Abstract

The main objective of this work is to determine the most appropriate high dynamic range picture calibration procedure for measuring luminance and color, in the context of architecture. Three professional digital single-lens reflex cameras fitted with fisheye lenses were tested. Photometric and colorimetric accuracy was assessed in comparison with spectroradiometer measurements of 57 color samples (Macbeth chart and additional Munsell samples). The results demonstrate that using a calibration model specific to the camera–lens association rather than the standardized RGB to XYZ color transform matrix is necessary to achieve an acceptable colorimetric accuracy. Moreover, using a specific color transform matrix reduces the error in luminance, especially for colorful elements. The study also shows the opportunity to share the same color transform matrix between similar photographic materials (same brand and model). Finally, among the three tested devices, one camera–lens association has higher performance and produces better quality QuickTime Virtual Reality panoramas.

© 2019 Optical Society of America

Full Article  |  PDF Article
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References

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  1. G. Ward, hdrgen command-line (2006).
  2. G. Ward, Photosphere (2001).
  3. D. Dumortier, B. Coutelier, T. Faulcon, and F. Roy, “Photolux: a new luminance mapping system based on Nikon coolpix digital cameras,” in Lux Europa (2005), pp. 308–311.
  4. R. Mantiuk, G. Krawczyk, R. Mantiuk, and H.-P. Seidel, “High-dynamic range imaging pipeline: perception-motivated representation of visual content,” Proc. SPIE 6492, 649212 (2007).
    [Crossref]
  5. G. Ward, Raw2hdr perl script (2011), http://www.anyhere.com/gward/pickup/raw2hdr.tgz .
  6. M. Inanici, “Evaluation of high dynamic range photography as a luminance data acquisition system,” Light. Res. Technol. 38, 123–136 (2006).
    [Crossref]
  7. H. Cai and T. Chung, “Improving the quality of high dynamic range images,” Light. Res. Technol. 43, 87–102 (2011).
    [Crossref]
  8. B. Jung and M. Inanici, “Measuring circadian lighting through high dynamic range photography,” Light. Res. Technol. 51, 742–763 (2018).
    [Crossref]
  9. M. H. Kim and J. Kautz, “Characterization for high dynamic range imaging,” Comput. Graph. Forum 27, 691–697 (2008).
    [Crossref]
  10. P. E. Debevec and J. Malik, “Recovering high dynamic range radiance maps from photographs,” ACM SIGGRAPH 97, 369–378 (1997).
    [Crossref]
  11. D. Varghese, R. Wanat, and R. Mantiuk, “Colorimetric calibration of high dynamic range images with a ColorChecker chart,” in Proceedings of HDRi 2014, Second International Conference and SME Workshop on HDR Imaging, Sarajevo, Bosnia and Herzegovina (2014), 17–22.
  12. International Electrotechnical Commission, “Multimedia systems and equipment-colour measurement and management-part 2-1: colour management-default RGB colour space-sRGB,” (IEC, 1999), p. 2.
  13. C. Cauwerts, M. Bodart, and A. Deneyer, “Comparison of the vignetting effects of two identical fisheye lenses,” LEUKOS J. Illum. Eng. Soc. North Am. 8, 181–203 (2012).
    [Crossref]
  14. “HDRI mailing list, February 2012, hdrgen’s -x option,” 2019, https://discourse.radiance-online.org/t/hdrgens-x-option/3525/2 .
  15. MathWorks, MATLAB Release 2017a (MathWorks Inc., 2017).
  16. ISO/CIE, “Colorimetry—part 4: CIE 1976 L*a*b* colour space,” (CIE, 2008).
  17. J. J. McCann, V. Vonikakis, and A. Rizzi, HDR Scene Capture and Appearance (SPIE, 2017).
  18. CIE, “Colorimetry,” (CIE, 2018).
  19. W. Mokrzycki and M. Tatol, “Color difference delta E—a survey,” Mach. Graph. Vis. 20, 383–411 (2011).
  20. G. W. Meyer, “Wavelength selection for synthetic image generation,” Comput. Vis. Graph. Image Process. 41, 57–79 (1988).
    [Crossref]
  21. G. D. Finlayson, S. D. Hordley, and P. Morovic, “Using the spectracube to build a multispectral image database,” in Conference on Colour in Graphics, Imaging, and Vision, Aachen, Germany (Society for Imaging Science and Technology, 2004), pp. 268–274.
  22. I3A, Fundamentals and Review of Considered Test Methods (2007).
  23. C. Cauwerts, “Influence of presentation modes on visual perceptions of daylit spaces,” Ph.D. thesis (Université Catholique de Louvain, 2013).
  24. J. Kuang, G. M. Johnson, and M. D. Fairchild, “iCAM06: a refined image appearance model for HDR image rendering,” J. Vis. Commun. Image Represent. 18, 406–414 (2007).
    [Crossref]
  25. New House Internet Services BV, PTGui, v9.1.7 PRO (PTGui, 1996).

