The quality and spatial resolution of video can be improved by combining multiple pictures to form a single superresolution picture. We address the special problems associated with pictures of variable but somehow parameterized quality such as MPEG-decoded video. Our algorithm provides a unified approach to restoration, chrominance upsampling, deinterlacing, and resolution enhancement. A decoded MPEG-2 sequence for interlaced standard definition television (SDTV) in 4:2:0 is converted to: (1) improved quality interlaced SDTV in 4:2:0; (2) interlaced SDTV in 4:4:4; (3) progressive SDTV in 4:4:4; (4) interlaced high-definition TV (HDTV) in 4:2:0; (5) progressive HDTV in 4:2:0. These conversions also provide features such as freeze frame and zoom. The algorithm is mainly targeted at bit rates of 4-8 Mb/s. The algorithm is based on motion-compensated spatial upsampling from multiple images and decimation to the desired format. The processing involves an estimated quality of individual pixels based on MPEG image type and local quantization value. The mean-squared error (MSE) is reduced, compared to the directly decoded sequence, and annoying ringing artifacts, including mosquito noise, are effectively suppressed. The superresolution pictures obtained by the algorithm are of much higher visual quality and have lower MSE than superresolution pictures obtained by simple spatial interpolation.

The quality and the spatial resolution of video can be improved by combining multiple pictures to form a single superresolution picture. We address the special problems associated with pictures of variable but somehow parameterized quality such as MPEG-decoded video. Our algorithm provides a unified approach to restoration, chrominance upsampling, deinterlacing and superresolution as e.g. HDTV. The algorithm is mainly targeted at improving MPEG-2 decoding at high bit rates (4-8 Mbit/s). The mean squared error is reduced, compared to the directly decoded sequence, and annoying ringing artifacts including mosquito noise are effectively suppressed. The superresolution pictures obtained by the algorithm are of much higher visual quality and has lower mean squared error than superresolution pictures obtained by simple spatial interpolation.

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.

Summary form only given. We investigate lossless coding of video using predictive coding and motion compensation. The new coding methods combine state-of-the-art lossless techniques as JPEG (context based prediction and bias cancellation, Golomb coding), with high resolution motion field estimation, 3D predictors, prediction using one or multiple (k) previous images, predictor dependent error modelling, and selection of motion field by code length. We treat the problem of precision of the motion field as one of choosing among a number of predictors. This way, we can incorporate 3D-predictors and intra-frame predictors as well. As proposed by Ribas-Corbera (see PhD thesis, University of Michigan, 1996), we use bi-linear interpolation in order to achieve sub-pixel precision of the motion field. Using more reference images is another way of achieving higher accuracy of the match. The motion information is coded with the same algorithm as is used for the data. For slow pan or slow zoom sequences, coding methods that use multiple previous images perform up to 20% better than motion compensation using a single previous image and up to 40% better than coding that does not utilize motion compensation.

The Joint Bi-Level Image Experts Group (JBIG), an international study group affiliated with ISO/IEC and ITU-T, is in the process of drafting a new standard for lossy and lossless compression of bilevel 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 be obtained with imperceptible loss of quality. It is expected that JBIG2 will become an international standard by 2000.

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 the 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.

Presently, tree coders are the best bi-level image coders. The current ISO standard, JBIG, is a good example. By organising code length calculations properly a vast number of possible 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.