Picture Perfect RGB Rendering Using Spectral Prefiltering and Sharp Color Primaries

Accurate color rendering requires the consideration of many samples over the visible spectrum, and advanced rendering tools developed by the research community offer multispectral sampling towards this goal. However, for practical reasons including efficiency, white balance, and data demands, most commercial rendering packages still employ a naive RGB model in their lighting calculations. This results in colors that are often qualitatively different from the correct ones. In this paper, we demonstrate two independent and complementary techniques for improving RGB rendering accuracy without impacting calculation time: spectral prefiltering and color space selection. Spectral prefiltering is an obvious but overlooked method of preparing input colors for a conventional RGB rendering calculation, which achieves exact results for the direct component, and very accurate results for the interreflected component when compared with full-spectral rendering. In an empirical error analysis of our method, we show how the choice of rendering color space also affects final image accuracy, independent of prefiltering. Specifically, we demonstrate the merits of a particular transform that has emerged from the color research community as the best performer in computing white point adaptation under changing illuminants: the Sharp RGB space.