| TYPE OF COURSE UNIT | Elective Course |
| LEVEL OF COURSE UNIT | Master's Degree With Thesis |
| YEAR OF STUDY | - |
| SEMESTER | - |
| NUMBER OF ECTS CREDITS ALLOCATED | 10 |
| NAME OF LECTURER(S) | -
|
| LEARNING OUTCOMES OF THE COURSE UNIT |
At the end of this course, the students;
1) Know the requirements of data compression.
2) Identify the location of the concept of entropy in data compression.
3) Know the lossless data compression algorithms and apply them.
4) Know the how lossy data compression is done.
5) Learn and apply the quantization methods.
6) Learn the predictive, transform, and subband coding.
techniques.
7) Gain the ability to decide how the data should be compressed and apply.
8) Gain the ability to make a research on a matter in this field and presents the results.
|
| MODE OF DELIVERY | Face to face |
| PRE-REQUISITES OF THE COURSE | No |
| RECOMMENDED OPTIONAL PROGRAMME COMPONENT | None |
| COURSE DEFINITION | Mathematical preliminaries of lossless compression. Huffman coding, arithmetic coding, dictionary Techniques. Predictive coding. Mathematical preliminaries of lossy coding. Scalar quantization, vector quantization. Differential coding. Mathematical preliminaries of transforms, subbands, and wavelets. Transform coding, subband coding, wavelet based compression. Analysis/synthesis schemes. Introduction to Video coding. |
| COURSE CONTENTS | | WEEK | TOPICS |
|---|
| 1st Week | Introduction to compression | | 2nd Week | Brief introduction to information theory | | 3rd Week | Huffman coding | | 4th Week | Arithmetic coding I | | 5th Week | Arithmetic coding II | | 6th Week | Dictinary coding | | 7th Week | Lossless image compression | | 8th Week | Introduction to information theory II | | 9th Week | Midterm exam | | 10th Week | Scalar quantization | | 11th Week | Vector Quantization | | 12th Week | Predictive coding | | 13th Week | Transform coding | | 14th Week | Subband coding |
|
| RECOMENDED OR REQUIRED READING | 1. Introduction to Data Compression, K. Sayood, 3rd Ed. Morgan Kaufmann, 2005
2. Vector Quantization and Signal Compression, A. Gersho and R.M. Gray. Kluwer Academic Press, 1992
|
| PLANNED LEARNING ACTIVITIES AND TEACHING METHODS | Lecture,Questions/Answers,Project |
| ASSESSMENT METHODS AND CRITERIA | | | Quantity | Percentage(%) |
|---|
| Mid-term | 1 | 30 | | Assignment | 4 | 20 | | Project | 1 | 5 | | Total(%) | | 55 | | Contribution of In-term Studies to Overall Grade(%) | | 55 | | Contribution of Final Examination to Overall Grade(%) | | 45 | | Total(%) | | 100 |
|
| ECTS WORKLOAD |
| Activities |
Number |
Hours |
Workload |
| Midterm exam | 1 | 3 | 3 | | Preparation for Quiz | 0 | 0 | 0 | | Individual or group work | 14 | 8 | 112 | | Preparation for Final exam | 1 | 40 | 40 | | Course hours | 14 | 3 | 42 | | Preparation for Midterm exam | 1 | 30 | 30 | | Laboratory (including preparation) | 0 | 0 | 0 | | Final exam | 1 | 3 | 3 | | Homework | 2 | 35 | 70 | | Total Workload | | | 300 |
|---|
| Total Workload / 30 | | | 10 |
|---|
| ECTS Credits of the Course | | | 10 |
|---|
|
| LANGUAGE OF INSTRUCTION | Turkish |
| WORK PLACEMENT(S) | No |
| | |
