Low-Rate Semantic Communication with Codebook-based Conditional Generative Models

Kailang Ye; Mingze Gong; Shuoyao Wang; Daquan Feng

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arXiv · 2504.04977

Low-Rate Semantic Communication with Codebook-based Conditional Generative Models

Kailang Ye·Mingze Gong·Shuoyao Wang·Daquan Feng

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Abstract

Generative semantic communication models are reshaping semantic communication frameworks by moving beyond pixel-wise optimization to align with human perception. However, many existing approaches prioritize image-level perceptual quality, often neglecting alignment with downstream tasks, which can lead to suboptimal semantic representation. This paper introduces an Ultra-Low Bitrate Semantic Communication (ULBSC) system that employs a conditional generative model and a learnable condition codebook.By integrating saliency conditions and image-level semantic information, the proposed method enables high-perceptual-quality and controllable task-oriented image transmission. Recognizing shared patterns among objects, we propose a codebook-assisted condition transmission method, integrated with joint source-channel coding (JSCC)-based text transmission to establish ULBSC. The codebook serves as a knowledge base, reducing communication costs to achieve ultra-low bitrate while enhancing robustness against noise and inaccuracies in saliency detection. Simulation results indicate that, under ultra-low bitrate conditions with an average compression ratio of 0.57Z%o, the proposed system delivers superior visual quality compared to traditional JSCC techniques and achieves higher saliency similarity between the generated and source images compared to state-of-the-art generative semantic communication methods.

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BibTeX RIS

Kailang Ye, Mingze Gong, Shuoyao Wang, Daquan Feng. 2025-04-07. Low-Rate Semantic Communication with Codebook-based Conditional Generative Models. https://arxiv.org/abs/2504.04977

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