Tree coding of bilevel images

Presently, sequential tree coders are the best general purpose bilevel image coders and the best coders of halftoned images? The current ISO standard, Joint Bilevel Image Experts Group (JBIG), is a good example, A sequential tree coder encodes the data by feeding estimates of conditional probabilities to an arithmetic coder, The conditional probabilities are estimated from co-occurrence statistics of past pixels, the statistics are stored in a tree, By organizing code length calculations properly, a vast number of possible models (trees) reflecting different pixel orderings can be investigated within reasonable time prior to generating the code, A number of general-purpose coders are constructed according to this principle, Rissanen's one-p:lss algorithm, context, is presented in two modified versions, The baseline is proven to be a universal coder, The faster version, which is one order of magnitude slower than JBIG, obtains excellent and highly robust compression performance, A multipass free tree coding scheme produces superior compression results for all test images, A multipass free template coding scheme produces significantly better results than JBIG for difficult images such as halftones, By utilizing randomized subsampling in the template selection, the speed becomes acceptable for practical image coding.