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

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 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 spt correlate statistically significantly with subjective evaluations. Biological sex did not affect perceived spatial complexity. While RMSE and R 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.

Chaos in Motion: Measuring Visual Pollution with Tangential View Landscape Metrics

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Circadian metric variability measures for tunable LED light sources

“…Research studies can also utilize metrics that quantify rarely investigated aspects of visual perception, such as visual clarity or complexity. 27 The unit voxel size of this dimension depends on the metric chosen. However, it should be noted that JND for visual metrics may vary with the absolute values due to the non-linear sensitivity of the visual system.…”

Subjective Evaluation of Visual Complexity, Clarity, and Preference of Indoor Environments

“…While visual complexity and clarity are important concepts for visual perception of spaces, they have only been studied using a restricted set of metrics and conditions. Some of the commonly used image quality metrics include maximum local variation (MLV) (Bahrami and Kot, 2014), spatial frequency slope 𝛼, blind/referenceless image spatial quality evaluator (BRISQUE) (Mittal et al, 2012), entropy (E), International Telecommunication Union (ITU) spatial information (SI) (1999), detectability suprathreshold Rspt (Durmus, 2020), and colourfulness M (Hasler and Suesstrunk, 2003). Maximum local variation (MLV) is a measure of image sharpness, which is the general clarity of an image with respect to focus and contrast.…”

“…Similarly, the detectability suprathreshold Rspt quantifies the visual complexity of an image based on adaptive thresholding method. This suprathreshold detectability image complexity metric was proposed to measure the visual complexity of images (Durmus, 2020).…”

“…Visual task performance is often quantified by measuring time and accuracy of a completed task, while subjective evaluations, such as visual clarity, visual complexity, pleasantness, and colourfulness, are complex concepts that may be difficult to quantify. Research suggests that subjective assessments, such as visual complexity, visual clarity, visual preference, colourfulness, and the quality of digital images may be interrelated, although they are not interchangeable concepts (Hasler and Suesstrunk, 2003;Valtchanov and Ellard, 2015;Durmus, 2020). Visual clarity is affected by luminance and chromatic contrast of the visual environment.…”