Region-of-Interest-Guided Deep Joint Source-Channel Coding for Image Transmission

TL;DR

This paper introduces ROI-JSCC, a deep joint source-channel coding framework that enhances the quality of regions of interest in transmitted images, improving user experience without significant computational costs.

Contribution

The paper presents a novel ROI-guided deepJSCC approach with four key components, focusing on prioritizing ROI quality in image transmission over wireless networks.

Findings

Significantly improves ROI reconstruction quality.

Maintains competitive overall image quality.

Operates with minimal computational overhead.

Abstract

Deep joint source-channel coding (deepJSCC) methods have shown promising improvements in communication performance over wireless networks. However, existing approaches primarily focus on enhancing overall image reconstruction quality, which may not fully align with user experiences, often driven by the quality of regions of interest (ROI). Motivated by this, we propose ROI-guided joint source-channel coding (ROI-JSCC), a novel deepJSCC framework that prioritizes high-quality transmission of ROI. The ROI-JSCC consists of four key components: (1) Image ROI embedding, (2) ROI-guided split processing, (3) ROI-based loss function design, and (4) ROI-adaptive bandwidth allocation. Together, these components allow ROI-JSCC to selectively enhance the ROI reconstruction quality at varying ROI positions while maintaining overall image quality with minimal computational overhead. Experimental results under diverse communication environments demonstrate that ROI-JSCC significantly improves ROI reconstruction quality while maintaining competitive average image quality compared to recent state-of-the-art methods.