scholarly journals A Design Method of Saturation Test Image Based on CIEDE2000

2012 ◽  
Vol 2012 ◽  
pp. 1-4 ◽  
Author(s):  
Yang Yang ◽  
Jun Ming ◽  
Nenghai Yu
Keyword(s):  
Design Method ◽  
Human Perception ◽  
Color Difference ◽  
Image Similarity ◽  
Masking Effect ◽  
Test Image ◽  
Visual Effects ◽  
Color Test ◽  
Saturation Parameter ◽  
Difference Formula

In order to generate color test image consistent with human perception in aspect of saturation, lightness, and hue of image, we propose a saturation test image design method based on CIEDE2000 color difference formula. This method exploits the subjective saturation parameterC′ of CIEDE2000 to get a series of test images with different saturation but same lightness and hue. It is found experimentally that the vision perception has linear relationship with the saturation parameterC′. This kind of saturation test image has various applications, such as in the checking of color masking effect in visual experiments and the testing of the visual effects of image similarity component.

Color-Difference Threshold for Printed Images

2013 ◽  
Vol 469 ◽  
pp. 236-239
Author(s):  
Hao Xue Liu ◽  
Gui Hua Cui ◽  
Min Huang ◽  
Bing Wu ◽  
Yu Liu
Keyword(s):  
Color Difference ◽  
Psychophysical Experiment ◽  
Difference Threshold ◽  
Test Image ◽  
Color Distribution ◽  
Difference Formula ◽  
Color Differences ◽  
The Mean ◽  
Image Pairs ◽  
Test Color

Five ISO 400 images were used as test images and a method of limits psychophysical experiment was designed to test color-difference threshold in printed images. The color appearance of each original image was modified by an exponential function for CIELAB lightness and chroma, an offset function for CIELAB hue at 20 steps for each attributes respectively. The modified images and their originals were paired to form the test image pairs. The mean color differences of image pairs, ranged from 0 to 4 CIELAB units, were calculated by CIELAB color-difference formula and nearly uniformly divided into 21 grades for each attributes. The test image pairs were assessed in a CPC-8n lighting booth. 12 normal color vision observers took part in the experiment. The experimental results showed that the mean color-differences threshold for lightness, chroma and hue attributes were 1.49, 1.53 and 0.78 CIELAB units showing the threshold for hue was apparently smaller than that of lightness and chroma, and the thresholds of different images were dependent on the image content or color distribution.

A new color difference formula in RGBcolor space

2009 ◽  
Vol 29 (2) ◽  
pp. 465-467
Author(s):  
Zhen-ya YANG ◽  
Yong WANG ◽  
Zhen-dong YANG ◽  
Cheng-dao WANG
Keyword(s):  
Color Difference ◽  
Difference Formula

Application of CIEDE2000 Color Difference Formula in Jet Fuel Silver Strip Corrosion Color Recognition

2012 ◽  
Vol 531-532 ◽  
pp. 54-57 ◽  
Author(s):  
Li Ping Tong ◽  
Bin Peng ◽  
Yi Wei Fei ◽  
Hong Wei Yang
Keyword(s):  
Chromatic Aberration ◽  
Color Difference ◽  
Jet Fuel ◽  
Color Recognition ◽  
Difference Formula ◽  
Human Eyes

In order to eliminate the human eyes’ subjective differences in the level recognition of the jet fuel’s silver corrosion, chromatic aberration system is used to calculate chromatic aberration between the image of the standard colorimetric board and the experiment silver corrosion. By contrast, this paper chooses the least chromatic aberration to estimate the level of silver corrosion. The CIEDE2000 of the color difference formula, which can match with person vision theoretically, is applied in the chromatic aberration system at present, and can reflect the minor differences in color correctly. so this paper attempts to use CIEDE2000 color formula to determine the silver strip corrosion, which can reduce the influence of professionals’ subjective factors, and these results are more objective and accurate, which proves the actual feasibility of this method.

Evaluation of change in color of denim fabric after various industrial washing processes with different fiber composition

2014 ◽  
Vol 11 (2) ◽  
pp. 107-110
Author(s):  
Mushtaq Mangat ◽  
A. Abbasi ◽  
Jakub Wiener
Keyword(s):  
Color Space ◽  
Color Difference ◽  
Fiber Composition ◽  
Obvious Change ◽  
Washing Machines ◽  
Difference Formula ◽  
Denim Fabric ◽  
Cielab Color

Traditional denim made by using 100% cotton and novel denim made by using cotton in warp and spun PP in the weft were treated in 11 different ways on industrial garment washing machines with the help of various textile auxiliaries and pumice. There is an obvious change in color of denim. This change was measured by using Spectrophotometer. Reflectance was taken as a variable to observe the intensity of change. Color difference was measured by using the CIELab color difference formula 1976. Color space coordinates (L*, a*, b*) and color difference ΔE were calculated between the untreated denim and treated denim.

Color Difference Evaluation Based on Image Color Appearance Model

2012 ◽  
Vol 200 ◽  
pp. 637-640
Author(s):  
Jing Liang ◽  
Yong Bin Zhao ◽  
Hui Gao
Keyword(s):  
Calculation Method ◽  
Color Difference ◽  
Color Images ◽  
Color Appearance ◽  
Appearance Model ◽  
Difference Formula ◽  
Difference Calculation

The iCAM (image color appearance model) as the most advanced modern color appearance model is constantly being put into use. In color images industry, the evaluation of the color difference is significant. This article will focus on color difference formula of image color appearance model and analysis its reasonable color difference calculation method, which reflects the advantages of image color appearance model.

scholarly journals Comparison of color indexes for tomato ripening

2004 ◽  
Vol 22 (3) ◽  
pp. 534-537 ◽  
Author(s):  
Andrés F. López Camelo ◽  
Perla A. Gómez
Keyword(s):  
Color Index ◽  
Human Perception ◽  
Color Difference ◽  
Color Classification ◽  
Color Changes ◽  
Color Categories ◽  
Visual Color ◽  
Postharvest Life ◽  
Range Of Values

Color in tomato is the most important external characteristic to assess ripeness and postharvest life, and is a major factor in the consumer's purchase decision. Degree of ripening is usually estimated by color charts. Colorimeters, on the other hand, express colors in numerical terms along the L*, a* and b* axes (from white to black, green to red and blue to yellow, respectively) within the CIELAB color sphere which are usually mathematically combined to calculate the color indexes. Color indexes and their relationship to the visual color classification of tomato fruits vine ripened were compared. L*, a* and b* data (175 observations from eleven cultivars) from visually classified fruits at harvest in six ripening stages according to the USDA were used to calculate hue, chroma, color index, color difference with pure red, a*/b* and (a*/b*)². ANOVA analysis were performed and means compared by Duncan's MRT. Color changes throughout tomato ripening were the result of significant changes in the values of L*, a* and b*. Under the conditions of this study, hue, color index, color difference and a*/b* expressed essentially the same, and the color categories were significantly different in terms of human perception, with hue showing higher range of values. Chroma was not a good parameter to express tomato ripeness, but could be used as a good indicator of consumer acceptance when tomatoes are fully ripened. The (a*/b*)² relationship had the same limitations as chroma. For vine ripened fruits, hue, color index, color difference and a*/b* could be used as objective ripening indexes. It would be interesting to find out what the best index would be if ripening took place under inadequate conditions of temperature and ilumination.

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