考虑双目竞争视觉现象的非对称失真立体图像质量评价方法  

Binocular rivalry-based stereoscopic images quality assessment relevant to its asymmetric and distorted contexts

作　　者：唐祎玲 江顺亮[1] 徐少平[1] 肖建 陈晓军 Tang Yiling;Jiang Shunliang;Xu Shaoping;Xiao Jian;Chen Xiaojun(School of Mathematics and Computer Sciences,Nanchang University,Nanchang 330031,China)

机构地区：[1]南昌大学数学与计算机学院,南昌330031

出　　处：《中国图象图形学报》2023年第10期3049-3063,共15页Journal of Image and Graphics

基　　金：国家自然科学基金项目(62162042,62162043)。

摘　　要：目的 现有方法存在特征提取时间过长、非对称失真图像预测准确性不高的问题,同时少有工作对非对称失真与对称失真立体图像的分类进行研究,为此提出了基于双目竞争的非对称失真立体图像质量评价方法。方法依据双目竞争的视觉现象,利用非对称失真立体图像两个视点的图像质量衰减程度的不同,生成单目图像特征的融合系数,融合从左右视点图像中提取的灰度空间特征与HSV(hue-saturation-value)彩色空间特征。同时,量化两个视点图像在结构、信息量和质量衰减程度等多方面的差异,获得双目差异特征。并且将双目融合特征与双目差异特征级联为一个描述能力更强的立体图像质量感知特征向量,训练基于支持向量回归的特征-质量映射模型。此外,还利用双目差异特征训练基于支持向量分类模型的对称失真与非对称失真立体图像分类模型。结果 本文提出的质量预测模型在4个数据库上的SROCC(Spearman rank order correlation coefficient)和PLCC(Pearson linear correlation coefficient)均达到0.95以上,在3个非对称失真数据库上的均方根误差(root of mean square error,RMSE)取值均优于对比算法。在LIVE-Ⅱ(LIVE 3D image quality database phase Ⅱ)、IVC-Ⅰ(Waterloo-IVC 3D image quality assessment database phase Ⅰ和IVC-Ⅱ(Waterloo-IVC 3D image quality assessment database phase Ⅱ)这3个非对称失真立体图像测试数据库上的失真类型分类测试中,对称失真立体图像的分类准确率分别为89.91%、94.76%和98.97%,非对称失真立体图像的分类准确率分别为95.46%,92.64%和96.22%。结论 本文方法依据双目竞争的视觉现象融合左右视点图像的质量感知特征用于立体图像质量预测,能够提升非对称失真立体图像的评价准确性和鲁棒性。所提取双目差异性特征还能够用于将对称失真与非对称失真立体图像进行有效分类,分类准确性高。Objective Computer vision-related stereoscopic image quality assessment(SIQA)is focused on recently.It is essential for parameter setting and system optimizing for such domains of multiple stereoscopic image applications like image storage,compression,transmission,and display.Stereoscopic images can be segmented into two sorts of distorted images:symmetrically and asymmetrically distorted,in terms of the degree of degradation between the left and right views.For symmetric-based distorted stereoscopic images,the distortion type and degree occurred in the left and right views are basically in consistency.Early SIQA methods were effective in evaluating symmetrically distorted images by averaging scores or features derived from the two views.However,in practice,the stereoscopic images are often asymmetrically dis⁃torted,where the distortion type and level of the two views are different.Simply averaging the quality values of the two views cannot accurately simulate the binocular fusion process and the binocular rivalry phenomena in relevance to the human visual system.Consequently,the evaluation accuracy of these methods will be down to severe lower when the qual⁃ity of asymmetrically distorted stereoscopic images is estimated.Previous studies have shown that when the left and right views of a stereoscopic image exhibit varying levels or types of distortion,binocular rivalry is primarily driven by one of the views.Specially,in the process of evaluating the quality of a stereoscopic image,the visual quality of one view has a greater impact on the stereopair quality evaluation than the other view.To address this issue,some methods have simulated the binocular rivalry phenomenon in human visual system,and used a weighted average method to fuse the visual informa⁃tion in the two views of stereo-pairs as well.However,existing methods are still challenged for its lower prediction accuracy of asymmetrically distorted images,and its feature extraction process is also time-consuming.To optimize the evaluation accuracy of

关 键 词：立体图像质量评价(SIQA) 非对称失真 双目竞争 图像质量衰减系数 双目差异特征 

分 类 号：TN391[电子电信—物理电子学]