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Perception-JND-driven path tracing for reducing sample budget
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AI
DOI：10.1007/s00371-023-03199-w.png)
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摘要
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Monte Carlo path tracing is a widely used method for generating realistic rendering results in multimedia applications, but often suffers from poor convergence and heavy sampling budget. Insufficient path samples may lead to noisy results. Some noises are hidden in textures, and the human visual system cannot detect them all. Just noticeable difference (JND) quantifies this limitation as a full-reference perception threshold. In rendering, the reference is unavailable and a surrogate is required. This paper proposed a perception-JND-driven path tracing method for reducing sampling budget. We tested and verified the surrogate JND thresholds derived from current rendering results. Then, we introduced difference pooling module and shading restart module to control perceptual convergence. Further, to improve accuracy, we developed the strategy for optimizing sampling steps. Experiments showed that the proposed method outperformed the state-of-the-art method at moderately low sampling levels, offering a lightweight and efficient solution to reducing sample budget while improving visual quality.
Keyword:
Perception-driven rendering
Just noticeable difference
Monte Carlo path tracing
Adaptive sampling
期刊
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2.9
论文数:
4.7K
被引数:
6.5K
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