A challenging problem confronted when designing a blind/no-reference (NR) stereoscopic image quality assessment (SIQA) algorithm is to simulate the quality assessment (QA) behavior of the human visual system (HVS) during binocular vision. An effective way to solve this problem is to estimate the quality of the merged single view created in the human brain which is also referred to as the cyclopean image. However, due to the difficulty in modeling the binocular fusion and rivalry properties of the HVS, obtaining effective cyclopean images for QA is non-trivial, and consequently previous NR SIQA algorithms either require the MOS/DMOS values of the distorted 3D images for training or ignore the quality analysis of the merged cyclopean view. In this paper, we focus on (1) constructing accurate and appropriate cyclopean views for QA of stereoscopic images by adaptively analyzing the distortion information of two monocular views, and (2) training NR SIQA models without requiring the assistance of the MOS/DMOS values in existing databases. Accordingly, we present an effective opinion-unaware SIQA algorithm called MUSIQUE-3D, which blindly assesses the quality of multiply and singly distorted stereoscopic images by analyzing quality degradations of both monocular and cyclopean views. The monocular view quality is estimated by an extended version of the MUSIQUE algorithm, and the cyclopean view quality is computed from the distortion parameter values predicted by a two-layer classification-regression model trained on a large 3D image dataset. Tests on various 3D image databases demonstrate the superiority of our method as compared with other state-of-the-art SIQA algorithms.

设计盲/无参考（NR）立体图像质量评估（SIQA）算法时面临的一个挑战性问题是模拟双目视觉过程中人类视觉系统（HVS）的质量评估（QA）行为。解决此问题的有效方法是估计在人脑中创建的合并单视图的质量，该视图也称为独眼巨人图像。但是，由于难以对HVS的双眼融合和竞争特性进行建模，因此获得有效的QA独眼巨人图像并非易事，因此以前的NR SIQA算法要么需要失真的3D图像的MOS / DMOS值，要么进行训练或忽略合并的全景图视图的质量分析。在本文中，我们专注于（1）通过自适应地分析两个单眼视图的失真信息为立体图像的QA构建准确且适当的独眼动物视图，以及（2）在不需要现有数据库中MOS / DMOS值帮助的情况下训练NR SIQA模型。因此，我们提出了一种有效的，无见识的SIQA算法，称为MUSIQUE-3D，该算法通过分析单眼和双眼视图的质量下降来盲目评估倍增和单倍扭曲的立体图像的质量。单眼视图质量是通过MUSIQUE算法的扩展版本估算的，而单眼视图质量是根据在大型3D图像数据集上训练的两层分类回归模型预测的变形参数值计算出的。