Evaluation of the Bubble view Metaphor for the Crowdsourcing Study of Visual Attention Deployment in Tone-Mapped Images

ABSTRACT

Attention is an important attribute of human vision for study of user's quality of experience (QoE). The attention information collection from eye tracking is impossible in the current scenario of Covid-19. Different mouse metaphors have been proposed to study visual attention without eye tracking equipment. These methods have shown promising results on different types of images (visualizations, natural images and websites) with well-identified regions of interest. However, they have not been precisely tested for QoE applications, where natural images are processed with different algorithms (compression, tone-mapping, etc.) and visual content can induce more exploratory behavior. This paper studies and compares different configurations of bubble view metaphors for the study of visual attention in tone-mapped images. © 2021 IEEE.