Abstract

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.

© 2012 Optical Society of America

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References

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2011 (2)

2010 (1)

2008 (2)

X. D. Zhang and H. S. Xu, “Reconstructing spectral reflectance by dividing spectral space and extending the principal components in principal component analysis,” J. Opt. Soc. Am. A 25, 371–378 (2008).
[CrossRef]

S. Tsutsumil, M. R. Rosen, and R. S. Berns, “Spectral color management using interim connection spaces based on spectral decomposition,” Color Res. Appl. 33, 282–299 (2008).
[CrossRef]

2007 (4)

S. Tsutsumi, M. R. Rosen, and R. S. Berns, “Spectral gamut mapping using LabPQR,” J. Imaging Sci. Technol. 51, 473–485 (2007).
[CrossRef]

N. Shimano, K. Terai, and M. Hironaga, “Recovery of spectral reflectances of objects being imaged by multispectral cameras,” J. Opt. Soc. Am. A 24, 3211–3219 (2007).
[CrossRef]

S. Yamamoto, N. Tsumura, and T. Nakaguchi, “Development of multispectral scanner by using LEDs array for digital color proof,” J. Imaging Sci. Technol. 51, 61–69 (2007).
[CrossRef]

V. Bochko, N. Tsumura, and Y. Miyake, “Spectral color imaging system for estimating spectral reflectance of paint,” J. Imaging Sci. Technol. 51, 70–78 (2007).
[CrossRef]

2006 (2)

M. W. Derhak and M. R. Rosen, “Spectral colorimetry using LabPQR—an interim connection space,” J. Imaging Sci. Technol. 50, 53–63 (2006).
[CrossRef]

M. R. Rosen and M. W. Derhak, “Spectral gamuts and spectral gamut mapping,” Proc. SPIE 6062, 60620K (2006).

2005 (2)

A. Ribés, F. Schmitt, R. Pillay, and C. Lahanier, “Calibration and spectral reconstruction for CRISATEL: an art painting multispectral acquisition system,” J. Imaging Sci. Technol. 49, 563–573 (2005).

D. Y. Tzeng and R. S. Berns, “A review of principal component analysis and its applications to color technology,” Color Res. Appl. 30, 84–98 (2005).
[CrossRef]

2004 (1)

H. S. Fairman and M. H. Brill, “The principal components of reflectances,” Color Res. Appl. 29, 104–110 (2004).
[CrossRef]

2002 (2)

D. Dupont, “Study of the reconstruction of reflectance curves based on tristimulus values: comparison of methods of optimization,” Color Res. Appl. 27, 88–99 (2002).
[CrossRef]

J. Y. Hardeberg, F. Schmitt, and H. Brettel, “Multispectral color image capture using a liquid crystal tunable filter,” Opt. Eng. 41, 2532–2548 (2002).
[CrossRef]

1996 (1)

T. Johnson. “Methods for characterizing colour printers,” Displays 16, 193–202 (1996).
[CrossRef]

Berns, R. S.

S. Tsutsumil, M. R. Rosen, and R. S. Berns, “Spectral color management using interim connection spaces based on spectral decomposition,” Color Res. Appl. 33, 282–299 (2008).
[CrossRef]

S. Tsutsumi, M. R. Rosen, and R. S. Berns, “Spectral gamut mapping using LabPQR,” J. Imaging Sci. Technol. 51, 473–485 (2007).
[CrossRef]

D. Y. Tzeng and R. S. Berns, “A review of principal component analysis and its applications to color technology,” Color Res. Appl. 30, 84–98 (2005).
[CrossRef]

P. Urban, M. R. Rosen, and R. S. Berns, “Spectral gamut mapping framework based on human color vision,” in (CGIV) Fourth European Conference on Colour in Graphics, Imaging, and MCS/08 Vision 10th International Symposium on Multispectral Colour Science (Society for Imaging Science and Technology, 2008), pp. 548–553.

F. H. Imai, M. R. Rosen, and R. S. Berns, “Comparative study of metrics for spectral match quality,” in (CGIV) First European Conference on Colour Graphics, Imaging, and Vision (Society for Imaging Science and Technology, 2002), pp. 492–496 (2002).

Björck, Å.

Å. Björck, Numerical Methods for Least Squares Problems(Academic, 1996).

Bochko, V.

V. Bochko, N. Tsumura, and Y. Miyake, “Spectral color imaging system for estimating spectral reflectance of paint,” J. Imaging Sci. Technol. 51, 70–78 (2007).
[CrossRef]

Brettel, H.

J. Y. Hardeberg, F. Schmitt, and H. Brettel, “Multispectral color image capture using a liquid crystal tunable filter,” Opt. Eng. 41, 2532–2548 (2002).
[CrossRef]

Brill, M. H.

