Performance analysis of tabular nearest-neighbor encoding for joint image compression and ATR: I. Background and theory

Gary Key; Mark S. Schmalz; Frank M. Caimi

doi:10.1117/12.372746

16 December 1999 Performance analysis of tabular nearest-neighbor encoding for joint image compression and ATR: I. Background and theory

Gary Key, Mark S. Schmalz, Frank M. Caimi

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Proceedings Volume 3814, Mathematics of Data/Image Coding, Compression, and Encryption II; (1999) https://doi.org/10.1117/12.372746
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1999, Denver, CO, United States

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.

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Gary Key, Mark S. Schmalz, and Frank M. Caimi "Performance analysis of tabular nearest-neighbor encoding for joint image compression and ATR: I. Background and theory", Proc. SPIE 3814, Mathematics of Data/Image Coding, Compression, and Encryption II, (16 December 1999); https://doi.org/10.1117/12.372746

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