ELEC5306: Semester 1, 2025

Units ELEC5306 Semester 1 2025 [Normal day]

Unit outline_

ELEC5306: Video Intelligence and Compression

Semester 1, 2025 [Normal day] - Camperdown/Darlington, Sydney

Overview

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.

Unit details and rules

Academic unit	School of Electrical and Computer Engineering
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Basic understanding of digital signal processing (filtering, DFT) and programming skills (e.g. Matlab/Java/Python/C++)
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Huaming Chen, huaming.chen@sydney.edu.au
Lecturer(s)	Nan Yang, n.yang@sydney.edu.au
Tutor(s)	Yongkun Deng, yongkun.deng@sydney.edu.au
Tutor(s)	Zihao Yao, zihao.yao@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2025

Details
Criteria

Type	Description	Weight	Due	Length
Supervised exam ?	Final Exam Close book exam	40%	Formal exam period	2 hours
Outcomes assessed:
Assignment	Project 1 The report showing that the student understands the concepts well.	10%	Week 06 Due date: 03 Apr 2025 at 23:59	n/a
Outcomes assessed:
Assignment	Project 2 Evaluation on experiment report	20%	Week 10 Due date: 11 May 2025 at 23:59	n/a
Outcomes assessed:
Assignment	Project 3 Presentation and report in group.	30%	Week 13 Due date: 29 May 2025 at 23:59	From release of project to due date
Outcomes assessed:
= group assignment ? = AI allowed ?

Assessment summary

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Use of generative artificial intelligence (AI) and automated writing tools

Except for supervised exams or in-semester tests, you may use generative AI and automated writing tools in assessments unless expressly prohibited by your unit coordinator.

For exams and in-semester tests, the use of AI and automated writing tools is not allowed unless expressly permitted in the assessment instructions.

The icons in the assessment table above indicate whether AI is allowed – whether full AI, or only some AI (the latter is referred to as “AI restricted”). If no icon is shown, AI use is not permitted at all for the task. Refer to Canvas for full instructions on assessment tasks for this unit.

Your final submission must be your own, original work. You must acknowledge any use of automated writing tools or generative AI, and any material generated that you include in your final submission must be properly referenced. You may be required to submit generative AI inputs and outputs that you used during your assessment process, or drafts of your original work. Inappropriate use of generative AI is considered a breach of the Academic Integrity Policy and penalties may apply.

The Current Students website provides information on artificial intelligence in assessments. For help on how to correctly acknowledge the use of AI, please refer to the  AI in Education Canvas site.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction/digital image and video compression fundamentals (e.g., digital image/video representation, processing, quality assessment)	Lecture (2 hr)
Week 01	Literature readings, research&review, projects	Independent study (6 hr)
Week 02	Lossless compression (elements of information theory, run-length coding, Huffman coding, Arithmetic coding)	Lecture (2 hr)
	Colab & Python introduction	Practical (1 hr)
	Literature readings, research&review, projects	Independent study (6 hr)
Week 03	Lossless predictive coding (prediction, entropy coding)	Lecture (2 hr)
	Python Basics	Practical (1 hr)
	Literature readings, research&review, projects	Independent study (6 hr)
Week 04	Quantization (linear quantizer, optimal quantizer, lossy predictive coding)	Lecture (2 hr)
	Useful Libraries & Image manipulation	Practical (1 hr)
	Literature readings, research&review, projects	Independent study (6 hr)
Week 05	Discrete cosine transform and JPEG image compression standard	Lecture (2 hr)
	Visualization Tensor Operation	Practical (1 hr)
	Literature readings, research&review, projects	Independent study (6 hr)
Week 06	Motion compensated prediction and video coding standards	Lecture (2 hr)
	DFT DCT Quant	Practical (1 hr)
	Literature readings, research&review, projects	Independent study (6 hr)
Week 07	Introduction to machine learning	Lecture (2 hr)
	Image compression	Practical (1 hr)
	Literature readings, research&review, projects	Independent study (6 hr)
Week 08	Skip for holiday	Lecture (2 hr)
	Skip for holiday	Practical (1 hr)
	Literature readings, research&review, projects	Independent study (6 hr)
Week 09	Introduction to machine learning and deep learning -2	Lecture (2 hr)
	Pytorch Basics	Practical (1 hr)
	Literature readings, research&review, projects	Independent study (6 hr)
Week 10	Image Compression and Analysis based on Deep Learning	Lecture (2 hr)
	Neural Network training & testing	Practical (1 hr)
	Literature readings, research&review, projects	Independent study (6 hr)
Week 11	Video Compression and Understanding with Deep Learning -1	Lecture (2 hr)
	General Pipeline of PyTorch	Practical (1 hr)
	Literature readings, research&review, projects	Independent study (6 hr)
Week 12	Video Compression and Understanding with Deep Learning -2	Lecture (2 hr)
	Detailed Walkthrough of PyTorch	Practical (1 hr)
	Literature readings, research&review, projects	Independent study (6 hr)
Week 13	Project Presentation	Lecture (2 hr)
	Project presentation	Seminar (1 hr)
	Literature readings, research&review, projects	Independent study (6 hr)

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Understand the fundamental theory of digital image/video compression and understanding algorithms
LO2. Use the existing image/video compression and understanding methods.
LO3. Use appropriate software platforms and tools for a given image/video compression and understanding task
LO4. Demonstrate basic teamwork and project management skills through a group project
LO5. Apply the techniques to solve real world applications
LO6. Report results in a professional manner

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

Update the relevant material with assignment changes

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI) and automated writing tools

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links