Abstract
Color information is important to image quality assessment (IQA). However, most image quality assessment methods transform color image into gray scale, which fail to consider color information. In recent years, color image processing by using the algebra of quaternions has been attracting tremendous attention. Extensive moments based on quaternion have been introduced to deal with the red, green and blue channels of color images in a holistic manner, which have been proved more effective in color processing. With these inspirations, this paper presents a full-reference color image quality assessment metric based on Quaternion Tchebichef Moments (QTMs). QTMs are first employed to measure color and structure distortions simultaneously. Considering that moments are insensitive to weak distortions in high-quality images, gradient is incorporated as a complementary feature. Luminance is also considered as an auxiliary feature. Finally, a QTM-feature-based weighting map is proposed to conduct the pooling, producing an overall quality score. The experimental results on five public image quality databases demonstrate that the proposed method outperforms the state-of-the-arts.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Xia, Z.H., Wang, X.H., Sun, X.M., Wang, B.W.: Steganalysis of least significant bit matching using multi-order differences. Secur. Commun. Netw. 7(8), 1283–1291 (2014)
Li, J., Li, X.L., Yang, B., Sun, X.M.: Segmentation-based image copy-move forgery detection scheme. IEEE Trans. Inf. Forensics Secur. 10(3), 507–518 (2015)
Zheng, Y.H., Jeon, B., Xu, D.H., Wu, Q.J., Zhang, H.: Image segmentation by generalized hierarchical fuzzy C-means algorithm. J. Intell. Fuzzy Syst. 28(2), 961–973 (2015)
Lin, W.S., JayKuo, C.-C.: Perceptual visual quality metrics: a survey. J. Vis. Commun. Image Represent. 22(4), 297–312 (2011)
Cai, H., Li, L.D., Qian, J.S., Pan, J.S.: Image blur assessment with feature points. J. Inf. Hiding Multimedia Signal Proces. 6(3), 482–490 (2015)
Zhang, W., Li, L.D., Zhu, H.C., Cheng, D.Q., Chu, S.C., Roddick, J.F.: No-reference quality metric of blocking artifacts based on orthogonal moments. J. Inf. Hiding Multimedia Signal Proces. 5(4), 701–708 (2014)
Zhang, W., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multi-scale structural similarity for image quality assessment. In: Proceedings of IEEE Asilomar Conference on Signals, Systems and Computers, pp. 1398–1402 (2003)
Wang, Z., Li, Q.: Information content weighting for perceptual image quality assessment. IEEE Trans. Image Process. 20(5), 1185–1198 (2011)
Sheikn, H.R., Bovik, A.C.: Image information and visual quality. IEEE Trans. Image Process. 15(2), 430–444 (2006)
Larson, E.C., Chandler, D.M.: Most apparent distortion: full-reference image quality assessment and the role of strategy. J. Electr. Imaging 19(1), 001006:1–001006:21 (2010)
Zhang, L., Zhang, L., Mou, X.Q., Zhang, D.: FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process. 21(4), 1500–1512 (2012)
Liu, A.M., Lin, W.S., Narwaria, M.: Image quality assessment based on gradient similarity. IEEE Trans. Image Process. 20(8), 2378–2386 (2011)
Xue, W.F., Zhang, L., Mou, X.Q., Bovik, A.C.: Gradient magnitude similarity deviation: a highly efficient perceptual image quality index. IEEE Trans. Image Process. 22(2), 684–695 (2014)
Kolaman, A., Pecht, O.Y.: Quaternion structural similarity: a new quality index for color images. IEEE Trans. Image Process. 21(4), 1526–1536 (2012)
Hamilton, W.R.: Elements of Quaternions. Longmans Green, London (1866)
Sangwine, S.J.: Fourier transforms of color images using quaternion or hypercomplex numbers. Electr. Lett. 32(21), 1979–1980 (1996)
Kantor, I.L., Solodovnikov, A.S.: Hypercomplex Number: An Elementary Introduction to Algebras. Springer, New York (1989)
Mukundan, R., Ong, S.H., Lee, P.A.: Image analysis by Tchebichef moments. IEEE Trans. Image Process. 10(9), 1357–1364 (2001)
Zhu, H.Q., Li, Q., Liu, Q.: Quaternion discrete Tchebichef moments and their applications. Int. J. Signal Process. Image Process. Pattern Recogn. 7(6), 149–162 (2014)
Le Callet, P., Autrusseau, F.: Subjective Quality Assessment IR-CCyN/IVC Database. http://www.irccyn.ecnantes.fr/ivcdb/
Sheikh, H.R., Sabir, M.F., Bovik, A.C.: A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Trans. Image Process. 15(11), 3440–3451 (2006)
Ponomarenko, N., Lukin, V., Zelensky, A., Egiazarian, K., Carli, M., Battisti, F.: TID2008 - a database for evaluation of full-reference visual quality assessment metrics. Adv. Mod. Radioelectron. 10(4), 30–45 (2009)
Ponomarenko, N., Ieremeiev, O., Lukin, V., Egiazarian, K., Jin, L., Astola, J., Vozel, B., Chehdi, K., Carli, M., Battisti, F., Jay Kuo, C.-C.: Color image database TID2013: peculiarities and preliminary results. In: European Workshop on Visual Information Process, pp. 106–111 (2013)
Final Report from the Video Quality Experts Group on the Validation of Objective Models of Video Quality Assessment, Phase II. http://www.vqeg.org
Acknowledgment
This work is supported by the National Natural Science Foundation of China (61379143) and the Fundamental Research Funds for the Central Universities (2015XKMS032, 2015QNA66).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Zhang, W., Hu, B., Xu, Z., Li, L. (2016). Color Image Quality Assessment with Quaternion Moments. In: Sun, X., Liu, A., Chao, HC., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2016. Lecture Notes in Computer Science(), vol 10040. Springer, Cham. https://doi.org/10.1007/978-3-319-48674-1_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-48674-1_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48673-4
Online ISBN: 978-3-319-48674-1
eBook Packages: Computer ScienceComputer Science (R0)