Abstract

This work uses the S-CIELAB color model to compare images that have been calibrated and processed using a colorimetric dithering method which simulates increments in viewing distance. Firstly, we obtain XYZ calibrated images by applying the appropriate color transformations to the original images. These transformations depend on whether the image is viewed on a display device or encoded by a capture device, for example. Secondly, we use a colorimetric dithering method consisting of a partitive additive mixing of XYZ tristimulus values. The number of dithered pixels depends on simulated viewing distance. The dithered tristimulus values are transformed to digital data to observe the dithering effects in the image. Finally, we predict color differences using the S-CIELAB model as color appearance model for images. Moreover, this paper proposes some applications of this method to artistic and industrial problems where one must compare two images that appear different at close viewing distance, but match when they are seen from afar.

© 2007 Optical Society of America

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References

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  1. C. Hains, S. G. Wang, and K. Knox, “Digital color halftones,” in Digital Color Imaging handbook, G. Sharma, eds., (CRC PRESS, New York, 2003) pp. 385–490.
  2. V. Ostromoukhov, P. Emmel, N. Rudaz, I. Amidror, and R. D. Hersch, “Multi-level colour halftoning algorithms,” Proc. SPIE 2949, 332–340 (1997).
    [Crossref]
  3. H. R. Kang, Digital Color Halftoning (SPIE, 1999).
  4. L. Brun and A. Trémeau, “Color quantization,” in Digital Color Imaging handbookG. Sharma, ed., (CRC PRESS, New York, 2003), pp. 589–638.
  5. K. Man Kim, C. Soo Lee, E. Joo Lee, and Y. Ho Ha, “Color image quantization and dithering method based on human visual system characteristics,” J. Imaging Sci. Technol 40, 502–509 (1996).
  6. M. Petrou and P. García-Sevilla, “Non-stationary grey texture images,” in Image Processing. Dealing with TextureM. Petrou and P. García-Sevilla, (Wiley, England, 2006), pp. 297–606.
    [Crossref]
  7. J. Chen, T. N. Pappas, A. Mojsilovic, and B. E. Rogowitz, “Adaptive perceptual color-texture image segmentation,” IEEE Trans. Image Processing 14, 1524–1536 (2005).
    [Crossref]
  8. The Mathworks. “Image Processing Toolbox” http://www.mathworks.com/access/helpdesk/help/toolbox/images/index.html?/access/helpdesk/help/toolbo x/images/f8-18177.html .
  9. International Color Consortium, “A standard default color space for the internet: sRGB”, http://www.color.org/sRGB.html.
  10. H. R. Kang, “Regression,” in Color technology for electronic imaging devices, H.R. Kang, ed. (SPIE-Press, Washingon, 1997), pp. 55–63.
  11. G. Hong, M. R. Luo, and P. A. Rhodes, “A study of digital camera colorimetric characterization based on polynomial modeling,” Color. Res. Appl. 26, 76–84 (2000).
    [Crossref]
  12. F. Martínez-Verdú, J. Pujol, and P. Capilla, “Characterization of a digital camera as an absolute tristimulus colorimeter,” J. Imaging Sci. Technol. 47, 279–295 (2003).
  13. A. Poirson and B. Wandell, “S-CIELAB: A spatial extension to the CIEL*a*b* DeltaE Color Difference Metric”, http://white.stanford.edu/~brian/scielab/scielab.html.
  14. G. M. Johnson and M. D. Fairchild, “Measuring images: Differences, Quality, and Appearance,” Proc SPIE/IS&T Electronic Imaging Conference, Santa Clara,  5007, 51–60 (2003).
  15. M. D. Fairchild and G. M. Johnson, “The i-CAM framework for image appearance, image differences and image quality,” J. Electron. Imaging 13, 126–138 (2004).
    [Crossref]
  16. G. M. Johnson, “The quality of appearance,” in Proceedings of 10th Congress of the International Colour Association, J.L. Nieves and J. Hernández-Andrés, ed. (Granada, Spain, 2005), pp. 303–308, http://www.cis.rit.edu/people/faculty/johnson/pubs.html.
    [PubMed]
  17. F. Martínez-Verdú, R. Balboa, E. Chorro, J. C. Alcaraz, D. de Fez, and V. Viqueira, “Color measurement of natural stones using a calibrated digital camera,” in Proceedings of 10th Congress of the International Colour AssociationJ. L. Nieves and J. Hernández-Andrés, ed., (Granada, Spain, 2005) pp. 1267–1270.

