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Tone mapping with contrast preservation and lightness correction in high dynamic range imaging

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Abstract

In real-world environments, the human visual system perceives a wide range of luminance in a scene. However, the full range of tones in a high dynamic range (HDR) scene cannot be displayed on standard display devices due to their low dynamic range (LDR). Therefore, tone mapping is necessary to faithfully display a HDR scene on an LDR display device. Existing tone mapping methods have problems because details and contrast in a scene are not preserved faithfully, and they also distort the colors in a scene. Thus, we propose a tone mapping method for preserving the detail in an HDR scene using a weighted least squares filter, which preserves the global contrast in a scene by using a competitive learning neural network, before applying a tone reproduction operator to preserve the color without shifting the lightness. According to the Helmholtz–Kohlrausch effect, the perception of brightness depends on the lightness, chroma, and hue of a color. For example, the luminance of pixels with specific colors such as red and blue is low in an HDR scene. The proposed method corrects the lightness of pixels according to the color (i.e., lightness, chroma, and hue) of a tone-mapped image. Experimental results with several test sets of images demonstrated that the proposed tone mapping method with contrast preservation and lightness correction is more suitable for dynamic range compression than existing tone mapping methods, while it also preserves the color of a scene in an effective way.

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References

  1. Debevec, P.E., Malik, J.: Recovering high dynamic range radiance maps from photographs. In: ACM SIGGRAPH 2008 Classes (SIGGRAPH ’08), 31:1–31:10, Los Angeles, CA, USA (2008)

  2. Min, T.-H., Park, R.-H.: Noise reduction in high dynamic range images. SIViP 5(3), 315–328 (2011)

    Article  Google Scholar 

  3. Tsai, C.-Y.: A Fast Dynamic range compression with local contrast preservation algorithm and its application to real-time video enhancement. IEEE Trans. Multimed. 14(4), 1140–1152 (2012)

    Article  Google Scholar 

  4. Ma, K., Yeganeh, H., Zeng, K., Wang, Z.: High dynamic range image compression by optimizing tone mapped image quality index. IEEE Trans. Image Process. 24(10), 3086–3097 (2015)

    Article  MathSciNet  Google Scholar 

  5. Ke, P., Jung, C., Fang, Y.: Perceptual multi-exposure image fusion with overall image quality index and local saturation. Multimed. Syst. doi:10.1007/s00530-015-0480-7

  6. Farbman, Z., Fattal, R., Lischinski, D., Szeliski, R.: Edge-preserving decompositions for multi-scale tone and detail manipulation. ACM Trans. Graph. 27(3), 671–680 (2008)

    Article  Google Scholar 

  7. Subr, K., Soler, C., Durand, F.: Edge-preserving multiscale image decomposition based on local extrema. ACM Trans. Graph. 28(5), 1–9 (2009)

    Article  Google Scholar 

  8. Qui, G., Duan, J., Finlayson, G.D.: Learning to display high dynamic range images. Pattern Recogn. 40(10), 2641–2655 (2007)

    Article  MATH  Google Scholar 

  9. Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 3rd edn. Academic Press, San Diego (2006)

    MATH  Google Scholar 

  10. Du, K.-L.: Clustering: a neural network approach. Neural Netw. 23(1), 89–109 (2010)

    Article  Google Scholar 

  11. Galanopoulos, A.S., Ahalt, S.C.: Codeword distribution for frequency sensitive competitive learning with one-dimensional input data. IEEE Trans. Neural Netw. 7(3), 752–756 (1996)

    Article  Google Scholar 

  12. Mantiuk, R., Matiuk, R., Tomaszewska, A., Heidrich, W.: Color correction for tone mapping. Comput. Graph. Forum 28(2), 193–202 (2009)

    Article  Google Scholar 

  13. Durand, F., Dorsey, J.: Fast bilateral filtering for the display of high-dynamic-range images. ACM Trans. Graph. 21(3), 257–266 (2002)

