Iterated filter banks with rational rate changes connection with discrete wavelet transforms

Some properties of two-band filter banks with rational rate changes ("rational filter banks") are first reviewed. Focusing then on iterated rational filter banks, compactly supported limit functions are obtained, in the same manner as previously done for dyadic schemes, allowing a characterization of such filter banks. These functions are carefully studied and the properties they share with the dyadic case are highlighted. They are experimentally observed to verify a "shift property" (strictly verified in the dyadic ease) up to an error which can be made arbitrarily small when their regularity increases. In this case, the high-pass outputs of an iterated filter bank can be very close to samples of a discrete wavelet transform with the same rational dilation factor. Straightforward extension of the formalism of multiresolution analysis is also made. Finally, it is shown that if one is ready to put up with the loss of the shift property, rational iterated filter banks can be used in the same manner as if they were dyadic filter banks, with the advantage that rational dilation factors can be chosen closer to 1. >

[1]  Guoan Bi Minimisation of delay requirements for rational sampling rate alternating systems , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.

[2]  Jelena Kovacevic,et al.  Perfect reconstruction filter banks with rational sampling rate changes , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.

[3]  Martin Vetterli,et al.  A theory of multirate filter banks , 1987, IEEE Trans. Acoust. Speech Signal Process..

[4]  Stéphane Mallat,et al.  Multifrequency channel decompositions of images and wavelet models , 1989, IEEE Trans. Acoust. Speech Signal Process..

[5]  Thomas P. Barnwell,et al.  The design of perfect reconstruction nonuniform band filter banks , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.

[6]  Mark J. Shensa,et al.  The discrete wavelet transform: wedding the a trous and Mallat algorithms , 1992, IEEE Trans. Signal Process..

[7]  I. Daubechies Orthonormal bases of compactly supported wavelets , 1988 .

[8]  Gloria Faye Boudreaux-Bartels,et al.  A comparison of a wavelet functions for pitch detection of speech signals , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.

[9]  Chia-Chuan Hsiao Polyphase filter matrix for rational sampling rate conversions , 1987, ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[10]  A. Grossmann,et al.  Cycle-octave and related transforms in seismic signal analysis , 1984 .

[11]  Olivier Rioul,et al.  Fast algorithms for discrete and continuous wavelet transforms , 1992, IEEE Trans. Inf. Theory.

[12]  Someshwar C. Gupta,et al.  Multirate digital filters , 1979 .

[13]  O. Rioul Simple regularity criteria for subdivision schemes , 1992 .

[14]  L. Rabiner,et al.  A digital signal processing approach to interpolation , 1973 .

[15]  I. Daubechies,et al.  Two-scale difference equations I: existence and global regularity of solutions , 1991 .

[16]  P. Vaidyanathan Quadrature mirror filter banks, M-band extensions and perfect-reconstruction techniques , 1987, IEEE ASSP Magazine.

[17]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  P. P. Vaidyanathan,et al.  Multirate digital filters, filter banks, polyphase networks, and applications: a tutorial , 1990, Proc. IEEE.

[19]  I. Daubechies,et al.  Othonormal bases of compactly supported wavelets III: better frequency resolution , 1993 .

[20]  I. Daubechies,et al.  Biorthogonal bases of compactly supported wavelets , 1992 .

[21]  L.R. Rabiner,et al.  Interpolation and decimation of digital signals—A tutorial review , 1981, Proceedings of the IEEE.