Entropy-based Adaptive Supersampling

Ray tracing usually needs supersampling to reduce aliasingor noise in the final image. Not all the pixels in the image require the same quantity of rays, thusadaptive supersampling is implemented by adaptive subdivision of the sampling region, resulting in a refinement tree. We prese nt here a theoretically sound adaptive supersampling method based on entropy, an information theory approach wit h strong analogies to the decision tree problem where entropy is frequently used as a decision criterion. Ou r adaptive supersampling algorithm is implemented within a path tracing method and we show that our results comp are well to the ones obtained by a classic strategy.

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