    Article  Google Scholar 

  14. Lischinski, D., Farbman, Z., Uyttendaele, M., Szeliski, R.: Interactive local adjustment of tonal values. ACM Trans. Graph. 25(3), 646–653 (2006)

    Article  Google Scholar 

  15. Fattal, R., Lischinski, D., Werman, M.: Gradient domain high dynamic range compression. ACM Trans. Graph. 21(3), 249–256 (2002)

    Article  Google Scholar 

  16. Kuang, J., Johnson, G.M., Fairchild, M.D.: iCAM06: A refined image appearance model for HDR image rendering. J. Vis. Commun. Image Represent. 18(5), 406–414 (2007)

    Article  Google Scholar 

  17. Valberg, A.: Light Vision Color. Wiley, West Sussex (2005)

  18. Fairchild, M.D.: Color Appearance Models, 2nd edn. Wiley, West Sussex (2005)

    Google Scholar 

  19. Reinhard, E., Ward, G., Pattanaik, S., Debevec, P., Heidrich, W., Myszkowski, K.: High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting. Morgan Kaufmann, Burlington (2010)

    Google Scholar 

  20. Tumblin, J., Turk, G.: LCIS: A boundary hierarchy for detail-preserving contrast reduction. In: Proceedings 26th Annual Conference on Computer Graphics and Interactive Techniques, pp 83–90, Los Angeles, CA, USA (1999)

  21. Lee, J., Park, R.-H., Chang, S.: Local tone mapping using the K-means algorithm and automatic gamma setting. IEEE Trans. Consum. Electron. 57(1), 209–217 (2011)

    Article  Google Scholar 

  22. Reinhard, E., Khan, E.A., Akyuz, A.O., Johnson, G.M.: Color Imaging: Fundamentals and Applications. A. K. Peters Ltd., Natick (2008)

    Google Scholar 

  23. Wyszekci, G., Stiles, W.S.: Color Science: Concepts and Methods, Quantitative Data and Formulae, 2nd edn. Wiley, New York (2000)

    Google Scholar 

  24. Fairchild, M.D., Pirrotta, E.: Predicting the lightness of chromatic object colors using CIELAB. Color Res. Appl. 16(6), 385–393 (1991)

    Article  Google Scholar 

  25. Reinhard, E., Stark, M., Shirley, P., Ferwerda, J.: Photographic tone reproduction for digital images. ACM Trans. Graph. 21(3), 267–276 (2002)

    Article  Google Scholar 

  26. Duan, J., Qui, G.: Fast tone mapping for high dynamic range images. In : Proceedings of 17th International Conference on Pattern Recognition, pp. 847–850, Cambridge, UK (2004)

  27. Mantiuk, R., Myszkowski, K., Seidel, H.-P.: A perceptual framework for contrast processing of high dynamic range images. ACM Trans. Appl. Percept. 3(3), 286–308 (2006)

    Article  Google Scholar 

  28. Mai, Z., Mansour, H., Mantiuk, R., Nasiopoulos, P., Ward, R., Heidrich, W.: Optimizing a tone curve for backward-compatible high dynamic range image and video compression. IEEE Trans. Image Process. 20(6), 1558–1571 (2011)

    Article  MathSciNet  Google Scholar 

  29. Yeganeh, H., Wang, Z.: Objective quality assessment of tone-mapped images. IEEE Trans. Image Process. 22(2), 657–667 (2013)

    Article  MathSciNet  Google Scholar 

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Acknowledgments

This work was supported in part by Samsung Electronics Co., Ltd.

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Correspondence to Rae-Hong Park.

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Kim, BK., Park, RH. & Chang, S. Tone mapping with contrast preservation and lightness correction in high dynamic range imaging. SIViP 10, 1425–1432 (2016). https://doi.org/10.1007/s11760-016-0942-1

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  • DOI: https://doi.org/10.1007/s11760-016-0942-1

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