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ABSTRACT

This work outlines an evolutionary algorithm for image vector quantization. An integer-coded genetic algorithm (GA) that employs the maximum likelihood (ML) measure as the fitness function is introduced. The proposed algorithm allows for different chromosome representations and provides an adaptation to the genetic operators to suit the image quantization problem. The main objective of the algorithm is, for a codebook with a pre-defined size, to find the best set of image blocks that make up the codewords. Each codeword will be representative of a group of blocks.

The final codebook is formed from the set of groups' averages. Simulation results show the effectiveness of the algorithm especially when compared with the famous LBG vector quantizer.

REFERENCES

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.m Miscellaneous

General Terms:
Algorithms, Experimentation, Performance

Keywords:
clustering, evolutionary algorithms, image compression, maximum likelihood