Recent semantic communication methods explore effective ways to expand the communication paradigm and improve the system performance of the communication systems. Nonetheless, the common problem of these methods is that the essence of semantics is not explicitly pointed out and directly utilized. A new epistemology suggests that synonymy, which is revealed as the fundamental feature of semantics, guides the establishment of the semantic information theory from a novel viewpoint. Building on this theoretical basis, this paper proposes a semantic arithmetic coding (SAC) method for semantic lossless compression using intuitive semantic synonymy. By constructing reasonable synonymous mappings and performing arithmetic coding procedures over synonymous sets, SAC can achieve higher compression efficiency for meaning-contained source sequences at the semantic level and thereby approximate the semantic entropy limits. Experimental results on edge texture map compression show an evident improvement in coding efficiency using SAC without semantic losses, compared to traditional arithmetic coding, which demonstrates its effectiveness.

翻译：近期语义通信方法探索了扩展通信范式及提升通信系统性能的有效途径。然而，这些方法的共同问题在于未明确揭示并直接利用语义的本质。一种新的认识论指出，同义性作为语义的基本特征，从全新视角指导了语义信息理论的构建。基于这一理论基础，本文提出了一种利用直观语义同义性实现语义无损压缩的语义算术编码（SAC）方法。通过构建合理的同义映射并在同义集上执行算术编码流程，SAC能在语义层面实现更高压缩效率，从而逼近语义熵极限。边缘纹理图压缩实验结果表明，与传统算术编码相比，SAC在无语义损失的情况下显著提升了编码效率，验证了其有效性。