Course Catalog 2014-2015
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Course Catalog 2014-2015

SGN-22006 Signal Compression, 5 cr

Additional information

Suitable for postgraduate studies

Person responsible

Ioan Tabus

Lessons

Study type P1 P2 P3 P4 Summer Implementations Lecture times and places
Lectures
Excercises


 


 


 
 4 h/week
 2 h/week


 
SGN-22006 2014-01 Tuesday 12 - 14 , TB222
Thursday 12 - 14 , TB222

Requirements

Final examinaion and a homework assignment

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

Content Core content Complementary knowledge Specialist knowledge
1. Lossless techniques for data compression.     
2. Text compression.     
3. Lossless and lossy image compression.     
4. Speech and audio compression.     

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 solution will be prized with increasing the exam result by one grade if the threshold is passed.

Assessment scale:

Numerical evaluation scale (1-5) will be used on the course

Study material

Type Name Author ISBN URL Edition, availability, ... Examination material Language
Lecture slides     Ioan Tabus       http://www.cs.tut.fi/~tabus/SC.html   Yes    English  

Prerequisite relations (Requires logging in to POP)



Correspondence of content

Course Corresponds course  Description 
SGN-22006 Signal Compression, 5 cr SGN-2306 Signal Compression, 5 cr  

More precise information per implementation

Implementation Description Methods of instruction Implementation
SGN-22006 2014-01        

Last modified23.01.2014