IEEE Asilomar Conference on Signals, Systems, and Computers,
Nov. 3-6, 2002, Pacific Grove, CA USA.
Design of Optimum Multi-Dimensional Energy Compaction Filters
Brian L. Evans (2), and
Jamal Tuqan (3)
(1) Multimedia Understanding and Management,
1501 Page Mill Road,
Palo Alto, CA 94304 USA
(2) Embedded Signal Processing Laboratory,
Department of Electrical and Computer Engineering,
The University of Texas at Austin,
Austin, TX 78712-1084 USA
(3) Department of Electrical and Computer Engineering,
University of California, Davis,
2064 Engineering II
Davis, CA 95616-5294 USA
We discuss the design of optimum signal-adapted multi-dimensional energy
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
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.