Evolving optimized matched forward and inverse transform pairs via genetic algorithms

This research established a methodology for using a genetic algorithm to evolve coefficients for matched forward and inverse transform pairs. Beginning with an initial population of randomly mutated copies of the coefficients representing a standard wavelet, our GA consistently evolved transforms that outperformed wavelets for image compression and reconstruction applications under conditions subject to quantization error. Transforms optimized against a single representative image also outperformed wavelets when subsequently tested against other images from our test set. The new methodology has the potential to revolutionize the signal and image processing fields.