Gene Expression Programming Applied to Image Compression

We here describe an image compression algorithm that generates a set of mathematical functions capable of approximately reproducing the input image, but collectively smaller than the raw image data. These functions are created using an evolutionary algorithm, gene expression programming. When used with a 256 X 256 grayscale Lena image as input, the algorithm we developed created a compressed image with a root mean squared error of 16.3, and a compression ratio of about 1.5 : 1. Given the low quality of the compressed image, the minimal degree of compression, and the algorithm’s long running time, it is not a practical compression tool. However, we propose several methods that might be used to boost the algorithm’s performance.