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Color vision deficiency represents an inability to perceive differences between certain colors that can be distinguished in the case of regular color vision. This article proposes a new daltonization method for re-coloring image segments perceived as confusingly colored by color deficient observers and, thus, to improve their perceived image quality. The idea behind this approach is that only one image color center should be located on one confusion line. If colors of two or more segments lie on the same confusion line, then they should be remapped in a direction perpendicular to the confusion line taking into account the image content—the color distribution of other segments. The method conserves the image naturalness by restricting, for each segment center, an area of admissible remapping. This achieves re-coloring balance where colors are made sufficiently distinguishable from each other, but they do not deviate too much from the original image colors. The proposed re-coloring concept is applicable to all types of dichromacy and anomalous trichromacy with adjustment for their set of confusion lines. The simulation results show that the re-coloring converts an original image to a version with improved color distinction, confirmed by evaluations of eight color deficient users. An intentionally chosen example demonstrates when and why the proposed method performs better against content-independent methods. Furthermore, results and subjective evaluations indicate that the method provides more natural re-coloring results for anomalous trichromats than in the case of content-dependent methods optimized for dichromats.
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... However, this method cannot effectively enhance images that span almost the entire chromatic plane. In 2015, Milic et al. [20] proposed a color correction method based on confusion lines, which defines the remapping range of the center color and avoids creating new confusing colors for the center color. However, it cannot effectively avoid creating new confusing colors for those other than the center color. ...
... This study used comparative tests to verify the validity of the proposed method. The methods described by Milic et al. [20], Takimoto et al. [21] , Tennholtz et al. [22], Hassan were used for comparison. The corresponding parameter settings for the different methods are listed in Table 1. ...
... Parameter setting Milic et al.'s method [20] =0.5, =0.5 Takimoto et al.'s method [21] k = 5, -= 5°, -= 5°T ennenholtz et al's method [22] = is set from 0.1 to 1, which is used to weigh the maintenance of the color contrast of the original image and the improvement of the K-type chromatic contrast. When is small, the color correction range only considers the part of the color that is very similar. ...
Article
Images with rich color information are an important source of information that people obtain from the objective world. Occasionally, it is difficult for people with red-green color vision deficiencies to obtain color information from color images. We propose a method of color correction for dichromats based on the physiological characteristics of dichromats, considering hue information. First, the hue loss of color pairs under normal color vision was defined, an objective function was constructed on its basis, and the resultant image was obtained by minimizing it. Finally, the effectiveness of the proposed method is verified through comparison tests. Red-green color vision deficient people fail to distinguish between partial red and green colors. When the red and green connecting lines are parallel to the a* axis of CIE L*a*b*, red and green perception defectives cannot distinguish the color pair, but can distinguish the color pair parallel to the b* axis. Therefore, when two colors are parallel to the a* axis, their color correction yields good results. When color correction is performed on a color, the hue loss between the two colors under normal color vision is supplemented with b* so that red-green color vision-deficient individuals can distinguish the color difference between the color pairs. The magnitude of the correction is greatest when the connecting lines of the color pairs are parallel to the a* axis, and no color correction is applied when the connecting lines are parallel to the b* axis. The objective evaluation results show that the method achieves a higher score, indicating that the proposed method can maintain the naturalness of the image while reducing confusing colors.
... To our best knowledge, there are three major color adaptation methods that build upon the CIE Luv color space, namely those due to Ichikawa et al. [11], Oshima et al. [62], and Milic et al. [63]. [11]. ...
... Consequently, the three perceptual requirements of color consistency, naturalness and contrast are satisfied. [63]. As known, a CVD individual cannot distinguish certain colors from one another, which belong to the so-called confusion lines of a given color space [67]. ...
... We have found two color adaptation methods based on CIE Luv color space for dichromat people in the literature, namely those due to Nakauchi and Onouchi [88] and Milic et al. [63]. ...
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plenty of media elements like text, still images, video, sprites, and so on. Aware of the difficulties that color-blind people may face in interpreting colored contents, a significant number of recoloring algorithms have been proposed in the literature with the purpose of improving the visual perception of those people somehow. However, most of those algorithms lack a systematic study of subjective assessment, what undermines their validity, not to say usefulness. Thus, in the sequel of the research work behind this Ph.D. thesis, the central question that needs to be answered is whether recoloring algorithms are of any usefulness and help for colorblind people or not. With this in mind, we conceived a few preliminary recoloring algorithms that were published in conference proceedings elsewhere. Except the algorithm detailed in Chapter 3, these conference algorithms are not described in this thesis, though they have been important to engender those presented here. The first algorithm (Chapter 3) was designed and implemented for people with dichromacy to improve their color perception. The idea is to project the reddish hues onto other hues that are perceived more regularly by dichromat people. The second algorithm (Chapter 4) is also intended for people with dichromacy to improve their perception of color, but its applicability covers the adaptation of text and image, in HTML5-compliant web environments. This enhancement of color contrast of text and imaging in web pages is done while keeping the naturalness of color as much as possible. Also, to the best of our knowledge, this is the first web recoloring approach targeted to dichromat people that takes into consideration both text and image recoloring in an integrated manner. The third algorithm (Chapter 5) primarily focuses on the enhancement of some of the object contours in still images, instead of recoloring the pixels of the regions bounded by such contours. Enhancing contours is particularly suited to increase contrast in images, where we find adjacent regions that are color indistinguishable from dichromat’s point of view. To our best knowledge, this is one of the first algorithms that take advantage of image analysis and processing techniques for region contours. After accurate subjective assessment studies for color-blind people, we concluded that the CVD adaptation methods are useful in general. Nevertheless, each method is not efficient enough to adapt all sorts of images, that is, the adequacy of each method depends on the type of image (photo-images, graphical representations, etc.). Furthermore, we noted that the experience-based perceptual learning of colorblind people throughout their lives determines their visual perception. That is, color adaptation algorithms must satisfy requirements such as color naturalness and consistency, to ensure that dichromat people improve their visual perception without artifacts. On the other hand, CVD adaptation algorithms should be object-oriented, instead of pixel-oriented (as typically done), to select judiciously pixels that should be adapted. This perspective opens an opportunity window for future research in color accessibility in the field of in human-computer interaction (HCI).
