马尔可夫约束的离散扩散模型用于数字语义符号错误校正
Markov-Enforced Discrete Diffusion Model for Digital Semantic Symbol Error Correction
AI总结：
本文提出SSCDM模型，通过连续时间马尔可夫链理论构建离散时间转移动态，结合学习的VQ码本和自组织图损失，提升语义符号拓扑保持性，实验显示在低信噪比下图像重建质量显著提升。
摘要：
扩散模型（DMs）在各种领域取得了显著成功，得益于其强大的生成和去噪能力。同时，基于神经联合源-信道编码（JSCC）的语义通信已成为稳健高效的图像传输有前景的范式。然而，严重的信道噪声仍会扭曲传输的语义符号，导致显著的性能下降。将DMs应用于数字语义符号，特别是基于向量量化（VQ）的系统，本质上具有挑战性，因为马尔可夫假设不适用于符号转移动态。为了解决这个问题，我们引入了SSCDM，一种语义符号校正扩散模型，其离散时间转移动态是通过连续时间马尔可夫链理论的解决方案构建的。此外，为了促进DMs与JSCC之间的协同作用，我们的DM结构利用学习的VQ码本将离散符号嵌入到潜在特征空间中，并在码本学习期间结合基于自组织图的损失，以增强相邻数字符号之间的几何邻近性，从而促进拓扑保持的语义表示。实验结果表明，所提出的方法显著提高了图像重建质量，并在低信噪比场景和不同数据集上优于先前的符号级去噪技术。
英文摘要：
Diffusion models (DMs) have achieved remarkable success across various domains owing to their strong generative and denoising capabilities. Meanwhile, semantic communication based on neural joint source-channel coding (JSCC) has emerged as a promising paradigm for robust and efficient image transmission. However, severe channel noise can still distort the transmitted semantic symbols, resulting in significant performance degradation. Applying DMs to digital semantic symbols, particularly in vector quantization (VQ)-based systems, is fundamentally challenging because the Markov assumption does not hold for the symbol transition dynamics. To address this issue, we introduce SSCDM, a semantic symbol correcting diffusion model whose discrete-time transition dynamics are constructed using solutions from continuous-time Markov chain theory. Furthermore, to promote synergy between DMs and JSCC, our DM structure embeds discrete symbols into a latent feature space using a learned VQ codebook, and a self-organizing map-based loss is incorporated during codebook learning to enhance the geometric vicinity between neighboring digital symbols, thereby promoting topology-preserving semantic representations. Experimental results show that the proposed method significantly improves image reconstruction quality and outperforms previous symbol-level denoising techniques under low signal-to-noise ratio scenarios and different datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
