让图像传输保持结构完整，适合自动驾驶等场景
Topology-Preserving Deep Joint Source-Channel Coding for Semantic Communication
- 用拓扑正则项约束图像重建的连通性
- 低信噪比下拓扑保真度和PSNR均提升
- 无需额外信息，端到端训练适用性强
许多无线视觉应用（如自动驾驶）需要保留全局结构信息，而非仅关注像素级保真度。现有深度联合源信道编码（DeepJSCC）主要优化像素级损失，未显式保护连通性或拓扑结构。本文提出拓扑感知的DeepJSCC框架TopoJSCC，将持久同调正则项融入端到端训练。具体而言，通过惩罚原始与重建图像的立方体持久同调图之间的Wasserstein距离，以及信道前后潜在特征的Vietoris-Rips持久同调图间的距离，以确保潜在流形的鲁棒性。该方法无需额外侧信息，实验表明在低信噪比（SNR）和带宽比条件下，拓扑保真度与峰值信噪比（PSNR）均有显著提升。
原文摘要 · Abstract (English)
Many wireless vision applications, such as autonomous driving, require preservation of global structural information rather than only per-pixel fidelity. However, existing Deep joint source-channel coding (DeepJSCC) schemes mainly optimize pixel-wise losses and provide no explicit protection of connectivity or topology. This letter proposes TopoJSCC, a topology-aware DeepJSCC framework that integrates persistent-homology regularizers to end-to-end training. Specifically, we enforce topological consistency by penalizing Wasserstein distances between cubical persistence diagrams of original and reconstructed images, and between Vietoris--Rips persistence of latent features before and after the channel to promote a robust latent manifold. TopoJSCC is based on end-to-end learning and requires no side information. Experiments show improved topology preservation and peak signal-to-noise ratio (PSNR) in low signal-to-noise ratio (SNR) and bandwidth-ratio regimes.
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