Abstract

The color reproduction accuracy is a key factor to the overall perceived image quality in digital photography. In this framework, both the illuminant estimation process and the color correction matrix concur in the formation of the overall perceived image quality. To the best of our knowledge, the two processes have always been studied separately, thus ignoring the interactions between them. We investigate here these interactions, showing how the color correction transform amplifies the illuminant estimation errors. We demonstrate that incorporating knowledge about the illuminant estimation behavior in the optimization of the color correction matrix makes it possible to alleviate the error amplification. Different strategies to improve color accuracy under both perfect and imperfect white point estimations are investigated, and the experimental results obtained with a digital camera simulator are reported.

© 2012 Optical Society of America

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References

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  1. R. Ramanath, W. E. Snyder, Y. Yoo, and M. S. Drew, “Color image processing pipeline,” IEEE Signal Process. Mag. 22(1), 34–43 (2005).
    [CrossRef]
  2. S. D. Hordley, “Scene illuminant estimation: past, present, and future,” Color Res. Appl. 31, 303–314 (2006).
    [CrossRef]
  3. P. M. Hubel, J. Holm, G. D. Finlayson, and M. S. Drew, “Matrix calculations for digital photography,” Proceedings of the IS&T/SID Fifth Color Imaging Conference: Color Science, Systems, and Applications (Society for Imaging Science and Technology, 1997), pp. 105–111.
  4. S. Bianco, F. Gasparini, A. Russo, and R. Schettini, “A new method for RGB to XYZ transformation based on pattern search optimization,” IEEE Trans. Consum. Electron. 53, 1020–1028 (2007).
    [CrossRef]
  5. P. D. Burns and R. S. Berns, “Error propagation analysis in color measurement and imaging,” Color Res. Appl. 22, 280–289 (1997).
    [CrossRef]
  6. B. E. Bayer, “Color imaging array,” U.S. patent 3,971,065 (20 July 1976).
  7. J. von Kries, “Chromatic adaptation,” in Festschrift der Albrecht-Ludwig-Universität (Fribourg, 1902). Translation by D. L. MacAdam, Sources of Color Science (MIT Press, 1970).
  8. G. D. Finlayson and S. D. Hordley, “Color by correlation: a simple, unifying framework for color constancy,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1209–1221 (2001).
    [CrossRef]
  9. Y. Yang and A. Yuille, “Sources from shading,” in IEEE Conference on Computer Vision and Pattern Recognition CVPR ’91 (IEEE Computer Society, 1991), pp. 534–539.
  10. S. Bianco, F. Gasparini, and R. Schettini, “Consensus based framework for illuminant chromaticity estimation,” J. Electron. Imaging 17, 023013 (2008).
    [CrossRef]
  11. A. Gijsenij, T. Gevers, and M. P. Lucassen, “A perceptual analysis of distance measures for color constancy,” J. Opt. Soc. Am. A 26, 2243–2256 (2009).
    [CrossRef]
  12. J. Farrell, F. Xiao, P. Catrysse, and B. Wandell, “A simulation tool for evaluating digital camera image quality,” Proc. SPIE 5294, 124–131 (2003).
    [CrossRef]
  13. G. D. Finlayson, “Color constancy in diagonal chromaticity space,” in IEEE Proceedings of Fifth International Conference on Computer Vision (IEEE Computer Society, 1995), pp. 218–223.
  14. “Graphic technology—Standard object colour spectra database for colour reproduction evaluation (SOCS),” Technical Report ISO/TR 16066:2003(E) (International Organization for Standardization, 2003).
  15. K. Barnard, V. Cardei, and B. Funt, “A comparison of computational color constancy algorithms. I: Methodology and experiments with synthesized data,” IEEE Trans. Image Process. 11, 972–984 (2002).
    [CrossRef]
  16. G. Buchsbaum, “A spatial processor model for object color perception,” J. Franklin Inst. 310, 1–26 (1980).
    [CrossRef]
  17. E. H. Land, “The retinex theory of color vision,” Sci. Am. 237(6), 108–128 (1977).
    [CrossRef]
  18. D. A. Forsyth, “A novel algorithm for color constancy,” Int. J. Comput. Vis. 5, 5–35 (1990).
    [CrossRef]
  19. F. Wilcoxon, “Individual comparisons by ranking methods,” Biometrics 1, 80–83 (1945).
    [CrossRef]
  20. H. Lilliefors, “On the Kolmogorov-Smirnov test for normality with mean and variance unknown,” J. Am. Stat. Assoc. 62, 399–402 (1967).
    [CrossRef]
  21. N. Ohta and A. R. Robertson, Colorimetry: Fundamentals and Applications (Wiley, 2005).
  22. M. Stokes, M. Anderson, S. Chandrasekar, and R. Motta, “A standard default color space for the Internet—sRGB,” Version 1.10 (5Nov.1996) www.w3.org/Graphics/Color/sRGB.html .
  23. G. Wyszecki and W. S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulas (Wiley, 1967).
  24. G. Sharma, Digital Color Imaging Handbook (CRC Press, 2003).

