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Open Access Blind Quality Prediction of Stereoscopic 3D Images

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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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Keywords: 3D IMAGE; ASYMMETRIC DISTORTION; BLIND IMAGE QUALITY ASSESSMENT; DIVISIVE NORMALIZATION; STEREOSCOPIC IMAGE

Document Type: Research Article

Publication date: January 29, 2017

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