A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ten lectures on wavelets
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
JPEG 2000: Image Compression Fundamentals, Standards and Practice
JPEG 2000: Image Compression Fundamentals, Standards and Practice
A genetic algorithm for optimized reconstruction of quantized one-dimensional signals
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Effective image compression using evolved wavelets
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Evolutionary approach to improve wavelet transforms for image compression in embedded systems
EURASIP Journal on Advances in Signal Processing - Special issue on biologically inspired signal processing: analyses, algorithms and applications
Accelerating FPGA-based evolution of wavelet transform filters by optimized task scheduling
Microprocessors & Microsystems
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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.