The performance of image quality measures and spatial frequency information for visual complexity assessment

Citation Author(s):
Dorukalp
Durmus
Pennsylvania State University
Submitted by:
Dorukalp Durmus
Last updated:
Tue, 05/17/2022 - 22:17
DOI:
10.21227/4efq-9649
Data Format:
*.xlsx
Research Article Link:
Spatial Frequency and the Performance of Image-Based Visual Complexity Metrics
License:
Creative Commons Attribution
269 Views
Categories:
Image Processing
Keywords:
spatial frequency information, visual complexity, spatial image complexity, subjective assessments, psychophyiscs, image quality, lossy compression, root mean square error, entropy, image processing, edge detection algorithm, JPEG, GIF, color fractal images
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Abstract 

The dataset consists of subjective evaluations of 44 naive observers judging the visual complexity of 16 images. The subjective judgments were done using a 5-point Likert-type scale with a neutral midpoint. The items in the scale were “very complex,” “complex,” “medium,” “simple,” and “very simple.” The order of the images was randomized for every participant.

Spatial complexity properties of the images used in the experiment are quantified using nine spatial complexity measures: GIF, JPEG, root-mean-squared error, fractal D, entropy, spatial frequency information slope, suprathreshold detectability Rspt, and edge detection algorithms (Canny-GIF and Sobel-GIF). 

 

 

 

Instructions: 

DATA_subjective_evaluations.xlsx consists of subjective evaluations of 44 naive observers. Observers judged the visual complexity of 16 images using a 5-point Likert-type scale. The items in the scale were “very complex,” “complex,” “medium,” “simple,” and “very simple.” You can convert the data by allocating a number to each item (e.g., very complex =5, complex=4, and so on).

 

DATA_comp_measures.xlsx consists of the image complexity measures for nine metrics. 

 

 

For more details refer to the publication: Durmus, D. (2020) Spatial frequency and the performance of image based visual complexity metrics. IEEE Access.

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