H. S. Fairman and M. H. Brill, “The principal components of reflectances,” Color Res. Appl. 29, 104–110 (2004).
[CrossRef]

Derhak, M. W.

M. W. Derhak and M. R. Rosen, “Spectral colorimetry using LabPQR—an interim connection space,” J. Imaging Sci. Technol. 50, 53–63 (2006).
[CrossRef]

M. R. Rosen and M. W. Derhak, “Spectral gamuts and spectral gamut mapping,” Proc. SPIE 6062, 60620K (2006).

Dupont, D.

D. Dupont, “Study of the reconstruction of reflectance curves based on tristimulus values: comparison of methods of optimization,” Color Res. Appl. 27, 88–99 (2002).
[CrossRef]

Fairman, H. S.

H. S. Fairman and M. H. Brill, “The principal components of reflectances,” Color Res. Appl. 29, 104–110 (2004).
[CrossRef]

Guo, J. Y.

Hardeberg, J. Y.

J. Y. Hardeberg, F. Schmitt, and H. Brettel, “Multispectral color image capture using a liquid crystal tunable filter,” Opt. Eng. 41, 2532–2548 (2002).
[CrossRef]

Hironaga, M.

Imai, F. H.

F. H. Imai, M. R. Rosen, and R. S. Berns, “Comparative study of metrics for spectral match quality,” in (CGIV) First European Conference on Colour Graphics, Imaging, and Vision (Society for Imaging Science and Technology, 2002), pp. 492–496 (2002).

Johnson, T.

T. Johnson. “Methods for characterizing colour printers,” Displays 16, 193–202 (1996).
[CrossRef]

Lahanier, C.

A. Ribés, F. Schmitt, R. Pillay, and C. Lahanier, “Calibration and spectral reconstruction for CRISATEL: an art painting multispectral acquisition system,” J. Imaging Sci. Technol. 49, 563–573 (2005).

Luo, M. R.

Miyake, Y.

V. Bochko, N. Tsumura, and Y. Miyake, “Spectral color imaging system for estimating spectral reflectance of paint,” J. Imaging Sci. Technol. 51, 70–78 (2007).
[CrossRef]

Nakaguchi, T.

S. Yamamoto, N. Tsumura, and T. Nakaguchi, “Development of multispectral scanner by using LEDs array for digital color proof,” J. Imaging Sci. Technol. 51, 61–69 (2007).
[CrossRef]

Nakaya, F.

F. Nakaya and N. Ohta, “Applying LabRGB to real multi-spectral images,” in Sixteenth IS&T Colour Imaging Conference(Society for Imaging Science and Technology, 2008), pp. 289–294.

F. Nakaya and N. Ohta, “Spectral encoding/decoding using LabRGB,” in Fifteenth IS&T Colour Imaging Conference(Society for Imaging Science and Technology, 2007), pp. 190–194.

Ohta, N.

F. Nakaya and N. Ohta, “Spectral encoding/decoding using LabRGB,” in Fifteenth IS&T Colour Imaging Conference(Society for Imaging Science and Technology, 2007), pp. 190–194.

F. Nakaya and N. Ohta, “Applying LabRGB to real multi-spectral images,” in Sixteenth IS&T Colour Imaging Conference(Society for Imaging Science and Technology, 2008), pp. 289–294.

Pillay, R.

A. Ribés, F. Schmitt, R. Pillay, and C. Lahanier, “Calibration and spectral reconstruction for CRISATEL: an art painting multispectral acquisition system,” J. Imaging Sci. Technol. 49, 563–573 (2005).

Ribés, A.

A. Ribés, F. Schmitt, R. Pillay, and C. Lahanier, “Calibration and spectral reconstruction for CRISATEL: an art painting multispectral acquisition system,” J. Imaging Sci. Technol. 49, 563–573 (2005).

Rosen, M. R.

S. Tsutsumil, M. R. Rosen, and R. S. Berns, “Spectral color management using interim connection spaces based on spectral decomposition,” Color Res. Appl. 33, 282–299 (2008).
[CrossRef]

S. Tsutsumi, M. R. Rosen, and R. S. Berns, “Spectral gamut mapping using LabPQR,” J. Imaging Sci. Technol. 51, 473–485 (2007).
[CrossRef]

M. R. Rosen and M. W. Derhak, “Spectral gamuts and spectral gamut mapping,” Proc. SPIE 6062, 60620K (2006).

M. W. Derhak and M. R. Rosen, “Spectral colorimetry using LabPQR—an interim connection space,” J. Imaging Sci. Technol. 50, 53–63 (2006).
[CrossRef]

P. Urban, M. R. Rosen, and R. S. Berns, “Spectral gamut mapping framework based on human color vision,” in (CGIV) Fourth European Conference on Colour in Graphics, Imaging, and MCS/08 Vision 10th International Symposium on Multispectral Colour Science (Society for Imaging Science and Technology, 2008), pp. 548–553.

