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Exact global histogram specification optimized for structural similarity

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Abstract

An exact global histogram specification (EGHS) method modifies its input image to have a specified global histogram. Applications of EGHS include image (contrast) enhancement (e.g., by histogram equalization) and histogram watermarking. Performing EGHS on an image, however, may reduce its visual quality. Starting from the output of a generic EGHS method, we maximize the structural similarity index (SSIM) between the original image (before EGHS) and the EGHS result iteratively. Essential in this process is the computationally simple and accurate formula we derive for SSIM gradient. As it is based on gradient ascent, the proposed EGHS always converges. Experimental results confirm that while obtaining the histogram exactly as specified, the proposed method invariably outperforms the existing methods in terms of visual quality of the result. The computational complexity of the proposed method is shown to be of the same order as that of the existing methods.

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References

  1. D. Coltuc, P. Bolon, and J.-M. Chassery: IEEE Trans. Image Process. 15 (2006) 1143.

    Article  ADS  Google Scholar 

  2. Y. Wan and D. Shi: IEEE Trans. Image Process. 16 (2007) 2245.

    Article  MathSciNet  ADS  Google Scholar 

  3. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli: IEEE Trans. Image Process. 13 (2004) 600.

    Article  ADS  Google Scholar 

  4. M. Barni and F. Bartolini: Watermarking Systems Engineering (Marcel Dekker, New York, 2004) p. 113.

    Google Scholar 

  5. A. N. Avanaki: Code for SSIM-Optimal Exact Global Histogram Specification, to appear at http://www.geocities.com/dr.nasiri/optimEHS4ssim.html

  6. Z. Wang, Q. Li, and X. Shang: Proc. IEEE Int. Conf. Image Processing (ICIP’07), 2007, Vol. 2, p. 121.

    Google Scholar 

  7. A. Bevilacqua and P. Azzari: Proc. IEEE Int. Conf. Image Analysis and Processing (ICIAP’07), 2007, p. 623.

  8. I.-L. Jung and C.-S. Kim: Proc. IEEE Int. Conf. Image Processing (ICIP’07), 2007, Vol. 1, p. 545.

    Google Scholar 

  9. D. Coltuc and P. Bolon: Proc. IEEE Int. Conf. Image Processing (ICIP’99), 1999, Vol. 3, p. 150.

    Google Scholar 

  10. Z. Wang: The SSIM Index for Image Quality Assessment, Available: http://www.ece.uwaterloo.ca/~z70wang/research/ssim

  11. D. Coltuc and P. Bolon: Proc. IEEE Int. Conf. Image Processing (ICIP’99), 1999, Vol. 2, p. 236.

    Google Scholar 

  12. R. C. Gonzales and R. E. Woods: Digital Image Processing. (Prentice-Hall, Upper Saddle River, NJ, 2002) Sect. 3.3.

    Google Scholar 

  13. D. Coltuc and P. Bolon: Proc. European Signal Processing Conf. (EUSIPCO’98), Rhodes, Greece, 1998, p. 861–864.

  14. J. A. Stark: IEEE Trans. Image Process. 9 (2000) 889.

    Article  ADS  Google Scholar 

  15. V. Caselles, J. L. Lisani, J. M. Morel, and G. Sapiro: IEEE Trans. Image Process. 8 (1999) 220.

    Article  ADS  Google Scholar 

  16. Y. J. Zhang: Electron. Lett. 28 (1992) 213.

    Article  Google Scholar 

  17. E. L. Hall: IEEE Trans. Comput. 23 (1974) 207.

    Article  MATH  Google Scholar 

  18. J. Y. Kim, L. S. Kim, and S. H. Hwang: IEEE Trans. Circuits Syst. Video Technol. 11 (2001) 475.

    Article  Google Scholar 

  19. H. Zhu, F. H. Chan, and F. K. Lam: Comput. Vision Image Understand. 73 (1991) 281.

    Article  Google Scholar 

  20. P. J. S. G. Ferreira and A. J. Pinho: IEEE Signal Process. Lett. 9 (2002) 259.

    Article  ADS  Google Scholar 

  21. S.-C. Pei and Y.-C. Zeng: Proc. 17th Int. Conf. Pattern Recognition (ICPR—04), 2004, Vol. 4, p. 799.

    Article  Google Scholar 

  22. D. Coltuc and P. Bolon: Proc. IEEE Int. Conf. Image Processing (ICIP—00), 2000, Vol. 3, p. 698.

    Google Scholar 

  23. R. C. Gonzales and R. E. Woods: Digital Image Processing (Prentice-Hall, Upper Saddle River, NJ, 2002) Sect. 3.3, pp. 94–102.

    Google Scholar 

  24. T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein: Introduction to Algorithms (MIT Press, Cambridge, MA, 2002) Chap. 7.

    Google Scholar 

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Avanaki, A.N. Exact global histogram specification optimized for structural similarity. OPT REV 16, 613–621 (2009). https://doi.org/10.1007/s10043-009-0119-z

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  • DOI: https://doi.org/10.1007/s10043-009-0119-z

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