Design of efficient M-band coders with linear-phase and perfect-reconstruction properties

This paper presents a new design technique for obtaining M-band orthogonal coders where M=2/sup i/. The structures obtained using the proposed technique have the perfect reconstruction property. Furthermore, all filters that constitute the subband coder are linear-phase FIR-type filters. In contrast with conventional design techniques that attempt to find a unitary alias-component matrix in the frequency domain, we carry out the design in the time domain, based on time-domain orthonormality constraints that the filters must satisfy. The M-band design problem is reduced to the problem of finding a suitable lowpass filter h/sub 0/(n). Once a suitable lowpass filter is found, the remaining (M-1) filters of the coder are obtained through the use of shuffling operators on the lowpass filter. This approach leads to a set of filters that use the same numerical coefficient values in different shift positions, allowing very efficient numerical implementation of the subband coder. In addition, by imposing further constraints on the lowpass branch impulse response h/sub 0/(n), we are able to construct continuous bases of M-channel wavelets with good regularity properties. Design examples are presented for four-, eight-, and 16-band coders, along with examples of continuous wavelet bases that they generate. >

[1]  Yipeng Liu,et al.  Statistically optimized PR-QMF design , 1991, Other Conferences.

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

[3]  M. Vetterli Multi-dimensional sub-band coding: Some theory and algorithms , 1984 .

[4]  John W. Woods,et al.  Subband coding of images , 1986, IEEE Trans. Acoust. Speech Signal Process..

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

[6]  Henrique S. Malvar,et al.  The LOT: transform coding without blocking effects , 1989, IEEE Trans. Acoust. Speech Signal Process..

[7]  Truong Q. Nguyen,et al.  Improved technique for design of perfect reconstruction FIR QMF banks with lossless polyphase matrices , 1989, IEEE Trans. Acoust. Speech Signal Process..

[8]  P. Vaidyanathan,et al.  Design of doubly-complementary IIR digital filters using a single complex allpass filter, with multirate applications , 1987 .

[9]  Truong Q. Nguyen,et al.  Maximally decimated perfect-reconstruction FIR filter banks with pairwise mirror-image analysis (and synthesis) frequency responses , 1988, IEEE Trans. Acoust. Speech Signal Process..

[10]  Mark J. T. Smith,et al.  A new filter bank theory for time-frequency representation , 1987, IEEE Trans. Acoust. Speech Signal Process..

[11]  P. P. Vaidyanathan,et al.  The role of lossless systems in modern digital signal processing: a tutorial , 1989 .

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

[13]  A.N. Akansu,et al.  The Binomial QMF-Wavelet Transform for Multiresolution Signal Decomposition , 1993, IEEE Trans. Signal Process..

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

[15]  P. P. Vaidyanathan,et al.  Theory and design of M-channel maximally decimated quadrature mirror filters with arbitrary M, having the perfect-reconstruction property , 1987, IEEE Trans. Acoust. Speech Signal Process..

[16]  Martin Vetterli,et al.  Wavelets and filter banks: relationships and new results , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[17]  Georges Bonnerot,et al.  Digital filtering by polyphase network:Application to sample-rate alteration and filter banks , 1976 .

[18]  David H. Staelin,et al.  Encoding of images based on a lapped orthogonal transform , 1989, IEEE Trans. Commun..

[19]  James L. Flanagan,et al.  Digital coding of speech in sub-bands , 1976, The Bell System Technical Journal.

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

[21]  Martin Vetterli Wavelets and filter banks for discrete-time signal processing , 1992 .

[22]  R. Ansari,et al.  Two-dimensional IIR filters for exact reconstruction in tree-structured sub-band decomposition , 1987 .

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

[24]  Charles K. Chui,et al.  An Introduction to Wavelets , 1992 .

[25]  Henrique S. Malvar The LOT: a link between block transform coding and multirate filter banks , 1988, 1988., IEEE International Symposium on Circuits and Systems.

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

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

[28]  Martin Vetterli,et al.  Perfect reconstruction FIR filter banks: some properties and factorizations , 1989, IEEE Trans. Acoust. Speech Signal Process..

[29]  Mark J. T. Smith,et al.  Exact reconstruction techniques for tree-structured subband coders , 1986, IEEE Trans. Acoust. Speech Signal Process..

[30]  Hamid Gharavi,et al.  Sub-Band Coding Of Digital Images Using Two-Dimensional Quadrature Mirror Filtering , 1986, Other Conferences.

[31]  D. Esteban,et al.  Application of quadrature mirror filters to split band voice coding schemes , 1977 .

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

[33]  Ingrid Daubechies,et al.  The wavelet transform, time-frequency localization and signal analysis , 1990, IEEE Trans. Inf. Theory.

[34]  Martin Vetterli,et al.  Wavelets and filter banks: theory and design , 1992, IEEE Trans. Signal Process..