Color Estimation Error Trade-offs
نویسندگان
چکیده
Digital imager sensor responses must be transformed to calibrated (human) color representations for display or print reproduction. Errors in these color rendering transformations can arise from a variety of sources, including (a) noise in the acquisition process (including photon noise and sensor noise) and (b) sensor spectral responsivities inconsistent with those of the human cones. These errors can be summarized by the mean deviation and variance of the reproduced values. It is desirable to select a color transformation that produces both low mean deviations and low noise variance. We show that in some conditions there is an inherent trade-off between these two measures: when selecting a color rendering transformation either the mean deviation or the variance (caused by imager noise) can be minimized. We describe this trade-off mathematically, and we describe a methodology for choosing an appropriate transformation for different applications. We illustrate the methodology by applying it to the problem of color filter selection (CMYG vs. RGGB) for digital cameras. We find that under moderate illumination conditions photon noise alone introduces an uncertainty in the estimated CIELAB coordinates on the order of 1-2 ∆E units for RGGB sensors and in certain cases even higher uncertainty levels for CMYG sensors. If we choose color transformations that equate this variance, the color rendering accuracy of the CMYG and RGGB filters are similar.
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