No-reference image quality assessment based on AdaBoost BP neural network in wavelet domain

doi:10.21629/JSEE.2019.02.01

Journal of Systems Engineering and Electronics ›› 2019, Vol. 30 ›› Issue (2): 223-237.doi: 10.21629/JSEE.2019.02.01

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No-reference image quality assessment based on AdaBoost BP neural network in wavelet domain

Junhua YAN^1,^2,*(), Xuehan BAI¹(), Wanyi ZHANG¹(), Yongqi XIAO¹(), Chris CHATWIN²(), Rupert YOUNG²(), Phil BIRCH²()

¹ College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
² School of Engineering and Informatics, University of Sussex, Brighton BN1 9QT, UK

Received:2017-12-19 Online:2019-04-01 Published:2019-04-26
Contact: Junhua YAN E-mail:yjh9758@126.com;1595720931@qq.com;daisyzwy917@126.com;couragexyq@163.com;C.R.Chatwin@sussex.ac.uk;R.C.D.Young@sussex.ac.uk;p.m.birch@sussex.ac.uk
About author:YAN Junhua was born in 1972. She received her B.S. degree, M.S. degree and Ph.D. degree from Nanjing University of Aeronautics and Astronautics in 1993, 2001 and 2004, respectively. She is a professor at Nanjing University of Aeronautics and Astronautics. Her research interests include image quality assessment, multi-source information fusion, target detection, tracking and recognition. E-mail:yjh9758@126.com|BAI Xuehan was born in 1993. She received her B.S. degree from Harbin Institute of Technology in 2016. She is a graduate student at Nanjing University of Aeronautics and Astronautics. Her research interest is image quality assessment. E-mail:1595720931@qq.com|ZHANG Wanyi was born in 1992. She received her B.S. degree and M.S. degree from Nanjing University of Aeronautics and Astronautics in 2014 and 2017. Her research interest is image quality assessment. E-mail:daisyzwy917@126.com|XIAO Yongqi was born in 1994. He received his B.S. degree and M.S. degree from Nanjing University of Aeronautics and Astronautics in 2015 and 2018. His research interest is image quality assessment. E-mail:couragexyq@163.com|CHATWIN Chris was born in 1950. He received his B.S. degree from University of Aston in 1973, his M.S. and Ph.D. degrees from University of Birmingham in 1977, and 1980, respectively. He is a professor in Engineering at University of Sussex. His research interests are smart cameras and algorithms, UAV surveillance systems, multi camera tracking systems, image processing for cancer diagnostics. E-mail:C.R.Chatwin@sussex.ac.uk|YOUNG Rupert was born in 1959. He received his B.S. and Ph.D. degrees from University of Glasgow in 1986, and 1994, respectively. He is a reader at University of Sussex. His current research interests are machine vision, image processing, neural networks, and quantum computing. E-mail:R.C.D.Young@sussex.ac.uk|BIRCH Phil was born in 1973. He received his B.S., M.S. degree and Ph.D. degrees from University of Durham in 1994, and 1999, respectively. He is a senior lecturer at University of Sussex. His current research interests are optical signal processing, target detection and computer vision. E-mail:p.m.birch@sussex.ac.uk
Supported by:
the National Natural Science Foundation of China(61471194);the National Natural Science Foundation of China(61705104);the Science and Technology on Avionics Integration Laboratory and Aeronautical Science Foundation of China(20155552050);the Natural Science Foundation of Jiangsu Province(BK20170804);This work was supported by the National Natural Science Foundation of China (61471194; 61705104), the Science and Technology on Avionics Integration Laboratory and Aeronautical Science Foundation of China (20155552050), and the Natural Science Foundation of Jiangsu Province (BK20170804)

Abstract

Abstract:

Considering the relatively poor robustness of quality scores for different types of distortion and the lack of mechanism for determining distortion types, a no-reference image quality assessment (NR-IQA) method based on the AdaBoost BP neural network in the wavelet domain (WABNN) is proposed. A 36-dimensional image feature vector is constructed by extracting natural scene statistics (NSS) features and local information entropy features of the distorted image wavelet sub-band coefficients in three scales. The ABNN classifier is obtained by learning the relationship between image features and distortion types. The ABNN scorer is obtained by learning the relationship between image features and image quality scores. A series of contrast experiments are carried out in the laboratory of image and video engineering (LIVE) database and TID2013 database. Experimental results show the high accuracy of the distinguishing distortion type, the high consistency with subjective scores and the high robustness of the method for distorted images. Experiment results also show the independence of the database and the relatively high operation efficiency of this method.