2005 (1)

J. Chen, T. N. Pappas, A. Mojsilovic, and B. E. Rogowitz, “Adaptive perceptual color-texture image segmentation,” IEEE Trans. Image Processing 14, 1524–1536 (2005).
[Crossref]

2004 (1)

M. D. Fairchild and G. M. Johnson, “The i-CAM framework for image appearance, image differences and image quality,” J. Electron. Imaging 13, 126–138 (2004).
[Crossref]

2003 (2)

F. Martínez-Verdú, J. Pujol, and P. Capilla, “Characterization of a digital camera as an absolute tristimulus colorimeter,” J. Imaging Sci. Technol. 47, 279–295 (2003).

G. M. Johnson and M. D. Fairchild, “Measuring images: Differences, Quality, and Appearance,” Proc SPIE/IS&T Electronic Imaging Conference, Santa Clara,  5007, 51–60 (2003).

2000 (1)

G. Hong, M. R. Luo, and P. A. Rhodes, “A study of digital camera colorimetric characterization based on polynomial modeling,” Color. Res. Appl. 26, 76–84 (2000).
[Crossref]

1997 (2)

H. R. Kang, “Regression,” in Color technology for electronic imaging devices, H.R. Kang, ed. (SPIE-Press, Washingon, 1997), pp. 55–63.

V. Ostromoukhov, P. Emmel, N. Rudaz, I. Amidror, and R. D. Hersch, “Multi-level colour halftoning algorithms,” Proc. SPIE 2949, 332–340 (1997).
[Crossref]

1996 (1)

K. Man Kim, C. Soo Lee, E. Joo Lee, and Y. Ho Ha, “Color image quantization and dithering method based on human visual system characteristics,” J. Imaging Sci. Technol 40, 502–509 (1996).

Alcaraz, J. C.

F. Martínez-Verdú, R. Balboa, E. Chorro, J. C. Alcaraz, D. de Fez, and V. Viqueira, “Color measurement of natural stones using a calibrated digital camera,” in Proceedings of 10th Congress of the International Colour AssociationJ. L. Nieves and J. Hernández-Andrés, ed., (Granada, Spain, 2005) pp. 1267–1270.

Amidror, I.

V. Ostromoukhov, P. Emmel, N. Rudaz, I. Amidror, and R. D. Hersch, “Multi-level colour halftoning algorithms,” Proc. SPIE 2949, 332–340 (1997).
[Crossref]

Balboa, R.

F. Martínez-Verdú, R. Balboa, E. Chorro, J. C. Alcaraz, D. de Fez, and V. Viqueira, “Color measurement of natural stones using a calibrated digital camera,” in Proceedings of 10th Congress of the International Colour AssociationJ. L. Nieves and J. Hernández-Andrés, ed., (Granada, Spain, 2005) pp. 1267–1270.

Brun, L.

L. Brun and A. Trémeau, “Color quantization,” in Digital Color Imaging handbookG. Sharma, ed., (CRC PRESS, New York, 2003), pp. 589–638.

Capilla, P.

F. Martínez-Verdú, J. Pujol, and P. Capilla, “Characterization of a digital camera as an absolute tristimulus colorimeter,” J. Imaging Sci. Technol. 47, 279–295 (2003).

Chen, J.

J. Chen, T. N. Pappas, A. Mojsilovic, and B. E. Rogowitz, “Adaptive perceptual color-texture image segmentation,” IEEE Trans. Image Processing 14, 1524–1536 (2005).
[Crossref]

Chorro, E.

F. Martínez-Verdú, R. Balboa, E. Chorro, J. C. Alcaraz, D. de Fez, and V. Viqueira, “Color measurement of natural stones using a calibrated digital camera,” in Proceedings of 10th Congress of the International Colour AssociationJ. L. Nieves and J. Hernández-Andrés, ed., (Granada, Spain, 2005) pp. 1267–1270.

de Fez, D.

F. Martínez-Verdú, R. Balboa, E. Chorro, J. C. Alcaraz, D. de Fez, and V. Viqueira, “Color measurement of natural stones using a calibrated digital camera,” in Proceedings of 10th Congress of the International Colour AssociationJ. L. Nieves and J. Hernández-Andrés, ed., (Granada, Spain, 2005) pp. 1267–1270.

Emmel, P.

