Abstract
Quadrature mirror filters can be introduced by studying algebraic properties of discrete input signals viewed as square summable infinite sequences together with linear operators, termed filters, which act linearly through convolution on the input signals. The algebra is readily revealed through operations on complex variable transfer functions which are formally associated with the filters. By using the space of transfer functions, one can define how a signal can be decomposed into two subsidiary signals which can then be combined, using only certain linear mappings all along throughout the entire process. Ideally, the original signal may be recovered, and in this case the term perfect reconstruction is used. In addition to ideal recovery, it is desired to impose orthogonality conditions on the subsidiary signals. These constraints imply that one need only concentrate on the optimality of one of the subsidiary signals to guarantee the optimality of the other. The orthogonality properties are associated with the term quadrature mirror filters.
We describe what optimality can mean within this special structure by making a transition to a description of statistical properties which are reasonably representative of the actual transmission of discrete signals and their recovery after transmission. The concept of coding gain is reviewed, and we show how this objective function may be combined with constraining relations on the coefficients of the (chosen) primary filter necessary to formally guarantee perfect reconstruction. The constraints lead to a nonlinear transformation of the original filter coefficients to variables that appear in an equivalent linear semi-infinite programming problem developed by the second author. We show how an optimal solution in the original filter-variables may be obtained from an LSIP optimal solution by spectral decomposition. Finally, we review some elementary duality-based sensitivity analysis and present some previously published numerical results (by us with other co-authors).
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References
K. C. Aas, K. A. Duell, and C. T. Mullis. Synthesis of extremal wavelet-generating filters using Gaussian quadrature. IEEE Trans. Sig. Proc, 43:1045–1057, 1995.
P. N. Brown and Y. Saad. Hybrid Krylov methods for nonlinear systems of equations. SIAM J. Sc. Comput., 11:450–481, 1990.
H. Caglar, Y. Liu, and A. N. Akansu. Statistically optimized PR-QMF design. SPIE, 1605:86–94, 1991.
I. Daubechies. Ten Lectures on Wavelets. Number 61 in CBMS-NSF Regional Conference Series in Applied Mathematics. SIAM, Philadelphia, PA., 1992.
P. Delsarte, B. Macq, and D. T. M. Slock. Signal-adapted multiresolution transform for image coding. IEEE Trans. Info. Theory, 38:897–904, 1992.
J. Elzinga and T. G. Moore. A central cutting plane algorithm for the convex programming problem. Math. Programming, 8:134–145, 1975.
K. Georg and R. Hettich. On the numerical stability of the simplex algorithm: The package linop. Technical report, The University of Trier, Trier, Germany, April 1985.
K. Glashoff and S.-A. Gustafson. Linear Optimization and Approximation. Number 45 in Applied Mathematical Sciences. Springer-Verlag, Berlin-Heidelberg-New York, 1983.
P. R. Gribik. A central cutting plane algorithm for semi-infinite programming problem. In R. Hettich, editor, Semi-Infinite Programming, number 15 in Lecture Notes in Control and Information Sciences, pages 66–82. Springer-Verlag, 1979.
S. Gustafson and K. O. Kortanek. Numerical treatment of a class of semi-infinite programming problems. Naval Res. Logistics Quart., 20:477–504, 1973.
R. Hettich and K. O. Kortanek. Semi-infinite programming: theory, methods, and applications. SIAM Review, 35:380–429, 1993.
N. S. Jayant and P. Noll. Digital Coding of Waveforms. Prentice-Hall, 1984.
K. O. Kortanek. Vector-supercomputer experiments with the linear programming primal affine scaling algorithm. SIAM J. Scientific and Statistical Computing, 14:279–294, 1993.
K. O. Kortanek and H. No. A central cutting plane algorithm for convex semi-infinite programming problems. SIAM J. Optimization, 3:901–918, 1993.
M. Lang and B.-C. Frenzel. Software available by anonymous ftp from cml.rice.edu:/pub/markus/software, 1992. ©1992–4 LNT.
M. Lang and B.-C. Frenzel. Polynomial root finding. IEEE Sig. Proc. Lett., 1:141–143, 1994.
S. G. Mallat. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. on Pattern Analysis and Machine Intelligence, 11:674–693, 1989.
Y. Meyer. Wavelets Algorithms & Applications. SIAM Society for Industrial and Applied Mathematics, Philadelphia, PA, 1993. Translated and Revised by Robert D. Ryan,.
P. Moulin, M. Anitescu, K. O. Kortanek, and F. Potra. Design of signal-adapted FIR paraunitary filter banks. In Proc. ICASSP, volume 3, pages 1519–1522, Atlanta, GA, 1996.
P. Moulin, M. Anitescu, K. O. Kortanek, and F. Potra. The role of linear semi-infinite programming in signal-adapted QMF bank design. IEEE Transactions on Signal Processing, 45:2160–2174, 1997.
P. Moulin and K. M. Mihcak. Theory and design of signal-adapted FIR paraunitary filter banks. Technical report, The University of Illinois Beckmann Institute, Champaign/Urbana, IL, 1997. to appear in IEEE Transactions on Signal Processing, Special Issue on Applications of Wavelets and Filter Banks, 1998.
T. W. Parks and C. S. Burrus. Digital Filter Design. J. Wiley & Sons, 1987.
W. Press, B. Flannery, S. Teukolsky, and W. Vetterling. Numerical Recipes in C: The Art of Scientific Computing. Cambridge: Cambridge University Press, 1988.
R. Reemtsen. Discretization methods for the solution of semi-infinite programming problems. J. Opt. Theory and Appl., 71:85–103, 1991.
O. Rioul and P. Duhamel. A Remez exchange algorithm for orthonormal wavelets. IEEE Trans. Circ. and Syst. IL An. and Dig. Sig. Proc, 41:550–560, 1994.
M. J. T. Smith and T. P. B. III. Exact reconstruction techniques for tree-structured subband coders. IEEE Trans. ASSP, 34:434–441, 1986.
G. Strang and T. Nyugen. Wavelets and Filter Banks. Wellesley-Cambridge Press, Wellesley, MA, 1996.
M. Unser. An extension of the Karhunen-Loève transform for wavelets and perfect-reconstruction filterbanks. SPIE, 2034:45–56, 1883.
B. Usevitch and M. T. Orchard. Smooth wavelets, transform coding, and Markov-1 processes. In Proc. ISCAS’93, pages 527–530, 1993.
P. P. Vaidyanathan. Multirate Systems and Filter Banks. Prentice-Hall, 1993.
P. P. Vaidyanathan and P.-Q. Hoang. Lattice structures for optimal design and robust implementation of two-channel perfect-reconstruction QMF banks. IEEE Trans. ASSP, 36:81–94, 1988.
L. Vandendorpe. CQF filter banks matched to signal statistics. Signal Processing, 29:237–249, 1992.
M. Vetterli and J. Kovacevic. Wavelets and Subband Coding. Prentice-Hall, 1995.
B. Xuan and R. H. Bamberger. Multi-dimensional, paraunitary principal component filter banks. In Proc. ICASSP’95, pages 1488–1491, Detroit, 1995.
A. Zygmund. Trigonometric Series. Cambridge University Press, 1959.
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Kortanek, K.O., Moulin, P. (1998). Semi-Infinite Programming in Orthogonal Wavelet Filter Design. In: Reemtsen, R., Rückmann, JJ. (eds) Semi-Infinite Programming. Nonconvex Optimization and Its Applications, vol 25. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-2868-2_10
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DOI: https://doi.org/10.1007/978-1-4757-2868-2_10
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