A Study on Preconditioning Multiwavelet Systems for Image Compression
WAA '01 Proceedings of the Second International Conference on Wavelet Analysis and Its Applications
Context based multiwavelet image coding using SPIHT framework
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Construction of nonseparable multiwavelets for nonlinear image compression
EURASIP Journal on Applied Signal Processing
A multivariate thresholding technique for image denoising using multiwavelets
EURASIP Journal on Applied Signal Processing
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ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Odd-length armlets with flipping property and its application in image compression
Expert Systems with Applications: An International Journal
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In conventional wavelet transforms, prefiltering is not necessary due to the lowpass property of a scaling function. This is no longer true for multiwavelet transforms. A few research papers on the design of prefilters have appeared, but the existing prefilters are usually not orthogonal, which often causes problems in coding. Moreover, the condition on the prefilters was imposed based on the first-step discrete multiwavelet decomposition. We propose a new prefilter design that combines the ideas of the conventional wavelet transforms and multiwavelet transforms. The prefilters are orthogonal but nonmaximally decimated. They are derived from a very natural calculation of multiwavelet transform coefficients. In this new prefilter design, multiple step discrete multiwavelet decomposition is taken into account. Our numerical examples (by taking care of the redundant prefiltering) indicate that the energy compaction ratio with the Geronimo-Hardin-Massopust (1994) 2 wavelet transform and our new prefiltering is better than the one with Daubechies D4 wavelet transform