Semantic communication, leveraging advanced deep learning techniques, emerges as a new paradigm that meets the requirements of next-generation wireless networks. However, current semantic communication systems, which employ neural coding for feature extraction from raw data, have not adequately addressed the fundamental question: Is general feature extraction through deep neural networks sufficient for understanding semantic meaning within raw data in semantic communication? This article is thus motivated to clarify two critical aspects: semantic understanding and general semantic representation. This article presents a standardized definition on semantic coding, an extensive neural coding scheme for general semantic representation that clearly represents underlying data semantics based on contextual modeling. With these general semantic representations obtained, both human- and machine-centric end-to-end data transmission can be achieved through only minimal specialized modifications, such as fine-tuning and regularization. This article contributes to establishing a commonsense that semantic communication extends far beyond mere feature transmission, focusing instead on conveying compact semantic representations through context-aware coding schemes.

Accepted by IEEE COMSTD, project page: https://qin-jingyun.github.io/SemCod/