Measures in wavelet decompositions

In applications, choices of orthonormal bases in Hilbert space H may come about from the simultaneous diagonalization of some specific abelian algebra of operators. This is the approach of quantum theory as suggested by John von Neumann; but as it turns out, much more recent constructions of bases in wavelet theory, and in dynamical systems, also fit into this scheme. However, in these modern applications, the basis typically comes first, and the abelian algebra might not even be made explicit. It was noticed recently that there is a certain finite set of non-commuting operators F"i, first introduced by engineers in signal processing, which helps to clarify this connection, and at the same time throws light on decomposition possibilities for wavelet packets used in pyramid algorithms. There are three interrelated components to this: an orthonormal basis, an abelian algebra, and a projection-valued measure. While the operators F"i were originally intended for quadrature mirror filters of signals, recent papers have shown that they are ubiquitous in a variety of modern wavelet constructions, and in particular in the selection of wavelet packets from libraries of bases. These are constructions which make a selection of a basis with the best frequency concentration in signal or data-compression problems. While the algebra A generated by the F"i-system is non-abelian, and goes under the name ''Cuntz algebra'' in C^*-algebra theory, each of its representations contains a canonical maximal abelian subalgebra, i.e., the subalgebra is some C(X) for a Gelfand space X. A given representation of A, restricted to C(X), naturally induces a projection-valued measure on X, and each vector in H induces a scalar-valued measure on X. We develop this construction in the general context with a view to wavelet applications, and we show that the measures that had been studied earlier for a very restrictive class of F"i-systems (i.e., the Lemarie-Meyer quadrature mirror filters) in the theory of wavelet packets are special cases of this. Moreover, we prove a structure theorem for certain classes of induced scalar measures. In the applications, X may be the unit interval, or a Cantor set; or it may be an affine fractal, or even one of the more general iteration limits involving iterated function systems consisting of conformal maps.

[1]  Endomorphisms of B(H) , 1994, funct-an/9408001.

[2]  K. Falconer The geometry of fractal sets , 1985 .

[3]  O. Bratteli,et al.  Representation Theory and Numerical AF-Invariants: The Representations and Centralizers of Certain States on Od , 2004 .

[4]  P. Jorgensen MINIMALITY OF THE DATA IN WAVELET FILTERS , 2000, math/0004098.

[5]  M. Rieffel,et al.  Wavelet Filter Functions, the Matrix Completion Problem, and Projective Modules Over C(Tn) , 2003 .

[6]  I. Daubechies,et al.  A new technique to estimate the regularity of refinable functions , 1996 .

[7]  Palle E.T. Jorgensen,et al.  Iterated Function Systems and Permutation Representations of the Cuntz Algebra , 1996 .

[8]  Characterizations of orthonormal scale functions: A probabilistic approach , 2000, math/0110186.

[9]  Palle E. T. Jorgensen,et al.  Wavelets on Fractals , 2003, math/0305443.

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

[12]  Edward Nelson Topics in dynamics I: Flows , 1970 .

[13]  D. Ruelle Dynamical Zeta Functions for Piecewise Monotone Maps of the Interval , 1994 .

[14]  Wavelets in mathematical physics: q-oscillators , 2002, math/0212096.

[15]  Peter N. Heller,et al.  The application of multiwavelet filterbanks to image processing , 1999, IEEE Trans. Image Process..

[16]  P. Jorgensen,et al.  Wavelet representations and Fock space on positive matrices , 2002, math/0204034.

[17]  The free cover of a row contraction. , 2004, math/0403231.

[18]  A. Ron,et al.  The Sobolev Regularity of Refinable Functions , 2000 .

[19]  H. A. Schulke Matrix factorization , 1955, IRE Transactions on Circuit Theory.

[20]  M. Victor Wickerhauser,et al.  Adapted wavelet analysis from theory to software , 1994 .

[21]  Mary Beth Ruskai,et al.  Wavelets and their Applications , 1992 .

[22]  Joachim Cuntz,et al.  SimpleC*-algebra generated by isometries , 1977 .

[23]  Wei Lin,et al.  Wavelet Analysis and Applications , 2011 .

[24]  M. Yamaguti,et al.  Mathematics of Fractals , 1997 .

[25]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[26]  R. Gundy Low-Pass Filters, Martingales, and Multiresolution Analyses☆ , 2000 .