Key words: image quality assessment (IQA), AdaBoost BP neural network (ABNN), wavelet transform, natural scene statistics (NSS), local information entropy

Junhua YAN, Xuehan BAI, Wanyi ZHANG, Yongqi XIAO, Chris CHATWIN, Rupert YOUNG, Phil BIRCH. No-reference image quality assessment based on AdaBoost BP neural network in wavelet domain[J]. Journal of Systems Engineering and Electronics, 2019, 30(2): 223-237.

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Table 6

Comparison of the subjective consistency of different NRIQA methods on LIVE database"

Distortion type	Method	RMSE	LPCC	SROCC	KROCC
WN	BIQI	6.721 4	0.953 8	0.951 0	0.821 9
	DIIVINE	5.481 5	0.988 0	$\textbf{0.984 0}$	0.878 2
	BLIINDS-II	6.011 2	0.979 9	0.978 3	0.851 1
	NIQE	6.254 6	0.977 3	0.966 2	0.849 5
	BRISQUE	$\textbf{5.327 1}$	$\textbf{0.985 1}$	0.978 6	$\textbf{0.876 3}$
	SSEQ	5.928 9	0.980 6	0.978 4	0.859 7
	BHOD	6.554 4	0.980 1	0.972 8	0.864 5
	NRSL	$\textbf{5.384 6}$	$\textbf{0.986 8}$	$\textbf{0.980 1}$	$\textbf{0.891 6}$
	WABNN	$\textbf{5.439 4}$	$\textbf{0.985 5}$	$\textbf{0.981 6}$	$\textbf{0.870 8}$
BLUR	BIQI	8.964 8	0.829 3	0.846 3	0.779 5
	DIIVINE	7.996 1	0.923 0	0.921 0	0.796 1
	BLIINDS-II	7.6863	0.9381	0.9432	0.832 8
	NIQE	7.056 7	0.952 5	0.934 1	0.828 6
	BRISQUE	7.239 5	0.950 6	0.943 5	0.837 9
	SSEQ	$\textbf{6.864 9}$	$\textbf{0.960 7}$	$\textbf{0.948 3}$	$\textbf{0.846 1}$
	BHOD	$\textbf{6.166 9}$	$\textbf{0.966 0}$	$\textbf{0.964 9}$	$\textbf{0.854 7}$
	NRSL	7.850 4	0.944 7	0.942 0	0.808 1
	WABNN	$\textbf{6.633 9}$	$\textbf{0.957 2}$	$\textbf{0.9442 }$	$\textbf{0.840 7}$
JPEG	BIQI	10.144 0	0.901 1	0.891 4	0.783 9
	DIIVINE	9.5101	0.9210	0.910 0	0.808 5
	BLIINDS-II	8.654 1	0.937 6	0.931 1	0.825 7
	NIQE	8.298 2	0.941 4	0.938 2	0.821 8
	BRISQUE	$\textbf{7.204 2}$	$\textbf{0.973 4}$	$\textbf{0.964 7}$	$\textbf{0.846 6}$
	SSEQ	7.570 5	0.970 2	$\textbf{0.951 0}$	$\textbf{0.841 5}$
	BHOD	8.173 5	$\textbf{0.972 6}$	0.946 1	0.820 6
	NRSL	$\textbf{7.568 4}$	$\textbf{0.976 4}$	$\textbf{0.950 8}$	0.829 4
	WABNN	$\textbf{7.483 8}$	0.942 3	0.934 8	$\textbf{0.838 1}$
JP2K	BIQI	12.013 3	0.808 6	0.799 5	0.736 8
	DIIVINE	9.610 9	0.922 0	0.913 0	0.765 6
	BLIINDS-II	9.505 5	0.934 8	$\textbf{0.950 6}$	$\textbf{0.816 8}$
	NIQE	9.502 4	0.937 0	0.917 2	0.781 8
	BRISQUE	9.875 2	0.922 9	0.913 9	0.769 3
	SSEQ	$\textbf{8.165 3}$	$\textbf{0.946 4}$	0.942 0	0.801 1
	BHOD	$\textbf{7.482 1}$	$\textbf{0.966 2}$	$\textbf{0.946 2}$	$\textbf{0.818 0}$
	NRSL	$\textbf{7.311 1}$	$\textbf{0.967 1}$	$\textbf{0.951 4}$	$\textbf{0.824 3}$
	WABNN	8.916 5	0.941 2	0.931 0	0.795 1
FF	BIQI	18.103 2	0.732 8	0.706 7	0.606 4
	DIIVINE	14.429 5	0.888 0	0.863 0	0.668 6
	BLIINDS-II	13.505 5	0.895 5	0.865 7	0.684 1
	NIQE	12.605 8	0.912 8	0.859 4	0.665 3
	BRISQUE	$\textbf{11.799 6}$	0.914 8	0.886 1	0.721 6
	SSEQ	12.685 8	0.916 2	$\textbf{0.903 5}$	0.749 3
	BHOD	$\textbf{10.181 3}$	$\textbf{0.944 5}$	$\textbf{0.919 8}$	$\textbf{0.776 0}$
	NRSL	11.934 8	$\textbf{0.923 8}$	0.902 9	$\textbf{0.753 5}$
	WABNN	$\textbf{11.591 3}$	$\textbf{0.918 9}$	$\textbf{0.911 0}$	$\textbf{0.766 2}$

