Journal
IEEE TRANSACTIONS ON IMAGE PROCESSING
Volume 25, Issue 5, Pages 2059-2074Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2016.2538462
Keywords
Deep quality evaluator; blind image quality assessment; stereoscopic image; deep neural network
Funding
- Natural Science Foundation of China [61271021, U1301257]
- K. C. Wong Magna Fund in Ningbo University
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During recent years, blind image quality assessment (BIQA) has been intensively studied with different machine learning tools. Existing BIQA metrics, however, do not design for stereoscopic images. We believe this problem can be resolved by separating 3D images and capturing the essential attributes of images via deep neural network. In this paper, we propose a blind deep quality evaluator (DQE) for stereoscopic images (denoted by 3D-DQE) based on monocular and binocular interactions. The key technical steps in the proposed 3D-DQE are to train two separate 2D deep neural networks (2D-DNNs) from 2D monocular images and cyclopean images to model the process of monocular and binocular quality predictions, and combine the measured 2D monocular and cyclopean quality scores using different weighting schemes. Experimental results on four public 3D image quality assessment databases demonstrate that in comparison with the existing methods, the devised algorithm achieves high consistent alignment with subjective assessment.
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