Book contents
- Frontmatter
- Dedication
- Contents
- Preface
- 1 Introduction
- 2 A Warm-up
- 3 Random Sampling
- 4 List Ranking
- 5 Sorting Atomic Items
- 6 Set Intersection
- 7 Sorting Strings
- 8 The Dictionary Problem
- 9 Searching Strings by Prefix
- 10 Searching Strings by Substring
- 11 Integer Coding
- 12 Statistical Coding
- 13 Dictionary-Based Compressors
- 14 Block-Sorting Compression
- 15 Compressed Data Structures
- 16 Conclusion
- Index
12 - Statistical Coding
Published online by Cambridge University Press: 08 June 2023
- Frontmatter
- Dedication
- Contents
- Preface
- 1 Introduction
- 2 A Warm-up
- 3 Random Sampling
- 4 List Ranking
- 5 Sorting Atomic Items
- 6 Set Intersection
- 7 Sorting Strings
- 8 The Dictionary Problem
- 9 Searching Strings by Prefix
- 10 Searching Strings by Substring
- 11 Integer Coding
- 12 Statistical Coding
- 13 Dictionary-Based Compressors
- 14 Block-Sorting Compression
- 15 Compressed Data Structures
- 16 Conclusion
- Index
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.
- Type
- Chapter
- Information
- Pearls of Algorithm Engineering , pp. 210 - 239Publisher: Cambridge University PressPrint publication year: 2023