Jiangtao Kuang
Abstract
A series of three experiments has been performed to test both the preference and accuracy of high dynamic-range (HDR) rendering algorithms in digital photography application. The goal was to develop a methodology for testing a wide variety of previously published tone-mapping algorithms for overall preference and rendering accuracy. A number of algorithms were chosen and evaluated first in a paired-comparison experiment for overall image preference. A rating-scale experiment was then designed for further investigation of individual image attributes that make up overall image preference. This was designed to identify the correlations between image attributes and the overall preference results obtained from the first experiments. In a third experiment, three real-world scenes with a diversity of dynamic range and spatial configuration were designed and captured to evaluate seven HDR rendering algorithms for both of their preference and accuracy performance by comparing the appearance of the physical scenes and the corresponding tone-mapped images directly. In this series of experiments, a modified Durand and Dorsey's bilateral filter technique consistently performed well for both preference and accuracy, suggesting that it is a good candidate for a common algorithm that could be included in future HDR algorithm testing evaluations. The results of these experiments provide insight for understanding of perceptual HDR image rendering and should aid in design strategies for spatial processing and tone mapping. The results indicate ways to improve and design more robust rendering algorithms for general HDR scenes in the future. Moreover, the purpose of this research was not simply to find out the “best” algorithms, but rather to find a more general psychophysical experiment based methodology to evaluate HDR image-rendering algorithms. This paper provides an overview of the many issues involved in an experimental framework that can be used for these evaluations.
- Bartleson, C. J. and Grum, F. 1984. Optical Radiation Measurements. Vol. 5: Visual Measurements. Academic Press, New York. 467--471.Google Scholar
- Biggs, W. 2004. Perceptual accuracy of tone mapping algorithms. MS. Thesis, Dalhousie University.Google Scholar
- Braun, G. J. and Fairchild, M. D. 1999. Image lightness rescaling using Sigmoidal contrast enhancement functions. In IS&T/SPIE Electronic Imaging '99, Color Imaging: Device Independent Color, Color Hardcopy, and Graphic Arts IV. 96--105.Google Scholar
- Calabria, A. J. and Fairchild, M. D. 2002. Compare and contrast: perceived contrast of color images. In 10th Color Imaging Conference.Google Scholar
- Day, E. A., Taplin, L. A., and Berns, R. S. 2004. Colorimetric characterization of a computer-controlled liquid crystal display. Color Res. Appl. 29, 365--373.Google ScholarCross Ref
- Debevec, P. E. and Malik, J. 1997. Recovering high dynamic range radiance maps from photographs. In Proc. SIGGRAPH '97. 369--378. Google ScholarDigital Library
- Devlin, K. 2002. A review of tone reproduction techniques. Technical Report CSTR-02-005, Department of Computer Science, University of Bristol.Google Scholar
- Draper, N. and Smith, H. 1981. Applied Regression Analysis, 2nd ed. Wiley, New York. 307--312.Google Scholar
- Drago, F., Martens, W. L., Myszkowski, K., and Seidel, H. P. 2003. Perceptual evaluation of tone mapping operator. In ACM SIGGRAPH Conference Abstracts and Applications. Google ScholarDigital Library
- Durand, F. and Dorsey, J. 2002. Fast bilateral filtering for the display of high-dynamic-range image. In Proceedings of ACM SIGGRAPH 2002, Computer Graphics Proceedings, Annual Conf. Proc. 257--266. Google ScholarDigital Library
- Engeldrum, P. 2000. Psychometric Scaling: A Toolkit for Imaging Systems Development. Imcotek Press, Winchester.Google Scholar
- Frankle, J. and McCann, J. 1983. Method and apparatus for lightness imaging. US Patent #4,384,336.Google Scholar
- Funt, B., Ciurea, F., and McCann, J. 2000, Retinex in Matlab. In Proceedings of the IS&T/SID Eighth Color Imaging Conference: Color Science, Systems and Applications. 112--121.Google Scholar
- Funt, B., Ciurea, F., and McCann, J. 2002. Tuning Retinex parameters. In Proceedings of the IS&T/SPIE Electronic Imaging Conference.Google Scholar
- Ikeda, E. 1998. Image data processing apparatus for processing combined image signals in order to extend dynamic range, U.S. Patent 5801773.Google Scholar
- Johnson, G. M. 2005. Cares and concerns of CIE TC8-08: Spatial appearance modeling & HDR imaging. In SPIE/IS&T Electronic Imaging Conference, San Jose, CA.Google Scholar
- Johnson, G. M. and Fairchild, M. D. 2003. Rendering HDR images. In IS&T/SID 11th Color Imaging Conference, Scottsdale, AR. 36--41.Google Scholar
- Jones, L. A. and Condit, H. R. 1941. The brightness of exterior scenes and the computation of correct photographic exposure. Journal of the Optical Society of America A, 31, 651--666.Google ScholarCross Ref
