| Module Code | EEP55C37 |
| Module Name | Introduction To Motion Picture Engineering |
| ECTS Weighting2 | 5 ECTS |
| Semester taught | Semester 2 |
| Module Coordinator/s | Dr David Corrigan and Prof. Anil Kokaram |
Module Learning Outcomes
with reference to the Graduate Attributes and how they are developed in discipline
On successful completion of this module, students should be able to:
LO1. Design tools in a commercial video processing platform
LO2. Design motion picture processing algorithms to achieve a specified outcome (e.g. denoising) and critically compare them
LO3. Explain and apply video quality metrics for performance assessment and optimization
LO4. Describe and explain the use of AI and motion information in motion picture processing algorithms.
LO5. Describe the main components of current video compression standards and assess relative performance in terms of industry standard metrics and energy sustainability
LO6. Design and deploy Adaptive Bitrate strategies for video streaming
LO7. Describe aspects of the video technology industrial ecosystem in terms of sustainability and standardization
Graduate Attributes: levels of attainment
To act responsibly – Not embedded To think independently – Enhanced To develop continuously – Attained
To communicate effectively – Enhanced
Module Content
Motion Pictures in the form of Digital Video account for more than 70% of all internet traffic today. R&D in this area has inspired new industries in digital media creation, online video streaming and video media sharing. Industrial Light and Magic, The Foundry, YouTube, Netflix, Vimeo, Skype, Sky Digital are just a few of the well known large companies that now successfully operate in this space.
Motion Picture Engineering prepares the student for a career in these industries including post-production tool development and video streaming. The first part (before the reading week) introduces the underlying ideas in motion estimation and video processing in general including now the impact of AI on new techniques. The second part after the reading week will introduce modern compression standards such as H.264/5, VP9, AV1/2. The module incorporates a seminar program with guest lectures from domain experts.
Students develop practical skills in applied research and algorithmic development/testing that are common in companies developing tools for digital media. Students will be introduced to leading research papers in the field and develop video processing plugins for Nuke (www.thefoundry.co.uk), a leading video-processing platform in the Cinema Post-Production industry.
Teaching and Learning Methods
The module is mostly lab-based containing a mixture of tutorials and
conventional lab sessions where students will be able to seek assistance on their development assignments. There will be approximately 44 contact hours. The module also includes 4 guest lectures from leading industry experts in post production and video compression. We are delighted to have contributions on compression and AI from Dr. Darren Ramsook of Netflix and Vibhoothi from the AOMedia Consortium.The guideline for a 5 ECTS module is for 125 hours of student effort including class hours.
Assessment for 5C1 will be 60% based on Continuous Assessment and 40% Final Examination. Continuous Assessment will be a mixture of algorithm design assignments and in-class tests.
Syllabus
Video Quality Measurement (VQM, SSIM, PSNR, VMAF)
Motion Estimation – state of the art frameworks and implementations Optimisation – strategies for image/video processing applications such as image/video segmentation and motion estimation.
Deep Learning in Video – Recent topics in Deep Learning for motion estimation
Video Compression – an introduction to state of the art compression
standards (HEVC, VP9, AV1) and the influence of Royalty Free standards in shaping the future of the industry.
Assessment Details3
Please include the following:
- Assessment Component
- Assessment description
- Learning Outcome(s) addressed
- Assessment due date
- % of total
| Assessment Component | Assessment Description | LO Addressed | % of total | Week due |
| Exercise | Developing in NUKE | 1 | 5 | 4 |
| Presentation | Quality assessment exercise | 2,3,4 | 20 | 8 |
| Class Test | In class test | 1-4 | 20 | 6 |
| Interview | Compression design | 5,6,7 | 15 | 12 |
| Final Exam | Closed Book Exam | 1-7 | 40 | N/A |
Reassessment Requirements
Reassessment Exam
Contact Hours and Indicative Student Workload3
| Contact hours: 48 (22 Lecture hours, 4 Guest lectures, 22 Guided exercise and assignment hours) |
| Independent Study (preparation for course and review of materials): 48 |
| Independent Study (preparation for assessment, incl. completion of assessment): 29 |
Recommended Reading List
The Essential Guide to Video Processing. A. Bovik, Academic Press, 2009. ISBN: 978-0-12-374456-2
There are many other text books on Image and Video Processing and Computer Vision available in the library which you may wish to consult. Google scholar, arxiv.org and IEEE Xplore are essential resources for the research papers you will access over the duration of the module. The library also has paper versions of many relevant journals.
Deep Learning, DSP and Image Processing recommended
Module Pre-requisite
None.
Module Co-requisite
See departmental pages
Module Website
| Are other Schools/Departments involved in the delivery of this module? If yes, please provide details. | No |
| Module Approval Date | |
| Approved by | Prof Naomi Harte |
| Academic Start Year | September 2026 |
| Academic Year of Date | 2026-27 |