基于眼优势的非对称失真立体图像质量评价  

Asymmetrically Distorted Stereoscopic Image Quality Assessment Based on Ocular Dominance

作　　者：唐祎玲[1] 江顺亮[1] 徐少平[1] 刘婷云 李崇禧 TANG Yi-Ling;JIANG Shun-Liang;XU Shao-Ping;LIU Ting-Yun;LI Chong-Xi(School of Information Engineering,Nanchang University,Nanchang 330031)

机构地区：[1]南昌大学信息工程学院

出　　处：《自动化学报》2019年第11期2092-2106,共15页Acta Automatica Sinica

基　　金：国家自然科学基金(61662044,61163023,51765042);江西省自然科学基金(20171BAB202017)资助~~

摘　　要：针对现有立体图像质量评价算法对非对称失真立体图像的评价准确性及执行效率较低的问题,提出一种基于眼优势的非对称失真立体图像质量评价算法.首先采用梯度幅值响应来模拟左右眼输入的刺激强度,并根据人类视觉系统的眼优势原理分别以左和右视点图像作为主视图合成两幅融合图像;其次,利用旋转不变统一局部二值模式直方图、皮尔逊线性相关系数以及非对称广义高斯模型,获取左右融合图像以及左右梯度幅值响应图像中的多种能够反映立体图像质量好坏的特征;最后,利用自适应增强的支持向量回归模型将感知特征向量映射为图像质量值.在四个基准测试数据库上的实验结果表明:本文所提出算法大幅提升了非对称失真立体图像的评价准确性,且具有较高的执行效率.这些优势说明本文算法所提取的特征描述能力更强,质量映射模型的稳定性更好.Aiming at overcoming the low prediction accuracy and efficiency of current stereoscopic image quality assessment(SIQA) algorithms in evaluating the asymmetrically distorted stereoscopic images, this paper proposed an asymmetrically distorted SIQA algorithm based on ocular dominance theory. The proposed algorithm first uses the gradient magnitude response images(GMRIs) of the left and right views to model stimulus strength, and synthesizes two fusion images(FIs) by taking the left and the right views as dominant eye input, respectively, according to the theory of ocular dominance in human visual system. Then, the global histogram representations of the rotation invariant uniform local binary patterns, the Pearson linear correlation coefficient, and the asymmetric generalized Gaussian distribution model are employed to extract various perceptual features from the two GMRIs and the two FIs. Finally, with a pre-trained AdaBoosting support vector regression model, the extracted quality-aware feature vector of the stereoscopic image is mapped into the image quality score. Experimental results on four benchmark stereoscopic image databases show that, compared with the state-of-the-art SIQA algorithms, the prediction accuracy on asymmetrically distorted images of the proposed algorithm is greatly improved, and the implementation efficiency of the proposed algorithm is high. The results indicate that the extracted features of the proposed algorithm are more descriptive, and the quality mapping model is more stable.

关 键 词：立体图像质量评价 非对称失真 眼优势 双融合图像 自适应增强-支持向量回归 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]