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 …

Image quality assessment (IQA) has been a topic of intense research over the last several
decades. With each year comes an increasing number of new IQA algorithms, extensions of …

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 …

We propose a multi-task end-to-end optimized deep neural network (MEON) for blind image
quality assessment (BIQA). MEON consists of two sub-networks-a distortion identification …

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

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 …

Blind image quality assessment (BIQA) research aims to develop a perceptual model to
evaluate the quality of distorted images automatically and accurately without access to the …

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 …

We develop an efficient general-purpose no-reference (NR) image quality assessment (IQA)
model that utilizes local spatial and spectral entropy features on distorted images. Using a 2 …