2009

2008

S. Bianco, F. Gasparini, and R. Schettini, “Consensus based framework for illuminant chromaticity estimation,” J. Electron. Imaging 17, 023013 (2008).
[CrossRef]

2007

S. Bianco, F. Gasparini, A. Russo, and R. Schettini, “A new method for RGB to XYZ transformation based on pattern search optimization,” IEEE Trans. Consum. Electron. 53, 1020–1028 (2007).
[CrossRef]

2006

S. D. Hordley, “Scene illuminant estimation: past, present, and future,” Color Res. Appl. 31, 303–314 (2006).
[CrossRef]

2005

R. Ramanath, W. E. Snyder, Y. Yoo, and M. S. Drew, “Color image processing pipeline,” IEEE Signal Process. Mag. 22(1), 34–43 (2005).
[CrossRef]

2003

J. Farrell, F. Xiao, P. Catrysse, and B. Wandell, “A simulation tool for evaluating digital camera image quality,” Proc. SPIE 5294, 124–131 (2003).
[CrossRef]

2002

K. Barnard, V. Cardei, and B. Funt, “A comparison of computational color constancy algorithms. I: Methodology and experiments with synthesized data,” IEEE Trans. Image Process. 11, 972–984 (2002).
[CrossRef]

2001

G. D. Finlayson and S. D. Hordley, “Color by correlation: a simple, unifying framework for color constancy,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1209–1221 (2001).
[CrossRef]

1997

P. D. Burns and R. S. Berns, “Error propagation analysis in color measurement and imaging,” Color Res. Appl. 22, 280–289 (1997).
[CrossRef]

1990

D. A. Forsyth, “A novel algorithm for color constancy,” Int. J. Comput. Vis. 5, 5–35 (1990).
[CrossRef]

1980

G. Buchsbaum, “A spatial processor model for object color perception,” J. Franklin Inst. 310, 1–26 (1980).
[CrossRef]

1977

E. H. Land, “The retinex theory of color vision,” Sci. Am. 237(6), 108–128 (1977).
[CrossRef]

1967

H. Lilliefors, “On the Kolmogorov-Smirnov test for normality with mean and variance unknown,” J. Am. Stat. Assoc. 62, 399–402 (1967).
[CrossRef]

1945

F. Wilcoxon, “Individual comparisons by ranking methods,” Biometrics 1, 80–83 (1945).
[CrossRef]

Barnard, K.

K. Barnard, V. Cardei, and B. Funt, “A comparison of computational color constancy algorithms. I: Methodology and experiments with synthesized data,” IEEE Trans. Image Process. 11, 972–984 (2002).
[CrossRef]

Bayer, B. E.

B. E. Bayer, “Color imaging array,” U.S. patent 3,971,065 (20 July 1976).

Berns, R. S.

P. D. Burns and R. S. Berns, “Error propagation analysis in color measurement and imaging,” Color Res. Appl. 22, 280–289 (1997).
[CrossRef]

Bianco, S.

S. Bianco, F. Gasparini, and R. Schettini, “Consensus based framework for illuminant chromaticity estimation,” J. Electron. Imaging 17, 023013 (2008).
[CrossRef]

S. Bianco, F. Gasparini, A. Russo, and R. Schettini, “A new method for RGB to XYZ transformation based on pattern search optimization,” IEEE Trans. Consum. Electron. 53, 1020–1028 (2007).
[CrossRef]

Buchsbaum, G.