V. Ostromoukhov, P. Emmel, N. Rudaz, I. Amidror, and R. D. Hersch, “Multi-level colour halftoning algorithms,” Proc. SPIE 2949, 332–340 (1997).
[Crossref]

Fairchild, M. D.

M. D. Fairchild and G. M. Johnson, “The i-CAM framework for image appearance, image differences and image quality,” J. Electron. Imaging 13, 126–138 (2004).
[Crossref]

G. M. Johnson and M. D. Fairchild, “Measuring images: Differences, Quality, and Appearance,” Proc SPIE/IS&T Electronic Imaging Conference, Santa Clara,  5007, 51–60 (2003).

García-Sevilla, P.

M. Petrou and P. García-Sevilla, “Non-stationary grey texture images,” in Image Processing. Dealing with TextureM. Petrou and P. García-Sevilla, (Wiley, England, 2006), pp. 297–606.
[Crossref]

Hains, C.

C. Hains, S. G. Wang, and K. Knox, “Digital color halftones,” in Digital Color Imaging handbook, G. Sharma, eds., (CRC PRESS, New York, 2003) pp. 385–490.

Hersch, R. D.

V. Ostromoukhov, P. Emmel, N. Rudaz, I. Amidror, and R. D. Hersch, “Multi-level colour halftoning algorithms,” Proc. SPIE 2949, 332–340 (1997).
[Crossref]

Ho Ha, Y.

K. Man Kim, C. Soo Lee, E. Joo Lee, and Y. Ho Ha, “Color image quantization and dithering method based on human visual system characteristics,” J. Imaging Sci. Technol 40, 502–509 (1996).

Hong, G.

G. Hong, M. R. Luo, and P. A. Rhodes, “A study of digital camera colorimetric characterization based on polynomial modeling,” Color. Res. Appl. 26, 76–84 (2000).
[Crossref]

Johnson, G. M.

M. D. Fairchild and G. M. Johnson, “The i-CAM framework for image appearance, image differences and image quality,” J. Electron. Imaging 13, 126–138 (2004).
[Crossref]

G. M. Johnson and M. D. Fairchild, “Measuring images: Differences, Quality, and Appearance,” Proc SPIE/IS&T Electronic Imaging Conference, Santa Clara,  5007, 51–60 (2003).

G. M. Johnson, “The quality of appearance,” in Proceedings of 10th Congress of the International Colour Association, J.L. Nieves and J. Hernández-Andrés, ed. (Granada, Spain, 2005), pp. 303–308, http://www.cis.rit.edu/people/faculty/johnson/pubs.html.
[PubMed]

Joo Lee, E.

K. Man Kim, C. Soo Lee, E. Joo Lee, and Y. Ho Ha, “Color image quantization and dithering method based on human visual system characteristics,” J. Imaging Sci. Technol 40, 502–509 (1996).

Kang, H. R.

H. R. Kang, “Regression,” in Color technology for electronic imaging devices, H.R. Kang, ed. (SPIE-Press, Washingon, 1997), pp. 55–63.

H. R. Kang, Digital Color Halftoning (SPIE, 1999).

Knox, K.

C. Hains, S. G. Wang, and K. Knox, “Digital color halftones,” in Digital Color Imaging handbook, G. Sharma, eds., (CRC PRESS, New York, 2003) pp. 385–490.

Luo, M. R.

G. Hong, M. R. Luo, and P. A. Rhodes, “A study of digital camera colorimetric characterization based on polynomial modeling,” Color. Res. Appl. 26, 76–84 (2000).
[Crossref]

Man Kim, K.

K. Man Kim, C. Soo Lee, E. Joo Lee, and Y. Ho Ha, “Color image quantization and dithering method based on human visual system characteristics,” J. Imaging Sci. Technol 40, 502–509 (1996).

Martínez-Verdú, F.

F. Martínez-Verdú, J. Pujol, and P. Capilla, “Characterization of a digital camera as an absolute tristimulus colorimeter,” J. Imaging Sci. Technol. 47, 279–295 (2003).

F. Martínez-Verdú, R. Balboa, E. Chorro, J. C. Alcaraz, D. de Fez, and V. Viqueira, “Color measurement of natural stones using a calibrated digital camera,” in Proceedings of 10th Congress of the International Colour AssociationJ. L. Nieves and J. Hernández-Andrés, ed., (Granada, Spain, 2005) pp. 1267–1270.

Mojsilovic, A.

