ELEC5306: Semester 1, 2023

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Units ELEC5306 Semester 1 2023 [Normal day]

Unit outline_

ELEC5306: Video Intelligence and Compression

Semester 1, 2023 [Normal day] - Remote

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)	Zao Zhang, zao.zhang@sydney.edu.au
Tutor(s)	Yu Zhang, yu.zhang1@sydney.edu.au

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	n/a
Outcomes assessed:
Assignment	Project 2 Evaluation on experiment report	30%	Week 10 Due date: 07 May 2023 at 23:59	n/a
Outcomes assessed:
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:
= 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.

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.

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

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. 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
Learning outcomes

Responding to student feedback

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.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

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