A Color Image Quality Assessment Method Based onNon-negative Matrix Factorization

XU Haiyong; YU Mei; LUO Ting; Lü Yaqi; JIANG Gangyi

doi:10.11999/JEIT150610

Volume 38 Issue 3

Mar. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(3): 578-585

XU Haiyong, YU Mei, LUO Ting, Lü Yaqi, JIANG Gangyi. A Color Image Quality Assessment Method Based onNon-negative Matrix Factorization[J]. Journal of Electronics & Information Technology, 2016, 38(3): 578-585. doi: 10.11999/JEIT150610

Citation:

XU Haiyong, YU Mei, LUO Ting, Lü Yaqi, JIANG Gangyi. A Color Image Quality Assessment Method Based onNon-negative Matrix Factorization[J]. Journal of Electronics & Information Technology, 2016, 38(3): 578-585. doi: 10.11999/JEIT150610

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A Color Image Quality Assessment Method Based onNon-negative Matrix Factorization

doi: 10.11999/JEIT150610

1.
2.
(Faculty of Information Science and Engineering, Ningbo University, Ningbo 315211, China)

Funds:

The National Natural Science Foundation of China (U1301257, 61171163, 61271270, 61271021, 61311140262, 61501270), Zhejiang Provincial Natural Science Foundation of China (LY14F010004, LY15F010005), Open Fund of Zhejiang Provincial Key Academic Project (first level)

Received Date: 2015-05-25
Rev Recd Date: 2015-11-09
Publish Date: 2016-03-19

Abstract

Abstract

For the sparse representation of image quality assessment model are based on gray image and the lack of color information, a Non-negative Matrix Factorization (NMF)-based full reference color image quality assessment method is proposed. Firstly, from the natural color image in random sampling, training samples are got. Non-negative matrix factorization method is used to train and get a feature basis matrix. After using Schmidt orthogonalization, a feature extracting matrix is got. Secondly, according to the visual saliency model, maximum visual saliency is defined and significant difference of two steps is used to select visual important area. Finally, using the feature extraction matrix, low dimensional feature vectors and the final color image quality evaluation value are got. The experimental results show that the proposed method has good performance in the LIVE, CSIQ and TID2008 three image databases. The average results of three image quality assessment databases show that the proposed method outperforms other methods, which means that the proposed method has better correlation with the subjective perception.
- Color image quality assessment,
- Non-negative Matrix Factorization (NMF),
- Visual saliency

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References(26)

References

蒋刚毅, 黄大江, 王旭, 等. 图像质量评价方法研究进展[J]. 电子与信息学报, 2010, 32(1): 219-226. doi: 10.3724/SP.J. 1146.2009.00091.

JIANG Gangyi, HUANG Dajiang, WANG Xu, et al. Overview on image quality assessment methods[J]. Journal of Electronics Information Technology, 2010, 32(1): 219-226. doi: 10.3724/SP.J.1146.2009.00091.

ZHANG M, MURAMASTSU C, ZHOU X, et al. Blind image quality assessment using the joint statistics of generalized local binary pattern[J]. IEEE Signal Processing Letters, 2015, 22(2): 207-210.

宋洋, 郁梅, 蒋刚毅, 等. 基于人眼视觉特性的三维小波变换视频质量评价方法[J]. 光电子激光, 2014, 25(10): 1983-1988.

SONG Yang, YU Mei, JIANG Gangyi, et al. 3D discrete wavelet transform based video quality metric combining with human visual characteristics[J]. Journal of OptoelectronicsLaser, 2014, 25(10): 1983-1988.

MANTIUK R K, TOMASZEWSKA A, and MANTIUK R. Comparison of four subjective methods for image quality assessment[J]. Computer Graphics Forum, 2012, 31(8): 2478-2491.

ZHANG L, SHEN Y, and LI H. VSI: a visual saliency-induced index for perceptual image quality assessment[J]. IEEE Transactions on Image Processing, 2014, 23(10): 4270-4281.

HONG R, PAN J, Hao S, et al. Image quality assessment based on matching pursuit[J]. Information Sciences, 2014, 273: 196-211.

陈勇, 樊强, 帅锋. 基于小波分析的图像稀疏保真度评价[J]. 电子与信息学报, 2015, 37(9): 2055-2061. doi: 10.11999/ JEIT150173.

CHEN Yong, FAN Qiang, and SHUAI Feng. Sparse image fidelity evaluation based on wavelet analysis[J]. Journal of Electronics Information Technology, 2015, 37(9): 2055-2061. doi: 10.11999/JEIT150173.

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.

WANG Z, SIMONCELLI E P, and BOVIK A C. Multiscale structural similarity for image quality assessment[C]. IEEE Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA, 2003: 1398-1402.

SAMPAT M P, WANG Z, GUPTA S, et al. Complex wavelet structural similarity: A new image similarity index[J]. IEEE Transactions on Image Processing, 2009, 18(11): 2385-2401.

WANG Z and LI Q. Information content weighting for perceptual image quality assessment[J]. IEEE Transactions on Image Processing, 2011, 20(5): 1185-1198.

SHEIKH H R and BOVIK A C. Image information and visual quality[J]. IEEE Transactions on Image Processing, 2006, 15(2): 430-444.

CHANDLER D M and HEMAMI S S. VSNR: A wavelet-based visual signal-to-noise ratio for natural images[J]. IEEE Transactions on Image Processing, 2007, 16(9): 2284-2298.

LIU A, LIN W, and NARWARIA M. Image quality assessment based on gradient similarity[J]. IEEE Transactions on Image Processing, 2012, 21(4): 1500-1512.

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.

GUHA T, NEZHADARYA E, and WARD R K. Sparse representation-based image quality assessment[J]. Signal Processing: Image Communication, 2014, 29(10): 1138-1148.

MARTIN D, FOWLKES C, TAL D, et al. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics[C]. Proceedings of. Eighth IEEE International

Conference on Computer Vision, Vancouver, Canada, 2001, 2: 416-423.

LEE D D and SEUNG H S. Learning the parts of objects by non-negative matrix factorization[J]. Nature, 1999, 401(6755): 788-791.

ZHANG L, GU Z, and LI H. SDSP: A novel saliency detection method by combining simple priors[C]. IEEE International Conference on Image Processing, Australia, 2013: 171-175.

PONOMARENKO N, LUKIN V, ZELENSKY A, et al. TID2008A database for evaluation of full-reference visual quality assessment metrics[J]. Advances of Modern Radioelectronics, 2009, 10: 30-45.

SHEIKH H R, BOVIK A C, and DE VECIANA G. An information fidelity criterion for image quality assessment using natural scene statistics[J]. IEEE Transactions on Image Processing, 2005, 14(12): 2117-2128.

LARSON E C and CHANDLER D M. Most apparent distortion: full-reference image quality assessment and the role of strategy[J]. Journal of Electronic Imaging, 2010, 19(1): 011006.

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