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 natural scene statistic-based distortion-generic blind/no-reference (NR)
image quality assessment (IQA) model that operates in the spatial domain. The new model …

Objective assessment of image quality is fundamentally important in many image processing
tasks. In this paper, we focus on learning blind image quality assessment (BIQA) models …

Blind image quality assessment (BIQA) aims to evaluate the perceptual quality of a distorted
image without information regarding its reference image. Existing BIQA models usually …

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 …

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

In most practical situations, the compression or transmission of images and videos creates
distortions that will eventually be perceived by a human observer. Vice versa, image and …

The image gradient is a commonly computed image feature and a potentially predictive
factor for image quality assessment (IQA). Indeed, it has been successfully used for both full …

State-of-the-art algorithms for blind image quality assessment (BIQA) typically have two
categories. The first category approaches extract natural scene statistics (NSS) as features …

Abstract Image Quality Assessment (IQA) plays an important role in assessing any new
hardware, software, image acquisition techniques, image reconstruction or post-processing …