Data Compression

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Department of Theoretical Computer Science

Synopsis:

Students are introduced to the basic principles of data compression. They will learn the necessary theoretical background and get an overview of data compression methods being used in practice. The overview covers principles of integer coding and of statistical, dictionary, and context data compression methods. In addition, students learn the fundamentals of lossy data compression methods used in image, audio, and video compression.

Requirements:

Knowledge of basic data structures, fundamentals of computer programming, and theory of finite automata.

Syllabus of lectures:

1. Introduction, entropy, models, elementary methods.

2. Coding of integers.

3. [2] Statistical methods: Shannon-Fano coding, Huffman coding, arithmetic coding.

4. [2] Dictionary methods: LZ77, LZ78, LZW.

5. [3] Context methods: PPM, DCA, ACB.

6. Burrows-Wheeler compression.

7. Pattern matching in a compressed text.

8. Word-based compression.

9. Introduction to lossy compression (image, audio, video).

Syllabus of tutorials:

1. Entropy, models, elementary methods.

2. Coding of integers.

3. Statistical methods, Shannon-Fano coding, Huffman coding.

4. Statistical methods, Arithmetic coding.

5. Dictionary methods, LZ77, LZ78.

6. Dictionary methods, LZW.

7. Context methods, PPM.

8. Context methods, DCA.

9. Context methods, ACB.

10. Burrows-Wheeler compression.

11. Pattern matching in compressed text.

12. Word-based compression.

13. Introduction to lossy compression (image, audio, video).

Study Objective:

The course deals with elementary techniques of data compression. The introduction with theoretical background is followed by presentation of methods for coding integers and statistical, dictionary, and context data compression methods. The module ends with an introduction to lossy data compression used in image, audio, and video compression.

Study materials:

1. Salomon, D., Motta, G., Bryant, D. ''Data Compression: The Complete Reference''. Springer, 2006. ISBN 1846286026.

Note:

Further information:

https://courses.fit.cvut.cz/MIE-KOD/index.html

No time-table has been prepared for this course

The course is a part of the following study plans: