Outline

Overview

Digital media data such as audio, image, videos, graphics, and 3D are increasingly becoming indispensable for big data driven computing applications in many domains, such as social media, public security, education, commerce, entertainment, and healthcare. This unit aims to bring students the essential knowledge on digital media, various computing techniques and tools on digital media processing and analysis, and many cutting-edge digital media applications such as VR/AR and Internet of Things (IoT) enabled new media. It will help students build practical computing skills for digital media driven applications and utilise learned knowledge to produce creative and media rich solutions to real world problems.

Unit details and rules

Academic unit	Computer Science
Credit points	6
Prerequisites ?	DATA3888 or COMP3888 or COMP3988 or CSEC3888 or ISYS3888 or SOFT3888 or ENGG3112 or SCPU3001
Corequisites ?	Enrolment in a thesis unit. INFO4001 or INFO4911 or INFO4991 or INFO4992 or AMME4111 or BMET4111 or CHNG4811 or CIVL4022 or ELEC4712 or COMP4103 or SOFT4103 or DATA4103 or ISYS4103
Prohibitions ?	COMP5405 or COMP5114 or COMP9419
Assumed knowledge ?	A major in a computer science area. Experience with programming skills as covered in INFO1113 or COMP2123 or COMP2823 or INFO1105 or INFO1905 or other similar units
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Zhiyong Wang, zhiyong.wang@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Final exam Open book exam.	55%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Creative work	Project proposal Group project proposal. Each member of the group will receive the same mark, though there could be adjustment in regard to the roles and contributions of individuals.	10%	Week 06	n/a	AI allowed
Outcomes assessed:
Practical skill	Homework Submitted work.	15%	Week 08	n/a	AI allowed
Outcomes assessed:
Presentation	Project Final Group project final. Each member of the group will receive the same mark, though there could be adjustment in regard to the roles and contributions of individuals.	20%	Week 12	n/a	AI allowed
Outcomes assessed:
= group assignment ?

Assessment summary

Project work: provides students an opportunity to discover digital media computing applications, and devise and develop a solution for an application.

Final exam: assesses the acquisition, understanding, and application of knowledge taught throughout the unit

Details for each assessment can be found in Canvas.

Assessment criteria

Assessment grading

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)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Late penalties should be modified in accordance with the Assessment Procedures 2011.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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	Unit of study introduction	Lecture (2 hr)
Week 02	Digital media basics	Lecture (2 hr)
Week 02	Practice with Digital media basics	Tutorial (1 hr)
Week 03	Digital image processing I	Lecture (2 hr)
Week 03	Practice with digital image processing 1	Tutorial (1 hr)
Week 04	Digital image processing II	Lecture (2 hr)
Week 04	Practice with digital image processing II	Tutorial (1 hr)
Week 05	Digital image understanding 1	Lecture (2 hr)
Week 05	Practice with digital image understanding I	Tutorial (1 hr)
Week 06	Digital image understanding II	Lecture (2 hr)
Week 06	Practice with digital image understanding II	Tutorial (1 hr)
Week 07	Video processing	Lecture (2 hr)
Week 07	Practice with video processing	Tutorial (1 hr)
Week 08	Graphics and animation	Lecture (2 hr)
Week 08	Practice with graphics and animation	Tutorial (1 hr)
Week 09	Visual Computing Advanced I	Lecture (2 hr)
Week 09	Practice with Visual Computing Advanced I	Tutorial (1 hr)
Week 10	Visual Computing Advanced II	Lecture (2 hr)
Week 10	Visual Computing Advanced II	Tutorial (1 hr)
Week 11	Media compression	Lecture (2 hr)
Week 11	Practice with media compression	Tutorial (1 hr)
Week 12	Project presentations	Lecture (2 hr)
Week 12	Practice with digital image processing III	Tutorial (1 hr)
Week 13	Course review and revision	Lecture (2 hr)

Attendance and class requirements

Attendance: Students are expected to attend all scheduled lectures. Students are expected to attend all scheduled tutorials.

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.

Required readings

All readings for this unit can be accessed on the Library eReserve link available on Canvas.

The Science of Digital Media, Jennifer Burg, Prentice Hall, 2009, http://www.pearsonhighered.com/product?ISBN=0132435802

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. explain multimedia processing and analysis techniques widely used in general scenarios
LO2. have developed basic project management and team coordination skills in a small group for completing a project
LO3. perform prototype design for a given task
LO4. explain the digitization of media data (e.g., image, video, and audio) in terms of acquisition and storage
LO5. perform the practice of processing and analysing on digital media data with specific techniques
LO6. reflect on the state-of-the-art digital media driven applications
LO7. perform solution design for a given task
LO8. perform derivation of technical solutions for processing and analysing digital media data and practical programming to implement the solutions.

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.

No significant changes, except updating the content on Visual Computing Advanced.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

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)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

COMP4405: Digital Media Computing

Semester 1, 2026 [Normal evening] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links