A key notion in numerous data mining and machine learning (ML) algorithms says that the
training data and testing data are essentially in the similar feature space and also have the …

Abstract No-Reference Image Quality Assessment (NR-IQA) aims to assess the perceptual
quality of images in accordance with human subjective perception. Unfortunately, existing …

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 …

Underwater image enhancement is such an important low-level vision task with many
applications that numerous algorithms have been proposed in recent years. These …

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 …

We propose a deep bilinear model for blind image quality assessment that works for both
synthetically and authentically distorted images. Our model constitutes two streams of deep …

Recently, increasing interest has been drawn in exploiting deep convolutional neural
networks (DCNNs) for no-reference image quality assessment (NR-IQA). Despite of the …

Automatically learned quality assessment for images has recently become a hot topic due to
its usefulness in a wide variety of applications, such as evaluating image capture pipelines …

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 …

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 …