Title :
Filterbank optimization with convex objectives and the optimality of principal component forms
Author :
Akkarakaran, Sony ; Vaidyanathan, P.P.
Author_Institution :
Dept. of Electr. Eng., California Inst. of Technol., Pasadena, CA, USA
fDate :
1/1/2001 12:00:00 AM
Abstract :
This paper proposes a general framework for the optimization of orthonormal filterbanks (FBs) for given input statistics. This includes as special cases, many previous results on FB optimization for compression. It also solves problems that have not been considered thus far. FB optimization for coding gain maximization (for compression applications) has been well studied before. The optimum FB has been known to satisfy the principal component property, i.e., it minimizes the mean-square error caused by reconstruction after dropping the P weakest (lowest variance) subbands for any P. We point out a much stronger connection between this property and the optimality of the FB. The main result is that a principal component FB (PCFB) is optimum whenever the minimization objective is a concave function of the subband variances produced by the FB. This result has its grounding in majorization and convex function theory and, in particular, explains the optimality of PCFBs for compression. We use the result to show various other optimality properties of PCFBs, especially for noise-suppression applications. Suppose the FB input is a signal corrupted by additive white noise, the desired output is the pure signal, and the subbands of the FB are processed to minimize the output noise. If each subband processor is a zeroth-order Wiener filter for its input, we can show that the expected mean square value of the output noise is a concave function of the subband signal variances. Hence, a PCFB is optimum in the sense of minimizing this mean square error. The above-mentioned concavity of the error and, hence, PCFB optimality, continues to hold even with certain other subband processors such as subband hard thresholds and constant multipliers, although these are not of serious practical interest. We prove that certain extensions of this PCFB optimality result to cases where the input noise is colored, and the FB optimization is over a larger class that includes biorthogonal FBs. We also show that PCFBs do not exist for the classes of DFT and cosine-modulated FBs
Keywords :
AWGN; Wiener filters; channel bank filters; circuit optimisation; data compression; discrete Fourier transforms; encoding; filtering theory; signal reconstruction; DFT filterbanks; MSE minimization; additive white noise; biorthogonal filterbanks; coding gain maximization; colored input noise; compression applications; concave function; constant multipliers; convex objectives; cosine-modulated filterbanks; filterbank optimization; input statistics; mean square error; mean square value; mean-square error; noise-suppression applications; orthonormal filterbanks optimization; principal component filterbank; subband hard thresholds; subband signal variances; subband variances produced; zeroth-order Wiener filter; Additive white noise; Colored noise; Filter bank; Finite impulse response filter; Grounding; Helium; Mean square error methods; Signal processing; Statistics; Wiener filter;
Journal_Title :
Signal Processing, IEEE Transactions on