A statistical method for adaptive stochastic sampling

Abstract Stochastic sampling is a good alternative to pure oversampling in terms of image quality. A method for adaptively controlling the number of required samples to the complexity of the picture is presented. The quality of the obtained picture can be controlled by two well-understandable parameters, these parameters define an error interval size and the probability that a pixel lies within it. The usefulness of the method is described by applying it to distributed ray-tracing.

[1]  Scott D. Roth,et al.  Ray casting for modeling solids , 1982, Comput. Graph. Image Process..

[2]  T. Pavlidis.  Algorithms for Graphics and Image Processing , 1981, Springer Berlin Heidelberg.

[3]  Arthur Appel,et al.  The notion of quantitative invisibility and the machine rendering of solids , 1967, ACM National Conference.

[4]  Turner Whitted,et al.  An improved illumination model for shaded display , 1979, CACM.

[5]  Robert L. Cook,et al.  Distributed ray tracing , 1984, SIGGRAPH.

[6]  Roy Hall,et al.  A Testbed for Realistic Image Synthesis , 1983, IEEE Computer Graphics and Applications.

[7]  Samuel P. Uselton,et al.  Statistically optimized sampling for distributed ray tracing , 1985, SIGGRAPH.

[8]  J. Geertsema,et al.  Sequential Confidence Intervals Based on Rank Tests , 1970 .