- Keelan, B. 2002. Handbook of Image Quality: Characterization and Prediction. CRC, Boca Raton, FL.Google Scholar
- Kuang, J., Yamaguchi, H., Johnson, G. M., and Fairchild, M. D. 2004. Testing HDR image rendering algorithms. In IS&T/SID 12th Color Imaging Conference.Google Scholar
- Kuang, J., Johnson, G. M., and Fairchild, M. D. 2005. Image preference scaling for HDR image rendering. In IS&T/SID 13th Color Imaging Conference.Google Scholar
- Kuang, J., Liu, C., Johnson, G. M., and Fairchild, M. D. 2006. Evaluation of HDR image rendering algorithms using real-world scenes. In Conference of ICIS.Google Scholar
- Larson, G. W. 1998. LogLuv encoding for full-gamut, high-dynamic range images. Journal of Graphics Tools 3, 15--31. Google ScholarDigital Library
- Larson, G. W., Rushmeier, H., and Piatko, C. 1997. A visibility matching tone reproduction operator for high dynamic range scenes. In IEEE Transactions on Visualization and Computer Graphics. 291--306. Google ScholarDigital Library
- Ledda, P., Chalmers, A., and Seetzen, H. 2004a. HDR displays: a validation against reality. In IEEE International Conference on Systems, Man and Cybernetics.Google Scholar
- Ledda, P., Santos, L. P., and Chalmers, A. 2004b. A local model of eye adaptation for high dynamic range images. In Proceedings of the 3rd International Conference on Computer Graphics, Virtual Reality, Visualization and Interaction in Africa, AFRIGRAPH2004. Google ScholarDigital Library
- Ledda, P., Chalmers, A., Troscianko, T., and Seetzen, H. 2005. Evaluation of tone mapping operators using a high dynamic range display. In Proceeding of ACM SIGGRAPH 2005. 640--648. Google ScholarDigital Library
- McCann, J. 2004. Retinex at 40. Journal of Electronic Imaging 13, 1, 139--145.Google ScholarCross Ref
- McCann, J. 1999. Lessons learned from Mondrians applied to real images and color gamuts. In Proc. IS&T/SID 7th Color Imaging Conference: Color Science, Systems and Applications. 1--8.Google Scholar
- Meylan, L. and Susstrunk, S. 2005. High dynamic range image rendering using a Retinex-based adaptive filter. In IEEE Transactions on Image Processing. Google ScholarDigital Library
- Montag, E. 2004. Louis Leon Thurstone in Monte Carlo: Creating error bars for the method of paired comparison. In Proc. of SPIE-IS&T Electronic Imaging. 222--230.Google Scholar
- Murphy, E., Taplin, L. A., and Berns, R. S. 2005. Experimental evaluation of museum case study digital camera systems. In Proc. IS&T Second Image Archiving Conference.Google Scholar
- Naka, K. I. and Rushton, W. A. H. 1966. S-potential from colour units in the retina of fish. Journal of Physiology 185, 536--555.Google ScholarCross Ref
- Nayar, S. K. and Mitsunaga, T. 2000. High dynamic range imaging: Spatially varying pixel exposures. In Proc. IEEE CVPR, Vol. 1. 472--479.Google Scholar
- Nishisato, S. 1994. Elements of Dual Scaling: An Introduction to Practical Data Analysis. Lawrence Erlbaum Associates, Hillside, New Jersey.Google Scholar
- Reinhard, E. and Devlin, K. 2005. Dynamic range reduction inspired by photoreceptor physiology. In IEEE Transactions on Visualization and Computer Graphics. 12--24. Google ScholarDigital Library
- Reinhard, E., Stark, M., Shirley, P., and Ferwerda, J. 2002. Photographic tone reproduction for digital images. In Proceedings of ACM SIGGRAPH 2002, Computer Graphics Proceedings, Annual Conference Proceedings. 267--276. Google ScholarDigital Library
- Reinhard, E., Ward, G., Pattanaik, S., and Debevec, P. 2006. High Dynamic Range Imaging. Morgan Kaufmann Publishers, San Francisco, 223--323.Google Scholar
- Robertson, M. A., Borman, S., and Stevenson, R. L. 1999. Dynamic range improvement through multiple exposures. In IEEE International Conference on Image Processing.Google Scholar
- Schlick, C. 1994. Quantization techniques for the visualization of high dynamic range pictures. In Fifth Eurographics Workshop on Rendering. 7--18.Google Scholar
- Thurstone, L. L. 1927. A law of comparative judgment, Psychological Review 34, 273--286.Google ScholarCross Ref
- Ward, G. 2005. JPEG-HDR: A backwards-compatible, high dynamic range extension to JPEG. In IS&T/SID's 13th Color Imaging Conference. 283--290.Google Scholar
- Yoshida, A., Blanz, V., Myszkowski, K., and Seidel, H. P. 2005. Perceptual evaluation of tone mapping operators with real-world scenes. In Proceedings of the SPIE, Vol. 5666. 192--203.Google Scholar
Index Terms
- Evaluating HDR rendering algorithms
Recommendations
HDR light probe sequence resampling for realtime incident light field rendering
SCCG '09: Proceedings of the 25th Spring Conference on Computer GraphicsThis paper presents a method for resampling a sequence of high dynamic range light probe images into a representation of Incident Light Field (ILF) illumination which enables realtime rendering. The light probe sequences are captured at varying ...
Comments