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Proceedings Paper

Improved prediction for lossless compression of multispectral images
Author(s): James M. Spring; Glen G. Langdon
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Paper Abstract

We study the use of spectral deltas for lossless multispectral image compression. Spectral deltas are differences between prediction errors. When bands are correlated, the prediction errors between the two bands are similar, thus the difference results in a smaller value. Building upon earlier work, we examine methods of detecting correlations and harnessing current advances in lossless still image compression. THe result is an algorithm that works well over a broad set of test images.

Paper Details

Date Published: 4 April 1997
PDF: 8 pages
Proc. SPIE 3025, Very High Resolution and Quality Imaging II, (4 April 1997); doi: 10.1117/12.270041
Show Author Affiliations
James M. Spring, Univ. of California/Santa Cruz (United States)
Glen G. Langdon, Univ. of California/Santa Cruz (United States)


Published in SPIE Proceedings Vol. 3025:
Very High Resolution and Quality Imaging II
V. Ralph Algazi; Sadayasu Ono; Andrew G. Tescher, Editor(s)

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