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Proceedings Paper

Compression of large binary images in digital spatial libraries
Author(s): Eugene Jevgeni Ageenko; Pasi Franti
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Paper Abstract

A method for lossless compression of large binary images is proposed for applications where spatial access to the image is needed. The method utilizes the advantages of (1) variable-size context modeling in a form of context trees, and (2) forward-adaptive statistical compression. New strategies for constructing the context tree are considered, including a fast two-stage bottom-up approach. The proposed technique achieves higher compression rates and allows dense tiling of images down to 50 X 50 pixels without sacrificing the compression performance. It enables partial decompression of large images far more efficiently than if the standard JBIG was applied.

Paper Details

Date Published: 23 December 1999
PDF: 9 pages
Proc. SPIE 3972, Storage and Retrieval for Media Databases 2000, (23 December 1999); doi: 10.1117/12.373562
Show Author Affiliations
Eugene Jevgeni Ageenko, Univ. of Joensuu (Finland)
Pasi Franti, Univ. of Joensuu (Finland)


Published in SPIE Proceedings Vol. 3972:
Storage and Retrieval for Media Databases 2000
Minerva M. Yeung; Boon-Lock Yeo; Charles A. Bouman, Editor(s)

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