... One way researchers evaluate recolouring tools is simply by qualitative assessment of how their recolouring method assists people with CVD by using CVD simulations [10,27,28,41,45,47,53,[58][59][60][61]. Authors do this by describing how their method satisfes the three requirements listed above or by visually comparing their method to existing methods. ...
... Most methods consider only anomalous trichromacy in general, and the severity of the anomaly is not adjustable, i.e., they are not personalized CVD aids. Besides, most algorithms operate using a global color transform for the image, while only a few try to transform only colors that are confused by the subject, and leave the rest unaltered [15,17,18]. ...
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Color vision deficiency (CVD) has gained in relevance in the last decade, with a surge of proposals for aid systems that aim to improve the color discrimination capabilities of CVD subjects. This paper focuses on the proposal of a new metric called CVD-MET, that can evaluate the efficiency and naturalness of these systems through a set of images using a simulation of the subject’s vision. In the simulation, the effect of chromatic adaptation is introduced via CIECAM02, which is relevant for the evaluation of passive aids (color filters). To demonstrate the potential of the CVD-MET, an evaluation of a representative set of passive and active aids is carried out both with conventional image quality metrics and with CVD-MET. The results suggest that the active aids (recoloration algorithms) are in general more efficient and produce more natural images, although the changes that are introduced do not shift the CVD’s perception of the scene towards the normal observer’s perception.
... In previous works, studies to improve the color discrimination ability of people with CVD mainly dealt with recoloring methods, while studies that considered various design factors other than color were insufficient [30][31][32][33][34]. Regarding the design factors, Table 2 summarizes the visual design elements of symbols and icons presented in the existing literature. ...
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Digital clusters have been adopted as displays in vehicles, and various driving information is presented through the digital clusters with different colors. However, drivers with color vision deficiency (CVD) face difficulties in recognizing the information conveyed through color, which might lead to serious traffic accidents. In this paper, the usability of symbols in automobile digital clusters was evaluated from the perspective of people with CVD, and alternative designs were proposed and validated to improve recognition of the symbols. Twenty-seven participants with CVD and twenty-one participants with normal color vision (NCV) were recruited to investigate the influence of design elements, such as symbol color, stroke width, cluster background color, and adjacent symbol color. The choice reaction time and error rate were measured, and the perceived importance and visibility were collected using a questionnaire. As a result, the following four effects of symbol designs on the usability and recognition were identified: (1) if the existing color profile for symbols is applied, the symbol recognition was improved by modifying symbol design elements (e.g., stroke width); (2) For the symbol stroke width, a stroke width-to-height ratio of 0.12 or more was recommended; (3) Gray color is recommended for the background color, but lighting a red symbol on a gray background should be avoided for the participants with CVD; (4) When presenting a symbol adjacent to another one, presenting in red-red, green-green, orange-red, and orange-green combinations should be avoided. The results of this study can be used as reference materials when developing vehicle display interfaces that are accessible to all users including people with CVD.
... An alternative technique working in RGB uses a network that connects colours with generated confusions for dichromats and iteratively changes them until no connections within the colour network remains [27]. Bin-based algorithms that work on difference between bins for dichromats and trichromats [55], as well as clustering techniques using k-means clustering [84,119] have also been created. ...
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Colour vision deficiency is a common visual impairment that cannot be compensated for using optical lenses in traditional glasses, and currently remains untreatable. In our work, we report on research on Computational Glasses for compensating colour vision deficiency. While existing research only showed corrected images within the periphery or as an indirect aid, Computational Glasses build on modified standard optical see-through head-mounted displays and directly modulate the user’s vision, consequently adapting their perception of colours. In this work, we present an exhaustive literature review of colour vision deficiency compensation and subsequent findings; several prototypes with varying advantages—from well-controlled bench prototypes to less controlled but higher application portable prototypes; and a series of studies evaluating our approach starting with proving its efficacy, comparing to the state-of-the-art, and extending beyond static lab prototypes looking at real world applicability. Finally, we evaluated directions for future compensation methods for computational glasses.
... The evaluation of CVD aids is an important process that is not as simple as solving for the Ishihara plate test [8]. A lot of research looking to develop or improve CVD aids tends to take that approach [2,23,28,29,33,36,42], however, the Ishihara plate test does not accurately address the problems that individuals with CVD actually face [46]. The issues that individuals with Colour Blindness face with colour can be categorized into four uses of colour [9]: 1) comparative, 2) denotative, 3) connotative, and 4) aesthetic. ...
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