Visual saliency modelling is of fundamental importance in modern video processing and its applications. Our previous eye-tracking study revealed that signal distortions caused by editing, compression, or transmission alter gaze patterns and consequently induce saliency shifts in both spatial and temporal domains. Saliency shifts provide crucial insights into viewers’ behavioural responses to video distortions, facilitating the perception-based optimisation of video algorithms. However, the spatio-temporal saliency shifts and their measurable effects on perception related applications remain largely unexplored. In this paper, we first investigate the measurement of distortion-induced saliency shifts (DSS) in videos and analyse DSS behaviours as functions of video content, time order and critical distortion disruption. Second, based on our findings, we construct three vision models to quantitatively simulate distinct DSS behaviours and integrate them into a comprehensive DSS behaviour model. Finally, we demonstrate that the computational DSS model can enhance emerging video technologies.

Human viewers’ eye movements reflect their perceptual responses to visual signals. Previous research has shown that distortions in videos cause spatio-temporal gaze shifts, which means gaze behaviour is related to video quality perception. It would be highly beneficial to understand gaze behaviour of viewing videos of varying perceived quality. However, little is known about the interactions between gaze, video content and distortions. In this paper, based on our eye-tracking database for video quality (SVQ160), we perform systematic analyses to reveal the impact of video content (VC) and time order (TO) on gaze shifts. Findings and quantitative methods for gaze behaviour can be used to develop advanced video quality metrics and video processing algorithms.