中文摘要：

通过模拟人类视觉系统（HVS）的双目视觉行为,提出一种基于双目特征联合的无参考立体图像质量评价（NR-SIQA）方法。首先分析立体视觉感知中的双目联合行为,提出可应用于立体图像质量预测的双目联合模型;然后采用学习和统计分析的方法,分别提取局部和全局特征并联合作为感知特征;最后采用机器学习算法,建立特征和质量的关系模型,并结合基于特征的双目联合模型预测立体图像质量。实验结果表明,本文方法在对称立体图像库上的Pearson线性相关系数（PLCC）和Spearman等级系数（SRCC）高于0.93,在非对称库上高于0.87,优于现有评价方法。

英文摘要：

No-reference stereoscopic image quality assessment（NR-SIQA）predicts stereoscopic image quality without original information,which has been a hot and difficult topic in stereo video system.In this paper,by simulating the binocular vision behavior in human vision system（HVS）,a no-reference stereoscopic image quality assessment metric is proposed based on binocular feature combination.In the proposed method,we firstly analyze the binocular combination behavior in NR-SIQA,and propose a binocular combination mode for quality prediction.Then,in order to represent local and global properties of stereoscopic images,the features extracted by learning and statistic analysis are combined together as the final perceptual features.Finally,a regression model is learnt by machine learning algorithm to map human subjective scores and features,and the learnt regression model and the binocular combination model are utilized to predict the quality of a test stereoscopic image pair.Experimental results demonstrate that by applying the proposed model on three public databases,the Pearson linear correlation coefficient（PLCC）and Spearman rank order correlation coefficient（SROCC）of the proposed method are higher than0.93 in symmetric database and 0.87 in asymmetric databases.Compared with the state-of-art SIQA methods,the proposed one outperforms most of them,which indicates that the metric is fairly good and can predict human visual perception very well.