Re‐coloring Images for Gamuts of Lower Dimension

Rasche, Karl; Geist, Robert; Westall, James

doi:10.1111/j.1467-8659.2005.00867.x

Cited by 194 publications

(163 citation statements)

References 28 publications

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“…Hence, our algorithm could be implemented in hardware or embedded in the driver software that renders color images on a grayscale printer. Our running time is linear in the number of pixels, unlike the Gooch et al algorithm [1], and is independent of the number of colors in the image, unlike the Rasche et al algorithm [2]. Owing to their complexity, the optimization algorithms [1,2] offer few guarantees about their results.…”

Section: Previous Workmentioning

confidence: 99%

“…In 2005, there were two significant proposals [1,2] for treating color to grayscale conversion as an optimization problem involving either local contrasts between pixels [1] or global contrasts between colors [2]. However, unconstrained optimization over thousands of variables [1] or constrained optimization over hundreds of variables [2] is a slow process prone to local minima.…”

Section: Previous Workmentioning

confidence: 99%

“…However, unconstrained optimization over thousands of variables [1] or constrained optimization over hundreds of variables [2] is a slow process prone to local minima. It can prove impractical in applications that demand real-time, automated performance with mathematical guarantees on the results.…”

Section: Previous Workmentioning

confidence: 99%

“…Alternatively, compared with standard principal component color projection, our approach better preserves achromatic luminance contrasts in grayscale. Finally, compared with recent optimization methods [1,2] for contrast enhancing color to grayscale conversion, this approach proves much more computationally efficient at producing visually similar results with stronger mathematical guarantees on its performance.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Decolorize: Fast, contrast enhancing, color to grayscale conversion

Grundland

Dodgson

2007

Pattern Recognition

221

182

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We present a new contrast enhancing color to grayscale conversion algorithm which works in real-time. It incorporates novel techniques for image sampling and dimensionality reduction, sampling color differences by Gaussian pairing and analyzing color differences by predominant component analysis. In addition to its speed and simplicity, the algorithm has the advantages of continuous mapping, global consistency, and grayscale preservation, as well as predictable luminance, saturation, and hue ordering properties.

show abstract

Section: Previous Workmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Decolorize: Fast, contrast enhancing, color to grayscale conversion

Grundland

Dodgson

2007

Pattern Recognition

221

182

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“…Examples of approaches for automatically reducing the impact of CDV are the Daltonize algorithm [11] and the work of Rasche et al [12]. In both cases automatic adjustments are made to how colours are used in images.…”

Section: Related Workmentioning

confidence: 99%

A Method for the Automatic Analysis of Colour Category Pixel Shifts During Dichromatic Vision

Bennett

Quigley

2006

Advances in Visual Computing

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Abstract. In this paper we present a method for automatically evaluating the amount of colour changes images undergo when perceived by individuals with colour deficient vision. This measure enables the classification of images based on the extent images visually change when viewed by people with one of the three classes of dichromatic (protanopia, deuteranopia, and tritanopia) colour vision. By measuring the extent that colour images appear perceptually different a designer, or automated layout technique, will have an indication of whether the choice of colour usage in an image could lead to colour ambiguity or colour confusions.

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Color scheme adjustment by fuzzy constraint satisfaction for color vision deficiencies

Yanagida

Okajima

Mimura

2014

Color Research & Application

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Color schemes or color combinations are widely used in printed materials and their digital versions, including web pages. These schemes convey information and create aesthetic appeal. Consequently, graphic designers have to strike a balance between accessibility and aesthetics with the color schemes they select. However, certain color schemes make the represented information confusing when viewers have color vision deficiencies (CVDs), which may lead to decrease in discriminability between used colors. What we will call the color-scheme-recoloring problem is the difficulty of optimizing a color scheme so as to preserve its original design and yet be simultaneously discriminable for all viewers, with or without CVDs. To address the problem, we formulated it within a framework of a fuzzy constraint satisfaction problem, a technique of artificial intelligence, and developed a tool for graphic designers. By utilizing the practical color co-ordinate system (PCCS), we introduce concepts used in the field of graphic design. This work combines pre-publication recoloring techniques for CVD viewers with elements of artificial intelligence, psychophysics, and psychology.

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Re‐coloring Images for Gamuts of Lower Dimension

Abstract: Abstract

Cited by 194 publications

References 28 publications

Decolorize: Fast, contrast enhancing, color to grayscale conversion

Decolorize: Fast, contrast enhancing, color to grayscale conversion

A Method for the Automatic Analysis of Colour Category Pixel Shifts During Dichromatic Vision

Color scheme adjustment by fuzzy constraint satisfaction for color vision deficiencies

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