Scene Graph-Aided Probabilistic Semantic Communication for Image Transmission

TL;DR

This paper introduces a semantic communication framework for wireless image transmission that uses scene graphs and probabilistic compression to improve efficiency and semantic fidelity.

Contribution

It proposes a novel semantic communication scheme utilizing scene graphs and a two-stage compression algorithm, along with a multi-round semantic compression method and theoretical analysis.

Findings

Achieves higher transmission throughput.

Ensures better semantic alignment.

Validates effectiveness through simulations.

Abstract

Semantic communication emphasizes the transmission of meaning rather than raw symbols. It offers a promising solution to alleviate network congestion and improve transmission efficiency. In this paper, we propose a wireless image communication framework that employs probability graphs as shared semantic knowledge base among distributed users. High-level image semantics are represented via scene graphs, and a two-stage compression algorithm is devised to remove predictable components based on learned conditional and co-occurrence probabilities. At the transmitter, the algorithm filters redundant relations and entity pairs, while at the receiver, semantic recovery leverages the same probability graphs to reconstruct omitted information. For further research, we also put forward a multi-round semantic compression algorithm with its theoretical performance analysis. Simulation results demonstrate that our semantic-aware scheme achieves superior transmission throughput and satiable semantic alignment, validating the efficacy of leveraging high-level semantics for image communication.