Abstract

Digital halftoning algorithms can produce results of very different quality and characteristics. To evaluate and improve the algorithms, it is important to have robust image quality measures. We propose a method to evaluate objectively the quality of halftoned images. The method is capable of evaluating any kind of monochrome original and is not limited by the choice of halftoning method. To perform the evaluation with respect to the perception of printed halftones, we use models for dot gain in prints and the human visual system. The main contribution and novelty of the method is an adaptive filter used to separate the halftone characteristics from the information about the original in the halftoned image. This approach facilitates the evaluation of the halftoned image’s resemblance to the original as well as of the characteristics of the halftoning method.

© 1999 Optical Society of America

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References

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  1. R. A. Ulichney, Digital Halftoning (MIT Press, Cambridge, Mass., 1987).
  2. T. Mitsa, K. J. Parker, “Digital halftoning technique using a blue-noise mask,” J. Opt. Soc. Am. A 9, 1920–1929 (1992).
    [CrossRef]
  3. T. N. Pappas, D. L. Neuhoff, “Least-squares model-based halftoning,” in Human Vision, Visual Processing, and Digital Display III, B. E. Rogowitz, ed., Proc. SPIE1666, 165–176 (1992).
    [CrossRef]
  4. F. Nilsson, “Precomputed frequency modulated halftoning maps that meets the continuity criterion,” in Proceedings of the IS&T International Conference on Digital Printing Technologies (NIP12) (Society for Imaging Science and Technology, Springfield, Va., 1996), pp. 72–76.
  5. T. Mitsa, “Image quality metrics for halftone images,” in Imaging Technologies and Applications, R. J. Heaston, ed., Proc. SPIE1778, 196–207 (1992).
    [CrossRef]
  6. T. A. Grogan, “Image quality evaluation with a contour-based perceptual model,” in Human Vision, Visual Processing, and Digital Display III, B. E. Rogowitz, ed., Proc. SPIE1666, 188–197 (1992).
    [CrossRef]
  7. Q. Lin, “Halftone image quality analysis based on a human vision model,” in Human Vision, Visual Processing, and Digital Display IV, J. P. Allebach, B. E. Rogowitz, eds., Proc. SPIE1913, 378–389 (1993).
    [CrossRef]
  8. M. Analoui, J. P. Allebach, “Model based halftoning using direct binary search,” in Human Vision, Visual Processing, and Digital Display III, B. E. Rogowitz, ed., Proc. SPIE1666, 96–108 (1992).
    [CrossRef]
  9. S. Gooran, M. Österberg, B. Kruse, “Hybrid halftoning—a novel algorithm for using multiple halftoning technologies,” in Proceedings of the IS&T International Conference on Digital Printing Technologies (NIP12) (Society for Imaging Science and Technology, Springfield, Va., 1996), pp. 79–86.
  10. F. Nilsson, “Halftoning and objective quality measures for halftoned images,” Licentiate thesis, Linköping Studies in Science and Technology thesis 671 (Linköping University, Linköping, Sweden, 1998).
  11. S. Gustavson, “Dot gain in colour halftones,” Ph.D. dissertation, Linköping Studies in Science and Technology dissertation 492 (Linköping University, Linköping, Sweden, 1997).
  12. M. Wedin, “Modelling of dot gain in halftone colour prints,” Licentiate thesis, Linköping Studies in Science and Technology thesis 508 (Linköping University, Linköping, Sweden, 1995).
  13. J. A. C. Yule, W. J. Nielsen, “The penetration of light into paper and its effect on halftone reproduction,” in TAGA Proceedings (Technical Association of the Graphic Arts, Rochester, N.Y., 1951), Vol. 3, pp. 65–76.
  14. J. Sullivan, R. Miller, G. Pios, “Image halftoning using a visual model in error diffusion,” J. Opt. Soc. Am. A 10, 1714–1724 (1993).
    [CrossRef]
  15. J. L. Mannos, D. J. Sakrison, “The effects of a visual fidelity criterion on the encoding of images,” IEEE Trans. Inf. Theory IT-20, 252–253 (1974).
  16. M. A. Georgeson, G. D. Sullivan, “Contrast constancy: deblurring in human vision by spatial frequency channels,” J. Physiol. (London) 252, 627–656 (1975).
  17. R. W. Floyd, L. Steinberg, “An adaptive algorithm for spatial grey scale,” Proc. Soc. Inf. Disp. 17, 75–77 (1976).

1993 (1)

1992 (1)

1976 (1)

R. W. Floyd, L. Steinberg, “An adaptive algorithm for spatial grey scale,” Proc. Soc. Inf. Disp. 17, 75–77 (1976).

1975 (1)

M. A. Georgeson, G. D. Sullivan, “Contrast constancy: deblurring in human vision by spatial frequency channels,” J. Physiol. (London) 252, 627–656 (1975).

