Spectral gamuts and spectral gamut mapping

Abstract

All imaging devices have two gamuts: the stimulus gamut and the response gamut. The response gamut of a print engine is typically described in CIE colorimetry units, a system derived to quantify human color response. More fundamental than colorimetric gamuts are spectral gamuts, based on radiance, reflectance or transmittance units. Spectral gamuts depend on the physics of light or on how materials interact with light and do not involve the human's photoreceptor integration or brain processing. Methods for visualizing a spectral gamut raise challenges as do considerations of how to utilize such a data-set for producing superior color reproductions. Recent work has described a transformation of spectra reduced to 6-dimensions called LabPQR. LabPQR was designed as a hybrid space with three explicit colorimetric axes and three additional spectral reconstruction axes. In this paper spectral gamuts are discussed making use of LabPQR. Also, spectral gamut mapping is considered in light of the colorimetric-spectral duality of the LabPQR space.

Spatio-Spectral Gamut Mapping and Separation

“…Incorporated into the optimization shown in Eq. (9), this forces the separation image to mimic the local spatial correlation of the original image, i.e., avoiding banding artifacts but preserving (also metameric) edges.…”

Spectral color management using interim connection spaces based on spectral decomposition

“…A six-dimensional example of LabPQR has been discussed in the literature 5,7 and it has been demonstrated for use in spectral gamut mapping. 6,18 FIG. 1.…”

“…Derhak and Rosen proposed an ICS called LabPQR. [5][6][7] LabPQR is an ICS that has three colorimetric axes (CIELAB) plus additional spectral reconstruction axes (PQR). PQR describes a stimulus' metameric black, [8][9][10] a spectral difference between the actual spectrum and a spectrum derived only from knowledge of the CIELAB components.…”

“…In previous research, 17 an inversion of the printer characterization was successfully performed to choose fractional area coverages for a requested spectrum. In the inversion process, spectral gamut mapping [5][6][7] is necessary since an answer must be delivered for any arbitrary spectral request, even those outside of a printer's spectral gamut. Previous proposals include a six-dimensional spectral gamut mapping approach based on a single stage optimization for minimizing both colorimetric and spectral error, simultaneously.…”

Let's call it “color‐gamut rendering”

“…Rosen and Derhak considered similar issues as an introduction to their research on spectral gamut rendering. 11 They used the terms ''stimulus gamut'' and ''response gamut,'' and did not use the term ''color gamut.'' A casual reader might add ''color'' to their terms out of habit.…”