Note: This unit version is currently under review and is subject to change!

ELEC5306: Advanced Signal Processing: Video Compression (2019 - Semester 1)

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Unit: ELEC5306: Video Intelligence and Compression (6 CP)
Mode: Normal-Day
On Offer: Yes
Level: Postgraduate
Faculty/School: School of Electrical & Information Engineering
Unit Coordinator/s: Dr Ouyang, Wanli
Session options: Semester 1
Versions for this Unit:
Campus: Camperdown/Darlington
Pre-Requisites: None.
Brief Handbook Description: This unit of study introduces digital image and video compression algorithms and standards. This course mainly focuses on fundamental and advanced methods for digital video compression. It covers the following areas: digital video fundamentals, digital image and video compression standards, and video codec optimization.
Assumed Knowledge: Basic understanding of digital signal processing (filtering, DFT) and programming skills (e.g. Matlab/Java/Python/C++)
Additional Notes: From 2020, this unit will be renamed to Video Intelligence and Compression.
Lecturer/s: Dr Ouyang, Wanli
Timetable: ELEC5306 Timetable
Time Commitment:
# Activity Name Hours per Week Sessions per Week Weeks per Semester
1 Lecture 2.00 1 13
2 Laboratory 1.00 1 12
3 Independent Study 6.00 13
T&L Activities: Independent Study: Students are expected to undertake the prescribed reading and work on homework exercises and assignments.

Learning outcomes are the key abilities and knowledge that will be assessed in this unit. They are listed according to the course goal supported by each. See Assessment Tab for details how each outcome is assessed.

(6) Communication and Inquiry/ Research (Level 4)
1. To be able to report results in a professional manner
(7) Project and Team Skills (Level 3)
2. To be able to develop some basic teamwork and project management skills through a group project
(3) Problem Solving and Inventiveness (Level 4)
3. To be able to apply the techniques to solve real world applications
(2) Engineering/ IT Specialisation (Level 4)
4. To be able to use appropriate software platforms and tools for a given image/video compression task
5. To be able to understand the fundamental theory of digital image/video compression algorithms
6. To be able to use the existing image/video compression standards
Assessment Methods:
# Name Group Weight Due Week Outcomes
1 Final Exam No 60.00 Exam Period 3, 5, 6,
2 Project 1 No 20.00 Week 8 1, 3, 4, 5, 6,
3 Project 2 Yes 20.00 Week 12 1, 2, 3, 4, 5, 6,
Assessment Description: [1]Text-based similarity detecting software (Turnitin) will be used for all text-based written assignments.

[2]Late submission for lab reports: 1) There is no penalty for submissions until 11:59pm of the due day; 2) For submissions that are late than 11:59pm of the due day, 15% penalty will be applied for each day. Submissions that are late for one week will be given ZERO marks.
Prescribed Text/s: Note: Students are expected to have a personal copy of all books listed.
Recommended Reference/s: Note: References are provided for guidance purposes only. Students are advised to consult these books in the university library. Purchase is not required.

Note that the "Weeks" referred to in this Schedule are those of the official university semester calendar https://web.timetable.usyd.edu.au/calendar.jsp

Week Description
Week 1 Lecture: Introduction / Digital image and video compression fundamentals (e.g., digital image/video representation, processing, quality assessment)
Week 2 Lecture: Lossless compression (elements of information theory, run-length coding, Huffman coding, Arithmetic coding)
Week 3 Lecture: Lossless predictive coding (prediction, entropy coding)
Week 4 Lecture: Quantization (linear quantizer, optimal quantizer, lossy predictive coding)
Week 5 Lecture: Discrete Cosine Transform
Week 6 Lecture: JPEG image compression standard
Week 7 Lecture: Motion compensated prediction
Week 8 Lecture: H.261 video coding standard
Assessment Due: Project 1
Week 9 Lecture: MPEG-1 and MPEG-2 video coding standard
Week 10 Lecture: MPEG-4, H.264, H.265 video coding standards
Week 11 Lecture: Image Compression and Analysis based on Deep Learning
Week 12 Lecture: Video Compression and Understanding based on Deep Learning
Assessment Due: Project 2
Week 13 Lecture: Review and preparation for the exam
Exam Period Assessment Due: Final Exam

Course Relations

The following is a list of courses which have added this Unit to their structure.

Course Year(s) Offered
Bachelor of Advanced Computing/Bachelor of Commerce 2018, 2019, 2020
Bachelor of Advanced Computing/Bachelor of Science 2018, 2019, 2020
Bachelor of Advanced Computing/Bachelor of Science (Health) 2018, 2019, 2020
Bachelor of Advanced Computing/Bachelor of Science (Medical Science) 2018, 2019, 2020
Bachelor of Advanced Computing (Computational Data Science) 2018, 2019, 2020
Bachelor of Advanced Computing (Computer Science Major) 2018, 2019, 2020
Bachelor of Advanced Computing (Information Systems Major) 2018, 2019, 2020
Bachelor of Advanced Computing (Software Development) 2018, 2019, 2020
Electrical Mid-Year 2016, 2017, 2018, 2019, 2020
Electrical/ Project Management 2019, 2020
Electrical 2015, 2016, 2017, 2018, 2019, 2020
Electrical / Arts 2016, 2017, 2018, 2019, 2020
Electrical / Commerce 2018, 2019, 2020
Electrical / Medical Science 2016, 2017
Electrical / Music Studies 2016, 2017
Electrical / Project Management 2016, 2017, 2018, 2020
Electrical / Science 2016, 2017, 2018, 2019, 2020
Electrical/Science (Health) 2018, 2019, 2020
Electrical / Law 2016, 2017, 2018, 2019, 2020
Software Mid-Year 2019, 2020
Software/ Project Management 2019, 2020
Software 2019, 2020
Software / Arts 2019, 2020
Software / Commerce 2019, 2020
Software / Science 2019, 2020
Software/Science (Health) 2019, 2020
Software / Law 2019, 2020
Electrical/Science (Medical Science Stream) 2018, 2019, 2020
Master of Engineering 2018, 2019, 2020
Master of Professional Engineering (Accelerated) (Electrical) 2019, 2020
Master of Professional Engineering (Accelerated) (Intelligent Information Engineering) 2020
Master of Professional Engineering (Electrical) 2016, 2017, 2018, 2019, 2020
Master of Professional Engineering (Intelligent Information Engineering) 2020
Software/Science (Medical Science Stream) 2019, 2020

Course Goals

This unit contributes to the achievement of the following course goals:

Attribute Practiced Assessed
(6) Communication and Inquiry/ Research (Level 4) No 8%
(7) Project and Team Skills (Level 3) No 2%
(5) Interdisciplinary, Inclusiveness, Influence (Level 4) No 0%
(4) Design (Level 4) No 0%
(3) Problem Solving and Inventiveness (Level 4) No 32%
(2) Engineering/ IT Specialisation (Level 4) No 58%

These goals are selected from Engineering & IT Graduate Outcomes Table 2018 which defines overall goals for courses where this unit is primarily offered. See Engineering & IT Graduate Outcomes Table 2018 for details of the attributes and levels to be developed in the course as a whole. Percentage figures alongside each course goal provide a rough indication of their relative weighting in assessment for this unit. Note that not all goals are necessarily part of assessment. Some may be more about practice activity. See Learning outcomes for details of what is assessed in relation to each goal and Assessment for details of how the outcome is assessed. See Attributes for details of practice provided for each goal.