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Semantic Arithmetic Coding Using Synonymous Mappings.

Zijian Liang1, Kai Niu1,2, Jin Xu1

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Summary
This summary is machine-generated.

This study introduces semantic arithmetic coding (SAC) for lossless data compression. SAC leverages synonymity to enhance compression efficiency for meaning-contained data, outperforming traditional methods.

Keywords:
arithmetic codingsemantic lossless compressionsemanticssynonymitysynonymous mappings

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Area of Science:

  • Information Theory
  • Computer Science
  • Communication Systems

Background:

  • Semantic communication methods aim to improve system performance but often fail to explicitly utilize the essence of semantics.
  • A novel viewpoint establishes semantic information theory based on synonymity as a fundamental semantic feature.

Purpose of the Study:

  • To propose a novel semantic arithmetic coding (SAC) method for semantic lossless compression.
  • To leverage synonymity for improved compression efficiency at the semantic level.

Main Methods:

  • Developed a semantic arithmetic coding (SAC) method based on synonymity.
  • Constructed synonymous mappings and applied arithmetic coding over synonymous sets.
  • Evaluated SAC on edge texture map compression.

Main Results:

  • SAC achieves higher compression efficiency for meaning-contained source sequences by operating at the semantic level.
  • The method approximates semantic entropy limits.
  • Experimental results show significant coding efficiency improvement compared to traditional arithmetic coding without semantic loss.

Conclusions:

  • Semantic arithmetic coding (SAC) effectively enhances compression efficiency by utilizing synonymity.
  • The proposed method offers a significant advancement in semantic lossless compression.
  • SAC demonstrates practical effectiveness in applications like edge texture map compression.