Joint ROI Guidance and Spatial Analysis for Task-Aware Distributed Deep Joint Source-Channel Coding

In this paper, we investigate the system performance of deep joint source-channel coding (JSCC) for task-oriented transmission in the Wyner-Ziv scenario, i.e., a distributed coding scenario, aiming to improve the image reconstruction performance and task accuracy. Unlike existing deep JSCC based methods, we introduce regions of interest (ROI), which facilitates the effective utilization of side information for enhancing task performance. Meanwhile, we incorporate a spatial analysis mechanism to fuse the side information. By integrating these two mechanisms, we propose a novel distributed deep JSCC scheme that further leverages task relevance within the side information. Simulation results show that our proposed scheme outperforms the benchmark in terms of image reconstruction performance and task accuracy.