A survey of techniques for the display of continuous tone pictures on bilevel displays

Jarvis, J. F.; Judice, Charles N.; Ninke, William H.

doi:10.1016/s0146-664x(76)80003-2

Cited by 537 publications

(231 citation statements)

References 9 publications

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“…Halftoning techniques include error diffusion methods such as those by FloydSteinburg and Jarvis at el. [86,47,36,48]. At times these halftoned images may need resizing, enhancement, or removal of aliasing artifacts.…”

Section: Inverse-halftoningmentioning

confidence: 99%

Image Processing Using Shearlets

Easley

Labate

2012

Shearlets

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Since shearlets provide nearly optimally sparse representations for a large class of functions that are useful to model natural images, many image processing methods benefit from their use. In particular, the error rates of data estimation from noise are highly dependent on the sparsity properties of the representation, so that many successful applications of shearlets center around restoration tasks such as denoising and inverse problems. Other imaging problems, where also the application of the shearlet representation turns out to be very beneficial, include image enhancement, image separation, edge detection, and estimation of the geometric features of an object.

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Section: Inverse-halftoningmentioning

confidence: 99%

Image Processing Using Shearlets

Easley

Labate

2012

Shearlets

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“…The final step is the halftoning operation which is performed channel independently. We use scalar error diffusion (SED) halftoning technique [11] with Jarvis [12] filter to diffuse the error in the halftoning algorithm.…”

Section: Wf1: Colorant Separation and Scalar Error Diffusion By Colormentioning

confidence: 99%

“…Each spectral image have 31 channels since each spectral reflectance in our experiment is described by 31 discrete values equally spaced from 400nm to 700nm. The spectral patches are halftoned by sVED using [12] filter as in WF1 for the SED halftoning. For each pixel of a spectral image the distance to each NP is calculated, the smallest distance given colorant combination at the processed pixel.…”

Section: Wf2: Spectral Vector Error Diffusionmentioning

confidence: 99%

Simple Comparison of Spectral Color Reproduction Workflows

Gerhardt

Hardeberg

2009

Image Analysis

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Abstract. In this article we compare two workflows for spectral color reproduction : colorant separation (CS) followed by halftoning by scalar error diffusion (SED) of its resulting multi-colorant channel image and a second workflow by spectral vector error diffusion (sVED). Identical filters are used in both SED and sVED to diffuse the error. Gamut mapping is performed as pre-processing and the reproductions are compared to the gamut mapped spectral data. The inverse spectral Yule-Nielsen modified Neugebauer (YNSN) model is used for the colorant separation. To bring the improvement of the YNSN model upon the regular Neugebauer model into the sVED halftoning the n factor is introduced in the sVED halftoning. The performances of both workflows are evaluated in term of spectral and color differences but also visually with the dot distributions obtained by the two halftoning techniques. Experimental results have shown close performances for the compared workflows in term of color and spectral differences but visually cleaner and more stable dot distributions obtained by sVED.

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“…Received June 23, 2016; accepted for publication Oct. 3,2016; published online Nov. 14, 2016. Associate Editor: Chunghui Kuo.…”

Section: Introductionmentioning

confidence: 99%

“…In the past, a lot of work has been done on the selection of the weighting coefficients a j,k , for example using the larger matrix of Jarvis et al, 3 or by simplifying the weights as for example done by Fan et al, 4 or by optimizing the weights according to some criteria as in Kim and Gill 5 and Kolpatzik and Bouman 6 switching between different weight matrixes as in Eschbach 7 and adapting the weights to other requirements, e.g., complex-valued for holography 8 or based on hardware constraints (found mainly in the patent literature. 9,10 ) An additional area of work was the quantizer Q, which in the binary case is a simple step function with an optionally spatially varying threshold or distance metric 11 or an input dependent threshold.…”

Section: Introductionmentioning

confidence: 99%

On Large Local Error Accumulation in Multilevel Error Diffusion

Eschbach¹,

Pedersen²

2016

jist

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Abstract.Error diffusion is an often used method that transforms a continuous tone (multibit) image into an image of lower bit depth, most commonly into a binary output of black and white. The simplicity of the processing and the quality of the output have made error diffusion a frequently used tool. Part of the image quality is attributed to the minimization of quantization errors in the error-diffusion process. This article describes local instabilities in a color multilevel error-diffusion system that-in contrast-can lead to large local errors in the output, far exceeding the normally expected quantization errors. This can have serious negative effects specifically in connection with the design and incorporation of color calibration sheets. INTRODUCTIONError diffusion is a feedback-based halftoning technique that first was described for image data by Hale 1 -for a one-dimensional signal-and Floyd and Steinberg 2 -for a two-dimensional image in 1976. In a simple description, the input value at location (m, n) is quantized to its ''nearest'' possible output level. The error between the input at (m, n) and output at (m, n) is then compared and defined as an error. This error is subtracted from future pixels and this combination of actual input value and accumulated error value is considered the actual input for quantization. This can be written as:

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A survey of techniques for the display of continuous tone pictures on bilevel displays

Cited by 537 publications

References 9 publications

Image Processing Using Shearlets

Image Processing Using Shearlets

Simple Comparison of Spectral Color Reproduction Workflows

On Large Local Error Accumulation in Multilevel Error Diffusion

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A survey of techniques for the display of continuous tone pictures on bilevel displays

Cited by 537 publications

References 9 publications

Image Processing Using Shearlets

Image Processing Using Shearlets

Simple Comparison of Spectral Color Reproduction Workflows

On Large Local Error Accumulation in Multilevel Error&#xA0;Diffusion

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On Large Local Error Accumulation in Multilevel Error Diffusion