Abstract
There is a wide range of visual and spatial complexity measurement methods that aim to quantify perceived image complexity. While image-based calculation methods (edge detection, image compression, contrast) characterize a digital image, visual perception studies focus on fundamental visual mechanisms, such as contrast sensitivity and visual task performance. Despite the evidence from several vision studies, spatial frequency information has not been widely utilized to assess image complexity. Previous studies suggest that image-based performance metrics are limited in explaining perceived complexity due to confounding factors, such as context, memory, familiarity, and expectation. Here, a visual experiment is conducted to assess the performance of image-based metrics and spatial frequency information using 16 abstract and natural images. Anew image complexity metric(R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">spt</sub> ), based on detectability suprathreshold, was proposed to benchmark the performance of existing measures. Forty-four naïve participants used a 5-point Likert-type scale to judge the visual complexity of the images displayed on a tablet. Results indicate that root-mean-square error (RMSE) and R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">spt</sub> correlate statistically significantly with subjective evaluations. Biological sex did not affect perceived spatial complexity. While RMSE and R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">spt</sub> can potentially be used to estimate the spatial complexity of display images, the performance of spatial frequency information and image assessment measures in immersive viewing conditions require further research.
Highlights
IntroductionIn its simplest form, visual complexity refers to the level of detail within an image
Visual complexity is a widely discussed but not precisely defined term
While root-mean-square error (RMSE) and Rspt can potentially be used to estimate the spatial complexity of display images, the performance of spatial frequency information and image assessment measures in immersive viewing conditions require further research
Summary
In its simplest form, visual complexity refers to the level of detail within an image. Visual images, such as computer displays or photographs, are widely used to study visual mechanisms since three-dimensional environmental stimuli are reduced to two-dimensional retinal images. Several theories and models have been proposed based on a single visual form, visual arrays, information pickup, visual displays, perceptual learning, and neural circuit theory to explain perceived visual complexity [23]. Both empiricists’ and nativists’ theories investigated the roots of the perceived quality of images to build a theoretical framework. This study highlighted the shortcomings of image-based complexity metrics
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
