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

Abstract

We present an interactive application that enables users to improve the visual aesthetics of their digital photographs using spatial recomposition. Unlike earlier work that focuses either on photo quality assessment or interactive tools for photo editing, we enable the user to make informed decisions about improving the composition of a photograph and to implement them in a single framework. Specifically, the user interactively selects a foreground object and the system presents recommendations for where it can be moved in a manner that optimizes a learned aesthetic metric while obeying semantic constraints. For photographic compositions that lack a distinct foreground object, our tool provides the user with cropping or expanding recommendations that improve its aesthetic quality. We learn a support vector regression model for capturing image aesthetics from user data and seek to optimize this metric during recomposition. Rather than prescribing a fully-automated solution, we allow user-guided object segmentation and inpainting to ensure that the final photograph matches the user's criteria. Our approach achieves 86% accuracy in predicting the attractiveness of unrated images, when compared to their respective human rankings. Additionally, 73% of the images recomposited using our tool are ranked more attractive than their original counterparts by human raters.

Automatic salient object segmentation based on context and shape prior

“…The second one as a location prior, has been studied in photo quality assessment [4,8,20], known as Rule of Thirds. The rule indicates that to attract people's attention, the object of interest, or main element in a photograph should lie at one of the four intersections to approximate the "golden ratio" (about 0.618).…”

Aesthetic Communities Detection Using Latent Semantic Analysis for Image Enhancement

“…In [7] content based photo quality assessment is explained which mainly focuses on subject area detection with new global and regional features. Multimedia application [8] which most of the time users use for assessing the aesthetic quality of a photographers using geometric rule of composition. The spatial structure i.e.…”

Tell Me What You Like and I’ll Tell You What You Are: Discriminating Visual Preferences on Flickr Data

“…Motivated by this promising result, we investigated in detail the issues discussed in the two next sections. 5 The dataset is available upon request at http://profs.sci.univr.it/~{}cristanm/projects/perpre.html Fig. 2.…”

“…Other guidelines are rules-of-thumbs (e.g., "rule of thirds","visual weight balance", etc. [5]) and all of them are modeled in a computational sense by the field of Computational Media Aesthetics (CMA) [6]. Many CMA applications have been developed: from aesthetic photo ranking [7,8] and preference-aware view recommendation systems [9], to picture quality analysis [10,11].…”