2018 (1)

B. Jung and M. Inanici, “Measuring circadian lighting through high dynamic range photography,” Light. Res. Technol. 51, 742–763 (2018).
[Crossref]

2012 (1)

C. Cauwerts, M. Bodart, and A. Deneyer, “Comparison of the vignetting effects of two identical fisheye lenses,” LEUKOS J. Illum. Eng. Soc. North Am. 8, 181–203 (2012).
[Crossref]

2011 (2)

W. Mokrzycki and M. Tatol, “Color difference delta E—a survey,” Mach. Graph. Vis. 20, 383–411 (2011).

H. Cai and T. Chung, “Improving the quality of high dynamic range images,” Light. Res. Technol. 43, 87–102 (2011).
[Crossref]

2008 (1)

M. H. Kim and J. Kautz, “Characterization for high dynamic range imaging,” Comput. Graph. Forum 27, 691–697 (2008).
[Crossref]

2007 (2)

R. Mantiuk, G. Krawczyk, R. Mantiuk, and H.-P. Seidel, “High-dynamic range imaging pipeline: perception-motivated representation of visual content,” Proc. SPIE 6492, 649212 (2007).
[Crossref]

J. Kuang, G. M. Johnson, and M. D. Fairchild, “iCAM06: a refined image appearance model for HDR image rendering,” J. Vis. Commun. Image Represent. 18, 406–414 (2007).
[Crossref]

2006 (1)

M. Inanici, “Evaluation of high dynamic range photography as a luminance data acquisition system,” Light. Res. Technol. 38, 123–136 (2006).
[Crossref]

1997 (1)

P. E. Debevec and J. Malik, “Recovering high dynamic range radiance maps from photographs,” ACM SIGGRAPH 97, 369–378 (1997).
[Crossref]

1988 (1)

G. W. Meyer, “Wavelength selection for synthetic image generation,” Comput. Vis. Graph. Image Process. 41, 57–79 (1988).
[Crossref]

Bodart, M.

C. Cauwerts, M. Bodart, and A. Deneyer, “Comparison of the vignetting effects of two identical fisheye lenses,” LEUKOS J. Illum. Eng. Soc. North Am. 8, 181–203 (2012).
[Crossref]

Cai, H.

H. Cai and T. Chung, “Improving the quality of high dynamic range images,” Light. Res. Technol. 43, 87–102 (2011).
[Crossref]

Cauwerts, C.

C. Cauwerts, M. Bodart, and A. Deneyer, “Comparison of the vignetting effects of two identical fisheye lenses,” LEUKOS J. Illum. Eng. Soc. North Am. 8, 181–203 (2012).
[Crossref]

C. Cauwerts, “Influence of presentation modes on visual perceptions of daylit spaces,” Ph.D. thesis (Université Catholique de Louvain, 2013).

Chung, T.

H. Cai and T. Chung, “Improving the quality of high dynamic range images,” Light. Res. Technol. 43, 87–102 (2011).
[Crossref]

Coutelier, B.

