Undergraduate Course: Advanced Coding Techniques 5 (ELEE11092)
|School||School of Engineering
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 11 (Year 5 Undergraduate)
||Availability||Available to all students
|Summary||This course will cover the current topics of interest in Advanced Coding Techniques. In particular information theory fundamentals related to source coding and its extension to channel capacity are studied. Rate-distortion theory and quantisation for uncorrelated and correlated signals are of particular interest.
1. Scalar quantisation,
2. Asymptotic quantisation theory,
3. Vector quantisation,
4. Rate-distortion theory,
5. Channel capacity evaluations.
Practical examples of the above concepts are presented throughout the course.
2. Scalar Quantisation
3. Asymptotic Scalar Quantisation Theory and Variable Rate Encoding
4. Vector Quantisation
5. Rate Distortion Theory
6. Theoretical Channel Capacity Analysis
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| None
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Academic year 2016/17, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||Written Exam 100%
|No Exam Information
| The students will understand fundamentals as well as advanced concepts in source coding. They will be able to quantify the bit rate that is theoretically needed to perform source coding of continuous-valued signals with some given maximum distortion. They will be able to explain the complexity-quality trade-offs in practical systems and they will be able to quantify how close practical quantisation algorithms can get to the theoretical limits given by information theory. They will be able to design scalar and vector quantisers for practical signals. They will also understand how information theory can be used to predict the data capacity of communications channels.
|A. Gersho and R. M. Gray, Vector Quantization and Signal Compression. Kluwer Academic Publishers, 8th ed., 2001.|
T. Cover and J. Thomas, Elements of Information Theory. John Wiley & Sons, Inc., 1991.
|Graduate Attributes and Skills
|Keywords||Coding,Quantisation,Rate Distortion Theory,Channel Capacity
|Course organiser||Dr John Thompson
Tel: (0131 6)50 5585
|Course secretary||Miss Megan Inch
Tel: (0131 6)51 7079
© Copyright 2016 The University of Edinburgh - 3 February 2017 4:05 am