1974 (1)

J. L. Mannos, D. J. Sakrison, “The effects of a visual fidelity criterion on the encoding of images,” IEEE Trans. Inf. Theory IT-20, 252–253 (1974).

Allebach, J. P.

M. Analoui, J. P. Allebach, “Model based halftoning using direct binary search,” in Human Vision, Visual Processing, and Digital Display III, B. E. Rogowitz, ed., Proc. SPIE1666, 96–108 (1992).
[CrossRef]

Analoui, M.

M. Analoui, J. P. Allebach, “Model based halftoning using direct binary search,” in Human Vision, Visual Processing, and Digital Display III, B. E. Rogowitz, ed., Proc. SPIE1666, 96–108 (1992).
[CrossRef]

Floyd, R. W.

R. W. Floyd, L. Steinberg, “An adaptive algorithm for spatial grey scale,” Proc. Soc. Inf. Disp. 17, 75–77 (1976).

Georgeson, M. A.

M. A. Georgeson, G. D. Sullivan, “Contrast constancy: deblurring in human vision by spatial frequency channels,” J. Physiol. (London) 252, 627–656 (1975).

Gooran, S.

S. Gooran, M. Österberg, B. Kruse, “Hybrid halftoning—a novel algorithm for using multiple halftoning technologies,” in Proceedings of the IS&T International Conference on Digital Printing Technologies (NIP12) (Society for Imaging Science and Technology, Springfield, Va., 1996), pp. 79–86.

Grogan, T. A.

T. A. Grogan, “Image quality evaluation with a contour-based perceptual model,” in Human Vision, Visual Processing, and Digital Display III, B. E. Rogowitz, ed., Proc. SPIE1666, 188–197 (1992).
[CrossRef]

Gustavson, S.

S. Gustavson, “Dot gain in colour halftones,” Ph.D. dissertation, Linköping Studies in Science and Technology dissertation 492 (Linköping University, Linköping, Sweden, 1997).

Kruse, B.

S. Gooran, M. Österberg, B. Kruse, “Hybrid halftoning—a novel algorithm for using multiple halftoning technologies,” in Proceedings of the IS&T International Conference on Digital Printing Technologies (NIP12) (Society for Imaging Science and Technology, Springfield, Va., 1996), pp. 79–86.

Lin, Q.

Q. Lin, “Halftone image quality analysis based on a human vision model,” in Human Vision, Visual Processing, and Digital Display IV, J. P. Allebach, B. E. Rogowitz, eds., Proc. SPIE1913, 378–389 (1993).
[CrossRef]

Mannos, J. L.

J. L. Mannos, D. J. Sakrison, “The effects of a visual fidelity criterion on the encoding of images,” IEEE Trans. Inf. Theory IT-20, 252–253 (1974).

Miller, R.

Mitsa, T.

T. Mitsa, K. J. Parker, “Digital halftoning technique using a blue-noise mask,” J. Opt. Soc. Am. A 9, 1920–1929 (1992).
[CrossRef]

T. Mitsa, “Image quality metrics for halftone images,” in Imaging Technologies and Applications, R. J. Heaston, ed., Proc. SPIE1778, 196–207 (1992).
[CrossRef]

Neuhoff, D. L.

T. N. Pappas, D. L. Neuhoff, “Least-squares model-based halftoning,” in Human Vision, Visual Processing, and Digital Display III, B. E. Rogowitz, ed., Proc. SPIE1666, 165–176 (1992).
[CrossRef]

Nielsen, W. J.

J. A. C. Yule, W. J. Nielsen, “The penetration of light into paper and its effect on halftone reproduction,” in TAGA Proceedings (Technical Association of the Graphic Arts, Rochester, N.Y., 1951), Vol. 3, pp. 65–76.

Nilsson, F.

F. Nilsson, “Precomputed frequency modulated halftoning maps that meets the continuity criterion,” in Proceedings of the IS&T International Conference on Digital Printing Technologies (NIP12) (Society for Imaging Science and Technology, Springfield, Va., 1996), pp. 72–76.

F. Nilsson, “Halftoning and objective quality measures for halftoned images,” Licentiate thesis, Linköping Studies in Science and Technology thesis 671 (Linköping University, Linköping, Sweden, 1998).

Österberg, M.

S. Gooran, M. Österberg, B. Kruse, “Hybrid halftoning—a novel algorithm for using multiple halftoning technologies,” in Proceedings of the IS&T International Conference on Digital Printing Technologies (NIP12) (Society for Imaging Science and Technology, Springfield, Va., 1996), pp. 79–86.