D. Dumortier, B. Coutelier, T. Faulcon, and F. Roy, “Photolux: a new luminance mapping system based on Nikon coolpix digital cameras,” in Lux Europa (2005), pp. 308–311.

Debevec, P. E.

P. E. Debevec and J. Malik, “Recovering high dynamic range radiance maps from photographs,” ACM SIGGRAPH 97, 369–378 (1997).
[Crossref]

Deneyer, A.

C. Cauwerts, M. Bodart, and A. Deneyer, “Comparison of the vignetting effects of two identical fisheye lenses,” LEUKOS J. Illum. Eng. Soc. North Am. 8, 181–203 (2012).
[Crossref]

Dumortier, D.

D. Dumortier, B. Coutelier, T. Faulcon, and F. Roy, “Photolux: a new luminance mapping system based on Nikon coolpix digital cameras,” in Lux Europa (2005), pp. 308–311.

Fairchild, M. D.

J. Kuang, G. M. Johnson, and M. D. Fairchild, “iCAM06: a refined image appearance model for HDR image rendering,” J. Vis. Commun. Image Represent. 18, 406–414 (2007).
[Crossref]

Faulcon, T.

D. Dumortier, B. Coutelier, T. Faulcon, and F. Roy, “Photolux: a new luminance mapping system based on Nikon coolpix digital cameras,” in Lux Europa (2005), pp. 308–311.

Finlayson, G. D.

G. D. Finlayson, S. D. Hordley, and P. Morovic, “Using the spectracube to build a multispectral image database,” in Conference on Colour in Graphics, Imaging, and Vision, Aachen, Germany (Society for Imaging Science and Technology, 2004), pp. 268–274.

Hordley, S. D.

G. D. Finlayson, S. D. Hordley, and P. Morovic, “Using the spectracube to build a multispectral image database,” in Conference on Colour in Graphics, Imaging, and Vision, Aachen, Germany (Society for Imaging Science and Technology, 2004), pp. 268–274.

Inanici, M.

B. Jung and M. Inanici, “Measuring circadian lighting through high dynamic range photography,” Light. Res. Technol. 51, 742–763 (2018).
[Crossref]

M. Inanici, “Evaluation of high dynamic range photography as a luminance data acquisition system,” Light. Res. Technol. 38, 123–136 (2006).
[Crossref]

Johnson, G. M.

J. Kuang, G. M. Johnson, and M. D. Fairchild, “iCAM06: a refined image appearance model for HDR image rendering,” J. Vis. Commun. Image Represent. 18, 406–414 (2007).
[Crossref]

Jung, B.

B. Jung and M. Inanici, “Measuring circadian lighting through high dynamic range photography,” Light. Res. Technol. 51, 742–763 (2018).
[Crossref]

Kautz, J.

M. H. Kim and J. Kautz, “Characterization for high dynamic range imaging,” Comput. Graph. Forum 27, 691–697 (2008).
[Crossref]

Kim, M. H.

M. H. Kim and J. Kautz, “Characterization for high dynamic range imaging,” Comput. Graph. Forum 27, 691–697 (2008).
[Crossref]

Krawczyk, G.

R. Mantiuk, G. Krawczyk, R. Mantiuk, and H.-P. Seidel, “High-dynamic range imaging pipeline: perception-motivated representation of visual content,” Proc. SPIE 6492, 649212 (2007).
[Crossref]

Kuang, J.

J. Kuang, G. M. Johnson, and M. D. Fairchild, “iCAM06: a refined image appearance model for HDR image rendering,” J. Vis. Commun. Image Represent. 18, 406–414 (2007).
[Crossref]

Malik, J.

P. E. Debevec and J. Malik, “Recovering high dynamic range radiance maps from photographs,” ACM SIGGRAPH 97, 369–378 (1997).
[Crossref]

Mantiuk, R.

