TY - JOUR

T1 - Automation of the lifting factorisation of wavelet transforms

AU - Maslen, M.J.

AU - Abbott, Paul

PY - 2000

Y1 - 2000

N2 - Wavelets are sets of basis functions used in the analysis of signals and images. In contrast to Fourier analysis, wavelets have both spatial and frequency localization, making them useful for the analysis of sharply-varying or non-periodic signals. The lifting scheme for finding the discrete wavelet transform was demonstrated by Daubechies and Sweldens (1996). In particular, they showed that this method depends on the factorization of polyphase matrices, whose entries are Laurent polynomials, using the Euclidean algorithm extended to Laurent polynomials. Such factorization is not unique and hence there are multiple factorizations of the polyphase matrix. In this paper we outline a Mathematica program that finds all factorizations of such matrices by automating the Euclidean algorithm for Laurent polynomials. Polynomial reduction using Grobner bases was also incorporated into the program so as to reduce the number of wavelet filter coefficients appearing in a given expression through use of the relations they satisfy, thus permitting exact symbolic factorizations for any polyphase matrix. (C) 2000 Elsevier Science B.V. All rights reserved.

AB - Wavelets are sets of basis functions used in the analysis of signals and images. In contrast to Fourier analysis, wavelets have both spatial and frequency localization, making them useful for the analysis of sharply-varying or non-periodic signals. The lifting scheme for finding the discrete wavelet transform was demonstrated by Daubechies and Sweldens (1996). In particular, they showed that this method depends on the factorization of polyphase matrices, whose entries are Laurent polynomials, using the Euclidean algorithm extended to Laurent polynomials. Such factorization is not unique and hence there are multiple factorizations of the polyphase matrix. In this paper we outline a Mathematica program that finds all factorizations of such matrices by automating the Euclidean algorithm for Laurent polynomials. Polynomial reduction using Grobner bases was also incorporated into the program so as to reduce the number of wavelet filter coefficients appearing in a given expression through use of the relations they satisfy, thus permitting exact symbolic factorizations for any polyphase matrix. (C) 2000 Elsevier Science B.V. All rights reserved.

U2 - 10.1016/S0010-4655(99)00451-8

DO - 10.1016/S0010-4655(99)00451-8

M3 - Article

VL - 127

SP - 309

EP - 326

JO - Computer Physics Communications

JF - Computer Physics Communications

SN - 0010-4655

ER -