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Revolutionary image compression and reconstruction via evolutionary computation, part 2: multiresolution analysis transforms

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.