SGN-22006 Signal Compression, 5 cr

Additional information

Suitable for postgraduate studies.

Person responsible

Ioan Tabus

Lessons

Learning Outcomes

Student will learn about various signal compression methods and how to select proper methods for signal compression tasks at hand. After completing the course, the student - Understands the goals and restrictions of lossy and lossless compression for various signals - Understands the basic principles of entropy coding of data - Is exposed to using statistical modeling for modern data compression - Is familiar with the most important data compression techniques: Huffman coding, dictionary based methods, arithmetic coding, Burrows-Wheeler etc - Is able to choose between various compression methods for a given application - Is familiar with the state of the art methods for lossless and lossy image compression - Acquires practice on simulating compression algorithms with given input data and extracting useful performance indices helpful in comparing various algorithms.

Content

Instructions for students on how to achieve the learning outcomes

Course is graded on the basis of answers to exam questions. Very good grade is obtained when exam questions are correctly answered and homework is accepted. Course acceptance threshold is approx. half of the maximum exam points. By volunteering to show exersice solutions and solving bonus questions in the homework will be awarded with additional points, to be added to the exam points, and the sum will determine the final grade..

Assessment scale:

Numerical evaluation scale (0-5)

Study material

Correspondence of content