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Proceedings Paper

New global-search method for designing filter banks
Author(s): Yi Shang; Benjamin W. Wah
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Paper Abstract

In this paper, we present a new global-search method for designing QMF (quadrature-mirror-filter) filter banks. We formulate the design problem as a nonlinear constrained optimization problem, using the reconstruction error as the objective, and the other performance metrics as constraints. This formulation allows us to search for designs that improve over the best existing designs. Due to the nonlinear nature of the performance metrics, the design problem is a nonlinear constrained optimization problem with many local minima. We propose to solve this design problem use global- search methods based on Lagrangian formulations. After transforming the original constrained optimization problem into an unconstrained form using Lagrange multipliers, we apply a new global-search method to find good solutions. The method consists of a coarse-level global-search phase, a fine-level global-search phase, and a local search phase, and is suitable for parallel computation due to the minimal dependency between various key components. In our experiments, we show that our method finds better designs than existing global-search methods, including simulated annealing and genetic algorithms.

Paper Details

Date Published: 21 September 1998
PDF: 12 pages
Proc. SPIE 3452, Parallel and Distributed Methods for Image Processing II, (21 September 1998); doi: 10.1117/12.323461
Show Author Affiliations
Yi Shang, Univ. of Missouri/Columbia (United States)
Benjamin W. Wah, Univ. of Illinois/Urbana-Champaign (United States)

Published in SPIE Proceedings Vol. 3452:
Parallel and Distributed Methods for Image Processing II
Hongchi Shi; Patrick C. Coffield, Editor(s)

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