Performance analysis of tabular nearest-neighbor encoding for joint image compression and ATR: II. Results and analysis

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

doi:10.1117/12.372750

16 December 1999 Performance analysis of tabular nearest-neighbor encoding for joint image compression and ATR: II. Results and analysis

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

Author Affiliations +

Proceedings Volume 3814, Mathematics of Data/Image Coding, Compression, and Encryption II; (1999) https://doi.org/10.1117/12.372750
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1999, Denver, CO, United States

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.

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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: II. Results and analysis", Proc. SPIE 3814, Mathematics of Data/Image Coding, Compression, and Encryption II, (16 December 1999); https://doi.org/10.1117/12.372750

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