Naturalness- and information-preserving image recoloring for red–green dichromats

Zhu, Zhenyang; Toyoura, Masahiro; Go, Kentaro; Fujishiro, Issei; Kashiwagi, Kenji; Mao, Xiaoyang

doi:10.1016/j.image.2019.04.004

Cited by 22 publications

(51 citation statements)

References 21 publications

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“…This colour adaptation is called Daltonization, and is a complex problem of mapping the image gamut onto a reduced one [13]. Daltonization methods can be divided into two categoriescontent-independent and content-dependent.…”

Section: Daltonization Methodsmentioning

confidence: 99%

“…The focus of recent research is on Daltonization based on image content. Content-dependent categories include histogram-based [5], neighbourhood-based [20,21], and clustering-based [22][23][24] methods; optimization [13,[25][26][27][28][29][30], FIGURE 2 Confusion line and confusion point in Lu'v' colour space. (a) For protanopes, the red line represents the confusion line, and (0.678, 0.501) [34] is the confusion point.…”

Section: Daltonization Methodsmentioning

confidence: 99%

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A content‐dependent Daltonization algorithm for colour vision deficiencies based on lightness and chroma information

Shen

Feng

Zhang

2020

IET Image Processing

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To help CVD (colour vision deficiency) observers distinguish among different colours in digital images, this study proposes a content-dependent Daltonization algorithm based on lightness and chroma information. This improves the trade-offs in contrast, naturalness, and colour consistency deficiencies found in existing methods. Chroma remapping and brightness adjustments on image content are utilized to increase contrast and maximize the preservation of the original hue. In a quantitative study, the proposed method proved more useful for dichromats than did existing methods. The evaluation by research subjects showed that the method was superior to the current methods in balancing contrast, naturalness, and colour consistency.

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Section: Daltonization Methodsmentioning

confidence: 99%

Section: Daltonization Methodsmentioning

confidence: 99%

A content‐dependent Daltonization algorithm for colour vision deficiencies based on lightness and chroma information

Shen

Feng

Zhang

2020

IET Image Processing

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“…They have used the z-score method to evaluate the effectiveness of the CVD simulation algorithm. The authors obtained the simulated images created by authors of [50,52] (I1 and I2, respectively) against an original image. The authors generated the simulated image through a standard colour blind simulator tool Coblis [49] for the original image used (I3 and I4, respectively).…”

Section: Effectiveness By Proposed Benchmark Metric (%)mentioning

confidence: 99%

A novel image quality assessment method and coefficient of quality for digital solutions of colour blindness

Swaminathan

Singla

2021

IET Image Processing

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Eyesight is one of the primary senses that human beings have. Reports show that colour blindness, a form of colour vision deficiency (CVD), affects about 8% of the male population and 0.5% of female population. The Assistive Technology Act of 2004 lays focus on technologies that help individuals with disabilities and deficiencies. With the rapid advancement in technologies, several assistive solutions are available for visually impaired or CVD patients. Such solutions involve simulation and compensation of conflicting colours to help the colour blind in the visual perception of colours. Given the increased usage of the web, post the pandemic, these solutions improve the quality of life for the colour blind. Defining the image quality assessment criteria for such digital solutions becomes imperative. The study proposes a novel method for image quality assessment of digital solutions aimed at assisting the colour blind users. The proposed coefficient of quality (CQ) would be useful to rank colour compensation and recolouring algorithms. Experiments were conducted with a novel questionnaire set designed for this quality measurement. The results affirm the efficiency of the assessment method proposed. This will also provide objective feedback to the researchers and experts in this area to improve their solutions for CVD patients.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…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%

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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Naturalness- and information-preserving image recoloring for red–green dichromats

Cited by 22 publications

References 21 publications

A content‐dependent Daltonization algorithm for colour vision deficiencies based on lightness and chroma information

A content‐dependent Daltonization algorithm for colour vision deficiencies based on lightness and chroma information

A novel image quality assessment method and coefficient of quality for digital solutions of colour blindness

A Novel Approach to Image Recoloring for Color Vision Deficiency

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