Effective and efficient photo quality assessment

Abstract

Automatic photo quality assessment from the perspective of visual aesthetics is a hot research topic due to its potential need in numerous applications. It tries to automatically determine whether a given image has "high" or "low" quality according to the image's visual content. Most existing researches in photo quality assessment predominantly focus on exploring hand-crafted features which may be potentially related to highlevel aesthetic attributes. Most of those features are designed under the guidance of some common photography rules and prior knowledge. However, due to the subjectivity and complexity of humans' aesthetic activities, automatic image aesthetic quality assessment is very challenging. Those features are not effective enough and show varying performance on different datasets. Besides, they often require high computational cost. In this paper, we propose a set of compact aesthetic features which are not only effective but also highly efficient. We test those features on two large scale real world image datasets. The experimental results demonstrate that the proposed features achieve the best performance consistently over different datasets with a much lower computational complexity.

An Application for Aesthetic Quality Assessment in Photography with Interpretability Features

“…This was done not only in the training set, but also in the test set, which makes the problem easier, but further from reality. For example, in [17], we find a comparison between hand-crafted features within the AVA problem domain, where the proposed features outperform the general image descriptors. Nevertheless, this research work only employed 20% of the whole AVA dataset, which simplifies the original task.…”

Application of Partial Differential Equation Image Classification Methods to the Aesthetic Evaluation of Images

“…Similarly, the low-level and high-level features of images are also applied to the quality evaluation of art works. Dong and Tian [14] considered the importance of the main area, adopted the main background separation, and extracted the color histogram of the main area and the ratio of the main area to the size of the whole image to distinguish the quality of the picture. On the basis of previous studies, Wang et al [15] extracted features such as color, texture, depth of field, complexity, and color harmony and used these features to build an aesthetic classification model, which achieved good results.…”