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Unit outline_

ELEC5306: Video Intelligence and Compression

Semester 1, 2024 [Normal day] - Camperdown/Darlington, Sydney

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) Zao Zhang, zao.zhang@sydney.edu.au
Yu Zhang, yu.zhang1@sydney.edu.au
The census date for this unit availability is 2 April 2024
Type Description Weight Due Length
Assignment Project 1
The report showing that the student understands the concepts well.
20% Week 06
Due date: 28 Mar 2024 at 23:59
n/a
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Assignment group assignment Project 2
Evaluation on experiment report
30% Week 10
Due date: 05 May 2024 at 23:59
n/a
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Assignment group assignment Project 3
Presentation and report in group.
50% Week 13
Due date: 23 May 2024 at 23:59
From release of project to due date
Outcomes assessed: LO1 LO3 LO4 LO5 LO6
group assignment = group assignment ?

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.

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.

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

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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 2023 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 2023. 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

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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

Introduction to meachine learning and deep learning is provided earlier.

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.