计算机工程与科学2017，Vol.39Issue(4)：769-776,8.DOI:10.3969/j.issn.1007-130X.2017.04.022
多核学习纹理特征的立体图像质量评价
Quality assessment of stereoscopic images with texture features via multiple kernel learning
摘要
Abstract
To assess various distorted stereoscopic image quality efficiently,we propose a no reference stereoscopic image quality assessment model that utilizes 3D map information and 2D texture information to drive the multiple kernel learning machine (MKL).Firstly,the model utilizes the stereoscopic matching model to obtain disparity map and disparity map of error energy on the basis of left view and right view.Secondly,left view,right view,disparity map and disparity map of error energy are all transformed by phase congruency and structure tensor to obtain their marginal zone and planar zone.Thirdly,the model extracts the texture feature of the two zones from left view and right view respectively as plane information,and it extracts statistics feature and texture feature of the two zones respectively from disparity map and disparity map of error energy as 3D information.Finally,all features are input to the MKL to predict the quality of tested images.Experiments on the LIVE 3D image quality database demonstrate that the proposed method has good consistency with human subject quality and has high competitiveness in comparison with the state-of-the-art models.关键词
立体图像质量评价/通用无参考/多核学习/图像纹理Key words
stereoscopic image quality assessment/universal no-reference/multiple kernel learning/image texture分类
信息技术与安全科学引用本文复制引用
谭红宝,桑庆兵,严大卫..多核学习纹理特征的立体图像质量评价[J].计算机工程与科学,2017,39(4):769-776,8.基金项目
国家自然科学基金(61170120) （61170120）
江苏省产学研前瞻性联合研究项目(BY2013015-41) （BY2013015-41）