G. Buchsbaum, “A spatial processor model for object color perception,” J. Franklin Inst. 310, 1–26 (1980).
[CrossRef]

Burns, P. D.

P. D. Burns and R. S. Berns, “Error propagation analysis in color measurement and imaging,” Color Res. Appl. 22, 280–289 (1997).
[CrossRef]

Cardei, V.

K. Barnard, V. Cardei, and B. Funt, “A comparison of computational color constancy algorithms. I: Methodology and experiments with synthesized data,” IEEE Trans. Image Process. 11, 972–984 (2002).
[CrossRef]

Catrysse, P.

J. Farrell, F. Xiao, P. Catrysse, and B. Wandell, “A simulation tool for evaluating digital camera image quality,” Proc. SPIE 5294, 124–131 (2003).
[CrossRef]

Drew, M. S.

R. Ramanath, W. E. Snyder, Y. Yoo, and M. S. Drew, “Color image processing pipeline,” IEEE Signal Process. Mag. 22(1), 34–43 (2005).
[CrossRef]

P. M. Hubel, J. Holm, G. D. Finlayson, and M. S. Drew, “Matrix calculations for digital photography,” Proceedings of the IS&T/SID Fifth Color Imaging Conference: Color Science, Systems, and Applications (Society for Imaging Science and Technology, 1997), pp. 105–111.

Farrell, J.

J. Farrell, F. Xiao, P. Catrysse, and B. Wandell, “A simulation tool for evaluating digital camera image quality,” Proc. SPIE 5294, 124–131 (2003).
[CrossRef]

Finlayson, G. D.

G. D. Finlayson and S. D. Hordley, “Color by correlation: a simple, unifying framework for color constancy,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1209–1221 (2001).
[CrossRef]

P. M. Hubel, J. Holm, G. D. Finlayson, and M. S. Drew, “Matrix calculations for digital photography,” Proceedings of the IS&T/SID Fifth Color Imaging Conference: Color Science, Systems, and Applications (Society for Imaging Science and Technology, 1997), pp. 105–111.

G. D. Finlayson, “Color constancy in diagonal chromaticity space,” in IEEE Proceedings of Fifth International Conference on Computer Vision (IEEE Computer Society, 1995), pp. 218–223.

Forsyth, D. A.

D. A. Forsyth, “A novel algorithm for color constancy,” Int. J. Comput. Vis. 5, 5–35 (1990).
[CrossRef]

Funt, B.

K. Barnard, V. Cardei, and B. Funt, “A comparison of computational color constancy algorithms. I: Methodology and experiments with synthesized data,” IEEE Trans. Image Process. 11, 972–984 (2002).
[CrossRef]

Gasparini, F.

S. Bianco, F. Gasparini, and R. Schettini, “Consensus based framework for illuminant chromaticity estimation,” J. Electron. Imaging 17, 023013 (2008).
[CrossRef]

S. Bianco, F. Gasparini, A. Russo, and R. Schettini, “A new method for RGB to XYZ transformation based on pattern search optimization,” IEEE Trans. Consum. Electron. 53, 1020–1028 (2007).
[CrossRef]

Gevers, T.

Gijsenij, A.

Holm, J.

P. M. Hubel, J. Holm, G. D. Finlayson, and M. S. Drew, “Matrix calculations for digital photography,” Proceedings of the IS&T/SID Fifth Color Imaging Conference: Color Science, Systems, and Applications (Society for Imaging Science and Technology, 1997), pp. 105–111.

Hordley, S. D.

S. D. Hordley, “Scene illuminant estimation: past, present, and future,” Color Res. Appl. 31, 303–314 (2006).
[CrossRef]

G. D. Finlayson and S. D. Hordley, “Color by correlation: a simple, unifying framework for color constancy,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1209–1221 (2001).
[CrossRef]

Hubel, P. M.

