An evolutionary approach to vector quantizer design

Vecior quantization is a lossy coding technique for encoding a set of vectors from different sources such as image and speech. The design of vector quantizers that yields the lowesi disiortion is one of the mosi challenging problems in the field of soiurce coding. However, this problem is known to be dificult [3]. The conventional solution technique works through a process of iteraiive refinements which yield only locally opiimal results. In this paper, we design and evaluate three versions of genetic algorithms for computing vector quantizers. Our preliminary study wiih Gaussian-Markov sources showed that the genetic approach outperforms the conventional technique in mosi cases.