Please rank these features from low to high level features and argue for your ranking

Image Properties
For each image, we calculated thirteen image properties that covered diverse aspects of global image structure, as described in the following paragraphs. These properties were selected from an even larger set of image properties, which our group has studied previously in visual artworks and other visually pleasing images (for references, see below). From the original set, we omitted properties that correlated strongly with each other (Braun et al., 2013; Redies et al., 2017; Brachmann, 2018). Overall correlations between the remaining properties used in the present study are listed in Supplementary Table S1 for all datasets together.

Color Values (HSV Color Channels)
Color plays an important role in human preference for images (Palmer and Schloss, 2010; Mallon et al., 2014; Nascimento et al., 2017), and in the rating of affective pictures (Bekhtereva and Muller, 2017). In the present study, color was analyzed in the Hue-Saturation-Value (HSV) color space, which consists of channels for hue (H), saturation (S), and value (V), respectively. In a previous study, our group demonstrated that the three channels in this space relate to the beauty ratings of abstract artworks (Mallon et al., 2014). We converted the original RGB-coded pictures into the HSV color space and calculated the average pixel value for each of the three channels by using the rbg2hsv algorithm of the MATLAB Toolbox (The MathWorks, Natick, MA, United States, Release 2012a).

Symmetry
Scientists and artists have claimed that symmetry is a fundamental and universal principle of esthetics, to which the human brain is particularly sensitive (for a review, see Bode et al., 2017). To measure symmetry, we used filter responses from the first layer of a convolutional neural network (CNN) that closely match responses of neurons in the visual cortex of higher mammals (Brachmann and Redies, 2016). This approach has the advantage that it captures a higher-order symmetry not only based on the color, edges and texture of images, but also on shapes and objects, thereby performing closer to human vision. We calculated left-right and up-down symmetry according to the algorithm provided by Brachmann and Redies (2016).

Edge Density
In general, human observers prefer an intermediate level of complexity in visual stimuli (Berlyne, 1971; Spehar et al., 2016), but there are large between-subject differences (Güclütürk et al., 2016). As a measure of image complexity, we summed up all edge responses in Gabor-filtered images in the present study, as described in detail by Redies et al. (2017). Our complexity measure correlates highly with other complexity measures that are based on luminance gradients and relate to subjective complexity (Braun et al., 2013).

Self-Similarity
Traditional Western oil paintings are characterized by an intermediate to high degree of self-similarity (Redies et al., 2012). In the present study, we calculated self-similarity with a derivative of the PHOG descriptor, which measures how similar the histograms of orientated gradients (HOGs; Dalal and Triggs, 2005) for parts of an image are compared to the histogram of the entire image. We reduced each image to 100,000 pixels size and used 16 equally sized orientation bins covering 360° for the histograms. Histograms at levels 1–3 were compared to the ground level histogram. For a detailed description of the method, see the appendix in Braun et al. (2013).

Fourier Slope
In radially averaged log-log plots of spectral power versus spatial frequency, the slope of a straight line (here called Fourier slope) is indicative of the relative strength of high spatial frequencies (fine detail) versus low spatial frequencies (coarse structure) of luminance changes across an image. In general, this slope is around -2 for natural scenes as well as for large subsets of artworks and other visually pleasing images. Human observers thus prefer statistics that are similar to those of natural scenes (Graham and Field, 2007; Redies et al., 2007b). We converted each image to grayscale with the Photoshop CS5 program and padded the images according to square ones, followed by Fast Fourier Transformation, as described in Redies et al. (2007b). After radially averaging the power spectrum, we plotted Fourier power versus spatial frequency. For equally spaced intervals in log-log space, the data points were averaged and fitted to a straight line by least-square fitting (Redies et al., 2007b).

Fourier Sigma
The deviation of the log-log Fourier power spectrum from a straight line is here called Fourier sigma. In most natural images and artworks, spectral power decreases linearly with increasing spatial frequency so that the Fourier sigma is small (Graham and Field, 2007; Redies et al., 2007b). Interestingly, larger values for Fourier sigma, i.e., larger deviations from a straight line, have been described for some unpleasant images (Fernandez and Wilkins, 2008; O’Hare and Hibbard, 2011). We calculated Fourier sigma as the sum of the squared deviations of the data points, which were binned in log-log space, from the fitted straight line, divided by the number of data points (Redies et al., 2007b).

First-Order and Second-Order Edge-Orientation Entropies
First-order entropy of edge orientations is a measure of how uniformly the orientations of luminance edges in an image are distributed across all orientations (Redies et al., 2017). If all orientations are represented at equal strength, first-order entropy is maximal. Values become smaller as particular orientations predominate the image. Second-order entropy is a measure of how independent or randomly edge orientations are distributed across an image. Values are close to maximal if edge orientations at given positions in an image do not allow any predictions of orientations at other positions of the image. Values for both entropies are high in some photographs of natural objects (for example, lichen growth patterns) and in artworks of different cultural provenance (Redies et al., 2017). Moreover, the edge-orientation entropies are predictors for esthetic ratings in diverse other types of man-made visual stimuli, for example, photographs of building facades or artificial geometrical and line patterns (Grebenkina et al., 2018). We calculated the two entropies by the method described in detail by Redies et al. (2017).

Variances of Feature Responses in Convolutional Neural Networks (CNNs)
The response characteristics of lower-layer CNN features resemble neuronal responses at low levels of the visual system, such as the primary visual cortex (Brachmann et al., 2017). The CNN features show regularities when responding to traditional artworks; they possess a high richness and variability, two statistical properties that can be expressed in terms of the variances Pa and Pf, respectively (Brachmann et al., 2017). Richness implies that many features tend to respond at many positions in an image (low Pa). Despite this overall richness, the feature responses are relatively variable between the sections of an image (high to intermediate Pf) in traditional artworks. The two variances differ between artworks and several types of natural and man-made images. In the present study, we calculated the variances as described in Brachmann et al. (2017).