Topology-Preserving Deep Joint Source-Channel Coding for Semantic Communication
arXiv:2603.17126v1 Announce Type: new Abstract: 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 bandw
arXiv:2603.17126v1 Announce Type: new Abstract: 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.
Executive Summary
This paper proposes TopoJSCC, a novel deep joint source-channel coding framework that prioritizes the preservation of global structural information, specifically topology, in wireless vision applications. Building upon existing DeepJSCC schemes, TopoJSCC integrates persistent-homology regularizers for end-to-end training, enforcing topological consistency through Wasserstein distances between cubical persistence diagrams and latent features. Experiments demonstrate improved topology preservation and peak signal-to-noise ratio (PSNR) in low signal-to-noise ratio (SNR) and bandwidth-ratio regimes. The proposed framework has the potential to significantly enhance the robustness and reliability of wireless vision applications, such as autonomous driving, by protecting connectivity and topology. Its end-to-end learning approach and requirement for no side information further increase its practical appeal.
Key Points
- ▸ Topology-aware DeepJSCC framework for wireless vision applications
- ▸ Integration of persistent-homology regularizers for end-to-end training
- ▸ Enforcement of topological consistency through Wasserstein distances
- ▸ Improved topology preservation and PSNR in low SNR and bandwidth-ratio regimes
Merits
Strength in Topology Preservation
TopoJSCC effectively preserves global structural information, enabling robustness in wireless vision applications.
Efficient End-to-End Learning
The proposed framework allows for end-to-end learning, eliminating the need for side information and facilitating practical implementation.
Demerits
Limited Generalizability
The framework's performance and efficacy in other domains beyond wireless vision applications remain to be explored.
Computational Complexity
The integration of persistent-homology regularizers may introduce additional computational complexity, potentially impacting real-time applications.
Expert Commentary
The introduction of TopoJSCC marks a significant advancement in the field of deep joint source-channel coding, particularly in the context of wireless vision applications. By prioritizing topology preservation, the proposed framework effectively addresses the critical need for robustness in these applications. While the work demonstrates promising results, further research is necessary to fully explore its generalizability and potential computational complexities. The implications of this work extend beyond the technical community, with potential policy and regulatory implications arising from the increased use of topology-aware deep learning frameworks.
Recommendations
- ✓ Future research should focus on exploring the generalizability of TopoJSCC in other domains and evaluating its performance in diverse wireless vision applications.
- ✓ The development of efficient and scalable algorithms for persistent-homology regularizers is crucial for practical implementation and real-time applications.
