img_simplicity returns the simplicity of an image as 1 minus the complexity of the image. Higher values indicated higher image simplicity.

img_simplicity(imgfile, algorithm = "zip", rotate = FALSE)

Arguments

imgfile

Either a character string containing the path to the image file (or URL) or an an image in form of a matrix (grayscale image) or array (color image) of numeric values representing the pre-loaded image (e.g. by using img_read()).

algorithm

Character string that specifies which image compression algorithm to use. Currently implemented are zip with deflate compression, jpg, gif, and png.

rotate

logical. Should the compressed file size of the rotated image also be computed? (see details)

Value

a numeric value: 1 minus the ratio of compressed divided by uncompressed file size (i.e., the compression rate)

Details

Image simplicity is calculated as 1 minus the ratio between the compressed and uncompressed file size (i.e., the compression rate). Values can range between 0 (no compression possible, thus extremely complex image) and almost 1 (virtually completely compressed image, thus extremly simple image). Different compression algorithms are implemented. For details, see img_complexity.

References

Donderi, D. C. (2006). Visual complexity: A Review. Psychological Bulletin, 132, 73--97. doi:10.1037/0033-2909.132.1.73

Forsythe, A., Nadal, M., Sheehy, N., Cela-Conde, C. J., & Sawey, M. (2011). Predicting Beauty: Fractal Dimension and Visual Complexity in Art. British Journal of Psychology, 102, 49--70. doi:10.1348/000712610X498958

Mayer, S. & Landwehr, J, R. (2018). Quantifying Visual Aesthetics Based on Processing Fluency Theory: Four Algorithmic Measures for Antecedents of Aesthetic Preferences. Psychology of Aesthetics, Creativity, and the Arts, 12(4), 399--431. doi:10.1037/aca0000187

See also

Examples

# Example image with low simplicity: trees
trees <- img_read(system.file("example_images", "trees.jpg", package = "imagefluency"))
#
# display image
grid::grid.raster(trees)
#
# get complexity
img_simplicity(trees)

# Example image with high simplicity: sky
sky <- img_read(system.file("example_images", "sky.jpg", package = "imagefluency"))
#
# display image
grid::grid.raster(sky)
#
# get complexity
img_simplicity(sky)