用轻量扩散模型提升图像语义通信的重建质量
SC-CDM: Enhancing Quality of Image Semantic Communication with a Compact Diffusion Model
- 用改进的Swin Transformer提取高效语义特征
- 重建图像PSNR比传统方法提升超17%
- 适合带宽受限场景下的高质量图像传输
语义通信（SC）是6G移动通信系统中备受关注的新兴技术，但现有研究对重建图像的感知质量考虑不足。为此，本文提出一种面向无线图像传输的生成式语义通信框架（SC-CDM），采用轻量级扩散模型提升重建图像的保真度与语义准确性，确保在带宽受限环境下关键内容得以保留。具体而言，将Swin Transformer重构为高效语义特征提取与压缩的新骨干网络；接收端集成轻量化先验与图像重建网络。相较于传统扩散模型，该方法利用其强大的分布映射能力生成紧凑条件向量，引导图像恢复，从而增强重建图像的感知细节。一系列评估与消融实验验证了算法的有效性与鲁棒性，相比基于CNN的DeepJSCC，在峰值信噪比（PSNR）上提升超过17%。
原文摘要 · Abstract (English)
Semantic Communication (SC) is an emerging technology that has attracted much attention in the sixth-generation (6G) mobile communication systems. However, few literature has fully considered the perceptual quality of the reconstructed image. To solve this problem, we propose a generative SC for wireless image transmission (denoted as SC-CDM). This approach leverages compact diffusion models to improve the fidelity and semantic accuracy of the images reconstructed after transmission, ensuring that the essential content is preserved even in bandwidth-constrained environments. Specifically, we aim to redesign the swin Transformer as a new backbone for efficient semantic feature extraction and compression. Next, the receiver integrates the slim prior and image reconstruction networks. Compared to traditional Diffusion Models (DMs), it leverages DMs' robust distribution mapping capability to generate a compact condition vector, guiding image recovery, thus enhancing the perceptual details of the reconstructed images. Finally, a series of evaluation and ablation studies are conducted to validate the effectiveness and robustness of the proposed algorithm and further increase the Peak Signal-to-Noise Ratio (PSNR) by over 17% on top of CNN-based DeepJSCC.
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