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Proceedings Paper

Performance analysis of tabular nearest-neighbor encoding for joint image compression and ATR: I. Background and theory
Author(s): Gary Key; Mark S. Schmalz; Frank M. Caimi
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Paper Abstract

In this series of two papers, a high-level overview of the Tabular Nearest Neighbor Encoding (TNE) algorithm is presented. The performance of TNE is analyzed using training images having different size, statistical properties, and noise level than the source image. TNE is compared with several published algorithms such as visual pattern image coding, JPEG, and EBLAST. The latter is a relatively new, high-compression image transform that has compression ratio CR approximately equals 200:1 that can be consistently achieved with low MSE. Analysis focuses on the ability of TNE to provide low to moderate compression ratios at high computational efficiency on small- to large-format text and surveillance images.

Paper Details

Date Published: 16 December 1999
PDF: 12 pages
Proc. SPIE 3814, Mathematics of Data/Image Coding, Compression, and Encryption II, (16 December 1999); doi: 10.1117/12.372746
Show Author Affiliations
Gary Key, Frontier Technology, Inc. (United States)
Mark S. Schmalz, Univ. of Florida (United States)
Frank M. Caimi, Harbor Branch Oceanographic Institute (United States)


Published in SPIE Proceedings Vol. 3814:
Mathematics of Data/Image Coding, Compression, and Encryption II
Mark S. Schmalz, Editor(s)

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