Abstract
Photorealistic rendering, based on unbiased stochastic global illumination, is now within reach of any computer artist by using commercially or freely available softwares. One of the drawbacks of these softwares is that they do not provide any tool for detecting when convergence is reached, relying entirely on the user for deciding when stopping the computations. In this paper we detail two methods that aim at finding perceptual convergence thresholds for solving this problem. The first one uses the VDP image quality measurement for providing a global threshold. The second one uses SVM classifiers which are trained and used on small subparts of images and allow to take into account the heterogeneity of convergence through the image area. These two approaches are validated by using experimentations with human subjects.
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References
Davis, L.S., Huang, C., Townshend, J.R.G.: An assessment of support vector machines for land cover classification. International Journal of Remote Sensing 23, 725–749 (2002)
Daly, S.: The visible differences predictor: an algorithm for the assessment of image fidelity. In: Digital Images and Human Vision, vol. 4, pp. 124–125 (1993)
Farrugia, J.-P., Péroche, B.: A progressive rendering algorithm using an adaptive perceptually based image metric. Comput. Graph. Forum 23(3), 605–614 (2004)
Itti, L.: Models of Bottom-Up and Top-Down Visual Attention. bu|td|mod|psy|cv, Pasadena, California (January 2000)
Joachims, T.: Estimating the generalization performance of a SVM efficiently. In: Langley, P. (ed.) Proceedings of ICML 2000, 17th International Conference on Machine Learning, Stanford, US, pp. 431–438. Morgan Kaufmann Publishers, San Francisco (2000)
Kajiya, J.T.: The rendering equation. SIGGRAPH Comput. Graph. 20(4), 143–150 (1986)
Koch, C., Ullman, S.: Shifts in selective visual attention: Towards the underlying neural circuitry. Human Neurobiology 4, 219–227 (1985)
Longhurst, P., Chalmers, A.: User validation of image quality assessment algorithms. In: Theory and Practice of Computer Graphics, EGUK 2004, pp. 196–202. IEEE Computer Society, Los Alamitos (2004)
LuxRender, http://www.luxrender.net/en_GB/index
Melgani, F., Bruzzone, L.: Classification of Hyperspectral Remote Sensing Images With Support Vector Machines. IEEE Transactions on Geoscience and Remote Sensing 42, 1778–1790 (2004)
Myszkowski, K.: The visible differences predictor: applications to global illumination problems. In: Eurographics Rendering Workshop, pp. 233–236 (1998)
NextLimit, http://www.maxwellrender.com/
Niebur, E., Itti, L., Koch, C.: Controlling the focus of visual selective attention. In: Van Hemmen, L., Domany, E., Cowan, J. (eds.) Models of Neural Networks IV. Springer, Heidelberg (2001)
Pattanaik, S.N., Ferwerda, J.A., Fairchild, M.D., Greenberg, D.P.: A multiscale model of adaptation and spatial vision for realistic image display. Computer Graphics 32(Annual Conference Series), 287–298 (1998)
Ramasubramanian, M., Pattanaik, S.N., Greenberg, D.P.: A perceptually based physical error metric for realistic image synthesis. In: Rockwood, A. (ed.) Siggraph 1999, Computer Graphics Proceedings, Los Angeles, pp. 73–82. Addison Wesley Longman, Amsterdam (1999)
Russ, J.C.: The Image Processing Handbook. CRC Press, Boca Raton (1992)
Sarnoff Corporation. Sarnoff JND vision model algorithm description and testing, VQEG (August 1997)
Takouachet, N.: Utilisation de critères perceptifs pour la dètermination d’une condition d’arrêt dans les mèthodes d’illumination gobale. PhD thesis, Universitè du Littoral Côte d’Opale (January 2009)
Takouachet, N., Delepoulle, S., Renaud, C.: A perceptual stopping condition for global illumination computations. In: Proc. Spring Conference on Computer Graphics (2007)
Treisman, A.M., Gelade, G.: A feature-integration theory of attention. Cognit. Psychol. 12(1), 97–136 (1980)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Vapnik, V.: Statistical Learning Theory. John Wiley and Sons, Inc., New York (1995)
Veach, E., Guibas, L.J.: Metropolis light transport. Computer Graphics 31(Annual Conference Series), 65–76 (1997)
Yee, H.: A perceptual metric for production testing. Journal of Graphics Tools 9(4), 33–40 (2004)
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Renaud, C., Delepoulle, S., Takouachet, N. (2012). Detecting Visual Convergence for Stochastic Global Illumination. In: Plemenos, D., Miaoulis, G. (eds) Intelligent Computer Graphics 2011. Studies in Computational Intelligence, vol 374. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22907-7_1
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DOI: https://doi.org/10.1007/978-3-642-22907-7_1
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