This paper explores the computational inference of aesthetic quality in photographic images using visual content. The authors treat the task as a machine learning problem, leveraging peer-rated data from the online photo-sharing website *Photo.net*. They extract 56 visual features based on intuition and observed trends, including exposure, colorfulness, saturation, hue, rule of thirds, familiarity, wavelet-based texture, size, aspect ratio, region composition, low depth of field, and shape convexity. These features are used to build classifiers and regression models to predict aesthetic ratings. The study finds significant correlations between visual properties and aesthetic scores, with certain features showing strong discrimination capabilities. The results suggest that visual features can predict human-rated aesthetics scores with some success, providing a foundation for content-based image retrieval and digital photography applications.This paper explores the computational inference of aesthetic quality in photographic images using visual content. The authors treat the task as a machine learning problem, leveraging peer-rated data from the online photo-sharing website *Photo.net*. They extract 56 visual features based on intuition and observed trends, including exposure, colorfulness, saturation, hue, rule of thirds, familiarity, wavelet-based texture, size, aspect ratio, region composition, low depth of field, and shape convexity. These features are used to build classifiers and regression models to predict aesthetic ratings. The study finds significant correlations between visual properties and aesthetic scores, with certain features showing strong discrimination capabilities. The results suggest that visual features can predict human-rated aesthetics scores with some success, providing a foundation for content-based image retrieval and digital photography applications.