[1]马月梅,付 浩,刘国军,等.基于极限学习机的底层特征全参考彩色图像质量评价方法[J].南京师大学报(自然科学版),2022,45(04):91-101.[doi:10.3969/j.issn.1001-4616.2022.04.013]
　Ma Yuemei,Fu Hao,Liu Guojun,et al.Full Reference Color Image Quality Assessment Method via Low-level Features Combination with Extreme Learning Machine[J].Journal of Nanjing Normal University(Natural Science Edition),2022,45(04):91-101.[doi:10.3969/j.issn.1001-4616.2022.04.013]
点击复制

基于极限学习机的底层特征全参考彩色图像质量评价方法()
分享到：

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

卷:
第45卷
期数:
2022年04期
页码:
91-101
栏目:
计算机科学与技术
出版日期:
2022-12-15

文章信息/Info

Title:
Full Reference Color Image Quality Assessment Method via Low-level Features Combination with Extreme Learning Machine
文章编号:
1001-4616(2022)04-0091-11
作者:
马月梅; 付 浩; 刘国军; 杨 玲; 魏立力
(宁夏大学数学统计学院,宁夏 银川 750021)
Author(s):
Ma Yuemei; Fu Hao; Liu Guojun; Yang Ling; Wei Lili
(School of Mathematics and Statistics,Ningxia University,Yinchuan 750021,China)
关键词:
彩色图像质量评价; 底层特征; 局部二值模式; 梯度; 结构对比度指标; 极限学习机
Keywords:
color image quality assessment; low-level features; local binary pattern; gradient; structural contrast index; extreme learning machine
分类号:
TP391.41; TP181
DOI:
10.3969/j.issn.1001-4616.2022.04.013
文献标志码:
A
摘要:
作为图像质量的监测和评价工具,图像质量评价(image quality assessment,IQA)在各种图像处理系统中发挥着重要的作用,理想的IQA方法应该与人类视觉系统(human visual system,HVS)相一致. 目前HVS对图像的理解主要是依据图像的底层特征,本文提出了一种新的全参考(full reference,FR)彩色图像IQA方法. 首先,提取了结构对比度指标(structural contrast index,SCI)、梯度、局部二值模式(local binary pattern,LBP)和色度四类底层特征图,用于刻画图像的不同特征属性; 其次,利用不同的特征池化策略对每类特征分别处理,将其组成一组相似特征向量作为图像质量的检测器并采用极限学习机(extreme learning machine,ELM)建立回归模型,得到客观的质量分数; 最后,与目前流行的8种FR IQA方法在5个标准IQA数据库上进行数值实验. 结果表明,该方法整体性能优于其他方法,能够有效地提高大多数失真类型的预测精度.
Abstract:
As an image quality monitoring and evaluation tool,image quality assessment(IQA)plays an important role in various image processing systems. The ideal IQA method should be consistent with human visual system(HVS). Suppose HVS understanding an image mainly according to its low-level features,a novel Full Reference(FR)color IQA method. Firstly,four different types of low-level feature maps are extracted,namely structural contrast index(SCI),gradient,local binary pattern(LBP),and chroma,which are used to characterize different feature attributes of the image. Secondly,different feature pooling strategies are employed to process each type of features respectively,and a set of similar feature vectors are formed as the detector of image quality. Then,extreme learning machine(ELM)is used to establish regression model and map the feature vectors into an objective quality score. Finally,extensive experiments performed on five benchmark IQA databases and compared with eight state-of-the-art FR IQA metrics. The results demonstrate that the overall performance of proposed method is better than other methods,and can effectively improve the accuracy of IQA index on most of distortions.

参考文献/References:

