Research on Intelligent Algorithm for Image Quality Evaluation Based on Image Distortion Type and Convolutional Neural Network

Currently, three-dimensional (3D) imaging has been successfully applied in medical health, movie viewing, games, and military. To make 3D images more pleasant to the eyes, the accurate judgement of image quality becomes the key step in content preparation, compression, and transmission in 3D imaging. However, there is not yet a satisfactory evaluation method that objectively assesses the quality of 3D images. To solve the problem, this paper explores the evaluation and optimization of 3D image quality based on convolutional neural network (CNN). Specifically, a 3D image quality evaluation model was constructed, and a 3D image quality evaluation algorithm was proposed based on global and local features. Next, the authors expounded on the preprocessing steps of salient regions in images, depicted the fusion process between global and local quality evaluations, and provided the way to process 3D image samples and acquire contrast-distorted images. The proposed algorithm was proved effective through experiments.

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

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2020.64.1.010505 ◽

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.

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Sonar image quality evaluation using deep neural network

IET Image Processing ◽

10.1049/ipr2.12199 ◽

2021 ◽

Author(s):

Huiqing Zhang ◽

Shuo Li ◽

Donghao Li ◽

Zichen Wang ◽

Qixiang Zhou ◽

...

Keyword(s):

Neural Network ◽

Image Quality ◽

Deep Neural Network ◽

Quality Evaluation ◽

Image Quality Evaluation ◽

Sonar Image

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Hyperspectral image quality evaluation using generalized regression neural network

Signal Processing Image Communication ◽

10.1016/j.image.2020.115785 ◽

2020 ◽

Vol 83 ◽

pp. 115785

Author(s):

Rui Hou ◽

Yang Hu ◽

YunHao Zhao ◽

Huan Liu

Keyword(s):

Neural Network ◽

Image Quality ◽

Quality Evaluation ◽

Hyperspectral Image ◽

Generalized Regression Neural Network ◽

Image Quality Evaluation ◽

Generalized Regression

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A Dual-Path Deep Neural Network for Sonar Image Quality Evaluation

2020 IEEE International Conference on Networking, Sensing and Control (ICNSC) ◽

10.1109/icnsc48988.2020.9238081 ◽

2020 ◽

Author(s):

Huiqing Zhang ◽

Shuo Li ◽

Donghao Li

Keyword(s):

Neural Network ◽

Image Quality ◽

Deep Neural Network ◽

Quality Evaluation ◽

Image Quality Evaluation ◽

Sonar Image

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Nonreference Image Quality Evaluation Algorithm Based on Wavelet Convolutional Neural Network and Information Entropy

Entropy ◽

10.3390/e21111070 ◽

2019 ◽

Vol 21 (11) ◽

pp. 1070 ◽

Cited By ~ 1

Author(s):

Jinhua Liu ◽

Mulian Xu ◽

Xinye Xu ◽

Yuanyuan Huang

Keyword(s):

Neural Network ◽

Wavelet Transform ◽

Image Quality ◽

Information Entropy ◽

Quality Evaluation ◽

Evaluation Method ◽

Visual Quality ◽

Image Quality Evaluation ◽

Band Image

The image quality evaluation method, based on the convolutional neural network (CNN), achieved good evaluation performance. However, this method can easily lead the visual quality of image sub-blocks to change with the spatial position after the image is processed by various distortions. Consequently, the visual quality of the entire image is difficult to reflect objectively. On this basis, this study combines wavelet transform and CNN method to propose an image quality evaluation method based on wavelet CNN. The low-frequency, horizontal, vertical, and diagonal sub-band images decomposed by wavelet transform are selected as the inputs of convolution neural network. The feature information in multiple directions is extracted by convolution neural network. Then, the information entropy of each sub-band image is calculated and used as the weight of each sub-band image quality. Finally, the quality evaluation values of four sub-band images are weighted and fused to obtain the visual quality values of the entire image. Experimental results show that the proposed method gains advantage from the global and local information of the image, thereby further improving its effectiveness and generalization.

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