Spatial Frequency and the Performance of Image-Based Visual Complexity Metrics

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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. A new image complexity metric ( $R_{\mathrm {spt}}$ ), 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 $R_{\mathrm {spt}}$ correlate statistically significantly with subjective evaluations. Biological sex did not affect perceived spatial complexity. While RMSE and $R_{\mathrm {spt}}$ 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.

Original language English (US) 9103062 100111-100119 9 IEEE Access 8 https://doi.org/10.1109/ACCESS.2020.2998292 Published - 2020

All Science Journal Classification (ASJC) codes

• Computer Science(all)
• Materials Science(all)
• Engineering(all)