A framework for photo-quality assessment and enhancement based on visual aesthetics

316 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Content-based photo quality assessment (2011, methodology): “ous relevant features such as “geometry composition”[12] and “visual balance”[1] in most categories.”
RAPID: Rating Pictorial Aesthetics using Deep Learning (2014, background): “Various handcrafted aesthetics-relevant features have been proposed [6, 13, 21, 3, 20, 7, 26, 27], including low-level image statistics such as distributions of edges and color histograms, and high-level photographic rules such as the rule of thirds.”
Image Aesthetic Assessment: An experimental survey (2016, methodology): “[16], [40] propose compositional features using relative foreground position and visual weight ratio to model the relations of foreground objects and the background scene, then an support vector regressor is trained.”
Automatic salient object segmentation based on context and shape prior (2011, background): “The second one as a location prior, has been studied in photo quality assessment [4, 8, 20], known as Rule of Thirds.”
Attentive Systems: A Survey (2017, methodology): “Photo-quality assessment and improvement are two areas that have particularly attracted research attention (Bhattacharya et al. 2010) apply a saliency map to estimate visual attention distribution in photographs in order to infer the geometric context of a scene. With the help of the above methods, ”