EQ2330 Image and Video Processing 7.5 credits

Bild- och videobehandling

This course introduces the principles of digital image and video processing, discusses current image and video processing technology, and provides hands-on experience with image/video processing and communication methods. The course includes topics on image filtering and restoration, image transform algorithms, multiresolution image processing, image matching and segmentation techniques, as well as image and video compression.

Show course information based on the chosen semester and course offering:

Offering and execution

No offering selected

Select the semester and course offering above to get information from the correct course syllabus and course offering.

Course information

Content and learning outcomes

Course contents *

This course introduces the principles of digital image and video processing, discusses current image and video processing technology, and provides hands-on experience with image/video processing and communication methods. The course includes topics on image filtering and restoration, image transform algorithms, multiresolution image processing, image matching and segmentation techniques, as well as image and video compression.

Intended learning outcomes *

After passing this course, participants should be able to

- describe and use the principles of digital image and video processing to develop image processing algorithms,

- develop image processing algorithms for image filtering and restoration, image transformation and multiresolution processing, image and video compression, as well as image matching and segmentation,

- implement (for example with MatLab) and assess the developed image processing algorithms, 

- explain algorithm design choices using the principles of digital image/video processing,

- develop image processing algorithms for a given practical image/video processing problem

- analyze given image/video processing problems, identify and explain the challenges, propose possible solutions, and explain the chosen algorithm design.

To achive higher grades, participants should also be able to

- solve more advanced problems in all areas mentioned above.

Course Disposition

No information inserted

Literature and preparations

Specific prerequisites *

For single course students: 120 credits and documented proficiency in English B or equivalent.

Recommended prerequisites

EQ1220 Signal Theory or equivalent

Equipment

No information inserted

Literature

See course homepage. Preliminary 

R.C. Gonzalez and R.E. Woods: Digital Image Processing, Prentice-Hall

Examination and completion

Grading scale *

A, B, C, D, E, FX, F

Examination *

  • INL1 - Assignment, 1.5 credits, Grading scale: P, F
  • TEN1 - Exam, 6.0 credits, Grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Other requirements for final grade *

Preparation assignments, course projects, written examination.

Preparation assignments 1.5 ECTS (P/F). Course Projects 3 ECTS (A-F), Exam 3 ECTS (A-F). The final grade is the average of course projects and exam.

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted

Examiner

Markus Flierl

Further information

Course web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Course web EQ2330

Offered by

EECS/Intelligent Systems

Main field of study *

Electrical Engineering

Education cycle *

Second cycle

Add-on studies

EQ2845 Information Theory and Source Coding

EQ2442 Project Course on Multimedia Signal Processing

EQ2430 Project Course in Signal Processing and Digital Communication

Contact

Markus Flierl (mflierl@kth.se)

Ethical approach *

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Supplementary information

In this course, the EECS code of honor applies, see:
http://www.kth.se/en/eecs/utbildning/hederskodex.