Multidimensional Digital Signal Processing

Lecturer (assistant)
Duration4 SWS
TermWintersemester 2019/20
Language of instructionEnglish
Position within curriculaSee TUMonline
DatesSee TUMonline


Admission information

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Note: Please register for the course via TUMonline


At the end of the module students are able to understand and apply the theoretical concepts of multidimensional digital signal processing. The students also gain a deep understanding on how to apply these concepts to images and video. Students will learn how to acquire, process and display two- or multidimensional signals. Students will learn the differences between one-dimensional and multi-dimensional DSP. Students will learn to move back and forth from spatial to frequency domain. Students will understand what representation of multidimensional signals is most suitable for manipulation and resolution adaptation. Students will learn how to solve problems in multidimensional DSP both analytically and by using Matlab


Differences and similarities between one-dimensional and multidimensional DSP, two-dimensional signals and systems, sampling of spatio-temporal signals, two- and multi-dimensional filters, linear block transforms, multi-dimensional filterbank transforms, lifting implementation, geometric wavelets, inverse problems for multi-dimensional signals, selected applications of multidimensional DSP im image and video processing


Linear algebra, signals and systems, digital signal processing, stochastic signals The following modules should be passed before taking the course: - EI0200 Signale (Signaldarstellung, Stochastische Signale) - EI0300 Systeme (Nachrichtentechnik 1) Some programming experience in Matlab is highly recommended. For participants with no or very little Matlab experience, significant additional effort at the beginning of the semester will be required.

Teaching and learning methods

Learning method: In addition to the individual methods of the students consolidated knowledge is aspired by repeated lessons in exercises and tutorials. Teaching method: During the lectures students are instructed in a teacher-centered style. The exercises are held in a student-centered way. Additionally, selected concepts are implemented using Matlab.


The type of examination is adapted to the different learning outcomes: Knowledge-based learning results are examined during a written exam with 180 minutes duration. Matlab assignments with voluntary participation are offered during the semester and can be used to improve the final grade of the course. The final grade is composed of the following elements: - 100% final exam Successful completion of the Matlab assignments leads to a bonus of 0.3 on the final grade in case the final is passed. The Matlab assignments are successfully completed if at least an average of 65% is obtained when submitting the solutions to the LMT mat-checker.

Recommended literature

Jens-Rainer Ohm, "Multimedia Communication Technology: Representation, Transmission and Identification of Multimedia Signals", Springer, 2004. D.E. Dudgeon, R.M. Mersereau, “Multidimensional Digital Signal Processing”, Prentice-Hall Signal Processing Series, 1984. R.C. Gonzalez, R.E. Woods, “Digital Image Processing", Prentice Hall International, 2007. A.K. Jain, “Fundamentals of Digital Image Processing”, Prentice Hall, 1989.