Table 6

Fig 11

Table 7

Comparison of the subjective consistency of different NRIQA methods on TID2013 database"

Distortion type	Method	RMSE	LPCC	SROCC	KROCC
WN	BIQI	$\textbf{0.292 2}$	$\textbf{0.910 6}$	$\textbf{0.901 5}$	$\textbf{0.737 7}$
	DIIVINE	0.338 3	0.877 3	0.810 4	0.708 9
	BLIINDS-II	0.385 7	0.839 5	0.821 5	0.626 7
	NIQE	0.399 4	0.842 8	0.815 5	0.605 0
	BRISQUE	0.330 5	0.884 1	0.868 8	0.683 3
	SSEQ	$\textbf{0.310 8}$	$\textbf{0.900 2}$	$\textbf{0.894 6}$	$\textbf{0.726 7}$
	BHOD	0.485 8	0.729 5	0.710 6	0.522 1
	NRSL	0.312 7	0.896 7	0.889 1	0.713 3
	WABNN	$\textbf{0.280 4}$	$\textbf{0.916 3}$	$\textbf{0.916 6}$	$\textbf{0.728 9}$
BLUR	BIQI	0.635 6	0.855 2	0.836 2	0.663 3
	DIIVINE	0.479 9	0.921 1	0.916 5	0.760 0
	BLIINDS-II	$\textbf{0.380 3}$	$\textbf{0.951 0}$	$\textbf{0.946 2}$	$\textbf{0.811 4}$
	NIQE	0.670 2	0.825 4	0.815 5	0.589 0
	BRISQUE	0.483 7	0.919 4	0.911 5	0.749 0
	SSEQ	0.531 2	0.901 2	0.893 1	0.720 0
	BHOD	0.668 2	0.919 3	0.910 7	0.746 7
	NRSL	$\textbf{0.396 5}$	$\textbf{0.945 6}$	$\textbf{0.934 6}$	$\textbf{0.793 3}$
	WABNN	$\textbf{0.430 3}$	$\textbf{0.934 5}$	$\textbf{0.928 7}$	$\textbf{0.779 6}$
JPEG	BIQI	1.022 0	0.728 8	0.670 3	0.493 3
	DIIVINE	0.684 5	0.884 4	0.810 4	0.620 0
	BLIINDS-II	$\textbf{0.461 5}$	$\textbf{0.950 7}$	$\textbf{0.905 4}$	$\textbf{0.733 0}$
	NIQE	0.559 5	0.926 8	0.866 5	0.649 8
	BRISQUE	0.521 2	0.936 1	0.870 8	0.700 0
	SSEQ	0.462 2	$\textbf{0.948 7}$	$\textbf{0.900 9}$	$\textbf{0.737 9}$
	BHOD	0.475 3	0.948 3	0.871 2	0.686 7
	NRSL	$\textbf{0.413 4}$	$\textbf{0.958 7}$	0.900 5	0.732 3
	WABNN	$\textbf{0.413 9}$	0.944 6	$\textbf{0.919 8}$	$\textbf{0.766 3}$
JP2K	BIQI	0.761 2	0.893 4	0.822 0	0.631 1
	DIIVINE	$\textbf{0.636 7}$	0.924 9	0.877 3	0.700 0
	BLIINDS-II	$\textbf{0.546 6}$	$\textbf{0.944 9}$	$\textbf{0.926 9}$	$\textbf{0.780 0}$
	NIQE	0.715 5	0.907 2	0.898 1	0.706 2
	BRISQUE	0.672 3	0.912 8	$\textbf{0.907 3}$	$\textbf{0.752 3}$
	SSEQ	0.601 1	$\textbf{0.932 8}$	0.899 9	0.737 9
	BHOD	$\textbf{0.609 3}$	$\textbf{0.933 2}$	0.890 7	0.720 0
	NRSL	0.656 2	0.920 4	0.883 1	0.713 3
	WABNN	0.699 6	0.913 6	$\textbf{0.906 6}$	$\textbf{0.749 3}$

