A differential evolution algorithm for optimizing signal compression and reconstruction transforms

State-of-the-art image compression and reconstruction techniques utilize wavelets. Beginning in 2004, however, a team of researchers at Wright-Patterson Air Force Base (WPAFB), the University of Alaska Anchorage (UAA), and the Air Force Institute of Technology (AFIT) has demonstrated that a genetic algorithm (GA) is capable of evolving non-wavelet transforms that consistently outperform wavelets when applied to a broad class of images under conditions subject to quantization error. Unfortunately, the computational cost of our GA-based approach has been enormous, necessitating hundreds of hours of CPU time, even on supercomputers provided by the Arctic Region Supercomputer Center (ARSC). The purpose of this investigation was to begin to determine whether an alternative approach based upon differential evolution (DE) [20] could be used to (a) optimize transforms capable of outperforming those evolved by the GA, (b) reduce the amount of computation necessary to evolve such transforms, and/or (c) further reduce the mean squared error (MSE) of transforms previously evolved via our GA.

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