World Library  
Flag as Inappropriate
Email this Article

PPMd

Article Id: WHEBN0024871611
Reproduction Date:

Title: PPMd  
Author: World Heritage Encyclopedia
Language: English
Subject: Lossless compression, Zip (file format), PAQ
Collection:
Publisher: World Heritage Encyclopedia
Publication
Date:
 

PPMd

Prediction by partial matching (PPM) is an adaptive statistical data compression technique based on context modeling and prediction. PPM models use a set of previous symbols in the uncompressed symbol stream to predict the next symbol in the stream. PPM algorithms can also be used to cluster data into predicted groupings in cluster analysis.

Theory

Predictions are usually reduced to symbol rankings. The number of previous symbols, n, determines the order of the PPM model which is denoted as PPM(n). Unbounded variants where the context has no length limitations also exist and are denoted as PPM*. If no prediction can be made based on all n context symbols a prediction is attempted with n − 1 symbols. This process is repeated until a match is found or no more symbols remain in context. At that point a fixed prediction is made.

Much of the work in optimizing a PPM model is handling inputs that have not already occurred in the input stream. The obvious way to handle them is to create a "never-seen" symbol which triggers the escape sequence. But what probability should be assigned to a symbol that has never been seen? This is called the zero-frequency problem. One variant uses the Laplace estimator, which assigns the "never-seen" symbol a fixed pseudocount of one. A variant called PPMD increments the pseudocount of the "never-seen" symbol every time the "never-seen" symbol is used. (In other words, PPMD estimates the probability of a new symbol as the ratio of the number of unique symbols to the total number of symbols observed).

Implementation

PPM compression implementations vary greatly in other details. The actual symbol selection is usually recorded using arithmetic coding, though it is also possible to use Huffman encoding or even some type of dictionary coding technique. The underlying model used in most PPM algorithms can also be extended to predict multiple symbols. It is also possible to use non-Markov modeling to either replace or supplement Markov modeling. The symbol size is usually static, typically a single byte, which makes generic handling of any file format easy.

Published research on this family of algorithms can be found as far back as the mid-1980s. Software implementations were not popular until the early 1990s because PPM algorithms require a significant amount of RAM. Recent PPM implementations are among the best-performing lossless compression programs for natural language text.

Trying to improve PPM algorithms led to the PAQ series of data compression algorithms.

A PPM algorithm, rather than being used for compression, is used to increase the efficiency of user input in the alternate input method program Dasher.

References

  • expand by hand
  • expand by hand
  • expand by hand
  • C. Bloom, Solving the problems of context modeling.
  • W.J. Teahan, Probability estimation for PPM.
  • expand by hand

See also

External links

  • Suite of PPM compressors with benchmarks
  • BICOM, a bijective PPM compressor
  • "Arithmetic Coding + Statistical Modeling = Data Compression", Part 2
  • (Russian) PPMd compressor by Dmitri Shkarin
  • PPM algorithm implementation (source code) by René Puchinger
This article was sourced from Creative Commons Attribution-ShareAlike License; additional terms may apply. World Heritage Encyclopedia content is assembled from numerous content providers, Open Access Publishing, and in compliance with The Fair Access to Science and Technology Research Act (FASTR), Wikimedia Foundation, Inc., Public Library of Science, The Encyclopedia of Life, Open Book Publishers (OBP), PubMed, U.S. National Library of Medicine, National Center for Biotechnology Information, U.S. National Library of Medicine, National Institutes of Health (NIH), U.S. Department of Health & Human Services, and USA.gov, which sources content from all federal, state, local, tribal, and territorial government publication portals (.gov, .mil, .edu). Funding for USA.gov and content contributors is made possible from the U.S. Congress, E-Government Act of 2002.
 
Crowd sourced content that is contributed to World Heritage Encyclopedia is peer reviewed and edited by our editorial staff to ensure quality scholarly research articles.
 
By using this site, you agree to the Terms of Use and Privacy Policy. World Heritage Encyclopedia™ is a registered trademark of the World Public Library Association, a non-profit organization.
 


Copyright © World Library Foundation. All rights reserved. eBooks from Project Gutenberg are sponsored by the World Library Foundation,
a 501c(4) Member's Support Non-Profit Organization, and is NOT affiliated with any governmental agency or department.