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 …

Diffusion models (DMs) have disrupted the image super-resolution (SR) field and further
closed the gap between image quality and human perceptual preferences. They are easy to …

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 …

Blind image quality assessment (BIQA) aims to automatically evaluate the perceived quality
of a single image, whose performance has been improved by deep learning-based methods …

Abstract The goal of No-Reference Image Quality Assessment (NR-IQA) is to estimate the
perceptual image quality in accordance with subjective evaluations, it is a complex and …

Blind image quality assessment (BIQA) for authentically distorted images has always been a
challenging problem, since images captured in the wild include varies contents and diverse …

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 …

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 …

Abstract Generative Adversarial Networks (GAN) have demonstrated the potential to recover
realistic details for single image super-resolution (SISR). To further improve the visual …

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 …