Color Enhancement of Digital Images by Experts and Preference Judgments by Observers

Boust, Clotilde; Brettel, Hans; Viénot, Françoise; Berche, Stéphane; Alquié, G.

doi:10.2352/j.imagingsci.technol.(2006)50:1(1)

Cited by 25 publications

(26 citation statements)

References 12 publications

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“…As color is a very subjective concept [5], we want to propose a tool for image retrieval that is perception-dependent. According to the user's eye sensibility, we suggest to modify the definition of the association color/hue (cf.…”

Section: Towards a Learning Processmentioning

confidence: 99%

Image Retrieval using Fuzzy Representation of Colors

2006

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The problem addressed in this article is image indexing and retrieval according to the color. Indeed we propose a classification based on the dominant color(s) of the images. The process consists in two steps: first, assigning a colorimetric profile to the image in HLS space (Hue, Lightness, Saturation) and then, handling the query for the retrieval. To achieve the first step, the definition of hue is done using a fuzzy representation that takes into account the nonuniformity of color distribution. Then, lightness and saturation are represented through linguistic qualifiers also defined in a fuzzy way. Finally, the profile is built through fuzzy functions representing the membership degree of the image to different classes. Thus, the query for image retrieval is a pair (hue, qualifier). The second step looks for a match between the query and the profiles. In order to improve the software and to make it more flexible, the user can re-define the fuzzy representation of Hue, Lightness and Saturation, according to his own perception.

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Section: Towards a Learning Processmentioning

confidence: 99%

Image Retrieval using Fuzzy Representation of Colors

2006

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“…Reproducing skin colors pleasingly is an important factor in photographic color reproduction [1][2][3][4][5][6]. Moving skin colors toward their preferred skin color center improves the color preference of skin color reproduction.…”

Section: Introductionmentioning

confidence: 99%

“…(5) to(6), λ 01 = λ 10 and λ 21 = λ 12 .SPIE-IS&T/ Vol. 7866 786613-2 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 06/20/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx…”

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confidence: 99%

Preferred skin color enhancement for photographic color reproduction

Zeng

Luo

2011

Color Imaging XVI: Displaying, Processing, Hardcopy, and Applications

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Skin tones are the most important colors among the memory color category. Reproducing skin colors pleasingly is an important factor in photographic color reproduction. Moving skin colors toward their preferred skin color center improves the color preference of skin color reproduction. Several methods to morph skin colors to a smaller preferred skin color region has been reported in the past. In this paper, a new approach is proposed to further improve the result of skin color enhancement. An ellipsoid skin color model is applied to compute skin color probabilities for skin color detection and to determine a weight for skin color adjustment. Preferred skin color centers determined through psychophysical experiments were applied for color adjustment. Preferred skin color centers for dark, medium, and light skin colors are applied to adjust skin colors differently. Skin colors are morphed toward their preferred color centers. A special processing is applied to avoid contrast loss in highlight. A 3-D interpolation method is applied to fix a potential contouring problem and to improve color processing efficiency. An psychophysical experiment validates that the method of preferred skin color enhancement effectively identifies skin colors, improves the skin color preference, and does not objectionably affect preferred skin colors in original images.

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“…Memory and preferred colors have also been suggested or used as an internal reference to assess object color appearance and color quality in color reproduction [21][22][23][24][25][26][27][28] and color rendering [11,12,29,30].…”

Section: Introductionmentioning

confidence: 99%

Cross-cultural variation of memory colors of familiar objects

Smet¹,

Lin²,

Nagy³

et al. 2014

Opt. Express

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Abstract:The effect of cross-regional or cross-cultural differences on color appearance ratings and memory colors of familiar objects was investigated in seven different countries/regions -Belgium, Hungary, Brazil, Colombia, Taiwan, China and Iran. In each region the familiar objects were presented on a calibrated monitor in over 100 different colors to a test panel of observers that were asked to rate the similarity of the presented object color with respect to what they thought the object looks like in reality (memory color). For each object and region the mean observer ratings were modeled by a bivariate Gaussian function. A statistical analysis showed significant (p < 0.001) differences between the region average observers and the global average observer obtained by pooling the data from all regions. However, the effect size of geographical region or culture was found to be small. In fact, the differences between the region average observers and the global average observer were found to of the same magnitude or smaller than the typical within region inter-observer variability. Thus, although statistical differences in color appearance ratings and memory between regions were found, regional impact is not likely to be of practical importance.

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Color Enhancement of Digital Images by Experts and Preference Judgments by Observers

Cited by 25 publications

References 12 publications

Image Retrieval using Fuzzy Representation of Colors

Image Retrieval using Fuzzy Representation of Colors

Preferred skin color enhancement for photographic color reproduction

Cross-cultural variation of memory colors of familiar objects

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