Multispectral gamut mapping and visualization: a first attempt

Abstract

A method is proposed for performing spectral gamut mapping, whereby spectral images can be altered to fit within an approximation of the spectral gamut of an output device. Principal component analysis (PCA) is performed on the spectral data, in order to reduce the dimensionality of the space in which the method is applied. The convex hull of the spectral device measurements in this space is computed, and the intersection between the gamut surface and a line from the center of the gamut towards the position of a given spectral reflectance curve is found. By moving the spectra that are outside the spectral gamut towards the center until the gamut is encountered, a spectral gamut mapping algorithm is defined. The spectral gamut is visualized by approximating the intersection of the gamut and a 2-dimensional plane. The resulting outline is shown along with the center of the gamut and the position of a spectral reflectance curve. The spectral gamut mapping algorithm is applied to spectral data from the Macbeth Color Checker and test images, and initial results show that the amount of clipping increases with the number of dimensions used.

Weighted LabPQR Interim Connection Space Based on Human Color Vision for Spectral Color Reproduction

“…because values of CIE1931 ( ), ( ), and ( ) colormatching function are not less than zero. According to (7), we proposed the third weight function 3 ( ) (WF3) generated by calculating the square root of adding the three matching functions, which normalize the maximum of value to be 1:…”

“…The first type is the algorithm that applies multivariate statistical analysis theory to optimize spectral color information. Bakke et al [7] proposed PCA-based ICS, which applied principal component analysis (PCA) on dimensionality reduction of the spectra and spectral reconstruction. Zhang et al [8] proposed two ICSs called ICS 2SI and ICS 3SI, which applied PCA on extracting the widely used illuminants and light sources.…”

Spatio-Spectral Gamut Mapping and Separation

“…Different approaches have been proposed for accessing the colorimetric gamut boundaries, 6,7 while only a few have been published so far to access the boundaries of a spectral printer gamut. For instance, Bakke et al 8 applied principal component analysis (PCA) on spectral data in order to reduce the high dimensionality of the spectral space. They used the convex hull in this lower dimensional space to describe the boundary of the spectral gamut.…”

Charamer mismatch-based spectral gamut mapping

“…The first category, which is called the spectral space-based approach, applies multivariate statistical analysis theory to minimize spectral differences in order to accomplish spectral gamut mapping. Bakke et al [1] applied principal component analysis (PCA) to produce a reduced-dimensionality spectral ICS and then used the convex hull in the ICS to describe the device gamut boundary. Finally, they performed a complete GMA by moving all out-of-gamut spectra into the device's spectral gamut along a line pointing toward the center of the gamut.…”