Swin-Transformer-Based JSCC Exploring Detail-Aware Enhancement and Progressive Semantic Fusion in Wireless Image Transmission

The diversified applications including tele-medical treatment, immersive experiences and embodied intelligence pose a tremendous challenge to the ongoing wireless transmission in terms of spectrum resource scarcity and adaptability to complex environments. Semantic communication, acting as an emerging task-oriented transmission paradigm, is viewed as a transformational enabler in future wireless systems. In this paper, a Swin-Transformer-based joint source-channel coding scheme that explores detail-aware enhancement and multi-level semantic fusion (DSF-JSCC) is proposed to improve the transmission quality of high-resolution images over various channel environments. Firstly, the framework of the DSF-JSCC is designed based on the combination of stacked Swin-Transformer blocks and multi-level semantic fusion to enhance the capability of feature representation and channel adaptability. Secondly, a detail-aware enhancement method (DAEM) is designed to facilitate the capture and reconstruction of textures and edges, where human visual attention to the details is modeled through the parallel depthwise separable convolution branches. Thirdly, a progressive-processing-based multi-level semantic fusion (PMSF) is designed to fully exploit multi-level semantic information and to bridge the semantic gap across different stages through spatial alignment, resolution matching and adaptive weighting fusion. Finally, the performance of our DSF-JSCC and relevant variants is evaluated through the comparison with other baselines including separated source-channel coding schemes and joint source-channel coding schemes, and the superiority of our DSF-JSCC is validated in terms of PSNR, MS-SSIM and LPIPS under diverse channel environments.