On cosine-modulated wavelet orthonormal bases

Multiplicity M, K-regular, orthonormal wavelet bases (that have implications in transform coding applications) have previously been constructed by several authors. The paper describes and parameterizes the cosine-modulated class of multiplicity M wavelet tight frames (WTFs). In these WTFs, the scaling function uniquely determines the wavelets. This is in contrast to the general multiplicity M case, where one has to, for any given application, design the scaling function and the wavelets. Several design techniques for the design of K regular cosine-modulated WTFs are described and their relative merits discussed. Wavelets in K-regular WTFs may or may not be smooth, Since coding applications use WTFs with short length scaling and wavelet vectors (since long filters produce ringing artifacts, which is undesirable in, say, image coding), many smooth designs of K regular WTFs of short lengths are presented. In some cases, analytical formulas for the scaling and wavelet vectors are also given. In many applications, smoothness of the wavelets is more important than K regularity. The authors define smoothness of filter banks and WTFs using the concept of total variation and give several useful designs based on this smoothness criterion. Optimal design of cosine-modulated WTFs for signal representation is also described. All WTFs constructed in the paper are orthonormal bases.

[1]  Ronald R. Coifman,et al.  Entropy-based algorithms for best basis selection , 1992, IEEE Trans. Inf. Theory.

[2]  Truong Q. Nguyen,et al.  Wavelets and filter banks , 1996 .

[3]  Michel Barlaud,et al.  Image coding using vector quantization in the wavelet transform domain , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[4]  Gregory W. Wornell,et al.  Wavelet-based representations for a class of self-similar signals with application to fractal modulation , 1992, IEEE Trans. Inf. Theory.

[5]  Michèle Basseville,et al.  Modeling and estimation of multiresolution stochastic processes , 1992, IEEE Trans. Inf. Theory.

[6]  Ronald A. DeVore,et al.  Image compression through wavelet transform coding , 1992, IEEE Trans. Inf. Theory.

[7]  Henrique S. Malvar Extended lapped transforms: properties, applications, and fast algorithms , 1992, IEEE Trans. Signal Process..

[8]  Peter No,et al.  Digital Coding of Waveforms , 1986 .

[9]  Ramesh A. Gopinath,et al.  Optimal wavelets for signal decomposition and the existence of scale-limited signals , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[10]  Ramesh A. Gopinath,et al.  Theory of modulated filter banks and modulated wavelet tight frames , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[11]  C. Burrus,et al.  Optimal wavelet representation of signals and the wavelet sampling theorem , 1994 .

[12]  Ramesh A. Gopinath,et al.  Wavelet-Galerkin approximation of linear translation invariant operators , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.

[13]  P. P. Vaidyanathan,et al.  Cosine-modulated FIR filter banks satisfying perfect reconstruction , 1992, IEEE Trans. Signal Process..

[14]  Peter N. Heller,et al.  Theory of regular M-band wavelet bases , 1993, IEEE Trans. Signal Process..

[15]  I. Daubechies,et al.  Two-scale difference equations II. local regularity, infinite products of matrices and fractals , 1992 .

[16]  R. Coifman,et al.  Fast wavelet transforms and numerical algorithms I , 1991 .

[17]  Ramesh A. Gopinath,et al.  Wavelet-based lowpass/bandpass interpolation , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[18]  Ahmed H. Tewfik,et al.  Discrete orthogonal M-band wavelet decompositions , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[19]  Michel Barlaud,et al.  Image coding using wavelet transform , 1992, IEEE Trans. Image Process..

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

[21]  W. Lawton Necessary and sufficient conditions for constructing orthonormal wavelet bases , 1991 .

[22]  André Barbé,et al.  A level-crossing-based scaling dimensionality transform applied to stationary Gaussian processes , 1992, IEEE Trans. Inf. Theory.

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

[24]  Henrique S. Malvar Modulated QMF filter banks with perfect reconstruction , 1990 .

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

[26]  Joseph A. O'Sullivan,et al.  A method of sieves for multiresolution spectrum estimation and radar imaging , 1992, IEEE Trans. Inf. Theory.

[27]  C. S. Burrus,et al.  Cosine-Modulated Orthonorma-L Wavelet Bases , 1992, The Digital Signal Processing workshop.

[28]  Deepen Sinha,et al.  On the optimal choice of a wavelet for signal representation , 1992, IEEE Trans. Inf. Theory.