Undergraduate Course: Advanced Coding Techniques 5 (ELEE11092)
Course Outline
| 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 |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course will cover current topics of interest in Advanced Coding Techniques. It will discuss different approaches to quantization using both scalar and vector quantization approaches. Information theory fundamentals related to source coding are also studied. Rate-distortion theory and quantisation for different types of signals are discussed. Practical examples of the above concepts are presented throughout the course. |
| Course description |
1. Introduction
2. Scalar Quantisation
3. Asymptotic Scalar Quantisation Theory and Variable Rate Encoding
4. Vector Quantisation
5. Rate Distortion Theory
6. Practical System Examples
|
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2021/22, Available to all students (SV1)
|
Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
98 )
|
| Assessment (Further Info) |
Written Exam
100 %,
Coursework
0 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Written Exam 100% |
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S2 (April/May) | | 2:00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Understand fundamentals as well as advanced concepts in source coding.
- Quantify the bit rate that is theoretically needed to perform source coding of continuous-valued signals with some given maximum distortion.
- Explain the complexity-quality trade-offs for different types of quantization scheme.
- Quantify how close practical quantisation algorithms can get to the theoretical limits given by information theory.
- Design scalar and vector quantisers for practical signals.
|
Reading List
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. |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Coding,Quantisation,Rate Distortion Theory,Channel Capacity |
Contacts
| Course organiser | Dr John Thompson
Tel: (0131 6)50 5585
Email: John.Thompson@ed.ac.uk |
Course secretary | Miss Jo Aitkenhead
Tel: (0131 6)50 5532
Email: Jo.Aitkenhead@ed.ac.uk |
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