J. Chen, T. N. Pappas, A. Mojsilovic, and B. E. Rogowitz, “Adaptive perceptual color-texture image segmentation,” IEEE Trans. Image Processing 14, 1524–1536 (2005).
[Crossref]

Ostromoukhov, V.

V. Ostromoukhov, P. Emmel, N. Rudaz, I. Amidror, and R. D. Hersch, “Multi-level colour halftoning algorithms,” Proc. SPIE 2949, 332–340 (1997).
[Crossref]

Pappas, T. N.

J. Chen, T. N. Pappas, A. Mojsilovic, and B. E. Rogowitz, “Adaptive perceptual color-texture image segmentation,” IEEE Trans. Image Processing 14, 1524–1536 (2005).
[Crossref]

Petrou, M.

M. Petrou and P. García-Sevilla, “Non-stationary grey texture images,” in Image Processing. Dealing with TextureM. Petrou and P. García-Sevilla, (Wiley, England, 2006), pp. 297–606.
[Crossref]

Poirson, A.

A. Poirson and B. Wandell, “S-CIELAB: A spatial extension to the CIEL*a*b* DeltaE Color Difference Metric”, http://white.stanford.edu/~brian/scielab/scielab.html.

Pujol, J.

F. Martínez-Verdú, J. Pujol, and P. Capilla, “Characterization of a digital camera as an absolute tristimulus colorimeter,” J. Imaging Sci. Technol. 47, 279–295 (2003).

Rhodes, P. A.

G. Hong, M. R. Luo, and P. A. Rhodes, “A study of digital camera colorimetric characterization based on polynomial modeling,” Color. Res. Appl. 26, 76–84 (2000).
[Crossref]

Rogowitz, B. E.

J. Chen, T. N. Pappas, A. Mojsilovic, and B. E. Rogowitz, “Adaptive perceptual color-texture image segmentation,” IEEE Trans. Image Processing 14, 1524–1536 (2005).
[Crossref]

Rudaz, N.

V. Ostromoukhov, P. Emmel, N. Rudaz, I. Amidror, and R. D. Hersch, “Multi-level colour halftoning algorithms,” Proc. SPIE 2949, 332–340 (1997).
[Crossref]

Soo Lee, C.

K. Man Kim, C. Soo Lee, E. Joo Lee, and Y. Ho Ha, “Color image quantization and dithering method based on human visual system characteristics,” J. Imaging Sci. Technol 40, 502–509 (1996).

Trémeau, A.

L. Brun and A. Trémeau, “Color quantization,” in Digital Color Imaging handbookG. Sharma, ed., (CRC PRESS, New York, 2003), pp. 589–638.

Viqueira, V.

F. Martínez-Verdú, R. Balboa, E. Chorro, J. C. Alcaraz, D. de Fez, and V. Viqueira, “Color measurement of natural stones using a calibrated digital camera,” in Proceedings of 10th Congress of the International Colour AssociationJ. L. Nieves and J. Hernández-Andrés, ed., (Granada, Spain, 2005) pp. 1267–1270.

Wandell, B.

A. Poirson and B. Wandell, “S-CIELAB: A spatial extension to the CIEL*a*b* DeltaE Color Difference Metric”, http://white.stanford.edu/~brian/scielab/scielab.html.

Wang, S. G.

C. Hains, S. G. Wang, and K. Knox, “Digital color halftones,” in Digital Color Imaging handbook, G. Sharma, eds., (CRC PRESS, New York, 2003) pp. 385–490.

Color. Res. Appl. (1)

G. Hong, M. R. Luo, and P. A. Rhodes, “A study of digital camera colorimetric characterization based on polynomial modeling,” Color. Res. Appl. 26, 76–84 (2000).
[Crossref]

IEEE Trans. Image Processing (1)

J. Chen, T. N. Pappas, A. Mojsilovic, and B. E. Rogowitz, “Adaptive perceptual color-texture image segmentation,” IEEE Trans. Image Processing 14, 1524–1536 (2005).
[Crossref]

J. Electron. Imaging (1)

M. D. Fairchild and G. M. Johnson, “The i-CAM framework for image appearance, image differences and image quality,” J. Electron. Imaging 13, 126–138 (2004).
[Crossref]

J. Imaging Sci. Technol (1)

K. Man Kim, C. Soo Lee, E. Joo Lee, and Y. Ho Ha, “Color image quantization and dithering method based on human visual system characteristics,” J. Imaging Sci. Technol 40, 502–509 (1996).

