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
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Effective image compression using evolved wavelets
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A satellite image set for the evolution of image transforms for defense applications
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
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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.