Copyright © 2000 Elsevier Science Ltd. All rights reserved.
Computer graphics in Russia
Lossless compression of large binary images in digital spatial libraries*1
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
- 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






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