Semantic Huffman Coding using Synonymous Mapping

TL;DR

This paper proposes a semantic Huffman coding method that leverages synonymous mapping to improve compression efficiency by reducing average code length, demonstrating advantages over classical Huffman coding in semantic lossless scenarios.

Contribution

It introduces a novel semantic Huffman coding scheme based on semantic information theory and synonymous sets, capable of approximating semantic entropy.

Findings

Semantic Huffman coding achieves shorter average code lengths.

It outperforms classical Huffman coding in semantic lossless compression.

Theoretical analysis shows capability to approximate semantic entropy.

Abstract

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.