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Unit outline_

COMP3419: Graphics and Multimedia

Semester 2, 2023 [Normal day] - Camperdown/Darlington, Sydney

This unit provides a broad introduction to the field of graphics and multimedia computing to meet the diverse requirements of application areas such as entertainment, industrial design, virtual reality, intelligent media management, social media and remote sensing. It covers both the underpinning theories and the practices of computing and manipulating digital media including graphics / image, audio, animation, and video. Emphasis is placed on principles and cutting-edge techniques for multimedia data processing, content analysis, media retouching, media coding and compression.

Unit details and rules

Academic unit Computer Science
Credit points 6
Prerequisites
? 
COMP2123 OR COMP2823 OR INFO1105 OR INFO1905
Corequisites
? 
None
Prohibitions
? 
None
Assumed knowledge
? 

Programming skills

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Weidong Cai, tom.cai@sydney.edu.au
Lecturer(s) Weidong Cai, tom.cai@sydney.edu.au
Type Description Weight Due Length
Supervised exam
? 
Final exam
Supervised final exam
60% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4
Assignment Assignment-1a
multimedia computing 1a
8% Week 08
Due date: 24 Sep 2023 at 23:59
n/a
Outcomes assessed: LO1 LO2 LO3 LO4
Assignment Assignment-1b
multimedia computing 1b
8% Week 11
Due date: 22 Oct 2023 at 23:59
n/a
Outcomes assessed: LO1 LO2 LO3 LO4
Assignment Assignment-2
object detection and recognition project
16% Week 13
Due date: 05 Nov 2023 at 23:59
n/a
Outcomes assessed: LO1 LO2 LO3 LO4
Small continuous assessment Lab checkpoints
take-home exercise submission based on specified lab exercise content
8% Weekly n/a
Outcomes assessed: LO1 LO4 LO3 LO2

Assessment summary

Lab checkpoints: weekly lab exercise submission;

Assignment-1a&-1b: multimedia computing;

Assignment-2: object detection and recognition project;

Final exam: supervised exam.

 

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.

It is a policy of the School of Computer Science that in order to pass this unit, a student must achieve at least 40% in the written examination. For subjects without a final exam, the 40% minimum requirement applies to the corresponding major assessment component specified by the lecturer. A student must also achieve an overall final mark of 50 or more. Any student not meeting these requirements may be given a maximum final mark of no more than 45 regardless of their average.

 

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.

WK Topic Learning activity Learning outcomes
Week 01 Introduction / Image data representation and enhancement Lecture (2 hr) LO1 LO2 LO4
Week 02 Morphological image processing Lecture (2 hr) LO1 LO2 LO4
Computing Lab with LCP Tutorial (2 hr) LO1 LO2 LO3 LO4
Week 03 Color models & color image processing Lecture (2 hr) LO1 LO2 LO4
Computing Lab with LCP Tutorial (2 hr) LO1 LO2 LO3 LO4
Week 04 Video data representation and processing Lecture (2 hr) LO1 LO2 LO4
Computing Lab with LCP Tutorial (2 hr) LO1 LO2 LO3 LO4
Week 05 Audio data representation and coding Lecture (2 hr) LO1 LO2 LO3 LO4
Computing Lab with LCP Tutorial (2 hr) LO1 LO2 LO3 LO4
Week 06 2D & 3D computer graphics Lecture (2 hr) LO1 LO2 LO4
Computing Lab with LCP Tutorial (2 hr) LO1 LO2 LO3 LO4
Week 07 Computer animation and multimedia authoring Lecture (2 hr) LO1 LO2 LO3 LO4
Computing Lab with LCP Tutorial (2 hr) LO1 LO2 LO3 LO4
Week 08 Image data analysis and retrieval Lecture (2 hr) LO1 LO2 LO4
Computing Lab with LCP Tutorial (2 hr) LO1 LO2 LO3 LO4
Week 09 Advanced visual content analysis Lecture (2 hr) LO1 LO2 LO3 LO4
Week 10 Video data analysis and retrieval Lecture (2 hr) LO1 LO2 LO4
Computing Lab with LCP Tutorial (2 hr) LO1 LO2 LO3 LO4
Week 11 Image coding and compression Lecture (2 hr) LO1 LO2 LO3 LO4
Computing Lab (assignment help session) Tutorial (2 hr) LO1 LO2 LO3 LO4
Week 12 Video coding and compression Lecture (2 hr) LO1 LO2 LO3 LO4
Computing Lab (assignment help session) Tutorial (2 hr) LO1 LO2 LO3 LO4
Week 13 Advanced multimedia computing Lecture (2 hr) LO1 LO2 LO3 LO4
Computing Lab (assignment help session) Tutorial (2 hr) LO1 LO2 LO3 LO4

Attendance and class requirements

  • Tutorials: Students are expected to attend all scheduled on-campus or remote/online zoom tutorials.
  • Independent Study: Students are expected to undertake prescribed reading and practical work, besides understanding lecutre contents. 6 hours of independent study per week is expected.

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. apply multimedia data processing and analysis techniques to real world applications
  • LO2. understand data representation and enhancement for different multimedia building blocks
  • LO3. understand various multimedia data coding and retrieval techniques
  • LO4. obtain practical skills in graphics / image, audio, and video processing and analysis.

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.

Except for changing to fully on-campus teaching mode from previous mix of on-campus and remote/online teaching and updating assessment including assignment due date changes, no significant changes have been made since this unit was last offered.

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.