Abstract
Color camera characterization, mapping outputs from the camera sensors to an independent color space, such as XY Z, is an important step in the camera processing pipeline. Until now, this procedure has been primarily solved by using a 3 × 3 matrix obtained via a least-squares optimization. In this paper, we propose to use the spherical sampling method, recently published by Finlayson et al., to perform a perceptual color characterization. In particular, we search for the 3 × 3 matrix that minimizes three different perceptual errors, one pixel based and two spatially based. For the pixel-based case, we minimize the CIE ΔE error, while for the spatial-based case, we minimize both the S-CIELAB error and the CID error measure. Our results demonstrate an improvement of approximately 3% for the ΔE error, 7% for the S-CIELAB error and 13% for the CID error measures.
Highlights
At first glance, it would seem that for a camera to accurately capture colors matching our perception, the color triplets obtained by the camera sensor(s) should correspond to the cone responses of the humanSensors 2014, 14 visual system
The other two experiments that we perform, which are based on minimizing a color appearance model (S-CIELAB) and an image quality metric (CID), are novel
The present work investigates a novel technique for mapping camera RGB responses to device independent, CIE XY Z, color co-ordinates
Summary
It would seem that for a camera to accurately capture colors matching our perception, the color triplets obtained by the camera sensor(s) should correspond to the cone responses of the humanSensors 2014, 14 visual system. Manufacturing processes and the properties of the materials used make it difficult to adjust at will the sensor response curves, and the Luther–Ives condition is usually not met in practice [3] Despite this fact, a three-channel camera with three arbitrary sensor response curves is able to estimate the tristimulus values of an object as long as the object’s spectral reflections are always composed of three principal components and they do not change steeply with respect to wavelength [3]. Where Sxyz denotes an m × 3 matrix of XY Z color matching functions sampled at m discrete wavelengths, Srgb is an m × 3 matrix of camera RGB responses and T is a 3 × 3 linear transform This can be shown to be the best approach when no other information about spectra is known (the maximum ignorance assumption) [13]; i.e., when spectra are pure noise stimuli. The minimization is usually performed as: arg min kMxyz − Mrgb T k2 ,
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