This article reviews recent computer vision techniques used in the assessment of image
aesthetic quality. Image aesthetic assessment aims at computationally distinguishing high …

Image quality assessment (IQA) is an important research topic for understanding and
improving visual experience. The current state-of-the-art IQA methods are based on …

Assessing the aesthetics of an image is challenging, as it is influenced by multiple factors
including composition, color, style, and high-level semantics. Existing image aesthetic …

Automatically learned quality assessment for images has recently become a hot topic due to
its usefulness in a wide variety of applications, such as evaluating image capture pipelines …

We study the problem of photo crop**, which aims to find a crop** window of an input
image to preserve as much as possible its important parts while being aesthetically …

We focus on a fundamental task of detecting meaningful line structures, aka, semantic line,
in natural scenes. Many previous methods regard this problem as a special case of object …

We propose an effective deep learning approach to aesthetics quality assessment that relies
on a new type of pre-trained features, and apply it to the AVA data set, the currently largest …

Computational image aesthetic evaluation is a computable human aesthetic perception and
judgment realized by machines, which has a significant impact on a variety of applications …

Deep convolutional neural networks (CNN) have recently been shown to generate
promising results for aesthetics assessment. However, the performance of these deep CNN …

Learning computational models of image aesthetics can have a substantial impact on visual
art and graphic design. Although automatic image aesthetics assessment is a challenging …