A substantially rectangular spectral representation is synthesized, which is adapted to produce image capture device sensor outputs if applied to an image capture device. The synthesized substantially rectangular spectral representation can be utilized in generating output color values of an output color space from image capture device sensor outputs, where the image capture device sensor outputs correspond to an image captured by an image capture device. The generated output color values correspond to colors perceived by the human visual system for the same image as that captured by the image capture device. Image capture device gamut is also determined.

Applying LabRGB to Real Multi-Spectral Images.

Spectral Synthesis for Image Capture Device Processing

To improve the spectral image color reproduction accuracy, two novel interim connection spaces (ICSs) were proposed. The dominant structure of spectral power distributions was extracted by principal component analysis for the widely used illuminants and light sources, and then further transformed to three synthetic illuminants. The CIEXYZ tristimulus under two or three synthetic illuminants was employed to construct two novel ICSs. The two ICSs were compared with LabPQR and the ICS with two sets of tristimulus under two real light sources according to the spectral and colorimetric representing accuracy of Munsell and Natural Color System (NCS) chips. The results indicated that the two ICSs proposed in this study outperformed the other two ICSs as a whole.

methods

The interim connection space based on human color vision for spectral color reproduction.

Spectral distribution can be written as a linear combination of eigenvectors and the eigenvectors method gives the least
estimation error, but eigenvectors depend on a sample selection of population and encoding values have no physical
meaning Recently reported LabPQR [1] is to convey physical values, but still is dependent on a sample selection of
population Thus, LabRGB [2][3][4] was proposed in 2007 LabRGB is to provide "sample selection of population" free
spectral encoding/decoding methods LabRGB consists of six unique trigonometric base functions and physically
meaningful encoding values LabRGB was applied to the real multispectral images and showed almost equal
performance to traditional orthogonal eigenvector method in spectral estimation, and even better performance in
colorimetric estimation In this paper, bit allocation to the weighting factors were examined in terms of spectral and
colorimetric distance of nearest neighbors The optimum way of minimizing the unusable combination of weighting
factors were obtained by using the correlation of the weighting factors The optimum way of minimizing the spectral and
colorimetric distance of nearest neighbors was also obtained by using the nonlinear mapping method The two methods thus obtained give a good clue for explicitly defining the number of bits of respective scores for the future applications and standardization

LabRGB : optimization of bit allocation

A substantially rectangular spectral representation is synthesized, which is adapted to produce either (a) image capture device sensor outputs if applied to an image capture device or (b) color values if applied to corresponding analysis functions. Spectral expansion, which can be used in various image processing methods, is achieved with the synthesized spectral representation.

Spectral image processing

https://summit.sfu.ca/system/files/iritems1/9587/ETD4620.pdf

Applying LabRGB to Real Multi-Spectral Images.

Citations

Spectral Synthesis for Image Capture Device Processing

The interim connection space based on human color vision for spectral color reproduction.

LabRGB : optimization of bit allocation

Spectral image processing

Spectral analysis of output devices, from printing to predicting

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