Evolving Nonlinear Predictive Models for Lossless Image Compression with Genetic Programming

Abstract

We describe a genetic programming system which learns nonlinear predictive models for lossless image compression. Sexpressions which represent nonlinear predictive models are learned, and the error image is compressed using a Hu#- man encoder. We show that the proposed system is capable of achieving compression ratios superior to that of the best known lossless compression algorithms, although it is significantly slower than standard algorithms.