Article Details

Title · Authors · Abstract · Keywords · DOI · References

Joint Source-Channel Coding Algorithm Based on Swin Transformer

LIAO Xiao, LI Zhi

(College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China)

Section：Communication Engineering · Vol. 2025, Issue 7 · Pages：1-4 · 2096-4706（2025）07-0001-04 · CLC number：TN911.22；TP391.4 · Document code：A · DOI：10.19850/j.cnki.2096-4706.2025.07.001

Abstract

Joint Source-Channel Coding (JSCC), as a key research direction in semantic communication, has achieved preliminary research results. However, with the increasing resolution of images, traditional JSCC algorithms based on Convolutional Neural Network (CNN) exhibit limitations in extracting image semantic features. To address this issue, this paper proposes a JSCC algorithm based on Swin Transformer. The algorithm firstly utilizes a Multi-Scale Large Kernel Attention (MLKA) mechanism to initially capture the local information and long-range dependencies of images. Subsequently, Swin Transformer is employed to further hierarchically extract image semantic features and perform adaptive rate coding. Experimental results demonstrate that, under the channel models of Additive White Gaussian Noise (AWGN) and Rayleigh, the proposed algorithm outperforms traditional algorithms in terms of Peak Signal-to-Noise Ratio (PSNR) and Multi-Scale Structural Similarity Index Measure (MS-SSIM).

Keywords

Joint Source-Channel Coding; Swin Transformer; Multi-Scale Large-Kernel Attention

References

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About the authors

Liao Xiao (2000.04-), male, Han ethnicity, from Renshou, Sichuan, is currently pursuing a master's degree in semantic communication.