A colour printer calibration method based on gamut division algorithms. (December 2014)

• Record Type: Journal Article
• Authors: Sun, Bangyong; Liu, Han; Zhou, Shisheng; Xing, Junhong; Cao, Congjun; Zheng, Yuanlin
• Is Part Of: Coloration technology. Volume 130:Number 6(2014)
• Page Start: 453
• Page End: 458
• Publication Date: 2014-12
• Subjects: Color -- Periodicals; Color in the textile industries -- Periodicals; Dyes and dyeing -- Periodicals; 667
• DOI: 10.1111/cote.12112
• Languages: English
• ISSNs: 1472-3581

Abstract

<abstract abstract-type="main" id="cote12112-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p>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 <italic>K</italic> values of sample data, and then the subgamuts, which enclose the given point in CIELab space, are found. Because the <italic>K</italic> 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 <inline-formula><alternatives><inline-graphic mimetype="image" xlink:href="ark:/27927/pgh2d2f4762" xlink:type="simple" xmlns:xlink="http://www.w3.org/1999/xlink" /><mml:math altimg="urn:x-wiley:14723581:media:cote12112:cote12112-math-0001" overflow="scroll" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi mathvariant="normal">Δ</mml:mi><mml:msubsup><mml:mi>E</mml:mi><mml:mrow><mml:mi mathvariant="normal">ab</mml:mi></mml:mrow><mml:mo>∗</mml:mo></mml:msubsup></mml:mrow></mml:math></alternatives></inline-formula>, 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.</p> </abstract>