Reduced-Reference Stereoscopic Image Quality Assessment Using Gradient Sparse Representation and Structural Degradation

Reduced-reference stereoscopic image quality assessment (RRSIQA) models evaluate stereoscopic image quality degradation with partial information about the “ideal-quality” reference stereopair. On one hand, sparse representation in recent theoretical studies of visual cognition...

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Autores principales:	Jian Ma, Guoming Xu, Xiyu Han
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Reduced-reference stereoscopic image quality assessment sparse representation structural degradation kernel ridge regressing Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/1a8d0b72c42a418185a34ab5eb79ea4e
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spelling	oai:doaj.org-article:1a8d0b72c42a418185a34ab5eb79ea4e2021-12-02T00:00:34ZReduced-Reference Stereoscopic Image Quality Assessment Using Gradient Sparse Representation and Structural Degradation2169-353610.1109/ACCESS.2021.3129814https://doaj.org/article/1a8d0b72c42a418185a34ab5eb79ea4e2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9623456/https://doaj.org/toc/2169-3536Reduced-reference stereoscopic image quality assessment (RRSIQA) models evaluate stereoscopic image quality degradation with partial information about the “ideal-quality” reference stereopair. On one hand, sparse representation in recent theoretical studies of visual cognition has been proved to resemble the strategy used to represent natural images in the primary visual cortex. On the other hand, the joint statistics of gradient magnitude (GM) and Laplacian of Gaussian (LOG) features are popularly utilized to form image semantic structures. Motivated by these findings, we present a new RRSIQA metric using gradient sparse representation and structural degradation in this paper. Concretely, the proposed metric is based on two main tasks: the first task extracts the distribution statistics of visual primitives by gradient sparse representation, while the second task measures structural degradation of stereoscopic image due to the presence of distortion by extracting the joint statistics of GM and LOG features. The former, so-called the binocular perceptual visual information (PVI), aims to effectively integrates the gradient map that is sparser than the image itself. Especially, the process of binocular fusion is simulated by using the mutual information of the gradient-based visual primitives between left and right view’s images as binocular cue. Furthermore, the perceptual loss vectors are taken as the differences of binocular perceptual visual information and structural degradation between reference and distorted stereopairs. Finally, the perceptual loss vectors are utilized to calculate the quality score by a prediction function which is trained using kernel ridge regressing (KRR). The experiments are performed on the popular LIVE 3D IQA databases and Waterloo IVC 3D databases, and experimental results show highly competitive performance with the state-of-the-art algorithms. Moreover, in some challenging cases with particular asymmetric distortion types, the proposed metric can achieves the best quality prediction accuracy in LIVE 3D phase II and Waterloo IVC 3D Phase II.Jian MaGuoming XuXiyu HanIEEEarticleReduced-referencestereoscopic image quality assessmentsparse representationstructural degradationkernel ridge regressingElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 157134-157150 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Reduced-reference stereoscopic image quality assessment sparse representation structural degradation kernel ridge regressing Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Reduced-reference stereoscopic image quality assessment sparse representation structural degradation kernel ridge regressing Electrical engineering. Electronics. Nuclear engineering TK1-9971 Jian Ma Guoming Xu Xiyu Han Reduced-Reference Stereoscopic Image Quality Assessment Using Gradient Sparse Representation and Structural Degradation
description	Reduced-reference stereoscopic image quality assessment (RRSIQA) models evaluate stereoscopic image quality degradation with partial information about the “ideal-quality” reference stereopair. On one hand, sparse representation in recent theoretical studies of visual cognition has been proved to resemble the strategy used to represent natural images in the primary visual cortex. On the other hand, the joint statistics of gradient magnitude (GM) and Laplacian of Gaussian (LOG) features are popularly utilized to form image semantic structures. Motivated by these findings, we present a new RRSIQA metric using gradient sparse representation and structural degradation in this paper. Concretely, the proposed metric is based on two main tasks: the first task extracts the distribution statistics of visual primitives by gradient sparse representation, while the second task measures structural degradation of stereoscopic image due to the presence of distortion by extracting the joint statistics of GM and LOG features. The former, so-called the binocular perceptual visual information (PVI), aims to effectively integrates the gradient map that is sparser than the image itself. Especially, the process of binocular fusion is simulated by using the mutual information of the gradient-based visual primitives between left and right view’s images as binocular cue. Furthermore, the perceptual loss vectors are taken as the differences of binocular perceptual visual information and structural degradation between reference and distorted stereopairs. Finally, the perceptual loss vectors are utilized to calculate the quality score by a prediction function which is trained using kernel ridge regressing (KRR). The experiments are performed on the popular LIVE 3D IQA databases and Waterloo IVC 3D databases, and experimental results show highly competitive performance with the state-of-the-art algorithms. Moreover, in some challenging cases with particular asymmetric distortion types, the proposed metric can achieves the best quality prediction accuracy in LIVE 3D phase II and Waterloo IVC 3D Phase II.
format	article
author	Jian Ma Guoming Xu Xiyu Han
author_facet	Jian Ma Guoming Xu Xiyu Han
author_sort	Jian Ma
title	Reduced-Reference Stereoscopic Image Quality Assessment Using Gradient Sparse Representation and Structural Degradation
title_short	Reduced-Reference Stereoscopic Image Quality Assessment Using Gradient Sparse Representation and Structural Degradation
title_full	Reduced-Reference Stereoscopic Image Quality Assessment Using Gradient Sparse Representation and Structural Degradation
title_fullStr	Reduced-Reference Stereoscopic Image Quality Assessment Using Gradient Sparse Representation and Structural Degradation
title_full_unstemmed	Reduced-Reference Stereoscopic Image Quality Assessment Using Gradient Sparse Representation and Structural Degradation
title_sort	reduced-reference stereoscopic image quality assessment using gradient sparse representation and structural degradation
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/1a8d0b72c42a418185a34ab5eb79ea4e
work_keys_str_mv	AT jianma reducedreferencestereoscopicimagequalityassessmentusinggradientsparserepresentationandstructuraldegradation AT guomingxu reducedreferencestereoscopicimagequalityassessmentusinggradientsparserepresentationandstructuraldegradation AT xiyuhan reducedreferencestereoscopicimagequalityassessmentusinggradientsparserepresentationandstructuraldegradation
_version_	1718403969244463104

Reduced-Reference Stereoscopic Image Quality Assessment Using Gradient Sparse Representation and Structural Degradation

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