TY - JOUR
T1 - Spatial Frequency and the Performance of Image-Based Visual Complexity Metrics
AU - Durmus, Dorukalp
N1 - Publisher Copyright:
© 2013 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
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U2 - 10.1109/ACCESS.2020.2998292
DO - 10.1109/ACCESS.2020.2998292
M3 - Article
AN - SCOPUS:85086307936
VL - 8
SP - 100111
EP - 100119
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
M1 - 9103062
ER -