Image Quality Assessment Using the SSIM and the Just Noticeable Difference Paradigm

Introduction/purpose: The paper presents interesting research related to the performance analysis of the picture-wise just noticeable difference (JND) prediction model and its application in the quality assessment of images with JPEG compression. Methods: The performance analysis of the JND model was conducted in an indirect way by using the publicly available results of subject-rated image datasets with the separation of images into two classes (above and below the threshold of visible differences). In the performance analysis of the JND prediction model and image quality assessment, five image datasets were used, four of which come from the visible wavelength range, and one dataset is intended for remote sensing and surveillance with images from the infrared part of the electromagnetic spectrum. Results: The pap 86 er shows that using a picture-wise JND model, subjective image quality assessment scores can be estimated with better accuracy, leading to significant performance improvements of the traditional peak signal-to-noise ratio (PSNR). The gain achieved by introducing the picture-wise JND model in the objective assessment depends on the chosen dataset and the results of the initial simple to compute PSNR measure, and it was obtained on all five datasets. The mean linear correlation coefficient (for five datasets) between subjective and PSNR objective quality estimates increased from 74% (traditional PSNR) to 90% (picture-wise JND PSNR). Conclusion: Further improvement of the JND-based objective measure can be obtained by improving the picture-wise model of JND prediction.

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Just-noticeable difference binary pattern for reduced reference image quality assessment

Optical Engineering ◽

10.1117/1.oe.58.9.093105 ◽

2019 ◽

Vol 58 (09) ◽

pp. 1

Author(s):

Xikui Miao ◽

Dah-Jye Lee

Keyword(s):

Image Quality ◽

Quality Assessment ◽

Image Quality Assessment ◽

Just Noticeable Difference ◽

Reference Image

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Multiple just-noticeable-difference-based no-reference stereoscopic image quality assessment

Applied Optics ◽

10.1364/ao.58.000340 ◽

2019 ◽

Vol 58 (2) ◽

pp. 340

Author(s):

Zijin Gu ◽

Yong Ding ◽

Ruizhe Deng ◽

Xiaodong Chen ◽

Andrey S. Krylov

Keyword(s):

Image Quality ◽

Quality Assessment ◽

Image Quality Assessment ◽

Just Noticeable Difference ◽

Stereoscopic Image

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A Novel Hybrid Image Quality Assessment Algorithm

Indian Journal Of Applied Research ◽

10.15373/2249555x/apr2014/31 ◽

2011 ◽

Vol 4 (4) ◽

pp. 107-108

Author(s):

Deepa Maria Thomas ◽

◽

S. John Livingston

Keyword(s):

Image Quality ◽

Quality Assessment ◽

Image Quality Assessment

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Implementation of No Reference Distortion Patch Features Image Quality Assessment Algorithm Based on Human Visual System

International Journal on Communications Antenna and Propagation (IRECAP) ◽

10.15866/irecap.v4i5.4166 ◽

2014 ◽

Vol 4 (5) ◽

pp. 195

Author(s):

Ch. Subrahmanyam ◽

D. Venkata Rao ◽

N. Usha Rani

Keyword(s):

Image Quality ◽

Visual System ◽

Quality Assessment ◽

Human Visual System ◽

Image Quality Assessment

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

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.9.iqsp-323 ◽

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.

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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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