title: Complexity of images: experimental and computational estimates compared.
abstract: We tested whether visual complexity can be modeled through the use of parameters relevant to known mechanisms of visual processing. In psychophysical experiments observers ranked the complexity of two groups of stimuli: 15 unfamiliar Chinese hieroglyphs and 24 outline images of well-known common objects. To predict image complexity, we considered: (i) spatial characteristics of the images, (ii) spatial-frequency characteristics, (iii) a combination of spatial and Fourier properties, and (iv) the size of the image encoded as a JPEG file. For hieroglyphs the highest correlation was obtained when complexity was calculated as the product of the squared spatial-frequency median and the image area. This measure accounts for the larger number of lines, strokes, and local periodic patterns in the hieroglyphs. For outline objects the best predictor of the experimental data was complexity estimated as the number of turns in the image, as Attneave (1957 Journal of Experimental Psychology 53 221-227) obtained for his abstract outlined images. Other predictors of complexity gave significant but lower correlations with the experimental ranking. We conclude that our modeling measures can be used to estimate the complexity of visual images but for different classes of images different measures of complexity may be required.
authors.affiliation: Pavlov Institute of Physiology, Russian Academy of Sciences, nab. Makarova 6, 199034 St Petersburg, Russia. niv@pavlov.infran.ru
authors.forename: Valeriy
authors.initials: V
authors.lastname: Chikhman
authors.forename: Valeriya
authors.initials: V
authors.lastname: Bondarko
authors.forename: Marina
authors.initials: M
authors.lastname: Danilova
authors.forename: Anna
authors.initials: A
authors.lastname: Goluzina
authors.forename: Yuri
authors.initials: Y
authors.lastname: Shelepin
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001288:Attention; D004192:Discrimination, Psychological; D005260:Female; D005583:Fourier Analysis; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D009949:Orientation; D010364:Pattern Recognition, Visual; D011601:Psychophysics / Q000706:statistics & numerical data*; D021641:Recognition, Psychology; D012858:Size Perception; D055815:Young Adult