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.
Saliency prediction has been extensively studied for natural images. In the area of video coding and video quality assessment, researchers attempt to integrate a saliency model to individual frames of a video sequence. In selecting bestperforming saliency models for these applications, the evaluation only considers the average model performance over all frames of a video. This may mask the defects of a saliency model and consequently hinder further improvement of the model. In this paper, we present the identification of pitfalls in the evaluation of saliency models for videos. We demonstrate the importance of considering the video content classification and temporal effect. Building on the findings, we make recommendations for saliency model evaluation and selection for videos.
Visual saliency prediction remains an academic challenge due to the diversity and complexity of natural scenes as well as the scarcity of eye movement data on where people look in images. In many practical applications, digital images are inevitably subject to distortions, such as those caused by acquisition, editing, compression or transmission. A great deal of attention has been paid to predicting the saliency of distortionfree pristine images, but little attention has been given to understanding the impact of visual distortions on saliency prediction. In this paper, we first present the CUDAS database -a new distortion-aware saliency benchmark, where eye-tracking data was collected for 60 pristine images and their corresponding 540 distorted formats. We then conduct a statistical evaluation to reveal the behaviour of state-of-the-art saliency prediction models on distorted images and provide insights on building an effective model for distortion-aware saliency prediction. The new database is made publicly available to the research community.
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