P. M. Hubel, J. Holm, G. D. Finlayson, and M. S. Drew, “Matrix calculations for digital photography,” Proceedings of the IS&T/SID Fifth Color Imaging Conference: Color Science, Systems, and Applications (Society for Imaging Science and Technology, 1997), pp. 105–111.

Land, E. H.

E. H. Land, “The retinex theory of color vision,” Sci. Am. 237(6), 108–128 (1977).
[CrossRef]

Lilliefors, H.

H. Lilliefors, “On the Kolmogorov-Smirnov test for normality with mean and variance unknown,” J. Am. Stat. Assoc. 62, 399–402 (1967).
[CrossRef]

Lucassen, M. P.

Ohta, N.

N. Ohta and A. R. Robertson, Colorimetry: Fundamentals and Applications (Wiley, 2005).

Ramanath, R.

R. Ramanath, W. E. Snyder, Y. Yoo, and M. S. Drew, “Color image processing pipeline,” IEEE Signal Process. Mag. 22(1), 34–43 (2005).
[CrossRef]

Robertson, A. R.

N. Ohta and A. R. Robertson, Colorimetry: Fundamentals and Applications (Wiley, 2005).

Russo, A.

S. Bianco, F. Gasparini, A. Russo, and R. Schettini, “A new method for RGB to XYZ transformation based on pattern search optimization,” IEEE Trans. Consum. Electron. 53, 1020–1028 (2007).
[CrossRef]

Schettini, R.

S. Bianco, F. Gasparini, and R. Schettini, “Consensus based framework for illuminant chromaticity estimation,” J. Electron. Imaging 17, 023013 (2008).
[CrossRef]

S. Bianco, F. Gasparini, A. Russo, and R. Schettini, “A new method for RGB to XYZ transformation based on pattern search optimization,” IEEE Trans. Consum. Electron. 53, 1020–1028 (2007).
[CrossRef]

Sharma, G.

G. Sharma, Digital Color Imaging Handbook (CRC Press, 2003).

Snyder, W. E.

R. Ramanath, W. E. Snyder, Y. Yoo, and M. S. Drew, “Color image processing pipeline,” IEEE Signal Process. Mag. 22(1), 34–43 (2005).
[CrossRef]

Stiles, W. S.

G. Wyszecki and W. S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulas (Wiley, 1967).

von Kries, J.

J. von Kries, “Chromatic adaptation,” in Festschrift der Albrecht-Ludwig-Universität (Fribourg, 1902). Translation by D. L. MacAdam, Sources of Color Science (MIT Press, 1970).

Wandell, B.

J. Farrell, F. Xiao, P. Catrysse, and B. Wandell, “A simulation tool for evaluating digital camera image quality,” Proc. SPIE 5294, 124–131 (2003).
[CrossRef]

Wilcoxon, F.

F. Wilcoxon, “Individual comparisons by ranking methods,” Biometrics 1, 80–83 (1945).
[CrossRef]

Wyszecki, G.

G. Wyszecki and W. S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulas (Wiley, 1967).

Xiao, F.

J. Farrell, F. Xiao, P. Catrysse, and B. Wandell, “A simulation tool for evaluating digital camera image quality,” Proc. SPIE 5294, 124–131 (2003).
[CrossRef]

Yang, Y.

Y. Yang and A. Yuille, “Sources from shading,” in IEEE Conference on Computer Vision and Pattern Recognition CVPR ’91 (IEEE Computer Society, 1991), pp. 534–539.

Yoo, Y.

R. Ramanath, W. E. Snyder, Y. Yoo, and M. S. Drew, “Color image processing pipeline,” IEEE Signal Process. Mag. 22(1), 34–43 (2005).
[CrossRef]

Yuille, A.

Y. Yang and A. Yuille, “Sources from shading,” in IEEE Conference on Computer Vision and Pattern Recognition CVPR ’91 (IEEE Computer Society, 1991), pp. 534–539.

Biometrics

F. Wilcoxon, “Individual comparisons by ranking methods,” Biometrics 1, 80–83 (1945).
[CrossRef]

Color Res. Appl.

S. D. Hordley, “Scene illuminant estimation: past, present, and future,” Color Res. Appl. 31, 303–314 (2006).
[CrossRef]

P. D. Burns and R. S. Berns, “Error propagation analysis in color measurement and imaging,” Color Res. Appl. 22, 280–289 (1997).
[CrossRef]

IEEE Signal Process. Mag.

