Adaptive Filtering in the Wavelet Transform Domain Via Genetic Algorithms

摘要

This report primarily addresses the problem of quantization noise since it is one of the very few processes that potentially eliminates valuable signal information. In a lossy compression system, the quantization step is totally responsible for information loss resulting in quality reduction of the reconstructed signal. For this effort, adaptive filtering techniques are utilized for modifying standard, off-the-shelf, discrete wavelet transform (DWT) coefficients in the hopes that the differential errors between the original uncorrupted signal and the corrupted signal will be minimized. These results show that coefficients evolved by a genetic algorithm (GA) can indeed outperform standard wavelet coefficients for reconstruction of one- and two-dimensional data subjected to quantization. This approach consistently identified coefficient sets that reduced mean squared error (MSE) and improved peak signal- to-noise ratio (PSNR) for inverse DWTs.