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
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
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
Hi-index | 0.00 |
Previous research demonstrated that a genetic algorithm (GA) can utilize supercomputers to evolve image compression and reconstruction transforms that reduce mean squared error (MSE) by more than 22% (1.126 dB) under conditions subject to quantization, while continuing to average the same amount of compression as the Daubechies-4 (D4) wavelet. This paper describes subsequent research that extends our GA to evolve multiresolution analysis (MRA) transforms. Test results indicate that our evolved MRA transforms can reduce MSE by an average of more than 10% (0.50 dB) at three levels of decomposition. This result substantially improves upon state-of-the-art MRA transforms for compression and reconstruction applications subject to quantization error.