R. Ramanath, W. E. Snyder, Y. Yoo, and M. S. Drew, “Color image processing pipeline,” IEEE Signal Process. Mag. 22(1), 34–43 (2005).
[CrossRef]

IEEE Trans. Consum. Electron.

S. Bianco, F. Gasparini, A. Russo, and R. Schettini, “A new method for RGB to XYZ transformation based on pattern search optimization,” IEEE Trans. Consum. Electron. 53, 1020–1028 (2007).
[CrossRef]

IEEE Trans. Image Process.

K. Barnard, V. Cardei, and B. Funt, “A comparison of computational color constancy algorithms. I: Methodology and experiments with synthesized data,” IEEE Trans. Image Process. 11, 972–984 (2002).
[CrossRef]

IEEE Trans. Pattern Anal. Mach. Intell.

G. D. Finlayson and S. D. Hordley, “Color by correlation: a simple, unifying framework for color constancy,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1209–1221 (2001).
[CrossRef]

Int. J. Comput. Vis.

D. A. Forsyth, “A novel algorithm for color constancy,” Int. J. Comput. Vis. 5, 5–35 (1990).
[CrossRef]

J. Am. Stat. Assoc.

H. Lilliefors, “On the Kolmogorov-Smirnov test for normality with mean and variance unknown,” J. Am. Stat. Assoc. 62, 399–402 (1967).
[CrossRef]

J. Electron. Imaging

S. Bianco, F. Gasparini, and R. Schettini, “Consensus based framework for illuminant chromaticity estimation,” J. Electron. Imaging 17, 023013 (2008).
[CrossRef]

J. Franklin Inst.

G. Buchsbaum, “A spatial processor model for object color perception,” J. Franklin Inst. 310, 1–26 (1980).
[CrossRef]

J. Opt. Soc. Am. A

Proc. SPIE

J. Farrell, F. Xiao, P. Catrysse, and B. Wandell, “A simulation tool for evaluating digital camera image quality,” Proc. SPIE 5294, 124–131 (2003).
[CrossRef]

Sci. Am.

E. H. Land, “The retinex theory of color vision,” Sci. Am. 237(6), 108–128 (1977).
[CrossRef]

Other

G. D. Finlayson, “Color constancy in diagonal chromaticity space,” in IEEE Proceedings of Fifth International Conference on Computer Vision (IEEE Computer Society, 1995), pp. 218–223.

“Graphic technology—Standard object colour spectra database for colour reproduction evaluation (SOCS),” Technical Report ISO/TR 16066:2003(E) (International Organization for Standardization, 2003).

N. Ohta and A. R. Robertson, Colorimetry: Fundamentals and Applications (Wiley, 2005).

M. Stokes, M. Anderson, S. Chandrasekar, and R. Motta, “A standard default color space for the Internet—sRGB,” Version 1.10 (5Nov.1996) www.w3.org/Graphics/Color/sRGB.html .

G. Wyszecki and W. S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulas (Wiley, 1967).

G. Sharma, Digital Color Imaging Handbook (CRC Press, 2003).

Y. Yang and A. Yuille, “Sources from shading,” in IEEE Conference on Computer Vision and Pattern Recognition CVPR ’91 (IEEE Computer Society, 1991), pp. 534–539.

B. E. Bayer, “Color imaging array,” U.S. patent 3,971,065 (20 July 1976).

J. von Kries, “Chromatic adaptation,” in Festschrift der Albrecht-Ludwig-Universität (Fribourg, 1902). Translation by D. L. MacAdam, Sources of Color Science (MIT Press, 1970).

P. M. Hubel, J. Holm, G. D. Finlayson, and M. S. Drew, “Matrix calculations for digital photography,” Proceedings of the IS&T/SID Fifth Color Imaging Conference: Color Science, Systems, and Applications (Society for Imaging Science and Technology, 1997), pp. 105–111.

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Figures (3)

Fig. 1.
Fig. 1.

