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Proceedings Paper

Evolved transforms for signal compression and reconstruction under quantization
Author(s): Frank W. Moore
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Paper Abstract

State-of-the-art signal compression and reconstruction techniques utilize wavelets. However, recently published research demonstrated that a genetic algorithm (GA) is capable of evolving non-wavelet inverse transforms that consistently outperform wavelets when used to reconstruct one- and two-dimensional signals under conditions subject to quantization error. This paper summarizes the results of a series of three follow-on experiments. First, a GA is developed to evolve matched forward and inverse transform pairs that simultaneously minimize the compressed file size (FS) and the squared error (SE) in the reconstructed file. Second, this GA is extended to evolve a single set of coefficients that may be used at every level of a multi-resolution analysis (MRA) transform. Third, this GA is expanded to achieve additional SE reduction by evolving a different set of coefficients for each level of an MRA transform. Test results indicate that coefficients evolved against a single representative training image generalize to effectively reduce SE for a broad class of reconstructed images.

Paper Details

Date Published: 20 May 2006
PDF: 15 pages
Proc. SPIE 6228, Modeling and Simulation for Military Applications, 62280T (20 May 2006); doi: 10.1117/12.668380
Show Author Affiliations
Frank W. Moore, Univ. of Alaska Anchorage (United States)


Published in SPIE Proceedings Vol. 6228:
Modeling and Simulation for Military Applications
Kevin Schum; Alex F. Sisti, Editor(s)

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