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Color Image Quality Assessment with Quaternion Moments

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Cloud Computing and Security (ICCCS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10040))

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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.

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References

  1. 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)

    Article  Google Scholar 

  2. 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)

    Article  Google Scholar 

  3. 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)

    Google Scholar 

  4. Lin, W.S., JayKuo, C.-C.: Perceptual visual quality metrics: a survey. J. Vis. Commun. Image Represent. 22(4), 297–312 (2011)

    Article  Google Scholar 

  5. 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)

    Google Scholar 

  6. 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)

    Google Scholar 

  7. 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)

    Article  Google Scholar 

  8. 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)

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  10. Sheikn, H.R., Bovik, A.C.: Image information and visual quality. IEEE Trans. Image Process. 15(2), 430–444 (2006)

    Article  Google Scholar 

  11. 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)

    Google Scholar 

  12. 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)

    Article  MathSciNet  Google Scholar 

  13. Liu, A.M., Lin, W.S., Narwaria, M.: Image quality assessment based on gradient similarity. IEEE Trans. Image Process. 20(8), 2378–2386 (2011)

    Article  MathSciNet  Google Scholar 

  14. 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)

    Article  MathSciNet  Google Scholar 

  15. Kolaman, A., Pecht, O.Y.: Quaternion structural similarity: a new quality index for color images. IEEE Trans. Image Process. 21(4), 1526–1536 (2012)

    Article  MathSciNet  Google Scholar 

  16. Hamilton, W.R.: Elements of Quaternions. Longmans Green, London (1866)

    Google Scholar 

  17. Sangwine, S.J.: Fourier transforms of color images using quaternion or hypercomplex numbers. Electr. Lett. 32(21), 1979–1980 (1996)

    Article  Google Scholar 

  18. Kantor, I.L., Solodovnikov, A.S.: Hypercomplex Number: An Elementary Introduction to Algebras. Springer, New York (1989)

    Book  MATH  Google Scholar 

  19. Mukundan, R., Ong, S.H., Lee, P.A.: Image analysis by Tchebichef moments. IEEE Trans. Image Process. 10(9), 1357–1364 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  20. 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)

    Google Scholar 

  21. Le Callet, P., Autrusseau, F.: Subjective Quality Assessment IR-CCyN/IVC Database. http://www.irccyn.ecnantes.fr/ivcdb/

  22. 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)

    Article  Google Scholar 

  23. 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)

    Google Scholar 

  24. 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)

    Google Scholar 

  25. Final Report from the Video Quality Experts Group on the Validation of Objective Models of Video Quality Assessment, Phase II. http://www.vqeg.org

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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).

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Authors and Affiliations

  1. School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, 221116, China

    Wei Zhang, Bo Hu, Zhao Xu & Leida Li

Authors
  1. Wei Zhang
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  2. Bo Hu
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  3. Zhao Xu
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  4. Leida Li
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Corresponding author

Correspondence to Bo Hu .

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Editors and Affiliations

  1. University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Michigan State University , EAST LANSING, Michigan, USA

    Alex Liu

  3. National Dong Hwa University , Shoufeng, Taiwan

    Han-Chieh Chao

  4. Purdue University , West Lafayette, Indiana, USA

    Elisa Bertino

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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

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  • DOI: https://doi.org/10.1007/978-3-319-48674-1_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48673-4

  • Online ISBN: 978-3-319-48674-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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