| 李柯蒙,邵枫,姜求平,蒋刚毅,郁梅.基于双目特征联合的无参考立体图像质量评价[J].光电子激光,2015,26(11):2224~2230 |
| 基于双目特征联合的无参考立体图像质量评价 |
| No-reference stereoscopic images quality assessment based on binocular feature combination |
| 投稿时间：2015-05-03 |
| DOI： |
| 中文关键词: 无参考立体图像质量评价(NR-SIQA) 双目视觉 双目联合 特征学习 |
| 英文关键词:no-reference stereoscopic image quality assessment (NR-SIQA) binocular vision binocular combination feature learning |
| 基金项目:国家自然科学基金(61271021)、宁波大学2014年研究生优秀学位论文培育基金(PY2014011) 和2015宁波大学研究生科研创新基金(G15008)资助项目 (宁波大学 信息科学与工程学院,浙江 宁波 315211) |
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| 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 stereosc opic 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-ref erence stereoscopic image quality assessment metric is proposed based on binocular feature combination.In the pro posed method,we firstly analyze the binocular combination behavior in NR-SIQA,and propose a binocular combinat ion mode for quality prediction.Then,in order to represent local and global prop erties of stereoscopic images,the features extracted by learning and statistic analysis are combined together as the final perceptual features.Finall y,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 stereo scopic image pair.Experimental results demonstrate that by applying the proposed model on three public database s,the Pearson linear correlation coefficient (PLCC) and Spearman rank order correlation coefficient (SROCC) of th e proposed method are higher than 0.93in symmetric database and 0.87in asymmetric databases.Compared with the state-of-art SIQA methods, the proposed one outperforms most of them,which indicates that the metric is fa irly good and can predict human visual perception very well. |
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