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
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
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Previously reported research efforts demonstrated that a genetic algorithm can evolve coefficients describing transforms that outperform standard wavelets, by reducing the mean squared error (MSE) apparent in reconstructed signals under conditions subject to quantization. This paper describes new results that substantially improve the state-of-the-art in evolved transform performance. Matched forward and inverse transform pairs trained against selected images consistently reduce MSE by more than 22% (1.126 dB) when applied to an arbitrary population of similarly quantized test images, yet still achieve the same amount of compression.