Most publicly available image quality databases have been created under highly controlled
conditions by introducing graded simulated distortions onto high-quality photographs …

An important aim of research on the blind image quality assessment (IQA) problem is to
devise perceptual models that can predict the quality of distorted images with as little prior …

We propose a highly unsupervised, training free, no reference image quality assessment
(IQA) model that is based on the hypothesis that distorted images have certain latent …

Existing blind image quality assessment (BIQA) methods are mostly opinion-aware. They
learn regression models from training images with associated human subjective scores to …

Our approach to blind image quality assessment (IQA) is based on the hypothesis that
natural scenes possess certain statistical properties which are altered in the presence of …

Many state-of-the-art perceptual image quality assessment (IQA) algorithms share a
common two-stage structure: local quality/distortion measurement followed by pooling …

Most existing blind image quality assessment (BIQA) methods learn a regression model to
predict scalar quality scores. Such a scheme ignores the fact that an image will receive …

Deep learning methods for image quality assessment (IQA) are limited due to the small size
of existing datasets. Extensive datasets require substantial resources both for generating …

This paper proposes a novel no-reference Perception-based Image Quality Evaluator
(PIQUE) for real-world imagery. A majority of the existing methods for blind image quality …

Models for image quality assessment (IQA) are generally optimized and tested by comparing
to human ratings, which are expensive to obtain. Here, we develop a blind IQA (BIQA) …