A method of image quality assessment based on visual low-level features

2015 ◽  
Vol 41 (3) ◽  
pp. 280-284
Author(s):  
刘玄玄 LIU Xuanxuan ◽  
王晓红 WANG Xiaohong ◽  
章婷 ZHANG Ting
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Low Level

scholarly journals A color intensity invariant low-level feature optimization framework for image quality assessment

2016 ◽  
Vol 10 (6) ◽  
pp. 1169-1176 ◽  
Author(s):  
Navaneeth K. Kottayil ◽  
Irene Cheng ◽  
Frederic Dufaux ◽  
Anup Basu
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Color Intensity ◽  
Low Level ◽  
Optimization Framework ◽  
Feature Optimization

scholarly journals Selecting Low-level Features for Image Quality Assessment by Statistical Methods

2010 ◽  
Vol 18 (2) ◽  
pp. 183 ◽  
Author(s):  
Atidel Lahouhou ◽  
Emmanuel Viennet ◽  
Azeddine Beghdadi
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Statistical Methods ◽  
Image Quality Assessment ◽  
Low Level

Multi-scale structural image quality assessment based on two-stage low-level features

2014 ◽  
Vol 40 (4) ◽  
pp. 1101-1110 ◽  
Author(s):  
Li Guo ◽  
Wei-long Chen ◽  
Yu Liao ◽  
Hong-hua Liao ◽  
Jia Hu
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Two Stage ◽  
Low Level ◽  
Multi Scale ◽  
Structural Image

A Novel Hybrid Image Quality Assessment Algorithm

2011 ◽  
Vol 4 (4) ◽  
pp. 107-108
Author(s):  
Deepa Maria Thomas ◽  
◽  
S. John Livingston
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment

Document Image Quality Assessment with Relaying Reference to Determine Minimum Readable Resolution for Compression

2020 ◽  
Vol 2020 (9) ◽  
pp. 323-1-323-8
Author(s):  
Litao Hu ◽  
Zhenhua Hu ◽  
Peter Bauer ◽  
Todd J. Harris ◽  
Jan P. Allebach
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Research Area ◽  
Input Image ◽  
Quality Score ◽  
Document Image ◽  
Digital Cameras ◽  
Active Research ◽  
Traditional Approaches

Image quality assessment has been a very active research area in the field of image processing, and there have been numerous methods proposed. However, most of the existing methods focus on digital images that only or mainly contain pictures or photos taken by digital cameras. Traditional approaches evaluate an input image as a whole and try to estimate a quality score for the image, in order to give viewers an idea of how “good” the image looks. In this paper, we mainly focus on the quality evaluation of contents of symbols like texts, bar-codes, QR-codes, lines, and hand-writings in target images. Estimating a quality score for this kind of information can be based on whether or not it is readable by a human, or recognizable by a decoder. Moreover, we mainly study the viewing quality of the scanned document of a printed image. For this purpose, we propose a novel image quality assessment algorithm that is able to determine the readability of a scanned document or regions in a scanned document. Experimental results on some testing images demonstrate the effectiveness of our method.

No-Reference Image Quality Assessment Based on Multi-Order Gradients Statistics

2020 ◽  
Vol 64 (1) ◽  
pp. 10505-1-10505-16
Author(s):  
Yin Zhang ◽  
Xuehan Bai ◽  
Junhua Yan ◽  
Yongqi Xiao ◽  
C. R. Chatwin ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Assessment Method ◽  
Image Distortion ◽  
Image Feature ◽  
Reference Image ◽  
Gradient Magnitude ◽  
Distortion Type

Abstract A new blind image quality assessment method called No-Reference Image Quality Assessment Based on Multi-Order Gradients Statistics is proposed, which is aimed at solving the problem that the existing no-reference image quality assessment methods cannot determine the type of image distortion and that the quality evaluation has poor robustness for different types of distortion. In this article, an 18-dimensional image feature vector is constructed from gradient magnitude features, relative gradient orientation features, and relative gradient magnitude features over two scales and three orders on the basis of the relationship between multi-order gradient statistics and the type and degree of image distortion. The feature matrix and distortion types of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion type; the feature matrix and subjective scores of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion degree. A series of comparative experiments were carried out using Laboratory of Image and Video Engineering (LIVE), LIVE Multiply Distorted Image Quality, Tampere Image, and Optics Remote Sensing Image databases. Experimental results show that the proposed method has high distortion type judgment accuracy and that the quality score shows good subjective consistency and robustness for all types of distortion. The performance of the proposed method is not constricted to a particular database, and the proposed method has high operational efficiency.

Image quality assessment based on structural orientation information

2010 ◽  
Vol 30 (6) ◽  
pp. 1622-1625
Author(s):  
Qiang WANG ◽  
De-qun LIANG ◽  
Sheng BI ◽  
Yu BO
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Structural Orientation ◽  
Orientation Information

Image quality assessment based on local invariant features

2013 ◽  
Vol 32 (12) ◽  
pp. 3369-3372 ◽  
Author(s):  
Ya-zhou YANG ◽  
Xiao-qing YING ◽  
Guang-quan CHENG ◽  
Dan TU
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Local Invariant ◽  
Invariant Features ◽  
Local Invariant Features
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