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Unit of study_

ELEC5306: Video Intelligence and Compression

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.


Academic unit Electrical and Information Engineering
Unit code ELEC5306
Unit name Video Intelligence and Compression
Session, year
Semester 1, 2023
Attendance mode Normal day
Location Camperdown/Darlington, Sydney
Credit points 6

Enrolment rules

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


Teaching staff and contact details

Coordinator Huaming Chen,
Lecturer(s) Nan Yang ,
Tutor(s) Zao Zhang ,
Yu Zhang,
Type Description Weight Due Length
Assignment Project 1
The report showing that the student understands the concepts well.
20% Week 06
Due date: 02 Apr 2023 at 23:59
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Assignment group assignment Project 2
Evaluation on experiment report
30% Week 10
Due date: 07 May 2023 at 23:59
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Assignment group assignment Project 3
Presentation and report in group.
50% Week 13
Due date: 25 May 2023 at 23:59
From release of project to due date
Outcomes assessed: LO1 LO3 LO4 LO5 LO6
group assignment = group assignment ?

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


High distinction

85 - 100



75 - 84



65 - 74



50 - 64



0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see

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.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

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.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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

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

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

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
Introduction to meachine learning and deep learning is provided earlier.


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