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
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State-of-the-art image compression and reconstruction techniques utilize wavelets. Recently published research 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. This paper describes new results that build upon previous research by demonstrating that a GA can evolve a single set of coefficients describing a matched forward and inverse transform pair that can be used at each level of a multi-resolution analysis (MRA) transform to simultaneously minimize the compressed file size (FS) and the squared error (SE) in the reconstructed file. Test results indicate that the benefits of using evolved transforms instead of wavelets increases in proportion to quantization level. Furthermore, coefficients evolved against a single representative training image generalize to effectively reduce SE for a broad class of reconstructed images.