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基于稀疏表示的SOM多失真图像质量评价方法
Multi-distorted image quality assessment algorithm based on sparse representation and SOM
- 液晶与显示 2018年33卷第10期 页码：877-883
- 作者机构：
1.中国科学院大学, 北京 100049
2.中国科学院 复杂航天系统电子信息技术重点实验室, 北京 100190
3.长春大学 信息工程学院, 吉林 长春 130022
- 作者简介：
[ "王春哲(1989-), 男, 吉林松原人, 博士研究生, 2012年于长春大学获得学士学位, 2015年于长春理工大学获得硕士学位, 现为中国科学院大学博士研究生, 主要从事深度学习及目标检测方面的研究。E-mail:wangchunzhe163@sina.com" ]
[ "安军社(1969-), 男, 陕西渭南人, 博士, 研究员, 1992年于北京航空航天大学获得学士学位, 1995年于北京科技大学获得硕士学位, 2004年于西北工业大学获得博士学位, 现为中国科学院国家空间科学中心研究员, 主要从事空间飞行器综合电子系统及深度学习方面的研究。E-mail:anjunshe@nssc.ac.com" ]
- 基金信息：
DOI：10.3788/YJYXS20183310.0877
中图分类号： TP391.41收稿日期：2018-04-04，
录用日期：2018-7-6，
纸质出版日期：2018-10-05
- 稿件说明：
移动端阅览
王春哲, 安军社, 姜秀杰, 等. 基于稀疏表示的SOM多失真图像质量评价方法[J]. 液晶与显示, 2018,33(10):877-883.
Chun-zhe WANG, Jun-she AN, Xiu-jie JIANG, et al. Multi-distorted image quality assessment algorithm based on sparse representation and SOM[J]. Chinese journal of liquid crystals and displays, 2018, 33(10): 877-883.
王春哲, 安军社, 姜秀杰, 等. 基于稀疏表示的SOM多失真图像质量评价方法[J]. 液晶与显示, 2018,33(10):877-883. DOI： 10.3788/YJYXS20183310.0877.
Chun-zhe WANG, Jun-she AN, Xiu-jie JIANG, et al. Multi-distorted image quality assessment algorithm based on sparse representation and SOM[J]. Chinese journal of liquid crystals and displays, 2018, 33(10): 877-883. DOI： 10.3788/YJYXS20183310.0877.
摘要
针对非线性回归下客观评分与主观评分一致性差的问题，本文提出一种基于稀疏表示的SOM多失真图像质量评价方法。首先，将参考图像及失真图像应用独立变量分析进行稀疏化表示，应用稀疏表示下的参考图像与失真图像间的结构相似度描述失真图像的质量，再使用SOM聚类算法和交叉验证方法提高非线性回归下的客观评分与主观评分之间的一致性。最后，在LIVE2，TID2013及IVC数据库中的实验结果显示，所提评价模型性能优越；3种数据库的平均结果说明，该文方法的总体性能高于现有的经典算法，表明该文方法能够很好地反映图像的视觉感知效果。通过对比时间效率，该方法基本能够满足实际要求，具有较高的适用性。
Abstract
Due to the problem of poor consistency between objective score in nonlinear regression and subjective scores
a multi-distorted image quality assessment method based on sparse representation and SOM is presented in this paper. Firstly
the reference image and the multi-distorted image are represented sparsely by independent component analysis
and the structural similarity index between the reference image and the distorted image under the sparse representation is computed to describe the quality of multi-distorted image. Secondly
the consistency between objective score in nonlinear regression and subjective score is improved by the SOM and cross-validation algorithms. Finally
the experimental results in LIVE2
TID2013 and IVC databases show that the proposed evaluation model has good performance. The average result of 3 kinds of databases shows that the overall performance of the method is higher than the existing classical algorithm
which indicates that the proposed method can reflect the visual perception of the image well. Comparison with the time efficiency
the proposed algorithm can basically meet the practical demand and has high practicability.
关键词
Keywords
references
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