Evolving better satellite image compression and reconstruction transforms
Brendan J. Babb, Frank W. Moore, Michael R. Peterson, Gary Lamont · 2008
This paper summarizes the results of a continuing investigation into the evolution of transforms that minimize the error present in satellite images compressed and subsequently reconstructed under conditions subject to quantization error. Using coefficients describing the Daubechies-4 (D4) discrete wavelet transform (DWT) as a starting point, our genetic algorithm (GA) evolves real-valued coefficients describing matched forward and inverse transform pairs that reduce mean squared error (MSE) by 17.9% (0.86 dB) on satellite images used for training, and by an average of more than 11.0% (0.5 dB) on a large test set of satellite images. This result improves upon previous work on satellite images, which evolved only the reconstruction transform, and establishes evolutionary computation as a viable methodology for identifying state-of-the-art solutions to this difficult class of problems.