The Community for Technology Leaders
RSS Icon
Issue No.12 - Dec. (2011 vol.17)
pp: 2144-2152
Weifeng Chen , Zhejiang University
Wei Chen , Zhejiang University
Hujun Bao , Zhejiang University
Color vision deficiency (CVD) affects a high percentage of the population worldwide. When seeing a volume visualization result, persons with CVD may be incapable of discriminating the classification information expressed in the image if the color transfer function or the color blending used in the direct volume rendering is not appropriate. Conventional methods used to address this problem adopt advanced image recoloring techniques to enhance the rendering results frame-by-frame; unfortunately, problematic perceptual results may still be generated. This paper proposes an alternative solution that complements the image recoloring scheme by reconfiguring the components of the direct volume rendering (DVR) pipeline. Our approach optimizes the mapped colors of a transfer function to simulate CVD-friendly effect that is generated by applying the image recoloring to the results with the initial transfer function. The optimization process has a low computational complexity, and only needs to be performed once for a given transfer function. To achieve detail-preserving and perceptually natural semi-transparent effects, we introduce a new color composition mode that works in the color space of dichromats. Experimental results and a pilot study demonstrates that our approach can yield dichromats-friendly and consistent volume visualization in real-time.
Dichromacy, direct volume rendering, volume classification, image recoloring.
Weifeng Chen, Wei Chen, Hujun Bao, "An Efficient Direct Volume Rendering Approach for Dichromats", IEEE Transactions on Visualization & Computer Graphics, vol.17, no. 12, pp. 2144-2152, Dec. 2011, doi:10.1109/TVCG.2011.164
[1] U. D. Bordoloi and H.-W. Shen, View selection for volume rendering. IEEE Transactions on Visualization and Computer Graphics, pages 487–494, 2005.
[2] H. Brettel, F. Viénot, and J. D. Mollon, Computerized simulation of color appearance for dichromats. J. Opt Soc. Am. A, 14 (10): 2647–2655, 1997.
[3] C. Brewer Colorbrewer 2.0. http:/
[4] M.-Y. Chan, Y. Wu, W.-H. Mak, W. Chen, and H. Qu, Perception-based transparency optimization for direct volume rendering. IEEE Transactions on Visualization and Computer Graphics, 15: 1283–1290, 2009.
[5] J. Chuang, D. Weiskopf, and T. Moeller, Hue-preserving color blending. IEEE Transactions on Visualization and Computer Graphics, 15 (6): 1275–1282, 2009.
[6] B. Dougherty and A. Wade Daltonize. http://www.vischeck.comdaltonize/.
[7] R. W. Fleming, and H. H. Büelthoff, Low-level image cues in the perception of translucent materials. ACM Transactions on Applied Perception, 2 (3): 346–382, 2005.
[8] W. Gerbino, C. I. Stultiens, J. M. Troost, and C. M. de Weert, Transparent layer constancy. Journal of Experimental Psychology: Human Perception and Performance, 16 (4): 3–20, 1990.
[9] S. Ishihara, Tests for colour-blindness. Tokio: Kanehara Shuppan Co, 1979.
[10] C. R. Johnson and C. D. Hansen, The Visualization Handbook. Academic Press, Inc., 2004.
[11] D. B. Judd, The color perceptions of deuteranopic and protanopic observers. Journal of Optical Society American, 39 (3): 252–256, 1949.
[12] G. R. Kuhn, M. M. Oliveira, and L. A. F. Fernandes, An efficient naturalness-preserving image-recoloring method for dichromats. IEEE Transactions on Visualization and Computer Graphics, 14 (6): 1747–1754, 2008.
[13] Y. Ma, X. Gu, and Y. Wang, Color discrimination enhancement for dichromats using self-organizing color transformation. Information Science, 179 (6): 830–843, 2009.
[14] G. M. Machado and M. M. Oliveira, Real-time temporal-coherent color contrast enhancement for dichromats. Computer Graphics Forum, 29 (3): 933–942, June 2010.
[15] G. M. Machado, M. M. Oliveira, and L. A. F. Fernandes, A physiologically-based model for simulation of color vision deficiency. IEEE Transactions on Visualization and Computer Graphics, 15 (6): 1291–1298, November 2009.
[16] N. Max, Optical models for direct volume rendering. IEEE Transactions on Visualization and Computer Graphics, 1 (2): 99–108, 1995.
[17] F. Metelli, O. D. Pos, and A. Cavedon, Balanced and unbalanced, complete and partial transparency. Perception and Psychophysics, 38 (4): 354–366, 1985.
[18] H. Pfister, B. Lorensen, C. Bajaj, G. Kindlmann, W. Schroeder, L. S. Avila, K. Martin, R. Machiraju, and J. Lee, The transfer function bake-off. IEEE Computer Graphics and Applications, 21 (3): 16–22, 2001.
[19] T. Porter and T. Duff, Compositing digital images. Computer Graphics (Proceedingds of ACM SIGGRAPH), 18 (3): 253–259, 1984.
[20] C. Rao, H. Toutenburg, A. Fieger, C. Heumann, T. Nittner, and S. Scheid, Linear Models: Least Squares and Alternatives. Springer Series in Statistics, 1999.
[21] K. Rasche, R. Geist, and J. Westall, Detail preserving reproduction of color images for monochromats and dichromats. IEEE Computer Graphics and Applications, 25 (3): 22–30, 2005.
[22] K. Rasche, R. Geist, and J. Westall, Re-coloring images for gamuts of lower dimension. Computer Graphics Forum, 24 (3): 423–432, 2005.
[23] P. Rheingans, Task-based color scale design. In Proceedings of SPIE on Applied Image and Pattern Recognition, pages 35–43, 1999.
[24] G. Sharma, Digital Color Imaging Handbook. CRC Press, Inc., 2002.
[25] L. T. Sharpe, A. Stockman, H. Jägle, and J. Nathans, Color Vision: From Genes to Perception. Cambridge University Press, 1999.
[26] M. Singh and B. L. Anderson, Towards a perceptual theory of transparency. Psychological Review, 109 (3): 492–519, 2002.
[27] T. Wachtler, U. Dohrmann, and R. Hertel, Modeling color percepts of dichromats. Vision Research, 44 (24): 2843–2855, 2004.
[28] L. Wang, J. Giesen, K. T. McDonnell, P. Zolliker, and K. Mueller, Color design for illustrative visualization. IEEE Transactions on Visualization and Computer Graphics, 14 (6): 1739–1754, 2008.
28 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool