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2007
IEEE

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

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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...
Kartic Subr, James Arvo
Added 04 Jun 2010
Updated 04 Jun 2010
Type Conference
Year 2007
Where PG
Authors Kartic Subr, James Arvo
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