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Volume: 29 | Article ID: art00008
Blind Quality Prediction of Stereoscopic 3D Images
  DOI :  10.2352/ISSN.2470-1173.2017.14.HVEI-379  Published OnlineJanuary 2017

Blind image quality assessment (BIQA) of distorted stereoscopic pairs without referring to the undistorted source is a challenging problem, especially when the distortions in the left- and right-views are asymmetric. Existing studies suggest that simply averaging the quality of the left- and right-views well predicts the quality of symmetrically distorted stereoscopic images, but generates substantial prediction bias when applied to asymmetrically distorted stereoscopic images. In this study, we propose a binocular rivalry inspired multi-scale model to predict the quality of stereoscopic images from that of the single-view images without referring to the original left- and right-view images. We apply this blind 2D-to-3D quality prediction model on top of ten stateof-the-art base 2D-BIQA algorithms for 3D-BIQA. Experimental results show that the proposed 3D-BIQA model, without explicitly identifying image distortion types, successfully eliminates the prediction bias, leading to significantly improved quality prediction performance. Among all the base 2D-BIQA algorithms, BRISQUE and M3 archive excellent tradeoffs between accuracy and complexity.

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Jiheng Wang, Qingbo Wu, Abdul Rehman, Shiqi Wang, Zhou Wang, "Blind Quality Prediction of Stereoscopic 3D Imagesin Proc. IS&T Int’l. Symp. on Electronic Imaging: Human Vision and Electronic Imaging,  2017,  pp 70 - 76,

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