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F. Drago 1 , W. Martens 2 , K. Myszkowski 3 , and N. Chiba 1

Design of a Tone Mapping Operator for High Dynamic Range Images based upon Psychophysical Evaluation and Preference Mapping. F. Drago 1 , W. Martens 2 , K. Myszkowski 3 , and N. Chiba 1 1 Iwate University and 2 Aizu University, Japan 3 Max-Planck-Institut f ü r Informatik, Germany. Overview.

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F. Drago 1 , W. Martens 2 , K. Myszkowski 3 , and N. Chiba 1

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  1. Design of a Tone Mapping Operator for High Dynamic Range Images based upon Psychophysical Evaluation and Preference Mapping F. Drago1, W. Martens2, K. Myszkowski3, and N. Chiba1 1Iwate University and 2Aizu University, Japan 3Max-Planck-Institut für Informatik, Germany

  2. Overview • Motivation • Previous work • Psychophysical experiment • Enhancements of Retinex for HDR images • Conclusions

  3. Motivation • Many applications • Lighting simulation and realistic rendering • High Dynamic Range photography • Multimedia: distributing HDR video streams

  4. HDR Photographs + Rendering: Real World Lighting 1) Photographs of mirror sphere at varying exposure times 3) Use as light source in Monte Carlo radiosity algorithm 2) High-dynamic range environment map Philippe Bekaert

  5. Goals • Technical requirement • Match the dynamic range of image to the range available on a given display device • Various objectives • Get good perceptual match between the real-world and corresponding images • Reproducing details • Maximize reproducible contrast • Just to get “nice-looking” images

  6. Various Classifications • Theoretical foundations • Perception-based • Pure image processing techniques • Mapping function • Global – the same for all pixels • Local – depends on local image contents • Temporal processing • Static • Dynamic

  7. Previous Work:Global Methods • Perception-based • Tumblin and Rushmeier (1993,1999) • Brightness matching • Ward (1994), Ferwerda et al. (1996) • Contrast matching (a linear function is used) • Ward et al. (1997) • Adjusting image histogram to avoid exceeding display contrast in respect to the real-world scene • Efficiency-driven • Schlick (1994) • Rational functions

  8. Examples Ferwerda et al. Tumblin (1999) Ward et al. Schlick

  9. Previous Work:Local Methods • Early methods – prone to halo artifacts • Chiu et al. (1993), Schlick (1994), • Land (1971), Jobson et al. (1997): Retinex • Pattanaik et al. (1998): The most comprehensive model of HVS used in CG • LCIS: Tumblin and Turk (1999) • Based on an anisotropic diffusion procedure • Emphasize on details but compress excessively contrast • New wave: Fattal et al., Reinhard et al., Durand and Dorsey, Ashikhmin (2002)

  10. Examples Tumblin and Turk Ashikhmin Retinex

  11. Examples Durand and Dorsey Fattal et al. Reinhard et al.

  12. Ashikhmin Durand and Dorsey Fattal et al. Reinhard et al.

  13. Psychophysical Experiment • Perceptual evaluation of subject preference by pairwise comparison of tone mapped images • Seven tone mapping algorithms examined: • Tumblin and Rushmeier (1993), • Ferwerda et al. (1996), • Ward et al. (1997), • Schlick (1994), • Retinex - based on Funt and Ciurea (2001) implementation but with our extensions toward suppressing halo • Reinhard et al. (2002) – photographic method • Tumblin and Turk (1999) - LCIS • Four scenes considered

  14. Statistical Data Processing • 11 subjects participated • Dissimilarity ratings for pairwise comparisons of images submitted to Individual Differences Scaling (INDSCAL) analysis • Stimulus Space configures the stimuli such that Euclidian distances between the stimuli match the obtained dissimilarity judgments • Axes labeled based upon correlation of the dimensional coordinates with independently generated attribute ratings (naturalness, detail and contrast reproduction) • “Ideal” preference point obtained through PREFMAP analysis

  15. Subject Preferences • T: Tumblin & R. • V: Ferwerda et al. • H: Ward et al. • Q: Schlick • X: Retinex • P: Reinhard et al.

  16. Retinex • We use the “Frankle-McCann Retinex” algorithm • ratio-product-reset-average • NP(x,y) new pixel value is obtained from the original image R() and previous iteration image OP() as follows: • Reset test • In each iteration (the number of iterations predefined by the user) • the distance D between pixels (x,y) and (xs,ys) is halved • the direction for pixel comparison is rotated 90o clockwise • Main problem: Suppressing halo effects

  17. Retinex Extensions: for HDR • Main problem: Suppressing halo effects • Adding counterclockwise rotation of the path • suggested by Coopers • Spatially varying levels of pixel interaction based contrast information • Suggested by Sobol, but we use a smooth function for clipping • Adjusting a reset ratio to the maximum luminance of the display device instead of the maximum luminance of the scene

  18. Halo Reduction: Retinex Rotation Clockwise CounterClockwise Both Ways All images for 40 iterations

  19. Halo Reduction:Retinex Contrast Crop with Bias

  20. Halo Reduction:Retinex Contrast Crop with Bias Standard Retinex 33 iterations cw and ccw The same settings but crop with bias added

  21. Halo Reduction:Retinex Contrast Crop with Bias 33 Retinex iterations 33 Retinex iterations

  22. Halo Reduction:Retinex Contrast Crop with Bias 4 Retinex iterations 30 Retinex iterations

  23. Retinex Maximum Reset Maximum = 226.5 cd/m^2 Maximum = 100 cd/m^2

  24. Linear mapping Retinex 4 iterations Extended Retinex 4 iterations Extended Retinex 4 iterations

  25. Retinex + Tone Mapping Op. Ferwerda et al. (1996) Logmap - new

  26. Logmap Equation

  27. Adaptive Logarithmic Mapping • Performance: • Software • 30 fps on PentiumIV, 2.2GHz • Hardware • ?

  28. Conclusions • We performed psychophysical of seven existing tone mapping operators. More details in our TechRep: • http://data.mpi-sb.mpg.de/internet/reports.nsf/AG4NumberView?OpenView • Good performance of Retinex in the experiment encouraged us extend it toward reducing hallo artifacts • Addind a regular tone mapping processing atop of Retinex results make the resulting images more independent on the number of Retinex iterations and improve the image naturalness • Future work: repeating psychophysical with all recent local tone mapping operators and our extended Retinex

  29. Color Balance Correction Retinex Applied to All Channels in LMS Color Space

  30. Stanford Memorial Church Photograph

  31. Stanford Memorial Church Photograph

  32. Acknowledgments • We would like to thank Michael Ashikhmin, Paul Debevec, Fredo Durand, Dani Lischinski, Eric Reinhard, and Greg Ward for providing us with some images used in this presentation. • We would like also to thank Greg Ward for his precious comments concerning our work.

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