Skip to main content
Dr Hantao Liu

Dr Hantao Liu

Director of International
Senior Lecturer

School of Computer Science and Informatics

+44 (0)29 2087 4598
Room S/3.11, Queen's Buildings, 5 The Parade, Newport Road, Cardiff, CF24 3AA
Available for postgraduate supervision


New Grant Awards!

Project: COVID-19 is Real: Making Crucial Health Information Available for All
This project will enable crucial health information to be delivered to communities and behaviour change by: Understanding perceptions of recipients; Producing tailored visual messages; Engaging communities. 
Partners: Mothers of Africa, Welsh Government

Project: COVID-19 Vaccination for Vulnerable Namibians
There is major COVID-19 vaccination resistance in Namibia, especially amongst vulnerable and remote groups. Ministry of Health and university partners will co-produce health promotion awareness campaigns for the most disadvantaged Namibians and then deliver the vaccination programme itself, transforming tens of thousands of lives. 
Partners: Phoenix Project, University of Namibia, Welsh Government


I am Director of International for the School of Computer Science and Informatics, Cardiff University. I am a member of the School’s Senior Management Team and am responsible for developing, leading and delivering the International Strategy for the School.
I am the Lead of Multimedia Computing Research Group, Cardiff University.
I am the Chair of International Committee of the Centre for Artificial Intelligence, Robotics and Human-Machine Systems (IROHMS), Cardiff University.

I am Associate Professor in Computer Science. I graduated from The University of Edinburgh, United Kingdom, and subsequently worked in the Department of Intelligent Systems at Delft University of Technology (TU Delft), The Netherlands for my PhD on Interactive Intelligence. My PhD research was funded by Philips Research Laboratories. I am a founder member of the Delft Image Quality Lab. Since 2006, I have been working closely with industry to develop next generation image and video technologies. I led a project funded by Philips Research Laboratories that developed novel algorithms for visual media quality assessment; and a project funded by Philips Healthcare that addressed a number of issues related to medical image quality.

My research interests sit at the intersection of Image and Video ProcessingAI/Machine Learning, Computer Vision, Applied Perception, Multimedia Computing, and Human-Technology Interaction.

Academic leadership

ChairIEEE Multimedia Communications Technical Committee, Interest Group on Quality of Experience for Multimedia Communications
EPSRC Associate College Member – EPSRC Peer Review College
Committee Member – Society for Information Display (SID), United Kingdom and Ireland Chapter
Associate Editor – IEEE Transactions on Multimedia [Impact Factor: 6.051] (2017-present)
Associate Editor – IEEE Transactions on Human-Machine Systems [Impact Factor: 3.374] (2015-present)
Associate Editor – Signal Processing: Image Communication (Elsevier) [Impact Factor: 2.779] (2014-present)
Associate Editor – Neurocomputing (Elsevier) [Impact Factor: 4.438] (2012-2018)
Associate Editor – Signal, Image and Video Processing (Springer) [Impact Factor: 1.794] (2012-2017)
Conference Chair  – IEEE International Conference on Multimedia and Expo (ICME) 2020 I British Machine Vision Conference 2019 I IEEE International Conference on Quality of Multimedia Experience 2021
Area Chair (Technical Program Committee) – IEEE International Conference on Multimedia and Expo (ICME), 2015-2017
Member (Technical Program Committee) – IEEE International Conference on Quality of Multimedia Experience (QoMEX), 2012-2019


Collaborative Research Center, SFB-TRR 161 Quantitative Methods for Visual Computing, funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), July 2019 - June 2023, Value: EUR 8.34 million
Co-I: Dr Hantao Liu (PI: Prof. Dietmar Saupe, University of Konstanz)
Project A05 | Image/Video Quality Assessment: From Test Databases to Similarity-Aware and Perceptual Dynamic Metrics
The project addresses methods for automated visual quality assessment and their validation beyond mean opinion scores. We propose to enhance the methods by including similarity awareness and predicted eye movement sequences, quantifying the perceptual viewing experience, and to apply the metrics for quality-aware media processing. Moreover, we will set up and apply media databases that are diverse in content and authentic in the distortions, in contrast to current scientific data sets.

Perceptually Salient Video Quality Awareness via Scene-Level Quality Assessment (funded by The Royal Society)
PI: Dr Hantao Liu
The project aims to develop technology to make any video camera automatically aware of its visual quality. In many scenarios, multiple images/videos of the same scene are captured; for example, videos of the same setting taken at different times, from different viewpoints, using different cameras, or even using the same camera with different settings. To evaluate, monitor, and optimise the system’s performance, there is a need to score/compare the images of the same scene in terms of visual quality.

Computational Models for Assessment of Diagnostic Image Quality (funded by EPSRC/GCRF)
PI: Dr Hantao Liu
The project aims to develop computational models that can automatically and reliably predict the task performance of the radiologist in the interpretation (e.g., lesion detection) of medical images. These models will be used either to support the human to augment diagnostic efficiency, or to train the human towards improved diagnostic accuracy.

Modelling Human Behavioural Responses to Distortions for Visual Quality Assessment (funded by The Royal Society)
PI: Dr Hantao Liu
Automatic visual quality assessment is the key for the optimisation of image/video acquisition, transmission, processing, and display systems. The research aims to better understand and model how the human visual system (HVS) perceives distortions in visual signals, and to develop algorithms for objective assessment of visual quality.

Medical Image Quality Assessment: Perceived Quality and Diagnostic Performance (funded by Cardiff University – KU Leuven)
PI: Dr Hantao Liu
The project aims to understand how the measured differences in image quality affect diagnostic performance, and to develop computational models that incorporate the knowledge of how radiologists understand medical images. These models will be used as valuable tools in future optimisation of medical systems and clinical procedures.



















Module Leader – Data Processing and Visualisation (undergraduate)
Module Leader – Human Centric Computing (postgraduate)

Research interests

My research interests sit at the intersection of visual signal processing, computer vision, machine learning, human perception and human-computer interaction.

Visual Experience Computing
I am interested in how humans perceive visual information and developing computational models of visual perception. I am interested in image processing and machine vision systems that have the skills of perceptual intelligence, helping people make decisions or improving their experiences.

Perceptual Image and Video Processing
I am interested in biologically motivated visual models and integrating the perceptual elements with image and video processing algorithms. I am interested in perceptually optimised image and video engineering applications that benefit from the use of quantitative visual models.

Eye Movements and Saliency Modelling
I am interested in eye movements and computational saliency models. I am interested in integrating aspects of human visual attention with imaging and computer vision systems.


Picture Quality Databases
TUD Eye-Tracking Database
Cardiff Visual Attention and Visual Quality Toolbox


Modelling Perceived Quality for Imaging Applications, 2011
Author: Hantao Liu
ISBN: 9789491211720