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23 December 1999 Compression of large binary images in digital spatial libraries
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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.
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Eugene Jevgeni Ageenko and Pasi Franti "Compression of large binary images in digital spatial libraries", Proc. SPIE 3972, Storage and Retrieval for Media Databases 2000, (23 December 1999);

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