Applying a Genetic Algorithm Solution to Improve Compression of Wavelet Coefficient Sign

Discrete Wavelet Transform has been widely used in image compression because it is able to compact frequency and spatial localization of image energy into a small fraction of coefficients. In recent years coefficient sign coding has been used to improve image compression. The potential correlation between the sign of a coefficient and the signs of its neighbors opens the possibility to use a sign predictor to improve the image compression process. However, this relationship is not uniform and constant for any image. Typically, image encoders use entropy coding to compact the wavelet coefficients information. This work analyzes the impact of the input parameters over a genetic algorithm that distributes into contexts (sets) the wavelet sign predictors in such a way that the overall aggregate entropy will be reduced as much as possible and a as a consequence higher compression rates can be achieved.