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Proceedings Paper

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

Vector quantization (VQ) is a well-established signal and image compression transform that exhibits several drawbacks. First, the VQ codebook generation process tends to be computationally costly, and can be prohibitive for high- fidelity compression in adaptive real-time applications. Second, codebook search complexity varies as a function of image statistics, codebook formation technique, and prespecified matching error. For large codebooks, search overhead can be prohibitive for VQ compression having stringent constraints on matching error. A third disadvantage of VQ is codebook size, which can be reduced at the cost of fidelity of reproduction in the decompressed image. Such issues were discussed in Part 1 of this series of two papers.

Paper Details

Date Published: 16 December 1999
PDF: 16 pages
Proc. SPIE 3814, Mathematics of Data/Image Coding, Compression, and Encryption II, (16 December 1999); doi: 10.1117/12.372750
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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