IEEE Asilomar Conference on Signals, Systems, and Computers, Nov. 3-6, 2002, Pacific Grove, CA USA.

Design of Optimum Multi-Dimensional Energy Compaction Filters

Niranjan Damera-Venkata(1), Brian L. Evans (2), and Jamal Tuqan (3)

(1) Multimedia Understanding and Management, Hewlett-Packard Laboratories, 1501 Page Mill Road, Palo Alto, CA 94304 USA
damera@exch.hpl.hp.com

(2) Embedded Signal Processing Laboratory, Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX 78712-1084 USA
bevans@ece.utexas.edu

(3) Department of Electrical and Computer Engineering, University of California, Davis, 2064 Engineering II Davis, CA 95616-5294 USA
tuqan@ece.ucdavis.edu

Paper - Talk - Literature Survey

Abstract

We discuss the design of optimum signal-adapted multi-dimensional energy compaction filters. As in the one-dimensional (1-D) case, the energy compaction problem is linear in the auto-correlation coefficients of the compaction filter which must also satisfy the multi-dimensional (m-D) equivalent of the Nyquist-(M) condition. If a minimum-phase spectral factor exists the optimum compaction filter is recovered using the m-D Discrete Hilbert Transform (DHT). If a minimum phase spectral factor does not exist we propose an iterative algorithm based on multi-objective goal attainment. We try to enforce the Nyquist-M condition while simultaneously forcing the autocorrelation coefficients of the compaction filter to be as close as possible to the coefficients of the product filter and the compaction gain of the optimum compaction filter to be close to the compaction gain produced by using the optimum product filter.

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Last Updated 01/30/03.