Context-based lossless halftone image compression

New applications such as printing on demand and personalized printing have increased the need for efficient lossless halftone image compression algori thms to lower the transmission time and the storage costs State-of-the-art lossless bilevel image compression schemes like JBIG achieve only moderate compression ratios because they do not fully take into account the special image characteristics. In this paper, we present an improvement on the cont ext modeling scheme by adapting the context template to the special pattern s of halftone images. This is a nontrivial problem for which we propose a f ast and efficient context template selection scheme based on the sorted aut ocorrelation function of a part of the image. We have experimented with cla ssical halftones of different resolutions and sizes, screened under differe nt angles, as well as with stochastic halftones. For classical halftones, t he global improvement with respect to JBIG in its best mode is about 30%-50 %. Far stochastic halftones, the autocorrelation-based template gives no im provement, though a much slower exhaustive search technique shows that gain s up to 70% are feasible using a suboptimal template. Binary free modeling increases the compression ratio by another 5%-10%. Context modeling can als o be used for other types of halftone image processing. (C) 1999 SPIE and I S&T.