img_complexity returns the complexity of an image via image compression. Higher values indicate higher image complexity.

img_complexity(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()). Character string that specifies which image compression algorithm to use. Currently implemented are zip with deflate compression (default), jpg, gif, and png. logical. Should the compressed file size of the rotated image also be computed? (see details)

## Value

a numeric value: the ratio of the compressed divided by the uncompressed image file size

## Details

The function returns the visual complexity of an image. Visual complexity is calculated as ratio between the compressed and uncompressed image file size. Preferably, the original image is an uncompressed image file.

The function takes the file path of an image file (or URL) or a pre-loaded image as input argument (imgfile) and returns the ratio of the compressed divided by the uncompressed image file size. Values can range between almost 0 (virtually completely compressed image, thus extremely simple image) and 1 (no compression possible, thus extremely complex image).

You can choose between different image compression algorithms. Currently implemented are zip with deflate compression (default), jpg, gif, and png. See Mayer & Landwehr (2018) for a discussion of different image compression algorithms for measuring visual complexity.

As most compression algorithms do not depict horizontal and vertical redundancies equally, the function includes an optional rotate parameter (default: FALSE). Setting this parameter to TRUE has the following effects: first, the image is rotated by 90 degrees. Second, a compressed version of the rotated image is created. Finally, the overall compressed image's file size is computed as the minimum of the original image's file size and the file size of the rotated image.

As R's built-in bmp device creates (a) indexed instead of True Color images and (b) creates files with different file sizes depending on the operating system, the function relies on the magick package to write (and read) images.

## 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

img_read, img_contrast, img_self_similarity, img_simplicity, img_symmetry, img_typicality,

## Examples

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

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