Coding and compression of media data
Study plans 2016-2017 - IMT4302 - 7.5 ECTS

On the basis of

Builds on some of the lectures in Image Processing and Analysis course - or similar.

Expected learning outcomes

This course is a graduate-level introductory course to the fundamentals of coding and compression of media data. It focuses on the fundamental principles of coding and compression and discusses several of the existing audio, image and video compression standards. On completion of this course the student will:


  • posses an understanding of the fundamental characteristics of data coding systems used widely in digital recording formats, software and hardware encoders.
  • understand the human visual system characteristics and deficiencies that can be exploited to compress audio-visual media efficiently.
  • understand the redundancies in audio-visual content and how to remove it when encoding this type of material. 
  • understand how subjective as well as objective metrics work, for the evaluation of media quality.
  • possess advanced knowledge of basic algorithms for lossless and lossy audio, image and video compression techniques and standards including preprocessing, transforms-based coding,  filtering, etc.
  • posses advanced knowledge of video sequences and how they differ from still images and how to exploit their inherent redundancies to compress this type of data. 
  • possess specialized insight and good understanding of the different media coding standards and their differences.


  • be able to use mathematical techniques for encoding different types of media and demonstrate the use of tools such as matlab, wavelets toolbox, to solve problems in data coding and compression.
  • be able to explore a range of practical techniques, by developing their own simple encoding functions using library facilities and tools such as Matlab.
  • be able to implement the techniques in the topics studied and compare their performances in certain coding tasks.
  • be able to use relevant and suitable methods when carrying out research and development activities in the area of media coding.
  • be able to present, to his colleagues and experts, his work in English and defend his ideas. 

General competence 

  • have the learning skills to continue acquiring new knowledge and skills in a manner that is largely self-directed.
  • be able to contribute to innovative thinking and innovation processes.


  •  Motivation for media data compression
  •  Media data redundancy and compression
  •  Fundamental digital image representation and processing
  •  Sampling and quantization
  •  Entropy coding, run-length coding, variable-length coding
  •  Lossy and lossless compression techniques
  •  Transform-based coding
  •  Compression of audio, image, and video data
  •  File formats and standards
  •  JPEG, JPEG2000
  •  Motion estimation, motion compensation, motion compensated prediction
  •  H.261, H.263, MPEG-1, MPEG-2, MPEG-4, MPEG-7, and newer coding standards
  •  Image quality

Teaching Methods

Net Support Learning
Project work

Teaching Methods (additional text)

The course will be offered both as an ordinary on-campus course and as a flexible course to off-campus students. Lecture notes in PDF, Audio recordings of the lectures and other types of e-learning material will be offered through Fronter. Communication between the teacher and the students, and among the students, will be facilitated via Fronter.

Form(s) of Assessment

Written exam, 4 hours

Grading Scale

Alphabetical Scale, A(best) – F (fail)

External/internal examiner

Internal examiner evaluates the written exam.

Re-sit examination

Ordinary re-sit examination in August.

Tillatte hjelpemidler

Code D: No printed or hand-written support material is allowed. A specific basic calculator is allowed.
Read more about permitted examination aids.

Coursework Requirements

Mandatory exercises reports (these will not be graded).

Teaching Materials


Replacement course for

IMT4451 Coding and compression of media data

Additional information

The course will run for the first time in 2017.