Spectral Gamut Mapping using LabPQR

Tsutsumi, Shohei; Rosen, Mitchell R.; Berns, Roy S.

doi:10.2352/j.imagingsci.technol.(2007)51:6(473)

Cited by 14 publications

(13 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another approach, proposed by Tsutsumi et al, 10 is also based on the LabPQR hybrid space. The main difference from the previous method is that in this approach both the colorimetric and the spectral parts are combined into a single objective function.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Spectral Gamut Mapping and Separation

Samadzadegan¹,

Urban²

2015

jist

View full text Add to dashboard Cite

Spectral printing aims to achieve an illuminant-invariant match between the original and the reproduction. Due to limited printer spectral gamuts, an errorless spectral reproduction is mostly impossible, and spectral gamut mapping is required to reduce perceptual errors. The recently proposed paramermismatch-based spectral gamut mapping (PMSGM) strategy minimizes such errors. However, due to its pixel-wise processing, it may result in severely different tonal values for spectrally similar adjacent pixels, causing unwanted edges (banding) in the final printout. While the addition of some noise to the a* and b* channels of the colorimetric (e.g., CIELAB) image-rendered for the first illuminant-prior to gamut mapping solves the banding problem, it adversely increases the image graininess. In this article, the authors combine the PMSGM strategy with subsequent spectral separation, considering the spatial neighborhood within the tonal-value space and the illuminant-dependent perceptual spaces to directly compute tonal values. Their results show significant improvements to the PMSGM method in terms of avoiding banding artifacts.

show abstract

Section: Introductionmentioning

confidence: 99%

Spatio-Spectral Gamut Mapping and Separation

Samadzadegan¹,

Urban²

2015

jist

View full text Add to dashboard Cite

show abstract

“…(2) Spectral consists of colorimetric values and metameric blacks under a particular viewing condition [3,17]. (3) Similar colorimetric values and spectral values save the same node address array.…”

Section: Figure 3 Schematic Diagram For the Spectral Gamut Mappingmentioning

confidence: 99%

“…The spectral reflectance is define as an object "fingerprinting" that accurately carry the fundamental color information, so spectral color reproduction could match original under arbitrary illuminants and observers [1][2][3]. However, a high-dimensional spectral data need large storage space and computational complexity.…”

Section: Introductionmentioning

confidence: 99%

“…However, a high-dimensional spectral data need large storage space and computational complexity. In addition, spectral color reproduction demands new approaches involving spectral-image processing, gamut boundary description and spectral gamut mapping in many applications such as textile color, art reproduction, printers with inks [3][4][5][6], so it is necessary to construct transformation chains that deliver high-quality results quickly.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spectral Color Reproduction from CIE Tristimulus Values Using a Node Address Array Selection Technique

Wu¹,

Liu²

2015

IJSIP

View full text Add to dashboard Cite

show abstract

“…Previous proposals include a six-dimensional spectral gamut mapping approach based on a single stage optimization for minimizing both colorimetric and spectral error, simultaneously. 18 In this research, LabPQR definition was evaluated in terms of how to derive a metameric black and what number of dimensions to include. Interactions between generalization and dimensions are discussed.…”

mentioning

confidence: 99%

Spectral color management using interim connection spaces based on spectral decomposition

Tsutsumi

Rosen

Berns

2008

Color Research & Application

Self Cite

View full text Add to dashboard Cite

A feasible approach to spectral color management was previously defined to include lookups performed within an interim connection space (ICS). ICS is relatively low in dimensions and is situated between a highdimensional spectral profile connection space and output units. The definition of ICS axes and the minimum number of ICS dimensions are explored here by considering the LabPQR, an ICS described in earlier research. LabPQR has three colorimetric dimensions (CIE L*a*b*) and additional dimensions to describe a metameric black (PQR). Several versions of LabPQR are explored. One type defines PQR axes based on metameric blacks generated from Cohen and Kappauf's spectral decomposition. The second type is constructed in an unconstrained way where metameric blacks are statistically derived based on the spectral characteristics of the target output device. For a six-dimensional LabPQR, one that uses three colorimetric and three metameric black dimensions, it was found that Cohen and Kappauf-based LabPQR was inferior for estimating the spectra when compared with the unconstrained method. However, when the limited spectral gamut of an output device was introduced through printer simulation and necessary spectral gamut mapping, the disadvantage of the six-dimensional Cohen and Kappauf-based LabPQR dissipated. On the other hand, reducing LabPQR to only five-dimensions (two metameric black dimensions) reintroduced the advantage of the unconstrained approach even after simulated printing including spectral gamut mapping. Importantly, it was found that the five-dimensional unconstrained approach achieved equivalent levels of performance to a full 31-dimensional approach within simulated printer spectral gamut limitations.

show abstract

Spectral Gamut Mapping using LabPQR

Cited by 14 publications

References 17 publications

Spatio-Spectral Gamut Mapping and Separation

Spatio-Spectral Gamut Mapping and Separation

Spectral Color Reproduction from CIE Tristimulus Values Using a Node Address Array Selection Technique

Spectral color management using interim connection spaces based on spectral decomposition

Contact Info

Product

Resources

About