In recent years, semantic communication based on deep joint source-channel coding (DEEPJSCC) has been demonstrated and widely investigated. However, existing DEEPJSCC schemes suffer from low efficiency in mining latent semantic representations, as well as large model size, high computational complexity, and redundant parameters. To address these issues, we meticulously establish a lightweight DEEPJSCC framework for wireless image semantic transmission, termed STARJSCC. The proposed method achieves flexible wireless image transmission by introducing an improved channel state adaptive module (CSA Mod) to adapt to different channel conditions, combined with a decoupled static semantic compression (SC) mask to control different transmission rates. Experimental results show that the STARJSCC framework outperforms other baseline schemes in terms of performance and adaptability across various transmission rates and signal-to-noise ratio (SNR) levels, achieving up to 2.73 dB improvement on high-resolution test set. Moreover, this solution significantly reduces model parameters, computational complexity, and storage overhead, providing a potential solution for high-quality wireless image transmission in resource-constrained scenarios.
Li et al. (Sun,) studied this question.