Seeing is Communicating: A Unified Visual Compression Method for Image-Text Multimodal Semantic Communication

As 6G networks evolve from bit-perfect transmission to semantic-oriented delivery, multimodal semantic communication (SemCom) has emerged as a key enabler for applications like the metaverse and autonomous driving. However, current multimodal systems suffer from “architecture islands”—relying on separate, redundant encoders for text and images—and often struggle with synchronization in dynamic wireless channels. To overcome these bottlenecks, we propose a unified vision-centric semantic communication (VC-SemCom) framework based on multimodal visual compression. By rendering non-visual modalities (e.g., text) into the visual domain, we utilize a single, parameter-efficient vision transformer (ViT) for unified encoding. We further address the vulnerability of semantic tokens in fading channels by introducing a semantic redundancy fusion mechanism. Finally, we demonstrate a dual-path decoding architecture suitable for edge computing. Simulation results show that our approach outperforms traditional multi-tower architectures in both compression efficiency and robustness against low signal-to-noise ratio (SNR). The visual compression-based unified image-text encoder provides an efficient and parameter-compact solution for multimodal semantic communication in 6G networks.