RGB Color Cluster and Graph Coloring Algorithm for Partial Color Blind Correction

Abstract

Partial color blindness is an anomaly occurring to 5-8% of the world's population. Color correction using re-coloring algorithm usually can be used to help partial color blindness patient. One of the existing re-coloring techniques is to use the RGB color cluster technique and combine it by utilizing the brute force algorithm to perform color tracing for correcting the color. It has massive time and memory complexity. This research aims to create a correction technique for color blind people using RGB Color Cluster combined with Graph Coloring Algorithm. The first process is to get the RGB color cluster for color blind subject. After getting RGB color cluster from the subject then the image which want to be corrected is grouped based on RGB color cluster. The threshold of color grouping in image is done by utilizing the upper and lower bound values of the RGB color cluster. After the cluster is grouped, then we can represent neighbourhood between the colors by utilizing graph. The adjacent color group will be a neighbour. The next process is color re-coloring using graph coloring algorithm. In graph coloring algorithm, same color group is prohibited to become neighbour. In this research, graph coloring algorithm is used to prevent 2 colors that are look almost similar for become neighbours because it will cause the subject cannot distinguish it. Re-coloring is done by increasing and decreasing the color intensity of a set of colors. This technique succeeds in decreasing the complexity of the brute force algorithm from O(N4) to O(2N2) where the first N2 is the complexity of building the cluster group and the second N2 is the complexity of the re- coloring. In addition, the color of the object becomes more natural because Re-coloring is based on color group not pixel based.

EnColor: Improving Visual Accessibility with a Deep Encoder-Decoder Image Corrector for Color Vision Deficient Individuals

“…As a consequence, this heuristic approach adds more contrast to these targeted pixels. Dody et al (2019) also proposed a similar approach yet identified clusters of problematic colors in images. Their correction application is then targeted to the relevant clusters only.…”

“…For comparative analysis, we adopt baseline imagecorrection methods mentioned above: Linear (Petrovic and For MSE, the lower the better, while for SSIM, the higher the better. Fujita 2017), Color Contraster (CC) (Yun, Michelson, and Brand 2007), Fuzzy (Lee and dos Santos 2011), and Clustering (Dody et al 2019). Because code for most the methods were not available, we implemented or reproduced them in Python, following the authors' instructions.…”

Development of a selection system for the colour of wardrobe furniture

“…The colour picker was connected to the computer software colour QC, and 10 data points were recorded. Finally, the average value of the data was recorded as the L* a* b* values (Dody et al 2019).…”

Saliency Consistency-Based Image Re-Colorization for Color Blindness

“…Huang [8] proposed a rapid recoloring method based on the best mapping relationship between colorblind images and standard images. Dody [9] RGB color cluster and graph coloring color blind image color correction algorithm. In addition to the color correction of color-blind images, color correction in multi-viewpoint imaging systems has also been extensively studied [10]- [12].…”