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.

翻译：许多无线视觉应用（如自动驾驶）需要保持全局结构信息，而不仅仅是逐像素保真度。然而，现有的深度联合信源信道编码（DeepJSCC）方案主要优化像素级损失，未对连通性或拓扑结构提供显式保护。本文提出TopoJSCC，一种拓扑感知的DeepJSCC框架，将持久同调正则化器集成到端到端训练中。具体而言，我们通过惩罚原始图像与重建图像的立方体持久图之间的Wasserstein距离，以及信道前后潜在特征的Vietoris–Rips持久性之间的Wasserstein距离，来强制拓扑一致性，从而促进鲁棒的潜在流形。TopoJSCC基于端到端学习，无需辅助信息。实验表明，在低信噪比（SNR）和低带宽比条件下，该方法在拓扑保持和峰值信噪比（PSNR）方面均有提升。