&lt;title&gt;Blue noise and model-based halftoning&lt;/title&gt;

Schulze, Mark A.; Pappas, Thrasyvoulos N.

doi:10.1117/12.172669

Cited by 13 publications

(14 citation statements)

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“…Thus, the values of the LSMB image quality metric agree with the visual evaluation. Using a blue-noise screen as a starting point also results in higher errors and grainy images [37]. In general, our experiments indicate that this scheme either preserves the original texture or converges to grainy patterns like those of Fig.…”

Section: Resultssupporting

confidence: 50%

“…We have found that the impulse responses of the 1-D eye filters we designed above are well approximated by a Gaussian shape with appropriate standard deviation. In fact, as was pointed out in [37], at 300 dpi and a viewing distance of 30 in, the impulse response of the 1-D eye filter and a Gaussian filter with are virtually identical. This is convenient because one can simply scale the standard deviation to obtain Gaussian filters for different viewing distances.…”

Section: Models Of Visual Perceptionsupporting

confidence: 52%

“…There could in fact be several local minima for a given image, depending on the starting point, as we show in the next section. More sophisticated (and computationally intensive) schemes use simulated annealing [24], [37] but have not yet shown any significant improvements in image quality.…”

Section: Then Choosingmentioning

confidence: 99%

“…The error criterion of the LSMB algorithm can be used to evaluate the performance of any halftoning technique (see the next section and [37]). It provides a measure of halftone image quality that takes into account the characteristics of both the display device (printer) and the human visual system.…”

Section: Then Choosingmentioning

confidence: 99%

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Least-squares model-based halftoning

Pappas¹,

Neuhoff

1999

IEEE Trans. on Image Process.

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A least-squares model-based (LSMB) approach to digital halftoning is proposed. It exploits both a printer model and a model for visual perception. It attempts to produce an optimal halftoned reproduction, by minimizing the squared error between the response of the cascade of the printer and visual models to the binary image and the response of the visual model to the original gray-scale image. It has been shown that the one-dimensional (1-D) least-squares problem, in which each row or column of the image is halftoned independently, can be implemented using the Viterbi algorithm to obtain the globally optimal solution. Unfortunately, the Viterbi algorithm cannot be used in two dimensions. In this paper, the two-dimensional (2-D) least-squares solution is obtained by iterative techniques, which are only guaranteed to produce a total optimum. Experiments show that LSMB halftoning produces better textures and higher spatial and gray-scale resolution than conventional techniques. We also show that the least-squares approach eliminates most of the problems associated with error diffusion. We investigate the performance of the LSMB algorithms over a range of viewing distances, or equivalently, printer resolutions. We also show that the LSMB approach gives us precise control of image sharpness.

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Section: Resultssupporting

confidence: 50%

Section: Models Of Visual Perceptionsupporting

confidence: 52%

Section: Then Choosingmentioning

confidence: 99%

Section: Then Choosingmentioning

confidence: 99%

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Least-squares model-based halftoning

Pappas¹,

Neuhoff

1999

IEEE Trans. on Image Process.

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“…On the other hand, the output values Li and the thresholds Ti used in the multi-level error diffusion process were calculated by Eqs. (l), (2). (1) dL(LmaxLmin)/(fl 1)…”

Section: Subjective Evaluationmentioning

confidence: 99%

<title>Evaluation of image quality for high-quality digital halftoning: image analysis and evaluation of multilevel error diffusion</title>

et al. 1997

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In this paper, we propose an evaluation method for digital halftone image produced by multi-level error diffusion technique. Tone reproduction, sharpness, graininess and color reproduction characteristics of the digital halftoning images with the resolution 200, 400, 800, 1600 DPI printed on transparency by digital film recorder were analyzed in both physically and subjectively. We also analyzed the relationship between the subjective evaluation and parameter calculated by linear combination of four physical parameters to evaluate of image quality.As a result of the experiment, it was found that the RMS granularity and parameter calculated by linear combination of each quality criterion were well correlated to the observer rating values.

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Model-based halftoning of color images

Pappas¹

1997

IEEE Trans. on Image Process.

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We present a new class of models for color printers. They form the basis for model-based techniques that exploit the characteristics of the printer and the human visual system to maximize the quality of the printed images. We present two model-based techniques, the modified error diffusion (MED) algorithm and the least-squares model-based (LSMB) algorithm. Both techniques are extensions of the gray-scale model-based techniques and produce images with high spatial resolution and visually pleasant textures. We also examine the use of printer models for designing blue-noise screens. The printer models cam account for a variety of printer characteristics. We propose a specific printer model that accounts for overlap between neighboring dots of ink and the spectral absorption properties of the inks. We show that when we assume a simple "one-minus-RGB" relationship between the red, green, and blue image specification and the corresponding cyan, magenta, and yellow inks, the algorithms are separable. Otherwise, the algorithms are not separable and the modified error diffusion may be unstable, The experimental results consider the separable algorithms that produce high-quality images for applications where the exact colorimetric reproduction of color is not necessary. They are computationally simple and robust to errors in color registration, but the colors are device dependent.

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<title>Blue noise and model-based halftoning</title>

Cited by 13 publications

References 0 publications

Least-squares model-based halftoning

Least-squares model-based halftoning

<title>Evaluation of image quality for high-quality digital halftoning: image analysis and evaluation of multilevel error diffusion</title>

Model-based halftoning of color images

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