J. Imaging Sci. Technol. (1)

F. Martínez-Verdú, J. Pujol, and P. Capilla, “Characterization of a digital camera as an absolute tristimulus colorimeter,” J. Imaging Sci. Technol. 47, 279–295 (2003).

Proc SPIE/IS&T Electronic Imaging Conference, Santa Clara (1)

G. M. Johnson and M. D. Fairchild, “Measuring images: Differences, Quality, and Appearance,” Proc SPIE/IS&T Electronic Imaging Conference, Santa Clara,  5007, 51–60 (2003).

Proc. SPIE (1)

V. Ostromoukhov, P. Emmel, N. Rudaz, I. Amidror, and R. D. Hersch, “Multi-level colour halftoning algorithms,” Proc. SPIE 2949, 332–340 (1997).
[Crossref]

Other (10)

H. R. Kang, Digital Color Halftoning (SPIE, 1999).

L. Brun and A. Trémeau, “Color quantization,” in Digital Color Imaging handbookG. Sharma, ed., (CRC PRESS, New York, 2003), pp. 589–638.

M. Petrou and P. García-Sevilla, “Non-stationary grey texture images,” in Image Processing. Dealing with TextureM. Petrou and P. García-Sevilla, (Wiley, England, 2006), pp. 297–606.
[Crossref]

The Mathworks. “Image Processing Toolbox” http://www.mathworks.com/access/helpdesk/help/toolbox/images/index.html?/access/helpdesk/help/toolbo x/images/f8-18177.html .

International Color Consortium, “A standard default color space for the internet: sRGB”, http://www.color.org/sRGB.html.

H. R. Kang, “Regression,” in Color technology for electronic imaging devices, H.R. Kang, ed. (SPIE-Press, Washingon, 1997), pp. 55–63.

C. Hains, S. G. Wang, and K. Knox, “Digital color halftones,” in Digital Color Imaging handbook, G. Sharma, eds., (CRC PRESS, New York, 2003) pp. 385–490.

A. Poirson and B. Wandell, “S-CIELAB: A spatial extension to the CIEL*a*b* DeltaE Color Difference Metric”, http://white.stanford.edu/~brian/scielab/scielab.html.

G. M. Johnson, “The quality of appearance,” in Proceedings of 10th Congress of the International Colour Association, J.L. Nieves and J. Hernández-Andrés, ed. (Granada, Spain, 2005), pp. 303–308, http://www.cis.rit.edu/people/faculty/johnson/pubs.html.
[PubMed]

F. Martínez-Verdú, R. Balboa, E. Chorro, J. C. Alcaraz, D. de Fez, and V. Viqueira, “Color measurement of natural stones using a calibrated digital camera,” in Proceedings of 10th Congress of the International Colour AssociationJ. L. Nieves and J. Hernández-Andrés, ed., (Granada, Spain, 2005) pp. 1267–1270.

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Figures (8)

Fig. 1.
Fig. 1.

Spatial dithering.

Fig. 2.
Fig. 2.

Natural image and simulated images with colorimetric dithering factor 2, 5 and 10.

Fig. 3.
Fig. 3.

Histogram of color difference are obtained when it compares both natural image and simulated images with factor 2, 5 and 10 (from left to right).

Fig. 4.
Fig. 4.

Comparison between the two original samples, viewed at near distance.

Fig. 5.
Fig. 5.

Comparison between the two different natural stone samples when the colorimetric dithering factor is 2.

Fig. 6.
Fig. 6.

Comparison between the two different natural stone samples when the colorimetric dithering factor is 5.

Fig. 7.
Fig. 7.

Comparison between the two different natural stone samples when the colorimetric dithering factor is 10.

Fig. 8.
Fig. 8.

Comparison between the two different natural stone samples when the colorimetric dithering factor is 20.

Tables (2)

Tables Icon

Table 1. Mean, median and colour difference with maximum frequency of the histograms belonging to the same image viewed under simulated different distances.

Tables Icon

Table 2. Mean, median and colour difference with maximum frequency of the histograms belonging to the same image pair viewed under simulated different distances.

Equations (4)

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

u = 2 arctan ( p 2 x )
X = 1 k 2 i , j = 1 k X i , j
Y = 1 k 2 i , j = 1 k Y i , j
Z = 1 k 2 i , j = 1 k z i , j

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