Image quality assessmentmethod based on nonlinear feature extraction in kernel space

Yong DING; Nan LI; Yang ZHAO; Kai HUANG

doi:10.1631/FITEE.1500439

Front. Inform. Technol. Electron. Eng ›› 2016, Vol. 17 ›› Issue (10) : 1008 -1017. DOI: 10.1631/FITEE.1500439

Article

Image quality assessmentmethod based on nonlinear feature extraction in kernel space

Author information +

History +

PDF (932KB)

Abstract

To match human perception, extracting perceptual features effectively plays an important role in image quality assessment. In contrast to most existing methods that use linear transformations or models to represent images, we employ a complex mathematical expression of high dimensionality to reveal the statistical characteristics of the images. Furthermore, by introducing kernel methods to transform the linear problem into a nonlinear one, a full-reference image quality assessment method is proposed based on high-dimensional nonlinear feature extraction. Experiments on the LIVE, TID2008, and CSIQ databases demonstrate that nonlinear features offer competitive performance for image inherent quality representation and the proposed method achieves a promising performance that is consistent with human subjective evaluation.

Keywords

Image quality assessment / Full-reference method / Feature extraction / Kernel space / Support vector regression

Cite this article

Download citation ▾

Yong DING, Nan LI, Yang ZHAO, Kai HUANG. Image quality assessmentmethod based on nonlinear feature extraction in kernel space. Front. Inform. Technol. Electron. Eng, 2016, 17(10): 1008-1017 DOI:10.1631/FITEE.1500439

登录浏览全文

4963

注册一个新账户忘记密码

References

Publishing order | Descend order by publishing year | Descend order by cited within

[1]	Abdi, H., Williams, L.J., 2010. Principal component analysis.Wiley Interdiscip. Rev. Comput. Stat., 2(4):433–459.

[2]	Bach, F.R., Jordan, M.I., 2003. Kernel independent component analysis. Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, p.IV-876–9.

[3]	Chang, C.C., Lin, C.J., 2011. LIBSVM: a library for support vector machines.ACM Trans. Intell. Syst. Technol., 2(3):Article 27.

[4]	Chang, H.W., Zhang, Q.W., Wu, Q.G., , 2015. Perceptual image quality assessment by independent feature detector.Neurocomputing, 151:1142–1152.

[5]	Ding, Y., Dai, H., 2014. Image quality assessment scheme with topographic independent components analysis for sparse feature extraction.Electron. Lett., 50(7):509–510.

[6]	Genton, M.G., 2001. Classes of kernels for machine learning: a statistics perspective. J. Mach. Learn. Res., 2:299–312.

[7]	Hyvärinen, A., Hurri, J., Hoyer, P.O., 2009. Natural Image Statistics: a Probabilistic Approach to Early Computational Vision. Springer-Verlag London, UK.

[8]	Jolliffe, I., 2002. Principal Component Analysis. Springer-Verlag New York, USA.

[9]	Larson, E.C., Chandler, D.M., 2010. Most apparent distortion: full-reference image quality assessment and the role of strategy.J. Electron. Imag., 19(1):011006.

[10]	Li, Q., Wang, Z., 2009. Reduced-reference image quality assessment using divisive normalization-based image representation.IEEE J. Sel. Topics Signal Process., 3(2):202–211.

[11]	Li, Y.C., Wu, K.H., Ma, Y.L., , 2007. Image digital watermarking technique based on kernel independent component analysis. Proc. 11th Int. Conf. on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, p.467–474.

[12]	Liu, A., Lin, W., Narwaria, M., 2012. Image quality assessment based on gradient similarity. IEEE Trans. Image Process., 21(4):1500–1512.

[13]	Liu, M., Yang, X., 2009. Image quality assessment using contourlet transform.Opt. Eng., 48(10):107201.

[14]	Liu, T.J., Lin, W., Kuo, C.C.J., 2013. Image quality assessment using multi-method fusion. IEEE Trans. Image Process., 22(5):1793–1807.

[15]	Ma, L., Li, S., Ngan, K.N., 2013. Reduced-reference image quality assessment in reorganized DCT domain. Signal Process.Image Commun., 28(8):884–902.

[16]	Mittal, A., Moorthy, A.K., Bovik, A.C., 2012. No-reference image quality assessment in the spatial domain.IEEE Trans. Image Process., 21(12):4695–4708.

[17]	Rao, D.V., Reddy, L.P., 2009. Contrast weighted perceptual structural similarity index for image quality assessment. Proc. Annual IEEE India Conf., p.1–4.

[18]	Rehman, A., Wang, Z., 2012. Reduced-reference image quality assessment by structural similarity estimation.IEEE Trans. Image Process., 21(8):3378–3389.

[19]	Schölkopf, B., Smola, A.J., 1998. Learning with Kernels. MIT Press.

[20]	Sheikh, H.R., Bovik, A.C., de Veciana, G., 2005. An information fidelity criterion for image quality assessment using natural scene statistics.IEEE Trans. Image Process., 14(12):2117–2128.

[21]	Video Quality Experts Group, 2003. Final Report from the Video Quality Experts Group on the Validation of Objective Models of Video Quality Assessment, Phase II (fr_tv2). Available from http://www.vqeg.org.

[22]	Wang, Z., Li, Q., 2011. Information content weighting for perceptual image quality assessment.IEEE Trans. Image Process., 20(5):1185–1198.

[23]	Wang, Z., Bovik, A.C., Lu, L.G., 2002. Why is image quality assessment so difficult? Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, p.1–4.

[24]	Wang, Z., Simoncelli, E.P., Bovik, A.C., 2003. Multiscale structural similarity for image quality assessment. Proc. 37th Asilomar Conf. on Signals, Systems and Computers, p.1398–1402.

[25]	Wang, Z., Bovik, A.C., Sheikh, H.R., , 2004. Image quality assessment: from error visibility to structural similarity.IEEE Trans. Image Process., 13(4):600–612.

[26]	Wu, Q., Li, H., Meng, F., , 2015. No reference image quality assessment metric via multi-domain structural information and piecewise regression.J. Vis. Commun. Image Represent., 32:205–216.

[27]	Wu, Q., Li, H., Meng, F., , 2016. Blind image quality assessment based on multichannel feature fusion and label transfer.IEEE Trans. Circ. Syst. Video Technol., 26(3):425–440.

[28]	Yang, C.A., Kaveh, M., 2010. Image quality assessment using singular vectors. Proc. SPIE, Article 752910.

[29]	Yang, J., Gao, X., Zhang, D., , 2005. Kernel ICA: an alternative formulation and its application to face recognition.Patt. Recog., 38(10):1784–1787.

[30]	Zhang, H., Ding, Y., Huang, K., , 2014. Image quality assessment by quantifying discrepancies of multifractal spectrums.IEICE Trans. Inform. Syst., 97(9):2453–2460.

[31]	Zhang, L., Zhang, L., Mou, X., , 2011. FSIM: a feature similarity index for image quality assessment.IEEE Trans. Image Process., 20(8):2378–2386.

[32]	Zhang, M., Muramatsu, C., Zhou, X., , 2015. Blind image quality assessment using the joint statistics of generalized local binary pattern.IEEE Signal Process. Lett., 22(2):207–210.

[33]	Zhang, Y., Chandler, D.M., 2013. No-reference image quality assessment based on log-derivative statistics of natural scenes.J. Electron. Imag., 22(4):043025.