Semantic communication stands out as a highly promising avenue for future developments in communications. Theoretically, source compression coding based on semantics can achieve lower rates than Shannon entropy. This paper introduces a semantic Huffman coding built upon semantic information theory. By incorporating synonymous mapping and synonymous sets, semantic Huffman coding can achieve shorter average code lengths. Furthermore, we demonstrate that semantic Huffman coding theoretically have the capability to approximate semantic entropy. Experimental results indicate that, under the condition of semantic lossless, semantic Huffman coding exhibits clear advantages in compression efficiency over classical Huffman coding.

翻译：语义通信作为通信领域未来发展的极具前景的方向脱颖而出。理论上，基于语义的信源压缩编码能够实现比香农熵更低的码率。本文提出了一种基于语义信息论的语义霍夫曼编码。通过引入同义映射与同义集，语义霍夫曼编码能够实现更短的平均码长。此外，我们证明了语义霍夫曼编码在理论上具有逼近语义熵的能力。实验结果表明，在语义无损条件下，语义霍夫曼编码在压缩效率上相较于经典霍夫曼编码展现出显著优势。