Lossless compression of large binary images in digital spatial libraries

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.