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.

Blind Image Quality Assessment Based on Natural Statistics of Double-Opponency

2018 ◽  
Vol 22 (5) ◽  
pp. 725-730 ◽  
Author(s):  
Edwin Sybingco ◽  
◽  
Elmer P. Dadios
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Quality Assessment ◽  
Gaussian Distribution ◽  
Image Quality Assessment ◽  
Assessment Method ◽  
Feedforward Neural Network ◽  
Reference Image ◽  
Generalized Gaussian Distribution ◽  
Natural Statistics

One of the challenges in image quality assessment (IQA) is to determine the quality score without the presence of the reference image. In this paper, the authors proposed a no-reference image quality assessment method based on the natural statistics of double-opponent (DO) cells. It utilizes the statistical modeling of the three opponency channels using the generalized Gaussian distribution (GGD) and asymmetric generalized Gaussian distribution (AGGD). The parameters of GGD and AGGD are then applied to feedforward neural network to predict the image quality. Result shows that for any opposing channels, its natural statistics parameters when applied to feedforward neural network can achieve satisfactory prediction of image quality.

Precise No-Reference Image Quality Evaluation Based on Distortion Identification

2021 ◽  
Vol 17 (3s) ◽  
pp. 1-21
Author(s):  
Chenggang Yan ◽  
Tong Teng ◽  
Yutao Liu ◽  
Yongbing Zhang ◽  
Haoqian Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Quality Assessment ◽  
Large Scale ◽  
Quality Evaluation ◽  
Image Quality Assessment ◽  
State Of The Art ◽  
Gaussian White Noise ◽  
The State ◽  
Reference Image

The difficulty of no-reference image quality assessment (NR IQA) often lies in the lack of knowledge about the distortion in the image, which makes quality assessment blind and thus inefficient. To tackle such issue, in this article, we propose a novel scheme for precise NR IQA, which includes two successive steps, i.e., distortion identification and targeted quality evaluation. In the first step, we employ the well-known Inception-ResNet-v2 neural network to train a classifier that classifies the possible distortion in the image into the four most common distortion types, i.e., Gaussian white noise (WN), Gaussian blur (GB), jpeg compression (JPEG), and jpeg2000 compression (JP2K). Specifically, the deep neural network is trained on the large-scale Waterloo Exploration database, which ensures the robustness and high performance of distortion classification. In the second step, after determining the distortion type of the image, we then design a specific approach to quantify the image distortion level, which can estimate the image quality specially and more precisely. Extensive experiments performed on LIVE, TID2013, CSIQ, and Waterloo Exploration databases demonstrate that (1) the accuracy of our distortion classification is higher than that of the state-of-the-art distortion classification methods, and (2) the proposed NR IQA method outperforms the state-of-the-art NR IQA methods in quantifying the image quality.

A WAVELET-BASED IMAGE QUALITY ASSESSMENT METHOD

2008 ◽  
Vol 06 (04) ◽  
pp. 541-551 ◽  
Author(s):  
WEN LU ◽  
XINBO GAO ◽  
DACHENG TAO ◽  
XUELONG LI
Keyword(s):  
Image Processing ◽  
Computational Complexity ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Assessment Method ◽  
Reference Image ◽  
Subjective Perception ◽  
Good Accordance ◽  
Quality Assessment Method

Image quality is a key characteristic in image processing,10,11 image retrieval,12,13 and biometrics.14 In this paper, a novel reduced-reference image quality assessment method is proposed based on wavelet transform. By simulating the human visual system, we take the variance of the visual sensitive coefficients into account to measure a distorted image. The computational complexity of the proposed method is much lower compared with some existing methods. Experimental results demonstrate its advantages in terms of correlation coefficient, outlier ratio, transmitted information, and CPU cost. Moreover, it is also illustrated that the proposed method has a good accordance with human subjective perception.

Generating Image Distortion Maps Using Convolutional Autoencoders With Application to No Reference Image Quality Assessment

2019 ◽  
Vol 26 (1) ◽  
pp. 89-93 ◽  
Author(s):  
Sathya Veera Reddy Dendi ◽  
Chander Dev ◽  
Narayan Kothari ◽  
Sumohana S. Channappayya
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Image Distortion ◽  
Reference Image
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