Probability estimation for the Q-Coder

Pennebaker, William B.; Mitchell, J. L.

doi:10.1147/rd.326.0737

Cited by 61 publications

(34 citation statements)

References 10 publications

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“…For data distributed with P(LPS) = 0.5, the probabilities used (effective probability) ranged from 2 −1 to 2 −3 . A similar phenomenon was observed during single-context coding of any fixed-probability file, as expected according to the analysis in [9]. In this method, the confidence estimate allows the coder to detect when it using an inappropriate effective probability and compensate proportionally.…”

Section: Probability Estimationsupporting

confidence: 76%

“…Our table is arranged in a similar fashion to the 'fastattack' (FA) tables of JBIG/JPEG. A table is 'fast-attack' when an uninterrupted sequence of MPS-renorms rapidly decreases the LPS probability, faster than would be warranted by the equations in [9]. In our tables, an early LPS-renorm moves the coder into a less confident state and two LPS-renorms move the coder into the steady-state mode, where the analysis from [9] is valid.…”

Section: Probability Estimationmentioning

confidence: 81%

“…For efficient compression a coder needs to have an accurate estimate of the probability of each symbol as it is compressed. A computationally 'cheap' approach is to use renormalisation-driven probability estimation, first used in the Q-coder (see [9] for a detailed analysis.) The basic idea is that the renormalisation step in the encoding algorithm can be used as a criterion to decide when to perform adaptation.…”

Section: Probability Estimationmentioning

confidence: 99%

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The B-coder: An Improved Binary Arithmetic Coder and Probability Estimator

Kelly

Brailsford

Data Compression Conference (DCC'06)

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A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription.For more information, please contact eprints@nottingham.ac.uk Foreword and ColophonThis paper reports on work performed in 2005-6 by my grad. student Benji Kelly, which eventually formed a major part of his Masters (M.Phil) thesis here at the University of Nottingham. He then went on to do a PhD at Cornell University.Benji's work began with an interest in Golomb coding but he soon extended the scope of his researches to take in adaptive binary arithmetic coding. The paper presented here was submitted to the annual Data Compression Conference in 2006 (DCC 2006). These conferences are held in Snowbird, Utah and are very well regarded in the DCC field. Although our paper was accepted only as a poster, we felt rather pleased to have had success of some sort at our first attempt.The version here is the original, unabbreviated, submission to DCC.The paper was created using L A T E X with the hyperref package and was then converted to PDF using pdfeTeX-1.21aDavid F. Brailsford. February 2015.The B-coder: An improved binary arithmetic coder and probability estimator AbstractIn this paper we present the B-coder, an efficient binary arithmetic coder that performs extremely well on a wide range of data. The B-coder should be classed as an 'approximate' arithmetic coder, because of its use of an approximation to multiplication. We show that the approximation used in the B-coder has an efficiency cost of 0.003 compared to Shannon entropy. At the heart of the B-coder is an efficient state machine that adapts rapidly to the data to be coded. The adaptation is achieved by allowing a fixed table of transitions and probabilities to change within a given tolerance. The combination of the two techniques gives a coder that out-performs the current state-of-the-art binary arithmetic coders.

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Section: Probability Estimationsupporting

confidence: 76%

Section: Probability Estimationmentioning

confidence: 81%

Section: Probability Estimationmentioning

confidence: 99%

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The B-coder: An Improved Binary Arithmetic Coder and Probability Estimator

Kelly

Brailsford

Data Compression Conference (DCC'06)

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“…A description of the Q-coder as well as the prior version of JBIG can be found in the November 1988 issue of the IBM Journal of Research and Development [5,6,7,8].…”

Section: Jbig Compression Standardmentioning

confidence: 99%

JBIG for Printer Pipelines: A Compression Test

Ravì

Meccio

Messina

et al. 2009

Lecture Notes in Computer Science

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Abstract. The proposed paper describes a compression test analysis of JBIG standard algorithm. The aim of such work is to proof the effectiveness of this standard for images acquired through scanners and processed into a printer pipeline. The main issue of printer pipelines is the necessity to use a memory buffer to store scanned images for multiple prints. This work demonstrates that for very large scales the buffer can be fixed using medium compression case, using multiple scans in case of uncommon random patterns.

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“…Drawing digital straight lines from the digital center of a cell to the four corners will partition the cell into four digital triangles (Figs. [3][4]. These triangles will be used when changing the color of the cells.…”

Section: B Digital Grids Cells and Trianglesmentioning

confidence: 99%