Work

Topology-aware Interpretable Image Semantic Communication

Year: 2025

Type: conference-paper

Abstract: In the 6 G era, semantic communication (SemCom), which transmits high-level semantics instead of bit-wise data, shows great potential to improve transmission efficiency and robustness. However, existing methods often rely on deep feature extraction, lacking interpretability and may lead to redundant transmission. This paper proposes an interpretable SemCom framework based on scene graphs. Firstly, we use explicit scene graphs to represent semantic content, and a topology-aware codec to efficiently compress semantic structures while preserving topological relationships. To verify semantic consistency, the reconstructed scene graph is further utilized to generate images in various styles using a large language model (LLM). Simulation results show that, compared with traditional SemCom approaches, the proposed method achieves a $\mathbf{1 2. 6 \%}$ improvement in semantic consistency score (SCS), a $\mathbf{1 0. 2 \%}$ increase in structural similarity index (SSIM), and a $\mathbf{3 2. 5 \%}$ reduction in graph edit distance (GED), with a compression rate exceeding 95%. These results highlight the ability of the framework to balance efficiency and semantic fidelity, which offers an effective and interpretable solution for future SemCom.

Source: IEEE/CIC International Conference on Communications in China (ICCC)

DOI: 10.1109/iccc65529.2025.11149288

Authors: Zijie Feng, Mingkai Chen, Xiaoming He, Ning Gao, Yao Sun, Shui Yu

Institutions: Nanjing University of Posts and Telecommunications, Southeast University, University of Glasgow, University of Technology Sydney

Language: English

Topic: Advanced Data Compression Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

Funder: National Natural Science Foundation of China