Modelling Human Behavioural Responses to Distortions for Visual Quality Assessment
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.

Computational Models for Assessment of Diagnostic Image Quality
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.

Medical Image Quality Assessment: Perceived Quality and Diagnostic Performance
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

Subjective and Objective Visual Quality Assessment
Co-I: Dr Hantao Liu

Visual media content rendered by current digital imaging systems differs in perceived quality depending on the system and its applications. The project investigates how the perceptual quality of an image or a video, as judged by an average human observer, is predicted by an algorithm. The research involves understanding the way human beings perceive visual quality and developing computational models that can automatically and reliably predict perceived visual quality.

Perceptual Quality Assessment of Medical Images
PI: Dr Hantao Liu

Visual signal distortions arising in medical image acquisition, processing, compression and transmission affect the perceptual quality of images and potentially impact diagnoses. To optimise clinical practice, we need to understand human perception of medical image quality in practical settings, and then use what is learned to develop useful solutions for improved image quality and better image-based diagnoses. This project focuses on developing methodologies for the assessment of the perceptual quality of medical images.