A Scalable Coding Method With Semantic Decomposition for Semantic Communication

Recent advancements in deep learning for semantic communication have been significant, yet fixed-length encoding techniques struggle to capture the complex and variable nature of semantic content, potentially compromising detail and transmission efficiency. This paper presents a novel semantic encoding framework featuring scalable coding to enhance the processing of diverse semantic targets within images. Our approach decomposes raw, unstructured images into hierarchically structured semantic features, thereby enriching semantic representation accuracy. The proposed scalable coding method employs a dynamic resource allocation strategy, guided by a semantic knowledge base and user intent, to selectively encode each semantic target. This approach yields substantial improvements in communication efficiency. Additionally, we introduce a semantic alignment strategy that optimizes reconstructed image edges and quality through specialized loss functions. Experimental results demonstrate that, compared with the latest methods, our approach achieves user-intent-aware flexible encoding and decoding without sacrificing performance. This highlights the effectiveness of our framework in maintaining high coding efficiency and image reconstruction quality, while enabling adaptive semantic communication.