Table 7

Table 8

Table 9

Table 10

Table 11

Table 12

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Vector elements	Meaning
$f_1$-$f_3 $	Variance $\sigma ^2$ of the three sub-bands in the horizontal orientations and the first, second and third scales
$f_4$-$f_6 $	Variance $\sigma ^2$ of the three sub-bands in the vertical orientations and the first, second and third scales
$f_7$-$f_9 $	Variance $\sigma ^2$ of the three sub-bands in the diagonal orientations and the first, second and third scales
$f_{10}$-$f_{12} $	Shape parameter $\gamma $ of the three sub-bands in the horizontal orientations and the first, second and third scales
$f_{13}$-$f_{15} $	Shape parameter $\gamma $ of the three sub-bands in the vertical orientations and the first, second and third scales
$f_{16}$-$f_{18} $	Shape parameter $\gamma $ of the three sub-bands in the diagonal orientations and the first, second and third scales

Vector elements	Meaning
$f_{19}$-$f_{21}$	Mean $\mu_w$ of sub-band local information entropy in the first scale and the horizontal, vertical and diagonal orientations
$f_{22}$-$f_{24}$	Mean $\mu_w$ of sub-band local information entropy in the second scale and the horizontal, vertical and diagonal orientations
$f_{25}$-$f_{27}$	Mean $\mu_w$ of sub-band local information entropy in the third scale and the horizontal, vertical and diagonal orientations
$f_{28}$-$f_{30}$	Skewness $S_w$ of sub-band local information entropy in the first scale and the horizontal, vertical and diagonal orientations
$f_{31}$-$f_{33}$	Skewness $S_w$ of sub-band local information entropy in the second scale and the horizontal, vertical and diagonal orientations
$f_{34}$-$f_{36}$	Skewness $S_w$ of sub-band local information entropy in the third scale and the horizontal, vertical and diagonal orientations

Dataset	RMSE	LPCC	SROCC	KROCC
Dataset 1	8.674 1	0.948 4	0.940 8	0.780 1
Dataset 2	9.135 9	0.922 7	0.917 3	0.763 5
Dataset 3	9.948 6	0.915 9	0.908 8	0.756 2
Dataset 4	8.825 4	0.935 5	0.926 5	0.768 7
Dataset 5	8.936 6	0.949 1	0.935 2	0.778 1
All	9.110 7	0.936 4	0.924 9	0.766 4

Dataset	WN	BLUR	JPEG	JPEG2000	FF	All
Dataset 1	96.67	90.00	97.37	85.29	86.67	91.36
Dataset 2	96.67	93.33	94.44	89.19	86.67	92.02
Dataset 3	100.00	90.00	91.89	88.24	90.00	91.93
Dataset 4	100.00	93.33	88.24	88.57	86.67	91.19
Dataset 5	96.00	92.00	96.30	89.66	88.00	93.13
All	97.87	91.73	93.65	88.19	87.60	91.93

Method	WN	Blur	JPEG	JP2K	ALL
BIQI	0.876 8	0.876 6	0.666 5	0.855 9	0.819 0
DIIVINE	0.883 9	0.893 9	0.852 4	0.789 5	0.854 9
BLIINDS-II	0.723 1	0.583 3	0.566 2	0.611 2	0.621 0
NIQE	0.815 5	0.815 5	0.866 5	0.898 1	0.848 9
BRISQUE	0.906 7	0.904 5	0.891 7	0.888 8	$\textbf{0.897 9 }$
SSEQ	0.861 6	0.898 6	0.918 3	0.902 3	$\textbf{0.895 2 }$
BHOD	0.748 9	0.914 8	0.881 5	0.915 5	0.865 2
NRSL	0.842 2	0.909 4	0.909 2	0.777 9	0.859 7
WABNN	0.901 0	0.897 1	0.922 1	0.841 2	$\textbf{0.890 4 }$

No-reference image quality assessment based on AdaBoost BP neural network in wavelet domain

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Dataset	Categories of distorted images	Types and numbers of distorted image
Dataset	Categories of distorted images	WN	BLUR	JPEG	JP2K	FF	All
1	Bikes, building2, buildings, caps, carnivaldolls, cemetry	30	30	38	34	30	162
2	Churchandcapitol, lighthouse, dancers, coinsinfountain, house, loweronih35	30	30	36	37	30	163
3	Lighthouse2, manfishing, monarch, ocean, parrots, paintedhouse	30	30	37	34	30	161
4	Plane, rapids, sailing1, sailing2, sailing3, sailing4	30	30	34	35	30	159
5	Statue, stream, womanhat, studentsculpture, woman	25	25	30	29	25	134
All distorted images in LIVE database		145	145	175	169	145	779

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	DIIVINE	BLIINDS-II	SSEQ	BRISQUE	BHOD	NRSL	WABNN
Mean time/s	19.645 0	62.372 0	2.135 7	0.171 8	0.827 3	0.296 4	0.251 3

	DIIVINE	BLIINDS-II	SSEQ	BRISQUE	BHOD	NRSL	WABNN
Mean time/s	14.733 8	62.372 0	1.414 6	0.111 7	0.549 8	0.170 3	0.238 6