Method of color correction that preserves perceived color differences

1992 ◽  
Author(s):  
Randall G. Guay
Keyword(s):  
Color Correction ◽  
Color Differences ◽  
Perceived Color

scholarly journals What Color Does the Consumer See? Perceived Color Differences in Plastic Products in an LED-Lit Environment

2019 ◽  
Vol 11 (21) ◽  
pp. 5985 ◽  
Author(s):  
Xiao Dou ◽  
Chih-Fu Wu ◽  
Kai-Chieh Lin ◽  
Jeih-Jang Liou
Keyword(s):  
Acrylonitrile Butadiene Styrene ◽  
Visual Sensitivity ◽  
Color Temperature ◽  
Light Sources ◽  
Color Differences ◽  
Market Opportunities ◽  
Actual Color ◽  
Human Eyes ◽  
Perceived Color ◽  
Plastic Products

To attract customers and increase market opportunities, retailers frequently use lighting to highlight the color of their products. However, differences between perceived and actual color, triggered by display lighting, can motivate buyers to discard products after purchase. Few studies have been reported on differences in perceived color, caused by LEDs. This study focuses on two correlated color temperatures (2800 K, 4000 K) and illuminance levels (500 lx, 1500 lx) to create four LED-lit environments, and measures the differences in the color perceived by 20 observers on acrylonitrile–butadiene–styrene (ABS) plastics, with different surfaces, under these four environments. The results reveal that correlated color temperature results in larger perceived differences in color than illuminance, and the effects of LED light sources on green and yellow ABS plastic products are more obvious than their effects on red and blue products. One possible reason for this can be attributed to the visual sensitivity effect of human eyes. The results of this study can serve as a reference for designers fabricating ABS plastic products for practical lighting applications, and improving the role of LED lighting in sustainable development.

Research on the Color Correction Algorithm of Images Based on Histogram Matching

2013 ◽  
Vol 469 ◽  
pp. 256-259 ◽  
Author(s):  
Jing Zhang ◽  
Guang Xue Chen
Keyword(s):  
Imaging System ◽  
Color Correction ◽  
Experimental Result ◽  
Reference Image ◽  
Atmospheric Conditions ◽  
Correction Algorithm ◽  
Lighting Conditions ◽  
Histogram Matching ◽  
Color Differences ◽  
Processing Software

Different rendering conditions (e.g., changes in lighting conditions or atmospheric conditions, changes of the imaging system) often cause significant color differences between two images. In the prepress process, the brightness and hue between two images should be adjusted to be as similar as possible. Currently, we generally use image processing software such as PhotoShop to adjust the image manually, it’s complex and time consuming. In this paper, the color correction algorithm based on histogram matching was put forward and implemented. Only one image needed to be adjusted well previously as the reference image, and the mapping relationship was established on pixels between the histogram of the source images and the reference image, then the source images would have the histograms similar to that of the reference image, so that the images would have similar color characteristic and achieve image color correction finally. The experimental result showed that the realized color correction algorithm was effective, it could not only maintain the visual effect of images, but also eliminate the color differences between the reference image and the source images.

scholarly journals Characterization of Color Differences for Color Palettes

2020 ◽  
Vol 2020 (28) ◽  
pp. 232-236
Author(s):  
Jialu Wu ◽  
Jie Yang ◽  
Minchen Wei ◽  
Kaida Xiao ◽  
Stephen Westland
Keyword(s):  
Computer Graphics ◽  
Color Difference ◽  
Difference Model ◽  
Color Palette ◽  
Reference Set ◽  
Color Differences ◽  
Multiple Patches ◽  
Different Color ◽  
Perceived Color

Various color difference metrics were developed for characterizing the perceived color difference between individual color patches. Color difference between palettes containing multiple color patches, however, is critically important in product design and computer graphics. This study aimed to investigate how the perceived color difference between a pair of color palettes containing more than a single color patch is affected by the order and number of color patches in the palette. Two reference color sets were generated and each set had four color palettes containing 1, 4, 9, and 16 color patches that were arranged as 1 × 1, 2 × 2, 3 × 3, and 4 × 4 patterns. Human observers scaled the color differences between a color palette of the reference set and a color palette that had revised colors, or revised orders, or a combination of revised colors and orders compared to the reference palette. The calculated color differences between the two palettes were derived using the Minimum Color Difference Model (MICDM) algorithm proposed in a recent work with different color difference metrics, including CIELAB, CMC, CIE94, and DE2000. It was found that the perceived color differences of pairs of individual color patches were significantly larger than those containing multiple patches, when the calculated color differences were the same. The color differences metrics, except for CIE94, had similar performance when characterizing perceived color differences between color palettes.

Color Similarity in Visual Search

2007 ◽  
Vol 66 (4) ◽  
pp. 191-199 ◽  
Author(s):  
Ester Reijnen ◽  
Dieter Wallach ◽  
Markus Stöcklin ◽  
Tanja Kassuba ◽  
Klaus Opwis
Keyword(s):  
Visual Search ◽  
Pairwise Comparison ◽  
Perceived Similarity ◽  
Guided Search ◽  
Color Categories ◽  
Color Differences ◽  
Color Similarity ◽  
Time Required ◽  
Perceived Color

In the present study, we investigated the role of phenomenologically perceived color differences between stimuli in determining visual search efficiency. We contrasted this with predictions based on the categorical color status, as proposed by Wolfe’s Guided Search 2 model. We first asked participants to rate the color similarity of each pair of stimuli (pairwise comparison). The results were combined using multidimensional scaling to produce a similarity metric, which was subsequently used to define stimulus similarities for two visual search experiments. The results demonstrate that the time required by participants to find a target is more adequately explained by the perceived similarity of colors than by color categories.

scholarly journals TDCCN: A Two-Phase Deep Color Correction Network for Traditional Chinese Medicine Tongue Images

2020 ◽  
Vol 10 (5) ◽  
pp. 1784
Author(s):  
Yunxi Lu ◽  
Xiaoguang Li ◽  
Zhaopeng Gong ◽  
Li Zhuo ◽  
Hui Zhang
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Color Correction ◽  
Work Environments ◽  
Second Phase ◽  
Perceptual Adaptation ◽  
Two Phase ◽  
Lighting Condition ◽  
Subjective Preference ◽  
Perceived Color

In Traditional Chinese Medicine (TCM), tongue diagnosis is an indispensable diagnostic method. Due to the limitations of acquisition devices and variations of the illumination, there is a significant color distortion between the captured tongue image and the human visual perceived image. In this paper, we proposed a Two-phase Deep Color Correction Network (TDCCN) for TCM tongue images. In the first phase, a deep color correction network was designed to model the mapping between the captured image and the target objective chromatic values under a standard lighting condition. The first phase provides consistent color tongue images by using different cameras and capture devices. The output tongue images at this phase can be used for further automatic quantitative analysis. The second phase provides flexible color adjusting options to adapt to different work environments and the subjective preference of doctors for visually perceived color appearance. Only three additional parameters are used to describe the adjustment operation. Experimental results show that our method achieved state-of-the-art performance in objective color correction and obtained satisfactory perceptual adaptation.

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