1 / 12

Source Encoding and Compression

Source Encoding and Compression. Jukka Teuhola University of Turku Dept. of Information Technology Spring 2014. General. Self-study course, starting lecture: 14.1.2014 Extent: 5 sp (3 cu) Level: Advanced

snix
Télécharger la présentation

Source Encoding and Compression

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Source Encoding and Compression Jukka Teuhola University of TurkuDept. of Information Technology Spring 2014 SEAC-1 J.Teuhola 2014

  2. General • Self-study course, starting lecture: 14.1.2014 • Extent: 5 sp (3 cu) • Level: Advanced • Preliminary knowledge: Data structures and algorithms I, basics of probability calculus • Material: Lecture notes and Powerpoint slides available via the course homepage. No textbook is needed. • Homework: 10 small exercise tasks will be given. Solutions must be submitted to the lecturer before taking the examination. Minimum: 5 solutions acceptably solved. • Examinations: Three attempts; March, April, May 2014 SEAC-1 J.Teuhola 2014

  3. Optional literature • T. C. Bell, J. G. Cleary, I. H. Witten: Text Compression, 1990. • R. W. Hamming: Coding and Information Theory, 2nd ed., Prentice-Hall, 1986. • K. Sayood: Introduction to Data Compression, 3rd ed., Morgan Kaufmann, 2006. • K. Sayood: Lossless Compression Handbook, Academic Press, 2003. • I. H. Witten, A. Moffat, T. C. Bell: Managing Gigabytes: compressing and indexing documents and images, Morgan Kaufmann, 1999. • Miscellaneous articles SEAC-1 J.Teuhola 2014

  4. Contents • Basic concepts • Coding-theoretic foundations • Information-theoretic foundations • Basic source coding methods • Predictive models for text compression • Dictionary models for text compression • Compression of digital images SEAC-1 J.Teuhola 2014

  5. 1. Basic concepts • Data compression: • Minimize the size of information representation. • Reduce the redundancy of the original representation. • Purposes: • Save storage space. • Reduce transmission time. • Basic approaches: • Lossless compression: decompression into exactly the original form (typical for text). • Lossy compression: decompression into approximately the original form (typical for signals and images). SEAC-1 J.Teuhola 2014

  6. Model Model Errors Source encoding Channel encoding Channel decoding Source decoding Source Sink Communication channel Basic concepts (cont.) • Fields of coding theory: • Source coding: purpose to minimize the size • Channel coding: detection and correction of transmission errors. • Also: cryptography: Encryption of private/secret information SEAC-1 J.Teuhola 2014

  7. Basic concepts (cont.) • Phases of data compression: • Modelling of the source • Source encoding (called also entropy coding), using the model • Other viewpoints: • Speed of compression / decompression • Size of the model • Classification by lengths of coding units: • Fixed-to-fixed coding • Variable-to-fixed coding • Fixed-to-variable coding • Variable-to-variable coding SEAC-1 J.Teuhola 2014

  8. 1. Character distribution Char Prob A 0.10 B 0.05 C 0.08 D 0.06 E 0.15 ….. ….. 2. Successor distributionChar Succ Prob A A 0.01 A B 0.20 A C 0.10 A D 0.25 ….. ….. …… B A 0.15 B B 0.02 B C 0.01 B D 0.01 ….. ….. ….. 3. DictionaryWord ProbALL 0.02ALWAYS 0.01ARE 0.05AS 0.03 AT 0.02BASIC 0.01BEGIN 0.01 ….. ….. Examples of models SEAC-1 J.Teuhola 2014

  9. Basic concepts (cont.) • Main classes of text compression methods: • Dictionary methods • Statistical methods • Classification based on availability of the source: • Off-line methods • On-line methods • Classification based on the status of the model: • Static methods • Semiadaptive methods • Adaptive methods • Measurement of compression efficiency: • Compression ratio: Source size / compressed size • Bits per source symbol (character, pixel, etc.) SEAC-1 J.Teuhola 2014

  10. Background knowledgeof the source data types Model Model Derived once Use Use Decoded message Sourcemessage Encoder Decoder Send Write Read Illustration of a static method SEAC-1 J.Teuhola 2014

  11. 1. Build Model Model 3. Send Sourcemessage Use 5. Use Decoded message 2. Read Encoder Decoder 4. Send 6. Write Illustration of a semiadaptive method SEAC-1 J.Teuhola 2014

  12. Initial model fixed Initial model fixed Processedpart Processedpart Model Model Dynamicupdate Dynamicupdate Use Use Sourcemessage Decoded message Encoder Decoder Send Read Write Illustration of an adaptive method Models are updated dynamically, based on thealready processed part of the source, known toboth encoder and decoder. SEAC-1 J.Teuhola 2014

More Related