Statistical Hypothesis Testing for Assessing Monte Carlo Estimators: Applications to Image Synthesis

Image synthesis algorithms are commonly compared on the basis of running times and/or perceived quality of the generated images. In the case of Monte Carlo techniques, assessment often entails a qualitative impression of convergence toward a reference standard and severity of visible noise; these amount to subjective assessments of the mean and variance of the estimators, respectively. In this paper we argue that such assessments should be augmented by well-known statistical hypothesis testing methods. In particular, we show how to perform a number of such tests to assess random variables that commonly arise in image synthesis such as those estimating irradiance, radiance, pixel color, etc. We explore five broad categories of tests: 1) determining whether the mean is equal to a reference standard, such as an analytical value, 2) determining that the variance is bounded by a given constant, 3) comparing the means of two different random variables, 4) comparing the variances of two dif...

• Type: Conference
• Year: 2007
• Where: PG
• Authors: Kartic Subr, James Arvo
• Keywords: Computer Graphics, Image Synthesis, Monte Carlo, PG 2007, Random Variables