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 …

We aim at advancing blind image quality assessment (BIQA), which predicts the human
perception of image quality without any reference information. We develop a general and …

We introduce a Depicted image Quality Assessment method (DepictQA), overcoming the
constraints of traditional score-based approaches. DepictQA leverages Multi-modal Large …

This paper investigates how to blindly evaluate the visual quality of an image by learning
rules from linguistic descriptions. Extensive psychological evidence shows that humans …

Blind or no-reference (NR) perceptual picture quality prediction is a difficult, unsolved
problem of great consequence to the social and streaming media industries that impacts …

Current top-performing blind perceptual image quality prediction models are generally
trained on legacy databases of human quality opinion scores on synthetically distorted …

The explosion of visual content available online underscores the requirement for an
accurate machine assessor to robustly evaluate scores across diverse types of visual …

Blind image quality assessment (BIQA) aims to predict perceptual image quality scores
without access to reference images. State-of-the-art BIQA methods typically require subjects …

We consider the problem of obtaining image quality representations in a self-supervised
manner. We use prediction of distortion type and degree as an auxiliary task to learn …

Abstract Automatic Perceptual Image Quality Assessment is a challenging problem that
impacts billions of internet, and social media users daily. To advance research in this field …