Binocular combination under different distortion types poses a great challenge to three-dimensional image quality assessment (3D-IQA). However, the research works on 3D-IQA with multiple distortion types are very limited. In this paper, we first construct a new multiply distorted stereoscopic image database (NBU-MDSID), which is composed of 270 multiply distorted stereoscopic images and 90 singly distorted stereoscopic images that are corrupted simultaneously and independently by blurring, JPEG compression, and noise injection. We then propose a new multimodal blind metric for quality assessment of multiply distorted stereoscopic images. Inspired by multimodal sparse representation framework, modality-specific dictionaries and the corresponding projection matrices are learned from the singly distorted training database at the training stage, and the testing stage only needs to estimate the quality score based on the reconstruction errors. Experimental results demonstrate the effectiveness of our blind metric.

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.