The Joint Bi-level Image Experts Group jbig, an international study group a liated with iso iec and itut, is in the process of drafting a new standard for lossy and lossless compression of bi-level images. The new standard, informally referred to as jbig2, will support model-based coding for text and halftones to permit compression ratios up to three times those of existing standards for lossless compression. jbig2 will also permit lossy preprocessing without specifying how it is to be done. In this case compression ratios up to eight times those of existing standards may beobtained with imperceptible loss of quality. It is expected that jbig2 will become an International Standard by 2000.

Abstract-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-pass 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.

 Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  You may not further distribute the material or use it for any profit-making activity or commercial gain  You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

No abstract

We present general and unified algorithms for lossy/lossless coding of bilevel images. The compression is realized by applying arithmetic coding to conditional probabilities. As in the current JBIG standard the conditioning may be specified by a template. For better compression, the more general free tree may be used. Loss may be introduced in a preprocess on the encoding side to increase compression. The primary algorithm is a rate-distortion controlled greedy flipping of pixels. Though being general, the algorithms are primarily aimed at material containing half-toned images as a supplement to the specialized soft pattern matching techniques that work better for text. Template based refinement coding is applied for lossy-to-lossless refinement. Introducing only a small amount of loss in half-toned test images, compression is increased by up to a factor of four compared with JBIG. Lossy, lossless, and refinement decoding speed and lossless encoding speed are less than a factor of two slower than JBIG. The (de)coding method is proposed as part of JBIG2, an emerging international standard for lossless/lossy compression of bilevel images.

 Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  You may not further distribute the material or use it for any profit-making activity or commercial gain  You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

Presently, tree coders are the best bi-level image coders. The current IS0 standard, JBIG, is a good example. By organising code length calculations properly a vast number of possibie models (trees) can be investigated within reasonable time prior to generating code. Three general-purpose coders are constructed by this principle. A multi-pass free tree coding scheme produces superior compression results for all test images. A multi-pass fast free template coding scheme produces much better results than JBIG for difficult images, such as halftonings. Rissanen's algorithm 'Context'is presented in a new version that without sacrificing speed brings it close to the multi-pass coders in compression performance.

Abstract-Vector Flow Imaging (VFI) has received an increasing attention in the scientific field of ultrasound, as it enables angle independent visualization of blood flow. VFI can be used in volume flow estimation, but a vessel segmentation is needed to make it fully automatic. A novel vessel segmentation procedure is crucial for wall-to-wall visualization, automation of adjustments, and quantification of flow in state-of-the-art ultrasound scanners. We propose and discuss a method for accurate vessel segmentation that fuses VFI data and B-mode for robustly detecting and delineating vessels. The proposed method implements automated VFI flow measures such as peak systolic velocity (PSV) and volume flow. An evaluation of the performance of the segmentation algorithm relative to expert manual segmentation of 60 frames randomly chosen from 6 ultrasound sequences (10 frame randomly chosen from each sequence) is also presented. Dice coefficient denoting the similarity between segmentations is used for the evaluation. The coefficient ranges between 0 and 1, where 1 indicates perfect agreement and 0 indicates no agreement. The Dice coefficient was 0.91 indicating to a very agreement between automated and manual expert segmentations. The flowrig results also demonstrated that the PSVs measured from VFI had a mean relative error of 14.5% in comparison with the actual PSVs. The error for the PSVs measured from spectral Doppler was 29.5%, indicating that VFI is 15% more precise than spectral Doppler in PSV measurement.