Evolutionary fractal image compression

Abstract

This paper introduces evolutionary computing to fractal image compression. In fractal image compression a partitioning of the image into ranges is required. We propose to use evolutionary computing to find good partitionings. Here ranges are connected sets of small square image blocks. Populations consist of N/sub p/ configurations, each of which is a partitioning with a fractal code. In the evolution each configuration produces /spl sigma/ children who inherit their parent partitionings except for two random neighboring ranges which are merged. From the offspring the best ones are selected for the next generation population based on a fitness criterion (collage error). We show that a far better rate-distortion curve can be obtained with this approach as compared to traditional quad-tree partitionings.