  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.
