Design of vector quantization codebooks using a genetic algorithm

Data compression techniques recode data into more compact forms. One such technique is vector quantization, which maps groups of input symbols, called vectors, onto a small set of vectors, called the codebook. Each vector in the codebook is a codeword. The indexes of the codewords represent the original vectors, and writing the codewords that the indexes indicate restores a facsimile of the original data. The similarity of the restored data to the original under vector quantization depends on the codebook, and several algorithms have been proposed for designing it from a training set of typical vectors. This paper describes a genetic algorithm for the problem of codebook design. The genetic algorithm's chromosomes represent partitions of the training set; each vector maps to the codeword that is the centroid of its set in the partition. To speed up its operation, the genetic algorithm uses fitness inheritance to assign fitness values to most new chromosomes, rather than evaluating them. Tests using five standard digitized images compare the genetic algorithm to a popular non-genetic algorithm for codebook design. The genetic algorithm is found to be effective, but slow.