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 …

Due to the attenuation and scattering of light by water, there are many quality defects in raw
underwater images such as color casts, decreased visibility, reduced contrast, et al.. Many …

Traditional blind image quality assessment (IQA) measures generally predict quality from a
sole distorted image directly. In this paper, we first introduce multiple pseudo reference …

Image recognition based on convolutional neural networks (CNNs) has recently been
shown to deliver the state-of-the-art performance in various areas of computer vision and …

Guided by the free-energy principle, generative adversarial networks (GAN)-based no-
reference image quality assessment (NR-IQA) methods have improved the image quality …

Traditional full-reference image quality assessment (IQA) metrics generally predict the
quality of the distorted image by measuring its deviation from a perfect quality image called …

Blind image quality assessment (BIQA) is a useful but challenging task. It is a promising idea
to design BIQA methods by mimicking the working mechanism of human visual system …

A good distortion representation is crucial for the success of deep blind image quality
assessment (BIQA). However, most previous methods do not effectively model the …

We propose an end-to-end saliency-guided deep neural network (SGDNet) for no-reference
image quality assessment (NR-IQA). Our SGDNet is built on an end-to-end multi-task …

We present a novel no-reference quality assessment metric, the image transferred point
cloud quality assessment (IT-PCQA), for 3D point clouds. For quality assessment, deep …