Selected research and applied projects in visual AI, human visual perception, embedded vision systems, video quality assessment, aerospace video transmission, and AI education.
Limited radio transmission bandwidth seriously restricts the use of wide-angle video in real-time technologies. This project develops human-vision-inspired processing for drone pilots, aiming to support real-time wide-angle perception beyond visual line of sight.
This project creates visual knowledge for training visual artificial intelligence. It reduces video dataset redundancy by leveraging human visual perception, improving internal computation and predictive accuracy in resource-constrained AI systems.
This project developed a deep learning-based non-reference video quality metric incorporating psychophysical features of human visual perception. The metric supports stable prediction of subjective video quality ratings across independent datasets.
This work measured large-scale thresholds of visibility for spatio-temporal sinusoidal variations using different spatial sizes and temporal modulation rates. The resulting model describes human contrast sensitivity under modern video presentation conditions.
This work analyses human perception of temporal frequencies in central and peripheral vision. It presents a new method for quality measurement based on contrast sensitivity models and visibility thresholds for spatio-temporal variations on modern displays.
Based on research into the limits of human vision, this project offers a method, software, and test equipment for measuring visual system characteristics using controlled spatial, temporal, and colour stimuli.
This work contributes to streaming quality assessment by creating a large-scale dataset with video compression and transmission artefacts. The final dataset includes millions of perceptual thresholds related to video quality.
Many students view AI as an all-knowing technology and feel anxious about its impact on learning and professional futures. This project explores how lecturers can help students understand AI tools, academic integrity, and responsible adoption across disciplines.