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Computers & Graphics
Volume 24, Issue 1, February 2000, Pages 91-98
 
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doi:10.1016/S0097-8493(99)00140-5    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2000 Elsevier Science Ltd. All rights reserved.

Computer graphics in Russia

Lossless compression of large binary images in digital spatial libraries*1

Eugene AgeenkoCorresponding Author Contact Information, E-mail The Corresponding Author and Pasi FräntiE-mail The Corresponding Author

Department of Computer Science, University of Joensuu, Box 111, FIN-80101 Joensuu, Finland

Available online 20 March 2000.

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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 introduced, including a fast two-stage bottom-up approach. The proposed technique achieves higher compression rates and allows dense tiling of images down to 50×50 pixels without sacrificing the compression performance. It enables partial decompression of large images far more efficiently than if the standard JBIG was applied.

Author Keywords: Image compression; Spatial access; Context tree; Context modeling; Binary images; Digital spatial libraries

Article Outline

1. Introduction
2. Compression system for digital spatial libraries
2.1. JBIG-based compression
2.2. Image tiling
2.3. Forward-adaptive statistical modeling
3. Variable-size context modeling
3.1. Context tree
3.2. Construction of the tree
3.3. Top-down construction
3.4. Bottom-up construction
3.5. Semi-adaptive and static variants
3.6. Combination with forward-adaptive modeling
3.7. Decompression
4. Empirical results
5. Conclusion
Acknowledgements
References







Computers & Graphics
Volume 24, Issue 1, February 2000, Pages 91-98
 
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