Topic
JBIG2
About: JBIG2 is a research topic. Over the lifetime, 488 publications have been published within this topic receiving 9381 citations. The topic is also known as: .jb2 & .jbig2.
Papers published on a yearly basis
Papers
TL;DR: LOCO-I as discussed by the authors is a low complexity projection of the universal context modeling paradigm, matching its modeling unit to a simple coding unit, which is based on a simple fixed context model, which approaches the capability of more complex universal techniques for capturing high-order dependencies.
Abstract: LOCO-I (LOw COmplexity LOssless COmpression for Images) is the algorithm at the core of the new ISO/ITU standard for lossless and near-lossless compression of continuous-tone images, JPEG-LS. It is conceived as a "low complexity projection" of the universal context modeling paradigm, matching its modeling unit to a simple coding unit. By combining simplicity with the compression potential of context models, the algorithm "enjoys the best of both worlds." It is based on a simple fixed context model, which approaches the capability of the more complex universal techniques for capturing high-order dependencies. The model is tuned for efficient performance in conjunction with an extended family of Golomb (1966) type codes, which are adaptively chosen, and an embedded alphabet extension for coding of low-entropy image regions. LOCO-I attains compression ratios similar or superior to those obtained with state-of-the-art schemes based on arithmetic coding. Moreover, it is within a few percentage points of the best available compression ratios, at a much lower complexity level. We discuss the principles underlying the design of LOCO-I, and its standardization into JPEC-LS.
1,893 citations
TL;DR: The CALIC obtains higher lossless compression of continuous-tone images than other lossless image coding techniques in the literature and can afford a large number of modeling contexts without suffering from the context dilution problem of insufficient counting statistics as in the latter approach.
Abstract: We propose a context-based, adaptive, lossless image codec (CALIC). The codec obtains higher lossless compression of continuous-tone images than other lossless image coding techniques in the literature. This high coding efficiency is accomplished with relatively low time and space complexities. The CALIC puts heavy emphasis on image data modeling. A unique feature of the CALIC is the use of a large number of modeling contexts (states) to condition a nonlinear predictor and adapt the predictor to varying source statistics. The nonlinear predictor can correct itself via an error feedback mechanism by learning from its mistakes under a given context in the past. In this learning process, the CALIC estimates only the expectation of prediction errors conditioned on a large number of different contexts rather than estimating a large number of conditional error probabilities. The former estimation technique can afford a large number of modeling contexts without suffering from the context dilution problem of insufficient counting statistics as in the latter approach, nor from excessive memory use. The low time and space complexities are also attributed to efficient techniques for forming and quantizing modeling contexts.
1,160 citations
1 Jan 2000
TL;DR: The embedded block coding algorithm at the heart of the JPEG2000 image compression standard achieves excellent compression performance, usually somewhat higher than that of SPIHT with arithmetic coding, but in some cases substantially higher.
Abstract: This paper describes the embedded block coding algorithm at the heart of the JPEG2000 image compression standard. The algorithm achieves excellent compression performance, usually somewhat higher than that of SPIHT with arithmetic coding, but in some cases substantially higher. The algorithm utilizes the same low complexity binary arithmetic coding engine as JBIG2. Together with careful design of the bit-plane coding primitives, this enables comparable execution speed to that observed with the simpler variant of SPIHT without arithmetic coding. The coder offers additional advantages including memory locality, spatial random access and ease of geometric manipulation.
190 citations
1 Sep 2016
TL;DR: For any type of image, this method performs as good or better (on average) than any of the existing image formats for lossless compression.
Abstract: We present a novel lossless image compression algorithm. It achieves better compression than popular lossless image formats like PNG and lossless JPEG 2000. Existing image formats have specific strengths and weaknesses: e.g. JPEG works well for photographs, PNG works well for line drawings or images with few distinct colors. For any type of image, our method performs as good or better (on average) than any of the existing image formats for lossless compression. Interlacing is improved compared to PNG, making the format suitable for progressive decoding and responsive web design.
173 citations
1 Jun 1994
TL;DR: This overview focuses on a comparison of lossless compression capabilities of the international standard algorithms for still image compression known as MH, MR, MMR, JBIG, and JPEC.
Abstract: This overview focuses on a comparison of lossless compression capabilities of the international standard algorithms for still image compression known as MH, MR, MMR, JBIG, and JPEC. Where the algorithms have parameters to select, these parameters have been carefully set to achieve maximal compression. Compression variations due to differences in data are illustrated and scaling of these compression results with spatial resolution or amplitude precision are explored. These algorithms are also summarized in terms of the compression technology they utilize, with further references given for precise technical details and the specific international standards involved. >
140 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 3 |
| 2020 | 2 |
| 2019 | 2 |
| 2018 | 5 |
| 2017 | 6 |
| 2016 | 13 |