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 …

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 …

No-reference (NR) perceptual video quality assessment (VQA) is a complex, unsolved, and
important problem for social and streaming media applications. Efficient and accurate video …

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

In this work, we investigate the use of deep learning for distortion-generic blind image
quality assessment. We report on different design choices, ranging from the use of features …

In recent years, no-reference/blind image quality assessment (NR-IQA), as a fundamental
but challenging research problem, has been attracting significant attention in the field of …

This paper studies one interesting problem: how does the deep neural network (DNN)
architecture affect the image quality assessment (IQA) performance? In order to find the …

Recently, the visual quality evaluation of screen content images (SCIs) has become an
important and timely emerging research theme. This article presents an effective and novel …

Blind image quality assessment (BIQA) aims at precisely estimating human perceived image
quality with no access to a reference. Recently, several attempts have been made to …

Blind image quality assessment (BIQA) methods aim to predict quality of images as
perceived by humans without access to a reference image. Recently, deep learning …