Course detail

Data Coding and Compression

FIT-KKOAcad. year: 2019/2020

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. Hardware support for data compression.

Learning outcomes of the course unit

Theoretical background of advanced data processing using compression.
Importance of advanced data compression.

Prerequisites

Knowledge of functioning of basic computer units.

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

Přednáškové materiály a studijní opory v elektronické formě.
Sayood, K.: Introduction to Data Compression, Fifth Edition, 2017, ISBN 978-0-12809-474-7
Salomon, D.: Data Compression. The Complete Reference, Fourth Edition, Springer 2007, ISBN 978-1-84628-605-5
Sayood, K.: Lossless Compression Handbook,  2003, ISBN 978-0-12620-861-0

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

Project designing and presentation.
Exam prerequisites:
Project designing and presentation. Min 10 points.

Language of instruction

Czech

Work placements

Not applicable.

Aims

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.

Classification of course in study plans

  • Programme IT-MGR-2 Master's

    branch MBI , any year of study, summer semester, 5 credits, compulsory-optional
    branch MGM , any year of study, summer semester, 5 credits, compulsory-optional
    branch MIS , any year of study, summer semester, 5 credits, optional
    branch MIN , any year of study, summer semester, 5 credits, optional
    branch MMI , any year of study, summer semester, 5 credits, optional
    branch MMM , any year of study, summer semester, 5 credits, compulsory-optional

  • Programme MITAI Master's

    specialization NADE , any year of study, summer semester, 5 credits, optional
    specialization NBIO , any year of study, summer semester, 5 credits, optional
    specialization NGRI , any year of study, summer semester, 5 credits, optional
    specialization NNET , any year of study, summer semester, 5 credits, optional
    specialization NVIZ , any year of study, summer semester, 5 credits, optional
    specialization NCPS , any year of study, summer semester, 5 credits, optional
    specialization NSEC , any year of study, summer semester, 5 credits, optional
    specialization NEMB , any year of study, summer semester, 5 credits, compulsory
    specialization NHPC , any year of study, summer semester, 5 credits, optional
    specialization NISD , any year of study, summer semester, 5 credits, optional
    specialization NIDE , any year of study, summer semester, 5 credits, optional
    specialization NISY , any year of study, summer semester, 5 credits, optional
    specialization NMAL , any year of study, summer semester, 5 credits, optional
    specialization NMAT , any year of study, summer semester, 5 credits, optional
    specialization NSEN , any year of study, summer semester, 5 credits, optional
    specialization NVER , any year of study, summer semester, 5 credits, optional
    specialization NSPE , any year of study, summer semester, 5 credits, optional

  • Programme IT-MGR-2 Master's

    branch MPV , 1. year of study, summer semester, 5 credits, compulsory
    branch MSK , 1. year of study, summer semester, 5 credits, compulsory-optional
    branch MBS , 1. year of study, summer semester, 5 credits, compulsory

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Syllabus


  • 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.
  • Other methods.
  • Hardware support for data compression, MXT.

Project

26 hours, compulsory

Teacher / Lecturer

Syllabus

Individual project assignment.

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