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Subjectively correlated estimation of noise due to blurriness distortion based on auto-regressive model using the Yule–Walker equations

Nakhaei, Arash Ashtari and Helfroush, Mohammad Sadegh and Danyali, Habibollah and Ghanbari, Mohammed (2018) 'Subjectively correlated estimation of noise due to blurriness distortion based on auto-regressive model using the Yule–Walker equations.' IET Image Processing, 12 (10). 1788 - 1796. ISSN 1751-9659

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Abstract

In this study, a block-based estimation of noise due to blurriness distortion is proposed based on auto-regressive (AR) modelling. In the proposed method; a de-correlated, low-energy version of the blurred image is auto regressively modelled. To this end, AR parameters are estimated using the Yule–Walker equations. As these equations include auto-correlation function (ACF) coefficients, ACF estimation is also required. The Yule–Walker equations are solved making use of Durbin–Levinson algorithm. Finally, noise energy is mathematically defined and computed for each block. Since blurriness is a signal-dependent image distortion, estimating and describing its characteristics via a noise like that of the AR model input, is significant. In fact, extracting features of such ‘noise’ can lead to the design and development of a new method of image quality metrics. Inspired by the ‘stem cells’ concept in medical science that is convertible to other cell types, the AR model input is called ‘stem noise’. To visualise contribution of the ‘Stem Noise’ in the reconstruction of blurriness image distortion, a map called stem noise energy map is created. It is shown that the characteristics of the estimated noise energy are well correlated with the human subjective scores.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 13 Sep 2018 14:29
Last Modified: 27 Mar 2019 11:16
URI: http://repository.essex.ac.uk/id/eprint/22388

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