Course details

Data Coding and Compression

KKO Acad. year 2023/2024 Summer semester 5 credits

Current academic year

Introduction to data compression theory. Lossy and lossless data compression, adaptive methods, statistical - Huffman and arithmetic coding, dictionary methods LZ77, LZ78, transform coding, Burrows-Wheeler transform.

Guarantor

Course coordinator

Language of instruction

Czech

Completion

Credit+Examination (written)

Time span

  • 26 hrs lectures
  • 26 hrs projects

Assessment points

  • 70 pts final exam
  • 30 pts projects

Department

Lecturer

Instructor

Course Web Pages

Aktuální informace jsou zveřejňovány na WIKI předmětu ve WISu.

Subject specific learning outcomes and competences

Theoretical background of advanced data processing using compression. 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 and hardware support for data compression.

Why is the course taught

Compression represents one of the most fundamental operations which is applied not only to improve the storage capacity, but also to lower the communication latency or increase throughput of the transmission channels. The goal of this course is to provide knowledge of compression techniques as well as the mathematical foundations of data compression. The students should develop transferable skills such as problem analysis and problem solving.

Prerequisite knowledge and skills

Knowledge of functioning of basic computer units.

Study literature

  • Sayood, K.: Introduction to Data Compression, Fifth Edition, 2017, ISBN 978-0-12809-474-7

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, LZ78.
  • Variants of LZW.
  • Transform coding, Burrows-Wheeler transform.
  • Advanced methods of data compression.

Syllabus - others, projects and individual work of students

Individual project assignment.

Progress assessment

An evaluated project for 30 points. A final examination for 70 points.

Exam prerequisites

Get at least 10 points for the project.

Course inclusion in study plans

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