Perceptual quality enhancements for 8K CCTV video compression
Start date
April 2022End date
February 2023Overview
Fatalities on the railways are a major source of disruption, however the visual evidence produced by rail forward-facing CCTV are usually of poor quality and this can have significant consequences on resolving incidents. When a “person struck by a train” incident happens the lack of good quality visual evidence can lead to the incident being declared as “unexplained” adding up to 2 hours to resolve such an incident. This project will build on the successes of a previous project and provide guidance on how to better capture rail forward-facing CCTV video based on improvements offered by 4KUHD and 8KUHD resolutions to rail emergency responders and video systems providers.
Project partner
This will be done in collaboration with project partner – Rail Innovations.
Team
Principal Investigator
Dr Femi Adeyemi-Ejeye
Associate Professor in Video Technology; Director of Postgraduate Research (PGR), School of Arts, Humanities and Creative Industries
Biography
Dr Femi Adeyemi-Ejeye is an Associate Professor in Video Technology at the University of Surrey whose research investigates how video compression, network conditions, and system design shape human perception of video and immersive media experiences. His work focuses on the evaluation and optimisation of Quality of Experience (QoE) in networked and next-generation media systems, spanning video compression, streaming, immersive media, XR, real-time communication, and safety-critical video applications.
His research sits at the intersection of multimedia systems, perceptual video quality, networked media delivery, and immersive technologies. Through experimental studies, perceptual evaluation, and systems-level analysis, he examines how technical choices such as compression strategies, transmission constraints, latency, and platform design affect the way users perceive and engage with digital media. A central aim of his work is to ensure that next-generation media technologies are designed, evaluated, and deployed with human experience at their core.
Dr Adeyemi-Ejeye contributes to the development of international multimedia quality standards through his work in ITU-T Study Group 12 and MPEG, helping to shape methodologies for the assessment of immersive and next-generation media systems. He is also a Board Member of the Video Quality Experts Group (VQEG) and a member of the IEEE Consumer Technology Society (CTSoc) Wireless and Network Technologies Technical Committee, reflecting his active role in the international research and standards community.
His research combines fundamental and applied perspectives, with a strong emphasis on translation into real-world systems. He collaborates with partners across the telecommunications, transport, and creative technology sectors to develop perceptually informed approaches to media system design and evaluation. This includes work on high-resolution video systems, immersive media delivery, and technologies used in operational and safety-critical environments.
An example of this applied research is his contribution to a £396,349 SBRI First of a Kind (FOAK) project led by Rail Innovations, which explored how 8K video, cloud technologies, and AI-based image recognition could enhance railway incident investigation and support faster restoration of services. Dr Adeyemi-Ejeye was the Surrey Principal Investigator on Surrey’s contribution to the project, working with partners including One Big Circle Ltd, Avanti West Coast, and Angel Trains Ltd.
Alongside his research, Dr Adeyemi-Ejeye contributes to teaching in video technology, multimedia systems, and computer imaging, supporting the development of students in areas such as video compression, networking, and immersive media systems. He supervises undergraduate and doctoral research and is committed to mentoring students and early-career researchers while fostering strong links between academic research and industry practice. He also currently serves as Director of Postgraduate Research in the School of Arts, Humanities and Creative Industries at the University of Surrey, where he supports doctoral training, interdisciplinary research culture, and collaboration between academia, industry, and public organisations.
He welcomes collaboration with researchers, industry partners, and public sector organisations interested in multimedia quality, video compression, networked media systems, immersive technologies, and the human-centred evaluation of next-generation digital media.
Co-Investigator
Outputs
Technical report GSTR-5GQoE agreed by ITU-T Study Group 12 in June is now available for download at https://www.itu.int/pub/T-TUT-QOS-2022-1
Impact
This project will provide both optimal visual quality parameters and video compression strategies for video storage for the rail industry. The impact generated by this project will be as follows:
Societal impact
This project will further help reduce the time taken to investigate incidents on trains, thereby making it quicker for services to resume rail services after a “person struck by a train” incident. This is because ultra-high-resolution videos will capture superior quality visual data to help emergency responders quickly ascertain the problem rather than categorise it as “unexplained”. In addition, due to improved visual quality data provided, computer vision algorithms can be applied in the future to notify train drivers of objects/persons on the railway, thereby making the driver slow down to prevent future incidents. This will be measured based on consumer feedback to rail operators.
Economic Impact
This project will generate results that will be used to optimise a product in the pipeline from a previous project. This will be considered successful when the product is launched. This product will provide storage cost saving, by reducing the time take to transfer high-quality video from train to shore or from train to cloud-based operations. This product will also provide human costs savings by reducing the time required to deal with incidents on the rail network. In addition, the resulting optimised parameters can be transferred to manufacturers and/or architects of CCTV applications to enable them to optimise their future products.
Academic Impact
This project aims to publish two outputs (1 journal paper and 1 ITU technical report). It will also generate a dataset that will be freely available for future research.
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