Image Compression Based on Genetic Algorithms and Rational Numbers Reduction

Fractal image compression has been developed in the last decades using different approaches such as cosine transforms and neural nets. The common objective is to reduce the size of an input image while maintain an acceptable quality of the reconstructed after decompression. In fact, a lossy algorithm is always applied in order to gain high compression ratios compared to lossless techniques. In this paper we introduce a new technique where we take advantage of the reduction characteristic of rational numbers in the benefit of reducing an image size. After dividing an image to several subimages, the best rational number representing each subimage is selected using genetic algorithms. The experimental results show competitive results with peer technique such as jpeg, png and tiff. Fractal image; lossy compression; rational numbers; genetic algorithms.