User Study in Non-static HDR Scenes Acquisition

  • Anna Tomaszewska
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7594)


We present a fast, robust and fully automatic method for high dynamic range (HDR) images acquisition for non-static scenes. To obtain high correctness of the approach, perceptual experiments were conducted. HDR images became popular for realistic scene acquisition, as they register much more information than standard images. The most common approach for their acquisition is a composition of photographs taken with a conventional camera. However, the approach suffers from some limitations caused by even the smallest camera movements as well as by objects in motion in the scene. The last one causes ghost artifacts visible in a final image. The key components of our technique include probability maps calculated on the basis of sequences of hand-held photographs and perceptual experiments. We obtained validation of our results by HDR VDP technique.


User Study High Dynamic Range Dynamic Scene High Dynamic Range Image Quality Assessment Method 
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  1. 1.
    Reinhard, E., Ward, G., Pattanaik, S., Debevec, P.: High Dynamic Range Imaging: Acquisition, Display and Image-Based Lighting. Morgan Kaufmann Publishers (2005)Google Scholar
  2. 2.
    Akyüz, A.O., Fleming, R.W., Riecke, B.E., Reinhard, E., Bülthoff, H.: Do HDR displays support LDR content? A psychophysical evaluation. ACM Trans. Graph 26(3), 38 (2007)CrossRefGoogle Scholar
  3. 3.
    Debevec, P.E., Malik, J.: Recovering High Dynamic Range Radiance Maps from Photographs. In: SIGGRAPH 1997, pp. 369–378 (1997)Google Scholar
  4. 4.
    Tomaszewska, A., Mantiuk, R.: Image Registration for Multi-exposure High Dynamic Range Image Acquisition. In: WSCG, Int. Conf. on Central Europe on Computer Graphics, Visualization and Computer Vision, pp. 49–56 (2007)Google Scholar
  5. 5.
    Rota, G.: Qtpfsgui - HDR Imaging Workflow Application (2007),
  6. 6.
    Grosch, T.: Fast and Robust High Dynamic Range Image Generation with Camera and Object Movement. In: Vision, Modeling and Visualization, RWTH Aachen, pp. 277–284 (2006)Google Scholar
  7. 7.
    HDRsoft: Photomatix Pro. (2003),
  8. 8.
    Mediachance: Dynamic Photo HDR (2008),
  9. 9.
    Khan, E.A., Akyüz, A.O., Reinhard, E.: Ghost Removal in High Dynamic Range Images. In: IEEE International Conference on Image Procesing, pp. 2005–2008 (2006)Google Scholar
  10. 10.
    Ward Larson, G.: Fast, Robust Image Registration for Compositing High Dynamic Range Photographs from Handheld Exposures. Exponent - Failure Analysis Assoc. (2003)Google Scholar
  11. 11.
    Selesnick, I., Wagner, C.: Double-Density Wavelet Software. Polytechnic University’s Brooklyn (2004)Google Scholar
  12. 12.
    Mantiuk, R., Daly, S., Myszkowski, K., Seidel, H.-P.: Predicting Visible Differences in High Dynamic Range Images - Model and its Calibration. In: Human Vision and Electronic Imaging X, IS&T/SPIE’s 17th Annual Symposium on Electronic Imaging, vol. 5666, pp. 204–214 (2005)Google Scholar
  13. 13.
    Min, T.-H., Park, R.-H., Chang, S.-K.: Histogram based ghost removal in high dynamic range images. In: IEEE International Conference on Multimedia and Expo, ICME 2009, pp. 530–533 (2009)Google Scholar
  14. 14.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. on Image Processing 13(4), 600–612 (2004)CrossRefGoogle Scholar
  15. 15.
    ITU-R.Rec.BT.500-11: Methodology for the Subjective Assessment of the Quality for Television Pictures (2002)Google Scholar
  16. 16.
    ITU-T.Rec.P.910: Subjective audiovisual quality assessment methods for multimedia applications (2008)Google Scholar
  17. 17.
    Ferwerda, J.A.: Psychophysics 101: how to run perception experiments in computer graphics. In: SIGGRAPH 2008: ACM SIGGRAPH 2008 Classes, pp. 1–60 (2008)Google Scholar
  18. 18.
    Torgerson, W.S.: Theory and methods of scaling. Wiley (1985)Google Scholar
  19. 19.
    Tomaszewska, A., Markowski, M.: Dynamic Scene Acquisition. In: Campilho, A., Kamel, M. (eds.) ICIAR 2010, Part II. LNCS, vol. 6112, pp. 345–354. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  20. 20.
    Tomaszewska, A.: Real-time algorithms optimization based on a gaze-point position. In: Bebis, G., et al. (eds.) ISVC 2012, Part II. LNCS, vol. 7432, pp. 746–755. Springer, Heidelberg (2012)Google Scholar
  21. 21.
    Tomaszewska, A.: Blind Noise Level Detection. In: Campilho, A., Kamel, M. (eds.) ICIAR 2012, Part I. LNCS, vol. 7324, pp. 107–114. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  22. 22.
    Tomaszewska, A., Stefanowski, K.: Real-Time Spherical Harmonics Based Subsurface Scattering. In: Campilho, A., Kamel, M. (eds.) ICIAR 2012, Part I. LNCS, vol. 7324, pp. 402–409. Springer, Heidelberg (2012)CrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Anna Tomaszewska
    • 1
  1. 1.Faculty of Computer Science and Information TechnologyWest Pomeranian University of Technology, SzczecinSzczecinPoland

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