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

In this article, we perform an exhaustive analysis of the use of Artificial Neural Networks and
Deep Learning in the Visual Arts. We begin by introducing changes in Artificial Intelligence …

Measuring the perception of visual content is a long-standing problem in computer vision.
Many mathematical models have been developed to evaluate the look or quality of an …

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 …

Generative AI models have shown impressive ability to produce images with text prompts,
which could benefit creativity in visual art creation and self-expression. However, it is …

Multi-modality large language models (MLLMs) as represented by GPT-4V have introduced
a paradigm shift for visual perception and understanding tasks that a variety of abilities can …

The rapid increase in user-generated-content (UGC) videos calls for the development of
effective video quality assessment (VQA) algorithms. However, the objective of the UGC …

We are witnessing a novel era of creativity where anyone can create digital content via
prompt-based learning (known as prompt engineering). This article investigates prompt …

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 …

As smartphones become people's primary cameras to take photos, the quality of their
cameras and the associated computational photography modules has become a de facto …