Variation operator performance for evolved image reconstruction transforms

Modern image processing applications often require robust performance in noisy or bandwidth-limited situations. In this research, we employ genetic algorithms (GAs) to evolve image transforms that reduce quantization error in reconstructed signals and images. The resulting transforms produce higher quality images than current wavelet-based transforms at a given compression ratio and thus allow transmission of compressed data at a lower bandwidth. We evaluate state-of-the-art variation operators for evolving reconstruction filters. Our results indicate that the careful selection of these operators has a strong positive effect upon the evolutionary search for superior image transforms.