F. H. Imai, M. R. Rosen, and R. S. Berns, “Comparative study of metrics for spectral match quality,” in (CGIV) First European Conference on Colour Graphics, Imaging, and Vision (Society for Imaging Science and Technology, 2002), pp. 492–496 (2002).

Schanda, J.

J. Schanda, “CIE colorimetry,” in Colorimetry: Understanding the CIE System (Academic, 2007), pp. 37–46.

Schmitt, F.

A. Ribés, F. Schmitt, R. Pillay, and C. Lahanier, “Calibration and spectral reconstruction for CRISATEL: an art painting multispectral acquisition system,” J. Imaging Sci. Technol. 49, 563–573 (2005).

J. Y. Hardeberg, F. Schmitt, and H. Brettel, “Multispectral color image capture using a liquid crystal tunable filter,” Opt. Eng. 41, 2532–2548 (2002).
[CrossRef]

Shen, H. L.

Shimano, N.

Terai, K.

Tsumura, N.

S. Yamamoto, N. Tsumura, and T. Nakaguchi, “Development of multispectral scanner by using LEDs array for digital color proof,” J. Imaging Sci. Technol. 51, 61–69 (2007).
[CrossRef]

V. Bochko, N. Tsumura, and Y. Miyake, “Spectral color imaging system for estimating spectral reflectance of paint,” J. Imaging Sci. Technol. 51, 70–78 (2007).
[CrossRef]

Tsutsumi, S.

S. Tsutsumi, M. R. Rosen, and R. S. Berns, “Spectral gamut mapping using LabPQR,” J. Imaging Sci. Technol. 51, 473–485 (2007).
[CrossRef]

Tsutsumil, S.

S. Tsutsumil, M. R. Rosen, and R. S. Berns, “Spectral color management using interim connection spaces based on spectral decomposition,” Color Res. Appl. 33, 282–299 (2008).
[CrossRef]

Tzeng, D.

D. Tzeng, “Spectral-based color separation algorithm development for multi-ink color reproduction,” Ph.D. thesis (Rochester Institute of Technology, 1999).

Tzeng, D. Y.

D. Y. Tzeng and R. S. Berns, “A review of principal component analysis and its applications to color technology,” Color Res. Appl. 30, 84–98 (2005).
[CrossRef]

Urban, P.

P. Urban, M. R. Rosen, and R. S. Berns, “Spectral gamut mapping framework based on human color vision,” in (CGIV) Fourth European Conference on Colour in Graphics, Imaging, and MCS/08 Vision 10th International Symposium on Multispectral Colour Science (Society for Imaging Science and Technology, 2008), pp. 548–553.

Wan, H. J.

Wang, B. Y.

Weng, C. W.

Xin, J. H.

Xu, H. S.

Yamamoto, S.

S. Yamamoto, N. Tsumura, and T. Nakaguchi, “Development of multispectral scanner by using LEDs array for digital color proof,” J. Imaging Sci. Technol. 51, 61–69 (2007).
[CrossRef]

Zhang, X. D.

Chin. Opt. Lett. (1)

Color Res. Appl. (4)

H. S. Fairman and M. H. Brill, “The principal components of reflectances,” Color Res. Appl. 29, 104–110 (2004).
[CrossRef]

D. Y. Tzeng and R. S. Berns, “A review of principal component analysis and its applications to color technology,” Color Res. Appl. 30, 84–98 (2005).
[CrossRef]

S. Tsutsumil, M. R. Rosen, and R. S. Berns, “Spectral color management using interim connection spaces based on spectral decomposition,” Color Res. Appl. 33, 282–299 (2008).
[CrossRef]

D. Dupont, “Study of the reconstruction of reflectance curves based on tristimulus values: comparison of methods of optimization,” Color Res. Appl. 27, 88–99 (2002).
[CrossRef]

Displays (1)

T. Johnson. “Methods for characterizing colour printers,” Displays 16, 193–202 (1996).
[CrossRef]

J. Imaging Sci. Technol. (5)

S. Tsutsumi, M. R. Rosen, and R. S. Berns, “Spectral gamut mapping using LabPQR,” J. Imaging Sci. Technol. 51, 473–485 (2007).
[CrossRef]

A. Ribés, F. Schmitt, R. Pillay, and C. Lahanier, “Calibration and spectral reconstruction for CRISATEL: an art painting multispectral acquisition system,” J. Imaging Sci. Technol. 49, 563–573 (2005).

