Title:

Data Coding and Compression

Code:KKO
Ac.Year:2017/2018
Term:Summer
Curriculums:
ProgrammeBranchYearDuty
IT-MGR-1HMGH-Recommended
IT-MSC-2MBI-Compulsory-Elective - group S
IT-MSC-2MBS1stCompulsory
IT-MSC-2MGM-Compulsory-Elective - group G
IT-MSC-2MIN-Elective
IT-MSC-2MIS-Elective
IT-MSC-2MMI-Elective
IT-MSC-2MMM-Compulsory-Elective - group B
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
Vašíček Zdeněk, doc. Ing., Ph.D., 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
Schedule:
DayLessonWeekRoomStartEndLect.Gr.St.G.EndG.
ThulecturelecturesD020610:0011:501MIT11 MBS11 MBS
ThulecturelecturesD020610:0011:501MIT17 MPV17 MPV
ThulecturelecturesD020610:0011:502MITxxxx
 
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, transform and context  methods, and hardware support for data compression. Data compression in the memory hierarchy.
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. Context methods. Hardware support for data compression, data compression in the memory hierarchy.
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:
 
  • 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.
  • Models of the context, context compression..
  • Hardware support for data compression, MXT. Data compression in the memory hierarchy. 
Syllabus - others, projects and individual work of students:
 Individual project assignment.
Fundamental literature:
 
  • Salomon, D.: Data Compression. The Complete Reference, Second Edition, Springer 2000, ISBN 0-387-95045-1
Study literature:
 
  • Lecture notes and study supports in e-format.
Progress assessment:
  Project designing and presentation.
Exam prerequisites:
  Project designing and presentation.