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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 fi...
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... An ongoing program of research conducted at Wright-Patterson Air Force Base and the University of Alaska Anchorage ([3], [4]) has provided considerable evidence suggesting that the search space of non-traditional transforms is indeed rich with such solutions. For selected classes of periodic, one-dimensional signals subjected to quantization, our GAs evolved DWT ...
Ongoing research has established a new methodology for using genetic algorithms (2) to evolve forward and inverse transforms that significantly reduce quantization error in reconstructed signals and images. The approach promises to revolutionize the signal and image processing field, producing both higher quality images and higher compression ratios than is currently possible with wavelet-based techniques.
... I chose to concentrate on the Daubechies-4 (D4) discrete wavelet transform (DWT), a standard wavelet designed by Ingrid Daubechies, although the research is applicable for any existing DWT [2,3,4]. The D4 is usually represented by the scaling numbers h1 and the set of wavelet numbers g1 (see Figure 1) and referred to as the forward transforms. ...
The use of digital images is increasing all the time in personal digital photography, medical imaging, and fingerprint image databases. The goal of this research is to improve the image quality of a given compressed digital image while maintaining the same file size of the image. Wavelet based image compression is improved upon by using Genetic Algorithms on a supercomputer to evolve transforms that have better image quality after image compression.
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This investigation uses a genetic algorithm to optimize coefficient sets describing inverse transforms that significantly reduce mean squared error of reconstructed images. Quantization error introduced during image compression and reconstruction is one of the worst noise sources, due to the fact that information is always permanently lost during the transformation process. Our approach establishes an adaptive filtering methodology for evolving transforms that outperform discrete wavelet inverse transforms for the reconstruction of images subjected to quantization error. Inverse transforms evolved against a single training image consistently generalize to exhibit superior performance against other images from the test set.
This paper describes a genetic algorithm that evolves optimized sets of coefficients for one-dimensional signal reconstruction under lossy conditions due to quantization. Beginning with a population of mutated copies of the set of coefficients describing a standard wavelet-based inverse transform, the genetic algorithm systemically evolves a new set of coefficients that significantly reduces mean squared error (relative to the performance of the selected wavelet) for various classes of one-dimensional signals. The evolved transforms also outperform wavelets when subsequently tested against random signals from the same class