Pappas, T. N.

T. N. Pappas, D. L. Neuhoff, “Least-squares model-based halftoning,” in Human Vision, Visual Processing, and Digital Display III, B. E. Rogowitz, ed., Proc. SPIE1666, 165–176 (1992).
[CrossRef]

Parker, K. J.

Pios, G.

Sakrison, D. J.

J. L. Mannos, D. J. Sakrison, “The effects of a visual fidelity criterion on the encoding of images,” IEEE Trans. Inf. Theory IT-20, 252–253 (1974).

Steinberg, L.

R. W. Floyd, L. Steinberg, “An adaptive algorithm for spatial grey scale,” Proc. Soc. Inf. Disp. 17, 75–77 (1976).

Sullivan, G. D.

M. A. Georgeson, G. D. Sullivan, “Contrast constancy: deblurring in human vision by spatial frequency channels,” J. Physiol. (London) 252, 627–656 (1975).

Sullivan, J.

Ulichney, R. A.

R. A. Ulichney, Digital Halftoning (MIT Press, Cambridge, Mass., 1987).

Wedin, M.

M. Wedin, “Modelling of dot gain in halftone colour prints,” Licentiate thesis, Linköping Studies in Science and Technology thesis 508 (Linköping University, Linköping, Sweden, 1995).

Yule, J. A. C.

J. A. C. Yule, W. J. Nielsen, “The penetration of light into paper and its effect on halftone reproduction,” in TAGA Proceedings (Technical Association of the Graphic Arts, Rochester, N.Y., 1951), Vol. 3, pp. 65–76.

IEEE Trans. Inf. Theory (1)

J. L. Mannos, D. J. Sakrison, “The effects of a visual fidelity criterion on the encoding of images,” IEEE Trans. Inf. Theory IT-20, 252–253 (1974).

J. Opt. Soc. Am. A (2)

J. Physiol. (London) (1)

M. A. Georgeson, G. D. Sullivan, “Contrast constancy: deblurring in human vision by spatial frequency channels,” J. Physiol. (London) 252, 627–656 (1975).

Proc. Soc. Inf. Disp. (1)

R. W. Floyd, L. Steinberg, “An adaptive algorithm for spatial grey scale,” Proc. Soc. Inf. Disp. 17, 75–77 (1976).

Other (12)

T. N. Pappas, D. L. Neuhoff, “Least-squares model-based halftoning,” in Human Vision, Visual Processing, and Digital Display III, B. E. Rogowitz, ed., Proc. SPIE1666, 165–176 (1992).
[CrossRef]

F. Nilsson, “Precomputed frequency modulated halftoning maps that meets the continuity criterion,” in Proceedings of the IS&T International Conference on Digital Printing Technologies (NIP12) (Society for Imaging Science and Technology, Springfield, Va., 1996), pp. 72–76.

T. Mitsa, “Image quality metrics for halftone images,” in Imaging Technologies and Applications, R. J. Heaston, ed., Proc. SPIE1778, 196–207 (1992).
[CrossRef]

T. A. Grogan, “Image quality evaluation with a contour-based perceptual model,” in Human Vision, Visual Processing, and Digital Display III, B. E. Rogowitz, ed., Proc. SPIE1666, 188–197 (1992).
[CrossRef]

Q. Lin, “Halftone image quality analysis based on a human vision model,” in Human Vision, Visual Processing, and Digital Display IV, J. P. Allebach, B. E. Rogowitz, eds., Proc. SPIE1913, 378–389 (1993).
[CrossRef]

M. Analoui, J. P. Allebach, “Model based halftoning using direct binary search,” in Human Vision, Visual Processing, and Digital Display III, B. E. Rogowitz, ed., Proc. SPIE1666, 96–108 (1992).
[CrossRef]

S. Gooran, M. Österberg, B. Kruse, “Hybrid halftoning—a novel algorithm for using multiple halftoning technologies,” in Proceedings of the IS&T International Conference on Digital Printing Technologies (NIP12) (Society for Imaging Science and Technology, Springfield, Va., 1996), pp. 79–86.

F. Nilsson, “Halftoning and objective quality measures for halftoned images,” Licentiate thesis, Linköping Studies in Science and Technology thesis 671 (Linköping University, Linköping, Sweden, 1998).

S. Gustavson, “Dot gain in colour halftones,” Ph.D. dissertation, Linköping Studies in Science and Technology dissertation 492 (Linköping University, Linköping, Sweden, 1997).

M. Wedin, “Modelling of dot gain in halftone colour prints,” Licentiate thesis, Linköping Studies in Science and Technology thesis 508 (Linköping University, Linköping, Sweden, 1995).