R. Mantiuk, G. Krawczyk, R. Mantiuk, and H.-P. Seidel, “High-dynamic range imaging pipeline: perception-motivated representation of visual content,” Proc. SPIE 6492, 649212 (2007).
[Crossref]

R. Mantiuk, G. Krawczyk, R. Mantiuk, and H.-P. Seidel, “High-dynamic range imaging pipeline: perception-motivated representation of visual content,” Proc. SPIE 6492, 649212 (2007).
[Crossref]

D. Varghese, R. Wanat, and R. Mantiuk, “Colorimetric calibration of high dynamic range images with a ColorChecker chart,” in Proceedings of HDRi 2014, Second International Conference and SME Workshop on HDR Imaging, Sarajevo, Bosnia and Herzegovina (2014), 17–22.

McCann, J. J.

J. J. McCann, V. Vonikakis, and A. Rizzi, HDR Scene Capture and Appearance (SPIE, 2017).

Meyer, G. W.

G. W. Meyer, “Wavelength selection for synthetic image generation,” Comput. Vis. Graph. Image Process. 41, 57–79 (1988).
[Crossref]

Mokrzycki, W.

W. Mokrzycki and M. Tatol, “Color difference delta E—a survey,” Mach. Graph. Vis. 20, 383–411 (2011).

Morovic, P.

G. D. Finlayson, S. D. Hordley, and P. Morovic, “Using the spectracube to build a multispectral image database,” in Conference on Colour in Graphics, Imaging, and Vision, Aachen, Germany (Society for Imaging Science and Technology, 2004), pp. 268–274.

Rizzi, A.

J. J. McCann, V. Vonikakis, and A. Rizzi, HDR Scene Capture and Appearance (SPIE, 2017).

Roy, F.

D. Dumortier, B. Coutelier, T. Faulcon, and F. Roy, “Photolux: a new luminance mapping system based on Nikon coolpix digital cameras,” in Lux Europa (2005), pp. 308–311.

Seidel, H.-P.

R. Mantiuk, G. Krawczyk, R. Mantiuk, and H.-P. Seidel, “High-dynamic range imaging pipeline: perception-motivated representation of visual content,” Proc. SPIE 6492, 649212 (2007).
[Crossref]

Tatol, M.

W. Mokrzycki and M. Tatol, “Color difference delta E—a survey,” Mach. Graph. Vis. 20, 383–411 (2011).

Varghese, D.

D. Varghese, R. Wanat, and R. Mantiuk, “Colorimetric calibration of high dynamic range images with a ColorChecker chart,” in Proceedings of HDRi 2014, Second International Conference and SME Workshop on HDR Imaging, Sarajevo, Bosnia and Herzegovina (2014), 17–22.

Vonikakis, V.

J. J. McCann, V. Vonikakis, and A. Rizzi, HDR Scene Capture and Appearance (SPIE, 2017).

Wanat, R.

D. Varghese, R. Wanat, and R. Mantiuk, “Colorimetric calibration of high dynamic range images with a ColorChecker chart,” in Proceedings of HDRi 2014, Second International Conference and SME Workshop on HDR Imaging, Sarajevo, Bosnia and Herzegovina (2014), 17–22.

Ward, G.

G. Ward, hdrgen command-line (2006).

G. Ward, Photosphere (2001).

ACM SIGGRAPH (1)

P. E. Debevec and J. Malik, “Recovering high dynamic range radiance maps from photographs,” ACM SIGGRAPH 97, 369–378 (1997).
[Crossref]

Comput. Graph. Forum (1)

M. H. Kim and J. Kautz, “Characterization for high dynamic range imaging,” Comput. Graph. Forum 27, 691–697 (2008).
[Crossref]

Comput. Vis. Graph. Image Process. (1)

G. W. Meyer, “Wavelength selection for synthetic image generation,” Comput. Vis. Graph. Image Process. 41, 57–79 (1988).
[Crossref]

J. Vis. Commun. Image Represent. (1)

J. Kuang, G. M. Johnson, and M. D. Fairchild, “iCAM06: a refined image appearance model for HDR image rendering,” J. Vis. Commun. Image Represent. 18, 406–414 (2007).
[Crossref]

