Photo quality assessment (PQA) aims at computationally and precisely evaluating the quality of images from the aspect of aesthetic. Image aesthetic is strongly correlated with composition. However, few existing works have taken composition into consideration. Besides, existing composition features are typically hand-crafted. In this paper, we propose a novel end-to-end framework for representation learning of image composition. Specially, we build a fully connected graph based on deep features in Convolutional Neural Networks (CNNs). In the graph, edge attributes i.e. similarities between deep features at different positions are used for representing image composition. Besides, we use global attributes of the graph to represent miscellaneous aesthetic aspects. Finally, we use a gate unit to combine both composition features and miscellaneous aesthetic features for aesthetic prediction. The whole network can be trained in an end-to-end manner. Experimental results show that the proposed techniques significantly improves the prediction precision of aesthetic and composition over various datasets. We have released our codes at: .

照片质量评估（PQA）旨在从美学的角度上，通过计算精确地评估图像的质量。图像美感与构图密切相关。但是，很少有现有的作品考虑到构图。此外，现有的构图特征通常是手工制作的。在本文中，我们提出了一种新颖的端到端框架，用于图像学习的表示学习。特别地，我们基于卷积神经网络（CNN）的深层功能构建完全连接的图。在图中，边缘属性（即不同位置的深层特征之间的相似性）用于表示图像合成。此外，我们使用图形的全局属性来表示其他美学方面。最后，我们使用门单元将构图特征和其他美学特征结合起来，以进行美学预测。可以以端到端的方式训练整个网络。实验结果表明，所提出的技术大大提高了各种数据集的美学和构图预测精度。我们已在以下位置发布了代码：。