Perceptual quality assessment plays a vital role in the visual communication systems owing
to the existence of quality degradations introduced in various stages of visual signal …

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 …

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 …

Comparative settings (eg. pairwise choice, listwise ranking) have been adopted by a wide
range of subjective studies for image quality assessment (IQA), as it inherently standardizes …

We propose a deep bilinear model for blind image quality assessment that works for both
synthetically and authentically distorted images. Our model constitutes two streams of deep …

Recently, increasing interest has been drawn in exploiting deep convolutional neural
networks (DCNNs) for no-reference image quality assessment (NR-IQA). Despite of the …

The rapid evolution of Multi-modality Large Language Models (MLLMs) has catalyzed a shift
in computer vision from specialized models to general-purpose foundation models …

We propose a no-reference image quality assessment (NR-IQA) approach that learns from
rankings (RankIQA). To address the problem of limited IQA dataset size, we train a Siamese …

The great content diversity of real-world digital images poses a grand challenge to image
quality assessment (IQA) models, which are traditionally designed and validated on a …

We introduce a Depicted image Quality Assessment method (DepictQA), overcoming the
constraints of traditional score-based methods. DepictQA allows for detailed, language …