Wireless semantic image transmission system based on deep flexible coding strategy and performance prediction
Year: 2025
Type: article
Abstract: To improve adaptivity and coding flexibility for diverse image content under complex and dynamic wireless channel conditions, a deeply flexible joint source-channel coding (DF-JSCC) scheme and a transmission performance prediction network (PreNet) for semantic image transmission (SIT) were proposed. Specifically, a multi-scale feature fusion module (MS-FFM) was incorporated into DF-JSCC to enhance detailed feature extraction. A feature enhancement module (FEM) was also designed to adaptively adjust channel weights in the feature maps to reinforce critical information. Additionally, PreNet was introduced to accurately predict the peak signal-to-noise ratio (PSNR) performance of reconstructed images by modeling feature distribution in detail-rich feature maps and utilizing multidimensional feature fusion and learning. Experimental results demonstrate that, compared with the existing state-of-the-art DeepJSCC method, DF-JSCC achieves at least 1.03 dB improvement in PSNR across various communication scenarios, while reducing bandwidth consumption by over 50% under equivalent transmission conditions. The PreNet effectively improves the prediction quality and robustness of the SIT in complex environments by reducing the mean squared error （MSE） by 20%.
Authors: YANG Shuhan, SHEN Bin, HUANG Xiaoge
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7044281234
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