Quality assessment measure based on image structural properties

Asatryan, David; Егиазарян, Карен

doi:10.1109/lnla.2009.5278400

Cited by 13 publications

(2 citation statements)

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“…Step 2 -Calculating the Gradient Value of R and P Structural information in the images is represented by the gradient, which is an intuitive and simple measure of structural distortion and is widely used in objective image quality assessment (Asatryan and Egiazarian, 2009;Liang et al, 2010;Kim, Han, and Park, 2010;Zhu and Wang, 2012). Two gradient values, G r for the raw image and G p for the processed image, are obtained as…”

Section: 22mentioning

confidence: 99%

Objective Image-Quality Assessment for High-Resolution Photospheric Images by Median Filter-Gradient Similarity

et al. 2015

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All next generation ground-based and space-based solar telescopes require a good quality assessment metric in order to evaluate their imaging performance. In this paper, a new image quality metric, the median filter gradient similarity (MFGS) is proposed for photospheric images. MFGS is a noreference/blind objective image quality metric (IQM) by a measurement result between 0 and 1 and has been performed on short-exposure photospheric images captured by the New Vacuum Solar Telescope (NVST) of the Fuxian Solar Observatory and by the Solar Optical Telescope (SOT) onboard the Hinode satellite, respectively. The results show that: (1)the measured value of MFGS changes monotonically from 1 to 0 with degradation of image quality; (2)there exists a linear correlation between the measured values of MFGS and root-mean-squarecontrast (RMS-contrast) of granulation; (3)MFGS is less affected by the image contents than the granular RMS-contrast. Overall, MFGS is a good alternative for the quality assessment of photospheric images.

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Section: 22mentioning

confidence: 99%

Objective Image-Quality Assessment for High-Resolution Photospheric Images by Median Filter-Gradient Similarity

et al. 2015

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“…However, formulas (1) -(2) allow, based on an approximate implementation of the central limit theorem, with at least some reservations for small neighborhoods of each pixel, to propose models that give approximate solutions of certain problems. Proceeding from this observation, for gradient magnitude distribution we take the two-parameter Weibull distribution density function [9]. Note that in the literature on image processing, the Weibull distribution, the generalized Gaussian distribution and other distributions from the exponential family are often used to describe the gradient magnitude.…”

Section: Basic Mathematical Modelsmentioning

confidence: 99%

Gradient-based technique for image structural analysis and applications

Asatryan¹

2019

Computer Optics

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This paper is devoted to application of gradients field characteristics in selected problems of image intellectual analysis and processing. To analyse the properties and structure of an image several approaches and models based on the use of the gradients field characteristics, are proposed. In this paper, models based on Weibull distribution are considered, an image dominant direction estimation algorithm using the parameters of scattering ellipse of gradients field components is proposed, and a similarity measure of two images with arbitrary dimensions and orientation is proposed. Some examples of applications of these models for estimation of blur and structuredness of an image, for the quality assessment of resizing and rotating algorithms, as well as for detection of a specified object on the image delivered by an unmanned aerial vehicle, are given.

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