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Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 132))

Abstract

This paper presents a new full reference image quality index for objective evaluation of color images in perceptually consistent manner with Human Visual System (HVS) characteristics. The quality index proposed in this paper evaluates the image quality across various types of distortions like: noise contamination, blurring, contrast stretching and compression; by considering the image degradations in terms of error, edge distortion, structural distortion, correlation degree and luminance. Simulation results obtained for the proposed quality index are subjectively validated using Mean Opinion Score (MOS) which ensures the capability and efficiency of the proposed index in evaluating color images according to the HVS characteristics.

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References

  1. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice Hall, Upper Saddle River (2001)

    Google Scholar 

  2. Wang, Z., Bovik, A.C., Lu, L.: Why is image quality assessment so difficult. In: Proc. IEEE Int. Conf. Acoust. Speech and Signal Processing, Orlando, vol. 4, pp. 3313–3316 (2002)

    Google Scholar 

  3. Wang, Z., Bovik, A.C.: Mean Squared Error: Love It or Leave It? IEEE Signal Processing Magazine, 98–117 (2009)

    Google Scholar 

  4. Wang, Z., Lu, L., Bovik, A.C.: Video quality assessment based on structural distortion measurement. Signal Process. Image Communication 19(2), 121–132 (2004)

    Article  Google Scholar 

  5. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image Quality Assessment: From Error Measurement to Structural Similarity. IEEE Transaction Image Processing 13(4), 600–612 (2004)

    Article  Google Scholar 

  6. Wharton, E., Panetta, K., Agatan, S.: Human Visual System Based Similarity metrics. In: Proc. of IEEE Conference on System, Man and Cybernetics, pp. 685–690 (2008)

    Google Scholar 

  7. Fu, W., Gu, X., Wang, Y.: Image Quality Assessment Using Edge and Contrast Similarity. In: Proc. of International Joint Conference on Neural Networks, pp. 852–855 (2008)

    Google Scholar 

  8. Han, H.S., Kim, D.O., Park, R.H.: Gradient Information- Based Image Quality Metric. In: Consumer Electronics (ICCE), 2010 Digest of Technical Papers, pp. 361–362 (2010)

    Google Scholar 

  9. Yutuo, C., Meijie, W., Yongchao, F.: Evaluation Method of Color Image Coding Quality integrating Visual Characteristics of Human Eye. In: Proc. of 2nd International Conference on Education Technology and Computer, Shanghai, China, vol. 2, pp. 562–566 (2010)

    Google Scholar 

  10. VQEG.: Final report from the video quality experts group on the validation of objective models of video quality assessment (March 2000), http://www.vqeg.org/

  11. Martens, J.B., Meesters, L.: Image dissimilarity. Signal Processing 70, 155–176 (1998)

    Article  MATH  Google Scholar 

  12. Eskicioglu, A.M., Fisher, P.S.: Image quality measures and their performance. IEEE Trans. Communications 43, 2959–2965 (1995)

    Article  Google Scholar 

  13. Pappas, T.N., Safranek, R.J.: Perceptual criteria for image quality evaluation. In: Bovik, A. (ed.) Handbook of Image and Video Proc. Academic Press (2000)

    Google Scholar 

  14. Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Analysis and Machine Intelligence 8(6), 679–698 (1986)

    Article  Google Scholar 

  15. Wang, Z., Bovik, A.C.: A universal image quality index. IEEE Signal Processing Lett. 9(3), 81–84 (2002)

    Article  Google Scholar 

  16. Tripathi, N., Wahidi, M.F., Gupta, A., Bhateja, V.: A Novel Spatial Domain Image Quality Metric. In: Proc. of (IEEE) 2011 World Congress on Engg. & Technology (CET-2011), Signal & Information Processing, Shanghai, paper-id: 24571 (in press, 2011)

    Google Scholar 

  17. Morrow, W.M., Paranjape, R.B., Rangayyan, R.M., Desautels, J.E.L.: Region Based Contrast Enhancement of Mammograms. IEEE Transactions on Medical Imaging 11(3), 392–406 (1992)

    Article  Google Scholar 

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Gupta, P., Srivastava, P., Bhardwaj, S., Bhateja, V. (2012). A Novel Full Reference Image Quality Index for Color Images. In: Satapathy, S.C., Avadhani, P.S., Abraham, A. (eds) Proceedings of the International Conference on Information Systems Design and Intelligent Applications 2012 (INDIA 2012) held in Visakhapatnam, India, January 2012. Advances in Intelligent and Soft Computing, vol 132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27443-5_28

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  • DOI: https://doi.org/10.1007/978-3-642-27443-5_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27442-8

  • Online ISBN: 978-3-642-27443-5

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