Abstract
Adaptive sampling is an interesting tool to eliminate noise, which is one of the main problems of Monte Carlo global illumination algorithms. We investigate the Tsallis entropy to do adaptive sampling. Implementation results show that adaptive sampling based on Tsallis entropy consistently outperforms the counterpart based on Shannon entropy.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Portes, M., Esquef, I.A., Gesualdi, A.R.: Image thresholding using Tsallis entropy. Pattern Recognition Letters 25, 1059–1065 (2004)
Bekaert, P.: Hierarchical and Stochastic Algorithms for Radiosity. Ph.D. Dissertation, Katholieke Universiteit Leuven (December 1999)
Blahut, R.E.: Principles and Practice of Information Theory. Addison-Wesley, Boston (1987)
Bolin, M.R., Meyer, G.W.: A perceptually based adaptive sampling algorithm. In: Cohen, M. (ed.) Proceedings SIGGRAPH 1998 Conference, Orlando, FL, USA, pp. 299–310 (1998)
Dippe, M.A.Z., Wold, E.H.: Antialiasing through Stochastic Sampling. Computer Graphics 19, 69–78 (1985)
Philippe, J., Peroche, B.: A Progressive Rendering Algorithm Using an Adaptive Perceptually Based Image Metric. In: Cani, M.-P., Slater, M. (eds.) Proceedings of Eurographics 2004, INRIA and Eurographics Association (2004)
Guo, B.: Progressive Radiance Evaluation using Directional Coherence Maps. In: Cohen, M. (ed.) Proceedings of the SIGGRAPH 1998 Conference, Orlando, FL, USA, pp. 255–266 (1998)
Kirk, D., Arvo, J.: Unbiased variance reduction for global illumination. In: Brunet, P., Jansen, F.W. (eds.) Proceedings of the 2nd Eurographics Workshop on Rendering. Barcelona, pp. 153–156 (1991)
Kapur, J.N., Kesavan, H.K.: Entropy Optimization Principles with Applications. Academic Press, New York (1992)
Lee, M.E., Redner, R.A., Uselton, S.P.: Statistically Optimized Sampling for Distributed Ray Tracing. Computer Graphics 19, 61–65 (1985)
Mitchell, D.P.: Generating Antialiased Images at Low Sampling Densities. Computer Graphics 21, 65–72 (1987)
Pharr, M., Humphreys, G.: Physically Based Rendering: From Theory to Implementation. Morgan Kaufmann, San Francisco (2004)
Painter, J., Sloan, K.: Antialiased Ray Tracing by Adaptive Progressive Refinement. Computer Graphics 23, 281–288 (1989)
Purgathofer, W.: A Statistical Method for Adaptive Stochastic Sampling. Computers Graphics 11, 157–162 (1987)
Renyi, A.: On measuresof entropy and information. Selected Papers of Alfred Renyi. 2, 525–580 (1976)
Rigau, J., Feixas, M., Sbert, M.: Entropy-based Adaptive Supersampling. In: Debevec, P., Gibson, S. (eds.) Proceedings of Thirteenth Eurographics Workshop on Rendering. Pisa, Italy (June 2002)
Rigau, J., Feixas, M., Sbert, M.: New Contrast Measures for Pixel Supersampling, pp. 439–451. Springer-Verlag London Limited, London (2002)
Rigau, J., Feixas, M., Sbert, M.: Entropy-based Adaptive Sampling. In: Proceedings of CGI 2003. IEEE Computer Society Press, Tokyo (2003)
Rigau, J., Feixas, M., Sbert, M.: Refinement Criteria Based on f-Divergences. In: Proceedings of Eurographics Symposium on Rendering 2003. Eurographics Association (2003)
Sharma, B.D., Autar, R.: An inversion theorem and generalized entropies for continuous distributions. SIAM J.Appl.Math. 25, 125–132 (1973)
Scheel, A., Stamminger, M., Putz, J., Seidel, H.: Enhancements to Directional Coherence Maps. In: Skala, V. (ed.) WSCG 2001 Proceedings of Ninth International Conference in Central Europeon Computer Graphics and Visualization. Plzen, Czech Republic, Plzen, February 5-9 (2001)
Santanna, A.P., Taneja, I.J.: Trigonometric entropies, Jensen difference divergence measures, and error bounds. Inf. Sci. 35, 145–156 (1985)
Smolikova, R., Wachowiak, M.P., Zurada, J.M.: An information-theoretic approach to estimating ultrasound backscatter characteristics. Computers in Biology and Medicine 34, 355–370 (2004)
Tsallis, C., Albuquerque, M.P.: Are citations of scientific paper a case of nonextensivity. Euro. Phys. J. B 13, 777–780
Tamstorf, R., Jensen, H.W.: Adaptive Sampling and Bias Estimation in Path Tracing. In: Dorsey, J., Slusallek, P. (eds.) Rendering Techniques 1997, pp. 285–295. Springer, Heidelberg (1997)
Tsallis, C.: Possible generalization of Boltzmann-Gibbls statistics. Journal of Statistical Physics 52, 480–487 (1988)
Tatsuaki, W., Takeshi, S.: When nonextensive entropy becomes extensive. Physica A 301, 284–290
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Xu, Q., Bao, S., Zhang, R., Hu, R., Sbert, M. (2005). Adaptive Sampling for Monte Carlo Global Illumination Using Tsallis Entropy. In: Hao, Y., et al. Computational Intelligence and Security. CIS 2005. Lecture Notes in Computer Science(), vol 3802. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11596981_147
Download citation
DOI: https://doi.org/10.1007/11596981_147
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30819-5
Online ISBN: 978-3-540-31598-8
eBook Packages: Computer ScienceComputer Science (R0)