Chao Li1,3,#, Chen Wang1,3,#, Caichang Ding2,*, Yonghao Liao1,3, Zhiwei Ye1,3
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3129-3149, 2025, DOI:10.32604/cmc.2025.067440
- 23 September 2025
Abstract Deep learning-based semantic communication has achieved remarkable progress with CNNs and Transformers. However, CNNs exhibit constrained performance in high-resolution image transmission, while Transformers incur high computational cost due to quadratic complexity. Recently, VMamba, a novel state space model with linear complexity and exceptional long-range dependency modeling capabilities, has shown great potential in computer vision tasks. Inspired by this, we propose MNTSCC, an efficient VMamba-based nonlinear joint source-channel coding (JSCC) model for wireless image transmission. Specifically, MNTSCC comprises a VMamba-based nonlinear transform module, an MCAM entropy model, and a JSCC module. In the encoding stage, the… More >
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