Showing papers by "Charles G. Boncelet published in 2003"

A context-weighting algorithm achieving model-adaptability in lossless bi-level image compression

Abstract: A context-weighting algorithm as an improvement of previously proposed block arithmetic coder for image compression (BACIC) is presented. The proposed algorithm weights two context models, so that it can automatically select the better model over different regions of an image, producing better probability estimates. The overall performance of this algorithm is better than single context BACIC; it is the same as JBIG1 for the eight CCITT business-type test images, and outclasses JBIG1 by 13.8% on halftone images, by 25.7% for images containing both text and halftones. Furthermore, users no longer need to select models as in JBIG1 and BACIC to get the better performance. Rather than using segmentation algorithm in JBIG2, the context-weighting BACIC may be a good choice in many applications due to its simplicity.