LEUKOS J. Illum. Eng. Soc. North Am. (1)

C. Cauwerts, M. Bodart, and A. Deneyer, “Comparison of the vignetting effects of two identical fisheye lenses,” LEUKOS J. Illum. Eng. Soc. North Am. 8, 181–203 (2012).
[Crossref]

Light. Res. Technol. (3)

M. Inanici, “Evaluation of high dynamic range photography as a luminance data acquisition system,” Light. Res. Technol. 38, 123–136 (2006).
[Crossref]

H. Cai and T. Chung, “Improving the quality of high dynamic range images,” Light. Res. Technol. 43, 87–102 (2011).
[Crossref]

B. Jung and M. Inanici, “Measuring circadian lighting through high dynamic range photography,” Light. Res. Technol. 51, 742–763 (2018).
[Crossref]

Mach. Graph. Vis. (1)

W. Mokrzycki and M. Tatol, “Color difference delta E—a survey,” Mach. Graph. Vis. 20, 383–411 (2011).

Proc. SPIE (1)

R. Mantiuk, G. Krawczyk, R. Mantiuk, and H.-P. Seidel, “High-dynamic range imaging pipeline: perception-motivated representation of visual content,” Proc. SPIE 6492, 649212 (2007).
[Crossref]

Other (15)

G. Ward, Raw2hdr perl script (2011), http://www.anyhere.com/gward/pickup/raw2hdr.tgz .

G. Ward, hdrgen command-line (2006).

G. Ward, Photosphere (2001).

D. Dumortier, B. Coutelier, T. Faulcon, and F. Roy, “Photolux: a new luminance mapping system based on Nikon coolpix digital cameras,” in Lux Europa (2005), pp. 308–311.

D. Varghese, R. Wanat, and R. Mantiuk, “Colorimetric calibration of high dynamic range images with a ColorChecker chart,” in Proceedings of HDRi 2014, Second International Conference and SME Workshop on HDR Imaging, Sarajevo, Bosnia and Herzegovina (2014), 17–22.

International Electrotechnical Commission, “Multimedia systems and equipment-colour measurement and management-part 2-1: colour management-default RGB colour space-sRGB,” (IEC, 1999), p. 2.

“HDRI mailing list, February 2012, hdrgen’s -x option,” 2019, https://discourse.radiance-online.org/t/hdrgens-x-option/3525/2 .

MathWorks, MATLAB Release 2017a (MathWorks Inc., 2017).

ISO/CIE, “Colorimetry—part 4: CIE 1976 L*a*b* colour space,” (CIE, 2008).

J. J. McCann, V. Vonikakis, and A. Rizzi, HDR Scene Capture and Appearance (SPIE, 2017).

CIE, “Colorimetry,” (CIE, 2018).

G. D. Finlayson, S. D. Hordley, and P. Morovic, “Using the spectracube to build a multispectral image database,” in Conference on Colour in Graphics, Imaging, and Vision, Aachen, Germany (Society for Imaging Science and Technology, 2004), pp. 268–274.

I3A, Fundamentals and Review of Considered Test Methods (2007).

C. Cauwerts, “Influence of presentation modes on visual perceptions of daylit spaces,” Ph.D. thesis (Université Catholique de Louvain, 2013).

New House Internet Services BV, PTGui, v9.1.7 PRO (PTGui, 1996).

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Figures (10)

Fig. 1.
Fig. 1. Experimental setup: acquisition with a spectroradiometer, shooting with a camera, and camera position.
Fig. 2.
Fig. 2. Data acquisition overview.
Fig. 3.
Fig. 3. Spectral radiances of training and test light sources.
Fig. 4.
Fig. 4. Coordinates of color samples lit by (a) the training source and (b) the test source in the CIE chromaticity diagram.
Fig. 5.
Fig. 5. Boxplot of relative difference of luminance (%), by device, for 57 samples (red line is the median; magenta circle is the mean).
Fig. 6.
Fig. 6. Relative difference of luminance (%) in the L*b* plane of the CIELAB color space, by device, after calibration with the minXYZ method.
Fig. 7.
Fig. 7. Boxplot of color differences ( $\Delta E_{ab}^{\rm *}$ ), by device, for 57 samples (red line is the median; magenta circle is the mean).
Fig. 8.
Fig. 8. Color differences ( $\Delta E_{ab}^{\rm *}$ ) in the a*b* plane of the CIELAB color space, by device, for 57 samples.
Fig. 9.
Fig. 9. Relative difference of luminance (%) on the Macbeth chart, by device and calibration method.
Fig. 10.
Fig. 10. Comparison of the images produced by each device.