Radar plot of the mean Δ E 94 errors obtained under the different illuminants considered by four different SILL approaches optimized for four different illuminants; D65, A, 2000 K, and F11.

Fig. 2.
Fig. 2.

Δ E 94 error distribution as the error in the illuminant estimation and compensation changes under the D65 illuminant: (a) no color correction and (b)  SILL D 65 color correction.

Fig. 3.
Fig. 3.

Composition rules for the generation of the acronyms of the proposed strategies.

Tables (9)

Tables Icon

Table 1. Average Δ E 94 Colorimetric Error Obtained by the Color Correction Matrices Optimized for the Different Illuminants, Evaluated on the Same Illuminant for Which the Optimization Is Carried Out

Tables Icon

Table 2. Average Δ E 94 Colorimetric Error Obtained by the Color Correction Matrices Optimized Simultaneously for the Different Illuminants, Evaluated on All the Considered Illuminants

Tables Icon

Table 3. Average Δ E 94 Colorimetric Error and Percentage Colorimetric Accuracy Improvement with Respect to the Most Performing Strategy, Obtained by All the Proposed Strategies

Tables Icon

Table 4. Average Δ E 94 Colorimetric Error and Average Slope of the SILL and SILL-WEB Color Correction Matrices

Tables Icon

Table 5. Brief Description of the Color Correction Strategies Compared

Tables Icon

Table 6. Average Δ E 94 Colorimetric Error and Percentage Colorimetric Accuracy Improvement with Respect to the State-of-the-Art Strategy ( SILL D 65 ), Obtained by All the Proposed Strategies

Tables Icon

Table 7. (Color online) Outputs of the Statistical Test for the Color Correction Strategies Considereda

Tables Icon

Table 8. Statistics for the Δ E 94 Colorimetric Error Obtained by the Color Correction Matrices Optimized for the Different Illuminants, Evaluated on the Same Illuminant for Which the Optimization Is Carried Out

Tables Icon

Table 9. Average Δ E 94 Colorimetric Error Obtained by the Color Correction Matrices Optimized Individually for the Different Illuminants, Evaluated on All the Illuminants Considered

Equations (15)

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

ρ ( x , y ) = ω I ( λ ) S ( x , y , λ ) C ( λ ) d λ ,
[ R G B ] out = ( α [ a 11 a 12 a 13 a 21 a 22 a 23 a 31 a 32 a 33 ] [ r a w b 0 0 0 g a w b 0 0 0 b a w b ] [ R G B ] in ) ,
RGB out = ( α A D · RGB in ) γ ,
M = arg ( min A R 3 × 3 k = 1 n E ( r k , ( α A D c k ) γ ) ) ,
M = arg ( min A R 3 × 3 j = 1 m w j ( k = 1 n E ( r k , ( α j A D j c k ) γ ) ) ) subject to j = 1 3 A ( i , j ) = 1 , i { 1 , 2 , 3 } .
M = arg ( min A R 3 × 3 j = 0 s u j ( k = 1 n E ( r k , ( α j A G j c k ) γ ) ) ) subject to j = 1 3 A ( i , j ) = 1 , i { 1 , 2 , 3 } ,
M = α SILL i + ( 1 α ) SILL j ,
α = d ( CCT , CCT j ) d ( CCT , CCT i ) + d ( CCT , CCT j ) .
M = α SILL WEB i + ( 1 α ) SILL WEB j ,
α = PED ( gains , gains j ) PED ( gains , gains i ) + PED ( gains , gains j ) .
C = { C / 12.92 , if C 0.04045 ( ( C + 0.055 ) / 1.055 ) 2.4 , if C > 0.04045 .
[ X Y Z ] = [ 0.4124564 0.3575761 0.1804375 0.2126729 0.7151522 0.0721750 0.0193339 0.1191920 0.9503041 ] [ R G B ] .
f C = { C 1 / 3 , if C > ϵ ( κ C + 16 ) / 116 , if C ϵ ,
[ L a b ] = [ 116 f Y 16 500 ( f X f Y ) 200 ( f Y f Z ) ] .
Δ E 94 = ( Δ L K L S L ) 2 + ( Δ C K C S C ) 2 + ( Δ H K H S H ) 2 ,

Metrics