Attractive images or videos are the visual backbones of journalism and social media to gain the user's attention. From trailers to teaser images to image galleries, appealing visuals have only grown in importance over the years. However, selecting eye-catching shots from a video or the perfect image from large image collections is a challenging and time-consuming task. We present our tool that can assess image and video content from an aesthetic standpoint. We discovered that it is possible to perform such an assessment by combining expert knowledge with data-driven information. We combine the relevant aesthetic features and machine learning algorithms into an aesthetics retrieval system, which enables users to sort uploaded visuals based on an aesthetic score and interact with additional photographic, cinematic, and person-specific features.

Computational inference of aesthetics is an ill-defined task due to its subjective nature. Many datasets have been proposed to tackle the problem by providing pairs of images and aesthetic scores based on human ratings. However, humans are better at expressing their opinion, taste, and emotions by means of language rather than summarizing them in a single number. In fact, photo critiques provide much richer information as they reveal how and why users rate the aesthetics of visual stimuli. In this regard, we propose the Reddit Photo Critique Dataset (RPCD), which contains tuples of image and photo critiques. RPCD consists of 74K images and 220K comments and is collected from a Reddit community used by hobbyists and professional photographers to improve their photography skills by leveraging constructive community feedback. The proposed dataset differs from previous aesthetics datasets mainly in three aspects, namely (i) the large scale of the dataset and the extension of the comments criticizing different aspects of the image, (ii) it contains mostly UltraHD images, and (iii) it can easily be extended to new data as it is collected through an automatic pipeline. To the best of our knowledge, in this work, we propose the first attempt to estimate the aesthetic quality of visual stimuli from the critiques. To this end, we exploit the polarity of the sentiment of criticism as an indicator of aesthetic judgment. We demonstrate how sentiment polarity correlates positively with the aesthetic judgment available for two aesthetic assessment benchmarks. Finally, we experiment with several models by using the sentiment scores as a target for ranking images. Dataset and baselines are available https://github.com/mediatechnologycenter/aestheval.