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Unbiased sampling techniques for image synthesis

We examine a class of adaptive sampling techniques employed in image synthesis and show that those commonly used for efficient anti-aliasing are statistically biased. This bias is dependent upon the image function being sampled as well as the strategy for determining the number of samples to use. It is most prominent in areas of high contrast and is attributable to early stages of sampling systematically favoring one extreme or the other. If the expected outcome of the entire adaptive sampling algorithm is considered, we find that the bias of the early decisions is still present in the final estimator. We propose an alternative strategy for performing adaptive sampling that is unbiased but potentially more costly. We conclude that it may not always be practical to mitigate this source of bias, but as a source of error it should be considered when high accuracy and image fidelity are a central concern.