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Proceedings Paper

Nonlinear predictors for lossless compression of AVHRR imagery
Author(s): Shawn D. Hunt
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Paper Abstract

Nonlinear predictors based on feedforward artificial neural networks are investigated for use in lossless compression of AVHRR Imagery. The FNN predictors are designed and compared to the optimum nonlinear Mean Square Error predictor, and to the linear predictor. The predictors are compared based on the first order entropy of the predictor error, on run time, and memory requirements. The FNN predictors can be designed to have a wide range of performance with a trade off between first order entropy error, and memory and run time. There is little difference in prediction errors between the linear and FNN predictors for large sample sizes, when the image is segmented into large areas. The difference can be greater for smaller sample sizes, when the image is segmented into smaller areas such as the typical 8 X 8 pixel size. The results indicate there is no advantage to using nonlinear predictors when compression and run time requirements are taken into account.

Paper Details

Date Published: 16 July 1999
PDF: 6 pages
Proc. SPIE 3717, Algorithms for Multispectral and Hyperspectral Imagery V, (16 July 1999); doi: 10.1117/12.353038
Show Author Affiliations
Shawn D. Hunt, Univ. of Puerto Rico/Mayaguez (United States)


Published in SPIE Proceedings Vol. 3717:
Algorithms for Multispectral and Hyperspectral Imagery V
Sylvia S. Shen; Michael R. Descour, Editor(s)

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