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.

The rule of thirds (ROT) is one of the best-known composition rules used in painting and photography. According to this rule, the focus point of an image should be placed along one of the third lines or on one of the four intersections of the third lines, to give aesthetically pleasing results. Recently, calculated saliency maps have been used in an attempt to predict whether or not images obey the rule of thirds. In the present study, we challenged this computer-based approach by comparing calculated ROT values with behavioral (subjective) ROT scores obtained from 30 participants in a psychological experiment. For photographs that did not follow the rule of thirds, subjective ROT scores matched calculated ROT values reasonably well. For photographs that followed the rule of thirds, we found a moderate correlation between subjective scores and calculated values. However, aesthetic rating scores correlated only weakly with subjective ROT scores and not at all with calculated ROT values. Moreover, for photographs that were rated as highly aesthetic and for a large set of paintings, calculated ROT values were about as low as in photographs that did not follow the rule of thirds. In conclusion, the computer-based ROT metrics can predict the behavioral data, but not completely. Despite its proclaimed importance in artistic composition, the rule of thirds seems to play only a minor, if any, role in large sets of high-quality photographs and paintings.

Contrast in visible images is one of the most relevant characteristics of visual signals. Since the pioneering works performed in vision psychology and optics, different definitions have been proposed in the literature. However, for the time being there exist no definition of contrast on which the vision research and visual processing scientific community can agree on. This makes it critical to have a clear view on the notion of contrast and its use in various applications. One issue to consider is how to define and particularly use this important measure in developing image processing and analysis methods. In this paper, we present a critical review of contrast measures and associated models developed by the scientific community in vision, optics, and image processing. We also provide learned lessons and guidelines on the use of appropriate contrast measures in selected visual information processing and analysis applications. We discuss challenges and propose research avenues for models enriched by recent findings in the field of human vision research and machine learning. We believe that this work serves as a guideline and can potentially open new research perspectives to the scientific community working on visual information processing and analysis.