCC outperforms G-UNet-JSCC in low-resolution image reconstruction

Both methods achieve superior perceptual quality and fidelity

cGAN-JSCC shows greater robustness to channel variations

Abstract

Deep learning (DL)-based joint source-channel coding (JSCC) methods have achieved remarkable success in wireless image transmission. However, these methods either focus on conventional distortion metrics that do not necessarily yield high perceptual quality or incur high computational complexity. In this paper, we propose two DL-based JSCC (DeepJSCC) methods that leverage deep generative architectures for wireless image transmission. Specifically, we propose G-UNet-JSCC, a scheme comprising an encoder and a U-Net-based generator serving as the decoder. Its skip connections enable multi-scale feature fusion to improve both pixel-level fidelity and perceptual quality of reconstructed images by integrating low- and high-level features. To further enhance pixel-level fidelity, the encoder and the U-Net-based decoder are jointly optimized using a weighted sum of structural similarity and mean-squared error (MSE) losses. Building upon G-UNet-JSCC, we further develop a DeepJSCC method called cGAN-JSCC, where the decoder is enhanced through adversarial training. In this scheme, we retain the encoder of G-UNet-JSCC and adversarially train the decoder's generator against a patch-based discriminator. cGAN-JSCC employs a two-stage training procedure. The outer stage trains the encoder and the decoder end-to-end using an MSE loss, while the inner stage adversarially trains the decoder's generator and the discriminator by minimizing a joint loss combining adversarial and distortion losses. Simulation results demonstrate that the proposed methods achieve superior pixel-level fidelity and perceptual quality on both high- and low-resolution images. For low-resolution images, cGAN-JSCC achieves better reconstruction performance and greater robustness to channel variations than G-UNet-JSCC.