1/f2 Characteristics and Isotropy in the Fourier Power Spectra of Visual Art, Cartoons, Comics, Mangas, and Different Categories of Photographs

Abstract

Art images and natural scenes have in common that their radially averaged (1D) Fourier spectral power falls according to a power-law with increasing spatial frequency (1/f2 characteristics), which implies that the power spectra have scale-invariant properties. In the present study, we show that other categories of man-made images, cartoons and graphic novels (comics and mangas), have similar properties. Further on, we extend our investigations to 2D power spectra. In order to determine whether the Fourier power spectra of man-made images differed from those of other categories of images (photographs of natural scenes, objects, faces and plants and scientific illustrations), we analyzed their 2D power spectra by principal component analysis. Results indicated that the first fifteen principal components allowed a partial separation of the different image categories. The differences between the image categories were studied in more detail by analyzing whether the mean power and the slope of the power gradients from low to high spatial frequencies varied across orientations in the power spectra. Mean power was generally higher in cardinal orientations both in real-world photographs and artworks, with no systematic difference between the two types of images. However, the slope of the power gradients showed a lower degree of mean variability across spectral orientations (i.e., more isotropy) in art images, cartoons and graphic novels than in photographs of comparable subject matters. Taken together, these results indicate that art images, cartoons and graphic novels possess relatively uniform 1/f2 characteristics across all orientations. In conclusion, the man-made stimuli studied, which were presumably produced to evoke pleasant and/or enjoyable visual perception in human observers, form a subset of all images and share statistical properties in their Fourier power spectra. Whether these properties are necessary or sufficient to induce aesthetic perception remains to be investigated.

How self-similar are artworks at different levels of spatial resolution?

“…In support of previously published ideas, their results showed that intermediate levels of complexity characterize visual artworks on average [Berlyne 1974;Forsythe et al 2011]. The anisotropy of orientation gradients in visual artworks is about as low as in images of diverse natural patterns and scenes (see also [Koch et al 2010]). …”

“…While it is believed that different factors such as the cultural background, age, gender, etc. of the observer play an important role in his decision whether or not an artwork is perceived as aesthetic by an individual, researchers have recently tried to find universal properties shared among artworks that are ranked as aesthetic, independent of the period during which they have been created, of the style they follow, the technique they use, or the subject matter they cover Koch et al 2010;Graham and Redies 2010;Amirshahi et al 2012;Redies et al 2012]. This type of research is in line with the notion that aesthetic artworks share specific and universal properties, which reflect functions of the human visual system in particular and of the human brain in general [Zeki 1999;Reber et al 2004;Redies 2007].…”

Navigation performance in virtual environments varies with fractal dimension of landscape

“…This procedure created a landscape pattern in which large scale low frequency waves had a large amplitude, while smaller scale higher frequency waves had a lower amplitude. Furthermore, the range of chosen frequencies generated a landscape that scaled across two orders of magnitude, which is representative of the extent to which many natural environments show fractal properties at varying scales (Balboa & Grzywacz 2003; Koch, Denzler & Redies 2010). Using numbers generated from a Gaussian distribution, the phases of the waves were then randomized to generate the statistical scale invariance of natural scenes.…”

Statistical image properties of print advertisements, visual artworks and images of architecture

“…In a similar vein, it was proposed that stimuli that can be processed fluently are more aesthetic in general (Reber et al, 2004). In the computational approach, a particular focus was placed on visual artworks and photographs (Datta et al, 2006; Li and Chen, 2009; Amirshahi et al, 2012; Redies et al, 2012), but other types of visually pleasing images, such as graphic novels (Koch et al, 2010) and aesthetic writings (Melmer et al, 2013) have also been studied.…”