Tables (9)

Tables Icon

Table 1. Specifications of the Three Tested Cameras and Picture Quality (Large/Fine Setting)

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Table 2. Camera Settings

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Table 3. Capture Settings

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Table 4. Calibration Factors

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Table 5. Mean (MAPE_lum) and Maximum Absolute Percentage Error of Luminance, by Device, for 57 Samples, with Test Source

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Table 6. Analysis of Variance Summary Table for Luminance Error

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Table 7. Color Differences ( $ \Delta E_{ab}^{*} $ ), by Device, for 57 Samples, with Test Source

Tables Icon

Table 8. Analysis of Variance Summary Table for Color Difference

Tables Icon

Table 9. Overview Table of Error Percentages in Luminance and Color Differences ( $\Delta {E_{00}}$ ) Observed in the Literature and Experimental Conditions

Equations (16)

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

L i j = C F 179 ( 0.2126 R i j E + 0.7152 G i j E + 0.0722 B i j E ) ,
R i j , c a m e r a = 179 R i j , H D R E , G i j , c a m e r a = 179 G i j , H D R E , B i j , c a m e r a = 179 B i j , H D R E ,
[ X c a m e r a Y c a m e r a Z c a m e r a ] = M s R G B D 65 [ R c a m e r a G c a m e r a B c a m e r a ] ,
M s R G B D 65 = [ .4124 .3576 .1805 .2126 .7152 .0722 .0193 .1192 .9505 ] .
[ X c a m e r a Y c a m e r a Z c a m e r a ] = M c a m e r a [ R c a m e r a G c a m e r a B c a m e r a ] ,
M c a m e r a = ( ( R G B c a m e r a t R G B c a m e r a ) 1 R G B c a m e r a t X Y Z s p e c t r o ) ,
[ X a d j Y a d j Z a d j ] = C F Y [ X c a m e r a Y c a m e r a Z c a m e r a ] ,
C F Y = Y g r e y t a r g e t , s p e c t r o Y g r e y t a r g e t , c a m e r a ,
{ X a d j = C F X X c a m e r a Y a d j = C F Y Y c a m e r a Z a d j = C F Z Z c a m e r a ,
C F X = X g r e y t a r g e t , s p e c t r o X g r e y t a r g e t , c a m e r a ( % ) , C F Y = Y g r e y t a r g e t , s p e c t r o Y g r e y t a r g e t , c a m e r a ( % ) , C F Z = Z g r e y t a r g e t , s p e c t r o Z g r e y t a r g e t , c a m e r a ( % ) ,
M A P E l u m = 100 n i = 1 n | Y i , c a m e r a Y i , s p e c t r o Y i , s p e c t r o | ( % ) ,
M 40 D f e 45 = [ .5939 .7048 .2598 .2624 1.4244 .0605 .0100 .1612 1.8226 ] ,
M 50 D f e 45 = [ .5832 .6349 .2671 .2493 1.2692 .0759 .0028 .1362 1.7204 ] ,
M 5 D f e 8 = [ .6408 .7341 .2378 .2737 1.4450 .0502 .0185 .1731 1.8815 ] .
L i j = 179 ( 0.2651 R i j E + 0.6701 G i j E + 0.0648 B i j E ) ,
M 50 D f e 45 b i s = [ .5025 .5673 .2454 .2168 1.1318 .0736 .0055 .1314 1.5919 ] .

Metrics