An efficient re-coloring method with information preserving for the color-blind

Kim, Hyunji; Wang, Tae-Shick; Yoon, Yeo-Jin; Ko, Sung-Jea

doi:10.1109/tce.2011.6131176

Cited by 21 publications

(7 citation statements)

References 9 publications

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“…To conduct the comparative quantitative evaluation of the four methods, two performance indices were employed. First the naturalness index [ 17 , 22 , 32 ]

…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…So far, a wide range of recoloring methods have been developed based on optimization of specially designed objective functions [ 11 , 12 , 13 , 14 , 15 , 16 ] or regularized objective functions [ 17 , 18 , 19 ] that uniformly combine the naturalness and contrast criteria, pixel-based classification [ 20 ], spectral filtering [ 21 ], cluster analysis [ 22 ], gradient domain recoloring [ 23 , 24 ], confusion-line based [ 7 , 8 , 25 ], color transformation and rotation/translation [ 26 , 27 , 28 , 29 ], neural networks [ 30 ], image retrieval [ 31 ], and deep learning [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Approach to Image Recoloring for Color Vision Deficiency

Tsekouras

Rigos

Chatzistamatis

et al. 2021

Sensors

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In this paper, a novel method to modify color images for the protanopia and deuteranopia color vision deficiencies is proposed. The method admits certain criteria, such as preserving image naturalness and color contrast enhancement. Four modules are employed in the process. First, fuzzy clustering-based color segmentation extracts key colors (which are the cluster centers) of the input image. Second, the key colors are mapped onto the CIE 1931 chromaticity diagram. Then, using the concept of confusion line (i.e., loci of colors confused by the color-blind), a sophisticated mechanism translates (i.e., removes) key colors lying on the same confusion line to different confusion lines so that they can be discriminated by the color-blind. In the third module, the key colors are further adapted by optimizing a regularized objective function that combines the aforementioned criteria. Fourth, the recolored image is obtained by color transfer that involves the adapted key colors and the associated fuzzy clusters. Three related methods are compared with the proposed one, using two performance indices, and evaluated by several experiments over 195 natural images and six digitized art paintings. The main outcomes of the comparative analysis are as follows. (a) Quantitative evaluation based on nonparametric statistical analysis is conducted by comparing the proposed method to each one of the other three methods for protanopia and deuteranopia, and for each index. In most of the comparisons, the Bonferroni adjusted p-values are <0.015, favoring the superiority of the proposed method. (b) Qualitative evaluation verifies the aesthetic appearance of the recolored images. (c) Subjective evaluation supports the above results.

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“…To conduct the comparative quantitative evaluation of the four methods, two performance indices were employed. First the naturalness index [ 17 , 22 , 32 ]

…”

Section: Experimental Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Image Recoloring for Color Vision Deficiency

Tsekouras

Rigos

Chatzistamatis

et al. 2021

Sensors

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“…In the literature, we find recoloring algorithms for still images and also for video [13] [15] [6] [9], although those for still images are predominant. The need for recoloring images comes from the fact that people with some degree of CVD may be not capable of distinguishing colors of adjacent regions of an image, what leads to misunderstanding and confusion about the image itself.…”

Section: Related Workmentioning

confidence: 99%

A skillet-based recoloring algorithm for dichromats

Ribeiro

Gomes

2013

2013 IEEE 15th International Conference on E-Health Networking, Applications and Services (Healthcom 2013)

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The color is a valuable communication resource, not just as part of the message itself, but also as a way of discriminating between the diverse contents of a given message. However, not all people have the ability to distinguish the color in a normal way. About 5% of the world population (8% of men) has a visual impairment, called color vision deficiency (CVD) or, simply, colorblindness. This vision deficiency also has a negative impact in both virtual and real worlds, because it compromises the correct interpretation of the contents across different media and envrironments. This issue is critical because of the increasing availability of multimedia contents in many places, particularly over the web. This article proposes a novel algorithm to adjust the colors for dichromate people (deuteranopes and protanopes), in particular for still images found over the web via browsers that take advantage of HTML5 markup language for graphics.

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“…Dysfunction or absence of two or more types of cones results in color vision deficiency (CVD), also known as color blindness (CB) [1,3,5]. People suffering from strong CVDs are subjected to various challenges in their everyday life, e.g.…”

Section: Introductionmentioning

confidence: 99%

“…To accomplish this, they designed an objective function, which quantified the naturalness and the contrast requirements. In this direction, Jeong et al [5] substantially improved the previous recoloring process by performing a translation of the colors not perceived correctly by the color-blind in distinguishable RGB color regions. Hassan and Paramersan [7] used the XYZ color space to carry out image enhancement in three steps namely, normalization, angular color rotation, and color un-normalization.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Methodology to Modify Color Images for Dichromatic Human Color Vision and its Application in Art Paintings

Rigos¹

2020

IJATCSE

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This paper introduces a systematic methodology to perform image recoloring for color-blind people that suffer from dichromacy, i.e. protanopia, deuteranopia, and tritanopia. The method is applied in digitized art paintings to alleviate accessibility problems related to art content consumption by the color-blind people, which is a very important issue. The methodology involves the RGB, the LMS, and the CIELab color spaces and comprises several steps implemented in sequence. To reduce the computational complexity, the colors of the original image are clustered. Each cluster center is a color. By using a standard technique, the cluster centers are transformed to simulate the effects of protanopia, deuteranopia, and tritanopia. To this end, a specialized objective function is minimized to recolor only the cluster centers that are significantly different from the respective simulated ones, because only these colors are confused by the color-blind. Finally, all colors of the original image belonging to the clusters associated with centers that have been recolored are appropriately recolored, also. The effectiveness of the proposed method is quantified in terms of comparative analysis over several experimental cases.

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An efficient re-coloring method with information preserving for the color-blind

Cited by 21 publications

References 9 publications

A Novel Approach to Image Recoloring for Color Vision Deficiency

A Novel Approach to Image Recoloring for Color Vision Deficiency

A skillet-based recoloring algorithm for dichromats

A Systematic Methodology to Modify Color Images for Dichromatic Human Color Vision and its Application in Art Paintings

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