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At the end of the module students are able to understand and apply advanced theoretical concepts of digital signal processing. The students also gain a deep understanding on how to apply these concepts to selected media content. 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 digital signals is most suitable for manipulation and resolution adaptation. Students will learn how to solve problems in 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, filterbank transforms, lifting implementation, geometric wavelets, inverse problems for multi-dimensional signals, selected applications of DSP in media processing.

Linear algebra, signals and systems, stochastic signals The following modules should be passed before taking the course: - EI00330 Signaltheorie - EI00340 Stochastische Signale- EI00440 Nachrichtentechnik 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.

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 Medienform: The following kinds of media are used: - Presentations - Lecture notes - Exercises with solutions - Live Matlab demos - Interactive Matlab lab sessions

During a written exam with 180 minutes duration and without aids students solve various advanced digital signal processing related tasks by calculation and answering related questions. 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 50% is obtained when submitting the solutions to the LMT mat-checker.

The following literature is recommended: - Jens-Rainer Ohm, ""Multimedia Communication Technology: Representation, Transmission and Identification ofMultimedia 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.