Abstract
Visual complexity, as an attribute of images related to human perception, has been widely studied in computer science and psychology. In conventional studies, the research objects have been limited to the traditional two-dimensional (2D) patterns or images. Therefore, if depth information is introduced into this scenario, how will it affect our perception of visual complexity of an image? To answer this question, we developed an experimental virtual reality system that enables control and display of three-dimensional (3D) visual stimuli. In this study, we aimed to investigate the effect of depth information on visual complexity perception by comparing 2D and 3D displays of the same stimuli. We scanned three textures with different characteristics to create the experimental stimuli and recruited 25 participants for the experiment. The results showed that depth information significantly increased the visual complexity perception of the texture images. Moreover, depth information had different degrees of impact on visual complexity for different textures. The higher the maximum depth introduced in the 3D image, the more significant the increase in visual complexity perception. The experimental virtual reality system used in this study also provides a feasible experimental tool for future experiments.
Highlights
Visual complexity is one of the subjective impressions associated with visual information of stimuli and plays an important role in human perception and recognition of patterns and images.The study of visual complexity is significant for visual understanding and aesthetic evaluation
We investigated the relationship between visual complexity perception and depth information, and found that depth information is essential in the visual complexity perception of 3D textures
We conducted an assessment of visual complexity perception for three sets of texture stimuli with different characteristics and analyzed the effect of depth information on visual complexity
Summary
Visual complexity is one of the subjective impressions associated with visual information of stimuli and plays an important role in human perception and recognition of patterns and images. The study of visual complexity is significant for visual understanding and aesthetic evaluation. It is useful in many applications, such as image segmentation, content-based image retrieval, and user interface design. It has been shown that visual complexity affects human emotions, memory, and active movement [1]. It is challenging to describe human sensitivity to perception of visual complexity quantitatively. The definition of visual complexity varies according to the research field and application [2,3,4]
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