Most visual advertisements are designed to attract attention, often by inducing a pleasant impression in human observers. Accordingly, results from brain imaging studies show that advertisements can activate the brain's reward circuitry, which is also involved in the perception of other visually pleasing images, such as artworks. At the image level, large subsets of artworks are characterized by specific statistical image properties, such as a high self-similarity and intermediate complexity. Moreover, some image properties are distributed uniformly across orientations in the artworks (low anisotropy). In the present study, we asked whether images of advertisements share these properties. To answer this question, subsets of different types of advertisements (single-product print advertisements, supermarket and department store leaflets, magazine covers and show windows) were analyzed using computer vision algorithms and compared to other types of images (photographs of simple objects, faces, large-vista natural scenes and branches). We show that, on average, images of advertisements and artworks share a similar degree of complexity (fractal dimension) and self-similarity, as well as similarities in the Fourier spectrum. However, images of advertisements are more anisotropic than artworks. Values for single-product advertisements resemble each other, independent of the type of product promoted (cars, cosmetics, fashion or other products). For comparison, we studied images of architecture as another type of visually pleasing stimuli and obtained comparable results. These findings support the general idea that, on average, man-made visually pleasing images are characterized by specific patterns of higher-order (global) image properties that distinguish them from other types of images. Whether these properties are necessary or sufficient to induce aesthetic perception and how they correlate with brain activation upon viewing advertisements remains to be investigated.

Previous research in computational aesthetics has led to the identification of multiple image features that, in combination, can be related to the aesthetic quality of images, such as photographs. Moreover, it has been shown that aesthetic artworks possess specific higher-order statistical properties, such as a scale-invariant Fourier spectrum, that can be linked to coding mechanisms in the human visual system. In the present work, we derive novel measures based on a PHOG representation of images for image properties that have been studied in the context of the aesthetic assessment of images previously. We demonstrate that a large dataset of colored aesthetic paintings of Western provenance is characterized by a specific combination of the PHOG-derived aesthetic measures (high self-similarity, moderate complexity and low anisotropy). In this combination, the artworks differ significantly from seven other datasets of photographs that depict various types of natural and manmade scenes, patterns and objects. To the best of our knowledge, this is the first time that these features have been derived and evaluated on a large dataset of different image categories.

The weighted principal component analysis technique is employed for reconstruction of reflectance spectra of surface colors from the related tristimulus values. A dynamic eigenvector subspace based on applying certain weights to reflectance data of Munsell color chips has been formed for each particular sample and the color difference value between the target, and Munsell dataset is chosen as a criterion for determination of weighting factors. Implementation of this method enables one to increase the influence of samples which are closer to target on extracted principal eigenvectors and subsequently diminish the effect of those samples which benefit from higher amount of color difference. The performance of the suggested method is evaluated in spectral reflectance reconstruction of three different collections of colored samples by the use of the first three Munsell bases. The resulting spectra show considerable improvements in terms of root mean square error between the actual and reconstructed reflectance curves as well as CIELAB color difference under illuminant A in comparison to those obtained from the standard PCA method.