Skin‐color correction method based on hue template mapping for wide color gamut liquid crystal display devices

Chen, Hung‐Shing; Wang, Te-Mei; Chen, Shih-Han; Liu, Jin-Sin

doi:10.1002/col.20636

Cited by 11 publications

(5 citation statements)

References 3 publications

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“…When a subgamut enclosing a given colour in CIELab space is found, the K value of CMYK is determined, and the remaining CMY values can be obtained by using three‐ink conversion methods. The polynomial regression method is selected in this paper as the colour conversion algorithm , as it obtains high accuracy with less sample data compared with the 3D interpolation method.…”

Section: Cmyk Four‐colour Conversion Processmentioning

confidence: 99%

A colour printer calibration method based on gamut division algorithms

Sun

Liu

Zhou

et al. 2014

Coloration Technology

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A colour printer calibration method based on gamut division algorithms is proposed in this paper, with the main task of converting colours from CIELab coordinates to CMYK space. The printer 3D gamut is firstly divided into seven subgamuts according to the K values of sample data, and then the subgamuts, which enclose the given point in CIELab space, are found. Because the K value stays the same for all sample colours of the determined subgamuts, a polynomial regression method is employed to convert the colours from CIELab space to CMY. Finally, by using black generation coefficients, the complete CMYKs are obtained according to the image characteristics. In experiments, for 256 test colours, the mean colour error of our algorithm, measured as ΔEab∗, is 1.45, which is acceptable for most printing products. Furthermore, the errors are less than those in both Neugebauer equations and the neural network method. These positive features suggest that the precision of our method is acceptable.

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Section: Cmyk Four‐colour Conversion Processmentioning

confidence: 99%

A colour printer calibration method based on gamut division algorithms

Sun

Liu

Zhou

et al. 2014

Coloration Technology

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“…Subsequently, can be derived by Eq. , where T denotes the matrix transpose

PX = Q

P \overset{\land}{X} ≅ Q

\overset{\land}{X} = {(P^{T} Q)}^{- 1} P^{T} Q

…”

Section: Cmtmentioning

confidence: 99%

Applying image‐based color palette for achieving high image quality of displays

Chen

Chao

et al. 2012

Color Research & Application

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In the highly competitive display market, manufacturers continuously develop new technologies to improve the image quality of displays. However, color measurement and visual assessment are time-consuming to production lines. A new method to measure and improve color quality of the displays automatically therefore, is urgently needed to the manufacturers. This article proposes a familiar color correction strategy to optimize the colors of different displays by means of creating an image-based color palette which enables color correction for familiar objects (e.g., facial skin, blue sky, or green grass) in the multidisplay systems. To produce the imagebased color palette, the 8-bit RGB value of each pixel in an image is transformed to L*d*n* (lightness/dominant color/nondominant color) color channels, and the dominant-color regions in an image are subsequently extracted from the dominant color (d*) channel. The memory color data of familiar objects can be set in reference monitor in advance to determine the dominant color (d*) channel. Then a series of palette colors are generated around a displayed image. The color palette will be displayed as a target for two-dimensional colorimeter shooting to obtain the measured color data. The familiar color correction model was established based on a first-order polynomial regression to achieve a polynomial fit between the measured color data and the reference color data on the color palette. The proposed method provides a solution to correct familiar colors on a displayed image, and maintains the original color gamut and tone characteristic in the multidisplay systems simultaneously. It is possible to achieve the preferred intent of the displayed images by using the proposed familiar color correction method.

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“…Simultaneously, higher saturation will get higher evaluation from observers, but the adjustment of saturation cannot exceed the critical value to avoid make people feel unnatural. Especially, for skin-tone memory color, the users will get a higher evaluation if the enhancement range of saturation is appropriately reduced 7 .…”

Section: Introductionmentioning

confidence: 99%