Personalized Digital Semantic Communication for Image Transmission with Vision-Language Models

Abstract

Semantic communication (SC) enables bandwidth-efficient wireless image transmission, but most existing SC schemes are user-agnostic and ignore receiver-dependent semantics. To address this issue, we propose a personalized digital semantic communication (PDSC) framework that integrates a vision-language model (VLM)-based semantic encoder with a latent diffusion model (LDM)-based semantic decoder. Specifically, the semantic encoder extracts source-aware personalized semantic tokens from both the source image and the receiver's historical interactions. These tokens are vector-quantized into discrete semantic indices and further encoded into a compact fixed-length bitstream, enabling compatibility with digital transmission. At the receiver, the semantic decoder reconstructs a personalized image conditioned on the recovered semantic tokens. Furthermore, we formulate a capacity-constrained personalized semantic rate-distortion problem and introduce a semantic distortion metric that jointly characterizes source-semantic fidelity and user-preference alignment. Experiments show that PDSC achieves superior source-semantic consistency and personalization over state-of-the-art SC baselines, including CDDM and MoS, under bandwidth-limited wireless transmission.