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Visual discomfort relaxation for stereoscopic 3D images by adjusting zero-disparity plane for projection.

, , , , , and . Displays, (2015)

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Supervised dictionary learning for blind image quality assessment., , , , and . VCIP, page 1-4. IEEE, (2015)Seam Manipulator: Leveraging Pixel Fusion for Depth-Adjustable Stereoscopic Image Retargeting., , , and . IEEE Access, (2019)Multistage Pooling for Blind Quality Prediction of Asymmetric Multiply-Distorted Stereoscopic Images., , , , and . IEEE Trans. Multimedia, 20 (10): 2605-2619 (2018)Measuring Depth Perception for Stereoscopic Images Using a Phase-shift Model., , , and . JSW, 9 (10): 2665-2671 (2014)Unified No-Reference Quality Assessment of Singly and Multiply Distorted Stereoscopic Images., , , , , and . IEEE Trans. Image Processing, 28 (4): 1866-1881 (2019)3D Visual Comfort Assessment via Sparse Coding., and . ICIG (1), volume 9217 of Lecture Notes in Computer Science, page 10-20. Springer, (2015)Authentically Distorted Image Quality Assessment by Learning From Empirical Score Distributions., , , and . IEEE Signal Process. Lett., 26 (12): 1867-1871 (2019)SGDNet: An End-to-End Saliency-Guided Deep Neural Network for No-Reference Image Quality Assessment., , , and . ACM Multimedia, page 1383-1391. ACM, (2019)A Full-Reference Stereoscopic Image Quality Measurement Via Hierarchical Deep Feature Degradation Fusion., , , , , and . IEEE Trans. Instrum. Meas., 69 (12): 9784-9796 (2020)Deep Road Scene Understanding., , , and . IEEE Signal Process. Lett., 26 (4): 587-591 (2019)