Special Section on Perceptually Driven Visual Information Analysis

Parts-based stereoscopic image assessment by learning binocular manifold color visual properties

[+] Author Affiliations
Haiyong Xu, Ting Luo

Ningbo University, Faculty of Information Science and Engineering, Ningbo 315211, China

Ningbo University, College of Science and Technology, Ningbo 315212, China

Mei Yu, Gangyi Jiang

Ningbo University, Faculty of Information Science and Engineering, Ningbo 315211, China

Yun Zhang

Chinese Academy of Sciences, Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China

J. Electron. Imaging. 25(6), 061611 (Oct 26, 2016). doi:10.1117/1.JEI.25.6.061611
History: Received April 30, 2016; Accepted October 3, 2016
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Abstract.  Existing stereoscopic image quality assessment (SIQA) methods are mostly based on the luminance information, in which color information is not sufficiently considered. Actually, color is part of the important factors that affect human visual perception, and nonnegative matrix factorization (NMF) and manifold learning are in line with human visual perception. We propose an SIQA method based on learning binocular manifold color visual properties. To be more specific, in the training phase, a feature detector is created based on NMF with manifold regularization by considering color information, which not only allows parts-based manifold representation of an image, but also manifests localized color visual properties. In the quality estimation phase, visually important regions are selected by considering different human visual attention, and feature vectors are extracted by using the feature detector. Then the feature similarity index is calculated and the parts-based manifold color feature energy (PMCFE) for each view is defined based on the color feature vectors. The final quality score is obtained by considering a binocular combination based on PMCFE. The experimental results on LIVE I and LIVE Π 3-D IQA databases demonstrate that the proposed method can achieve much higher consistency with subjective evaluations than the state-of-the-art SIQA methods.

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© 2016 SPIE and IS&T

Citation

Haiyong Xu ; Mei Yu ; Ting Luo ; Yun Zhang and Gangyi Jiang
"Parts-based stereoscopic image assessment by learning binocular manifold color visual properties", J. Electron. Imaging. 25(6), 061611 (Oct 26, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.6.061611


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