No-reference image quality assessment with deep convolutional neural networks

2016 ◽  
Author(s):  
Yuming Li ◽  
Lai-Man Po ◽  
Litong Feng ◽  
Fang Yuan
Keyword(s):  
Neural Networks ◽  
Image Quality ◽  
Quality Assessment ◽  
Convolutional Neural Networks ◽  
Image Quality Assessment ◽  
Reference Image ◽  
Deep Convolutional Neural Networks

Evaluating Convolutional Neural Networks for No -Reference Image Quality Assessment

2021 ◽  
Author(s):  
Kyriakos D. Apostolidis ◽  
Theodore Polyzos ◽  
Ioannis Grigoriadis ◽  
George A. Papakostas
Keyword(s):  
Neural Networks ◽  
Image Quality ◽  
Quality Assessment ◽  
Convolutional Neural Networks ◽  
Image Quality Assessment ◽  
Reference Image

scholarly journals Improve of contrast-distorted image quality assessment based on convolutional neural networks

2019 ◽  
Vol 9 (6) ◽  
pp. 5604
Author(s):  
Ismail Taha Ahmed ◽  
Chen Soong Der ◽  
Norziana Jamil ◽  
Mohamad Afendee Mohamed
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Image Quality ◽  
Quality Assessment ◽  
Convolutional Neural Networks ◽  
Image Quality Assessment ◽  
Reference Image ◽  
Human Intervention ◽  
Domain Expertise ◽  
Image Quality Metric

<span lang="EN-US">Many image quality assessment algorithms (IQAs) have been developed during the past decade. However, most of them are designed for images distorted by compression, noise and blurring. There are very few IQAs designed specifically for Contrast Distorted Images (CDI), e.g. Reduced-reference Image Quality Metric for Contrast-changed images (RIQMC) and NR-IQA for Contrast-Distorted Images (NR-IQA-CDI). The existing NR-IQA-CDI relies on features designed by human or handcrafted features because considerable level of skill, domain expertise and efforts are required to design good handcrafted features. Recently, there is great advancement in machine learning with the introduction of deep learning through Convolutional Neural Networks (CNN) which enable machine to learn good features from raw image automatically without any human intervention. Therefore, it is tempting to explore the ways to transform the existing NR-IQA-CDI from using handcrafted features to machine-crafted features using deep learning, specifically Convolutional Neural Networks (CNN).The results show that NR-IQA-CDI based on non-pre-trained CNN (NR-IQA-CDI-NonPreCNN) significantly outperforms those which are based on handcrafted features. In addition to showing best performance, NR-IQA-CDI-NonPreCNN also enjoys the advantage of zero human intervention in designing feature, making it the most attractive solution for NR-IQA-CDI.</span>

Sign in / Sign up

Export Citation Format

Share Document