S. Yamamoto, N. Tsumura, and T. Nakaguchi, “Development of multispectral scanner by using LEDs array for digital color proof,” J. Imaging Sci. Technol. 51, 61–69 (2007).
[CrossRef]

V. Bochko, N. Tsumura, and Y. Miyake, “Spectral color imaging system for estimating spectral reflectance of paint,” J. Imaging Sci. Technol. 51, 70–78 (2007).
[CrossRef]

M. W. Derhak and M. R. Rosen, “Spectral colorimetry using LabPQR—an interim connection space,” J. Imaging Sci. Technol. 50, 53–63 (2006).
[CrossRef]

J. Opt. Soc. Am. A (4)

Opt. Eng. (1)

J. Y. Hardeberg, F. Schmitt, and H. Brettel, “Multispectral color image capture using a liquid crystal tunable filter,” Opt. Eng. 41, 2532–2548 (2002).
[CrossRef]

Proc. SPIE (1)

M. R. Rosen and M. W. Derhak, “Spectral gamuts and spectral gamut mapping,” Proc. SPIE 6062, 60620K (2006).

Other (9)

International Color Consortium, “ICC.1, Version 4.2,” 6–8 (2004).

P. Urban, M. R. Rosen, and R. S. Berns, “Spectral gamut mapping framework based on human color vision,” in (CGIV) Fourth European Conference on Colour in Graphics, Imaging, and MCS/08 Vision 10th International Symposium on Multispectral Colour Science (Society for Imaging Science and Technology, 2008), pp. 548–553.

D. Tzeng, “Spectral-based color separation algorithm development for multi-ink color reproduction,” Ph.D. thesis (Rochester Institute of Technology, 1999).

Å. Björck, Numerical Methods for Least Squares Problems(Academic, 1996).

F. H. Imai, M. R. Rosen, and R. S. Berns, “Comparative study of metrics for spectral match quality,” in (CGIV) First European Conference on Colour Graphics, Imaging, and Vision (Society for Imaging Science and Technology, 2002), pp. 492–496 (2002).

F. Nakaya and N. Ohta, “Spectral encoding/decoding using LabRGB,” in Fifteenth IS&T Colour Imaging Conference(Society for Imaging Science and Technology, 2007), pp. 190–194.

F. Nakaya and N. Ohta, “Applying LabRGB to real multi-spectral images,” in Sixteenth IS&T Colour Imaging Conference(Society for Imaging Science and Technology, 2008), pp. 289–294.

The National Gallery, London, “Spectral power distribution (SPD) curves,” http://research.ng-london.org.uk/scientific/spd/ .

J. Schanda, “CIE colorimetry,” in Colorimetry: Understanding the CIE System (Academic, 2007), pp. 37–46.

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Figures (3)

Fig. 1.
Fig. 1.

Normalized SPD of CIE illuminants and LED.

Fig. 2.
Fig. 2.

Selected SPD for extracting the synthetic illuminants.

Fig. 3.
Fig. 3.

Three constructed synthetic illuminants.

Tables (5)

Tables Icon

Table 1. Spectral Representing Accuracy Comparison of the Four ICSs with Odd and Even Chips of Munsell as Training and Testing Samples, Respectively

Tables Icon

Table 2. Colorimetric Representing Accuracy Comparison of the Four ICSs with Odd and Even Chips of Munsell as Training and Testing Samples, Respectively

Tables Icon

Table 3. Spectral Representing Accuracy Comparison of the Four ICS with Odd Chips of Munsell and NCS as Training and Testing Samples, Respectively

Tables Icon

Table 4. Colorimetric Representing Accuracy Comparison of the Four ICSs with Odd Chips of Munsell and NCS as Training and Testing Samples, Respectively

Tables Icon

Table 5. Overall Performance of the Four ICSs

Equations (7)

Equations on this page are rendered with MathJax. Learn more.

si(λ)=pc(λ)pcminpcmaxpcmin,
[X1Y1Z1X2Y2Z2X3Y3Z3]=[si1(λ)x¯(λ)si1(λ)y¯(λ)si1(λ)z¯(λ)si2(λ)x¯(λ)si2(λ)y¯(λ)si2(λ)z¯(λ)si3(λ)x¯(λ)si3(λ)y¯(λ)si3(λ)z¯(λ)]r.
t=Mr,
r=r¯+Va,
a=(MV)1(tMr¯),
r=r¯+V(MV)1(tMr¯).
r=M1t

Metrics