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12 - Statistical Coding

Published online by Cambridge University Press:  08 June 2023

Paolo Ferragina
Affiliation:
Università di Pisa
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Summary

This chapter deals with a classic topic in data compression and information theory, namely the design of compressors based on the statistics of the symbols present in the text to be compressed. This topic is addressed by means of an algorithmic approach that gives much attention to the time efficiency and algorithmic properties of the discussed statistical coders, while also evaluating their space performance in terms of the empirical entropy of the input text. The chapter deals in detail with the classic Huffman coding and arithmetic coding, and also discusses their engineered versionsc known as canonical Huffman coding and range coding. Its final part is dedicated to describing and commenting on the prediction by partial matching (PPM) coder, whose algorithmic structure is at the core of some of the best statistical coders to date.

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Publisher: Cambridge University Press
Print publication year: 2023

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  • Statistical Coding
  • Paolo Ferragina, Università di Pisa
  • Book: Pearls of Algorithm Engineering
  • Online publication: 08 June 2023
  • Chapter DOI: https://doi.org/10.1017/9781009128933.013
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  • Statistical Coding
  • Paolo Ferragina, Università di Pisa
  • Book: Pearls of Algorithm Engineering
  • Online publication: 08 June 2023
  • Chapter DOI: https://doi.org/10.1017/9781009128933.013
Available formats
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Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Statistical Coding
  • Paolo Ferragina, Università di Pisa
  • Book: Pearls of Algorithm Engineering
  • Online publication: 08 June 2023
  • Chapter DOI: https://doi.org/10.1017/9781009128933.013
Available formats
×