Perceptual full-reference quality assessment of stereoscopic images by considering binocular visual characteristics. (Article, 2013) [WorldCat.org]
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Perceptual full-reference quality assessment of stereoscopic images by considering binocular visual characteristics.
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Perceptual full-reference quality assessment of stereoscopic images by considering binocular visual characteristics.

Author: F Shao Affiliation: Faculty of Information Science and Engineering, Ningbo University, Ningbo 315211, China. shaofeng@nbu.edu.cn; W Lin; S Gu; G Jiang; T Srikanthan
Edition/Format: Article Article : English
Publication:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2013 May; 22(5): 1940-53
Other Databases: WorldCatWorldCat
Summary:
Perceptual quality assessment is a challenging issue in 3D signal processing research. It is important to study 3D signal directly instead of studying simple extension of the 2D metrics directly to the 3D case as in some previous studies. In this paper, we propose a new perceptual full-reference quality assessment metric of stereoscopic images by considering the binocular visual characteristics. The major technical  Read more...
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Details

Document Type: Article
All Authors / Contributors: F Shao Affiliation: Faculty of Information Science and Engineering, Ningbo University, Ningbo 315211, China. shaofeng@nbu.edu.cn; W Lin; S Gu; G Jiang; T Srikanthan
ISSN:1057-7149
Language Note: English
Unique Identifier: 830548430
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Abstract:

Perceptual quality assessment is a challenging issue in 3D signal processing research. It is important to study 3D signal directly instead of studying simple extension of the 2D metrics directly to the 3D case as in some previous studies. In this paper, we propose a new perceptual full-reference quality assessment metric of stereoscopic images by considering the binocular visual characteristics. The major technical contribution of this paper is that the binocular perception and combination properties are considered in quality assessment. To be more specific, we first perform left-right consistency checks and compare matching error between the corresponding pixels in binocular disparity calculation, and classify the stereoscopic images into non-corresponding, binocular fusion, and binocular suppression regions. Also, local phase and local amplitude maps are extracted from the original and distorted stereoscopic images as features in quality assessment. Then, each region is evaluated independently by considering its binocular perception property, and all evaluation results are integrated into an overall score. Besides, a binocular just noticeable difference model is used to reflect the visual sensitivity for the binocular fusion and suppression regions. Experimental results show that compared with the relevant existing metrics, the proposed metric can achieve higher consistency with subjective assessment of stereoscopic images.

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