J. A. C. Yule, W. J. Nielsen, “The penetration of light into paper and its effect on halftone reproduction,” in TAGA Proceedings (Technical Association of the Graphic Arts, Rochester, N.Y., 1951), Vol. 3, pp. 65–76.

R. A. Ulichney, Digital Halftoning (MIT Press, Cambridge, Mass., 1987).

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Figures (18)

Fig. 1
Fig. 1

Three different halftones representing the same gray level. The amounts of microdots are the same, but because of different structure properties, their appearances are totally different.

Fig. 2
Fig. 2

Principle and work flow of the evaluation model. The halftone is subjected to a model that simulates print. The result from this, as well as the original, is then subjected to a model for the observer. The evaluation of the image quality is done on the final two results.

Fig. 3
Fig. 3

Microscopic image of a 15% tone printed with a laser printer on copy paper at 60-lines per inch. The print is not compensated for the effects of dot gain before printing.

Fig. 4
Fig. 4

Simulation of different amounts of mechanical dot gain. A wider kernel, still with a unity volume, is used to simulate an increase of the mechanical dot gain.

Fig. 5
Fig. 5

Simulation of optical dot gain on papers with (left to right) increasing scattering properties. Note the difference from the mechanical dot gain in Fig. 4.

Fig. 6
Fig. 6

Spatial representation of the MTF used to characterize the human visual system at a viewing distance of 300 mm.

Fig. 7
Fig. 7

Gray-scale original next to a binary halftone generated with an error diffusion method.

Fig. 8
Fig. 8

Results after the two images from Fig. 7 are subjected to the evaluation model with a viewing distance of 300 mm and an assumed print resolution of 300 dots per inch.

Fig. 9
Fig. 9

Spectrum of the perceived original (left) and spectrum of the perceived and printed error diffusion halftone (right). For low frequencies the two spectra have similar characteristics, whereas the halftone spectrum has much more energy for high frequencies.

Fig. 10
Fig. 10

Clustered-dot halftone (here the perceived and printed halftone) (left) and its spectrum (right). Note that the spectrum has a very different characteristic compared with the spectrum of the error diffusion method shown in Fig. 9.

Fig. 11
Fig. 11

Each frequency component of the Fourier transform can be represented with a vector in the complex plane. The phase and the amplitude of the component are described by the direction and the magnitude of the vector.

Fig. 12
Fig. 12

The frequency components of the reconstructed original Rˆ are derived from the projection of the original component Oˆ onto the halftone component Hˆ. In (a) a part of Hˆ is used as a descriptor of Oˆ. In (b) the projected vector is greater than Hˆ. Hˆ itself is therefore used as the reconstruction vector. In (c) the phase difference between Oˆ and Hˆ is greater than 90°, and thus no part of Hˆ is a good descriptor of Oˆ. Rˆ is therefore set to be the null vector.

Fig. 13
Fig. 13

Reconstruction from the error diffusion halftone in Fig. 8 next to the extracted halftone carrier. Their respective dc components have been adjusted for the purpose of illustration.

Fig. 14
Fig. 14

Reconstruction from the clustered-dot halftone in Fig. 10 next to the extracted halftone carrier. Their respective dc components have been adjusted for the purpose of illustration.

Fig. 15
Fig. 15

Difference functions for the error diffusion and clustered-dot methods used in the figures together with the error function for a second clustered-dot method [method (2)] with a smaller cell size than the first.

Fig. 16
Fig. 16

Proposed weight functions for evaluation of the halftones’ capability of reproducing smooth transitions and details. The smoothness is evaluated with the low-frequency deviation function, and the detail rendering is evaluated with the loss-of-details and loss-of-fine-details functions.

Fig. 17
Fig. 17

Carriers for the error diffusion and clustered-dot methods used in the figures together with the carrier for a clustered-dot method with a smaller cell size (method 2).

Fig. 18
Fig. 18

Proposed weight functions for evaluation of the disturbance caused by the carrier: the low-frequency disturbance function, the medium–high-frequency disturbance function, and the very-high-frequency disturbance function.

Tables (2)

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Table 1 Quality Measures Derived from the Error Functions

Tables Icon

Table 2 Quality Measures Derived from the Carrier Functions

Equations (8)

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(B*H)(x, y)=mnB(x-m, y-n)H(m, n).
P(x, y)=R0 a2πr exp(-ar)
T(x, y)=10-Dmax[(B * H)(x, y)],
R(x, y)={[I(x, y)T(x, y)]*P(x, y)}T(x, y),
Hrms(r)=u,vS(r)|F(u, v)|2u,vS(r)11/2,
r=δ(2n-1),n[1, 2,, N],
S(r)=[(u, v)|(r-δ<u2+v2r+δ)].
QMa=rH(r)Wa(r),

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