Faculty of Information Technology, BUT

Course details

Data Coding and Compression

KKO Acad. year 2007/2008 Summer semester 5 credits

Introduction to data compression theory. Lossy and lossless data compression, adaptive methods, statistical - Huffman and arithmetic coding, dictionary methods LZ77, 78, transform coding, Burrows-Wheeler transform. Hardware support for data compression.


Language of instruction



Credit+Examination (written)

Time span

26 hrs lectures, 26 hrs projects

Assessment points

70 exam, 30 projects




Gajda Zbyšek, Ing., Ph.D. (DCSY FIT BUT)
Šimek Václav, Ing. (DCSY FIT BUT)

Subject specific learning outcomes and competences

Theoretical background of advanced data processing using compression and error correction.

Generic learning outcomes and competences

Importance of advanced data compression.

Learning objectives

To give the students the knowledge of basic compression techniques, the methods for lossy and lossless data compression their efficiency, statistical and dictionary methods, hardware support for data compression.

Prerequisite kwnowledge and skills

Knowledge of functioning of basic computer units.

Study literature

  • Lecture notes in e-format.

Fundamental literature

  • Salomon, D.: Data Compression. The Complete Reference, Second Edition, Springer 2000, ISBN 0-387-95045-1

Syllabus of lectures

  • Introduction to compression theory. 
  • Basic compression methods.
  • Statistical and dictionary methods.
  • Huffman coding.
  • Adaptive Huffman coding.
  • Arithmetic coding. Text compression. 
  • Lossy and lossless data compression.
  • Dictionary methods, LZ77, 78.
  • Variants of LZW.
  • Transform coding, Burrows-Wheeler transform.
  • Other methods.
  • Hardware support for data compression, MXT.

Syllabus - others, projects and individual work of students

Individual project assignment.

Progress assessment

Project designing and presentation.

Exam prerequisites

Project designing and presentation.

Course inclusion in study plans

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