On the Use of Context-Weighting in Lossless Bilevel Image Compression

We present a context-weighting algorithm that adaptively weights in real-time three-context models based on their relative accuracy. It can automatically select the better model over different regions of an image, producing better probability estimates than using either one of these models exclusively. Combined with the previously proposed block arithmetic coder for image compression (BACIC), the overall performance is slightly better than JBIG for the eight CCITT business-type test images, outperforms JBIG by 13.8% on halftone images, and by 17.5% for compounded images containing both text and halftones. Furthermore, users no longer need to select models as in JBIG and BACIC to get the better performance