[1]WANG Z,BOVIK A C,SHEIKH H R,et al. Image quality assessment:from error visibility to structural similarity[J]. IEEE transactions on image processing,2004,13(4):600-612.
[2]JI W,WU J,SHI G,et al. Blind image quality assessment with semantic information[J]. Journal of visual communication and image representation,2019,58:195-204.
[3]SHEIKH H R,SABIR M F,BOVIK A C. A statistical evaluation of recent full reference image quality assessment algorithms[J]. IEEE transactions on image processing,2006,15(11):3440-3451.
[4]ZHANG L,ZHANG L,MOU X Q,et al. FSIM:A feature similarity index for image quality assessment[J]. IEEE transactions on image processing,2011,20(8):2378-2386.
[5]ZHANG L,SHEN Y,LI H. VSI:a visual saliency-induced index for perceptual image quality assessment[J]. IEEE transactions on image processing,2014,23(10):4270-4281.
[6]刘国军,高丽霞,陈丽奇. 广义平均的全参考型图像质量评价池化策略[J]. 光学精密工程,2017,25(3):742-748.
[7]NARWARIA M,LIN W. SVD-based quality metric for image and video using machine learning[J]. IEEE transactions on systems,man and cybernetics,part B(cybernetics),2012,42(2):347-364.
[8]LIU T J,LIN W,KUO C C J. Image quality assessment using multi-method fusion[J]. IEEE transactions on image processing,2013,22(5):1793-1807.
[9]KIM J,LEE S. Deep learning of human visual sensitivity in image quality assessment framework[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). USA:IEEE,2017.
[10]GAO F,WANG Y,LI P,et al. DeepSim:Deep similarity for image quality assessment[J]. Neurocomputing,2017,257(27):104-114.
[11]HUANG G B,ZHU Q Y,SIEW C K. Extreme learning machine:theory and applications[J]. Neurocomputing,2006,70(1-3):489-501.
[12]HUANG G B,ZHOU H,DING X,et al. Extreme learning machine for regression and multiclass classification[J]. IEEE transactions on systems,man and cybernetics,part B(cybernetics),2012,42(2):513-529.
[13]SURESH S,BABU R V,KIM H J. No-reference image quality assessment using modified extreme learning machine classifier[J]. Applied soft computing journal,2009,9(2):541-552.
[14]岳靖,刘国军,付浩. 四元数谱余量彩色图像质量评价[J]. 激光与光电子学进展,2019,56(3):31009-1-8.
[15]GEUSEBROEK J M,BOOMGAARD R V D,SMEULDERS A W M,et al. Color invariance[J]. IEEE transactions on pattern analysis and machine intelligence,2001,23(12):1338-1350.
[16]BAE S H,KIM M. A novel image quality assessment with globally and locally consilient visual quality perception[J]. IEEE transactions on image processing,2016,25(5):2392-2406.
[17]OJALA T,PIETIKAINEN M,MAENPAA T. Multire solution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE transactions on pattern analysis and machine intelligence,2002,24(7):971-987.
[18]刘丽,谢毓湘,魏迎梅,等. 局部二进制模式方法综述[J]. 中国图象图形学报,2018,19(12):1696-1720.
[19]LIU T J,LIN W,KUO C C J. Image quality assessment using multi-method fusion[J]. IEEE Transactions on image processing,2013,22(5):1793-1807.
[20]BARTLETT P L. The sample complexity of pattern classification with neural networks:The size of the weights is more important than the size of the network[J]. IEEE transactions on information theory,2002,44(2):525-536.
[21]RAO C R. Generalized inverse of matrices and its applications[M]. New York,USA:Wiley. 1971.
[22]HOERL A E,KENNARD R W. Ridge regression:biased estimation for nonorthogonal problems[J]. Technometrics,1970,12(1):55-67.
[23]PONOMARENKO N,JIN L,LEREMEIEV O,et al. Image database TID2013:peculiarities,results and perspectives[J]. Signal processing:image communication,2015,30:55-77.
[24]PONOMARENKO N,LUKIN V,ZELENSKY A,et al. TID 2008—a database for evaluation of full-reference visual quality assessment metrics[J]. Advances of modern radio electronics,2009,10:30-45.
[25]LARSON C,CHANDLER D M. Categorical image quality(CSIQ)database 2009[OL]. http://vision. okstate.edu/csiq.
[26]NINASSI A,CALLET P L,AUTRUSSEAU F. Subjective quality assessment IVC database 2005[OL]. http://www2.irccyn.ecnantes.fr/ivcdb.
[27]HORITA Y,SHIBATA K,KAWAYOKE Y,et al. MICT:Image quality evaluation database 2000[OL]. http://mict.eng.utoyama.Ac.jp/mictdb.html.
[28]高丽霞,刘国军,张选德. 快速提升的结构相似度IQA算法[J]. 计算机辅助设计与图形学学报,2017,29(07):1314-1323.
[29]KANG L,YE P,LI Y,et al. Convolutional neural networks for no-reference image quality assessment[C]//IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Beijing:IEEE Computer Society,2014:1733-1740.

备注/Memo

备注/Memo:
收稿日期:2022-03-28.
基金项目:国家自然科学基金项目(62061040)、宁夏区重点研发计划项目(2019BEG03056)、宁夏自然科学基金项目(2021AAC03039).
通讯作者:刘国军,博士,教授,博士生导师,研究方向:图像处理的小波和偏微分方程,图像质量评价,机器学习方法的研究. E-mail:liugj@nxu.edu.cn
更新日期/Last Update: 2022-12-15