Title:

Data Coding and Compression

Code:KKO
Ac.Year:2009/2010
Term:Summer
Study plans:
ProgramBranchYearDuty
IT-MSC-2MBI1stCompulsory-Elective - group S
IT-MSC-2MBS1stCompulsory
IT-MSC-2MGM-Compulsory-Elective - group G
IT-MSC-2MGM.1stElective
IT-MSC-2MIN-Elective
IT-MSC-2MIN.-Elective
IT-MSC-2MIS-Elective
IT-MSC-2MIS.-Elective
IT-MSC-2MMI-Elective
IT-MSC-2MMM2ndCompulsory-Elective - group B
IT-MSC-2MPS1stElective
IT-MSC-2MPV1stCompulsory
IT-MSC-2MSK1stCompulsory-Elective - group C
Language:Czech
Credits:5
Completion:accreditation+exam (written)
Type of
instruction:
Hour/semLecturesSem. ExercisesLab. exercisesComp. exercisesOther
Hours:2600026
 ExaminationTestsExercisesLaboratoriesOther
Points:7000030
Guarantee:Drábek Vladimír, doc. Ing., CSc., DCSY
Lecturer:Drábek Vladimír, doc. Ing., CSc., DCSY
Instructor:Drábek Vladimír, doc. Ing., CSc., DCSY
Šimek Václav, Ing., DCSY
Faculty:Faculty of Information Technology BUT
Department:Department of Computer Systems FIT BUT
 
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.
Description:
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.
Knowledge and skills required for the course:
Knowledge of functioning of basic computer units.
Subject specific learning outcomes and competences:
Theoretical background of advanced data processing using compression.
Generic learning outcomes and competences:
Importance of advanced data compression.
Syllabus of lectures:
  1. Introduction to compression theory.
  2. Basic compression methods.
  3. Statistical and dictionary methods.
  4. Huffman coding.
  5. Adaptive Huffman coding.
  6. Arithmetic coding. Text compression.
  7. Lossy and lossless data compression.
  8. Dictionary methods, LZ77, 78.
  9. Variants of LZW.
  10. Transform coding, Burrows-Wheeler transform.
  11. Other methods.
  12. Hardware support for data compression, MXT.
Syllabus - others, projects and individual work of students:
Individual project assignment.
Fundamental literature:
  1. Salomon, D.: Data Compression. The Complete Reference, Second Edition, Springer 2000, ISBN 0-387-95045-1
Study literature:
  1. Lecture notes and study supports in e-format.
Progress assessment:
Project designing with checked results.
Exam prerequisites:
Project designing with checked results.