基于边缘及特征点匹配的立体图像质量评价

田浩南; 张叶

doi:10.3788/YJYXS20153004.0666

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基于边缘及特征点匹配的立体图像质量评价

成像技术与图像处理 | 更新时间：2020-08-12

- 基于边缘及特征点匹配的立体图像质量评价
- Quality evaluation of stereo image based on edge and characteristic point matching
- 液晶与显示 2015年30卷第4期页码：666-672
- 作者机构：
  
  中国科学院长春光学精密机械与物理研究所,吉林长春,中国,130033
- 作者简介：
  
  [ "田浩南(1987-),男,吉林长春人,硕士,研究实习员,主要从事立体图像处理、超分辨率重构等方面的研究。E-mail:tju_thn@163.com" ]
  [ "张叶(1981-),女,吉林长春人,博士,副研究员,主要从事计算机视觉与模式识别等方面的研究。E-mail:zhangye@ciomp.ac.cn" ]
- 基金信息：
  
  吉林省重大科技攻关项目(No.11ZDGG001)();国家自然科学基金(No.60902067)()
- DOI：10.3788/YJYXS20153004.0666
  中图分类号： TP391.41
- 收稿日期：2014-08-26，
  
  修回日期：2014-09-30，
  
  纸质出版日期：2015-08-05
- 稿件说明：
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田浩南, 张叶. 基于边缘及特征点匹配的立体图像质量评价[J]. 液晶与显示, 2015,30(4): 666-672

TIAN Hao-nan, ZHANG Ye. Quality evaluation of stereo image based on edge and characteristic point matching[J]. , 2015,30(4): 666-672
田浩南, 张叶. 基于边缘及特征点匹配的立体图像质量评价[J]. 液晶与显示, 2015,30(4): 666-672 DOI： 10.3788/YJYXS20153004.0666.

TIAN Hao-nan, ZHANG Ye. Quality evaluation of stereo image based on edge and characteristic point matching[J]. , 2015,30(4): 666-672 DOI： 10.3788/YJYXS20153004.0666.

摘要

为了实现对立体图像质量的精确预测

提出了一种基于边缘和特征点匹配的立体图像客观质量评价方法。首先

对图像的质量进行评价

在基于结构相似度的质量评价方法的基础上

考虑了边缘信息对人眼视觉特性的重要性

加入了边缘结构相似度指标;然后

对图像的立体感进行评价

采用特征点匹配的方法提取立体感指标。最后

根据总体视差失真法将图像质量和立体感指标拟合为一个综合指标。实验结果表明

采用本文提出的方法对立体图像测试库进行评价

总体评价的PLCC(Pearson Linear Correlation Coefficient)均在0.94以上;与其他方法相比

本方法具有较高的预测精确性。

Abstract

In order to predict the quality of stereoscopic images accurately

an objective stereoscopic image quality assessment method based on edge and characteristic point matching is proposed. Firstly

for the quality assessment

a method that takes the edge information into account is presented

which is based on the plane image quality assessment method; secondly

for the stereo sense assessment

a stereo matching method is used for extracting stereo sense index. Finally

according to quality and stereo sense

a total evaluation index is proposed based on an appropriate weighted method. The experimental results indicate that compared with other methods

the overall Pearson linear correlation coefficient indicators are all above 0.94

which indicates that the proposed method can achieve higher prediction accuracy.

关键词

Keywords

references

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