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Gamut Boundaries expressed with Zernike polynomials

机译:用Zernike多项式表示的色域边界

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In cross-media color image reproduction, gamut mapping is needed due to gamut difference among different media. In order to implement gamut mapping, gamut boundaries of each medium involved should be first determined. It may be expected that an analytical expression for a boundary is preferred than a set of discrete data , since it would take less storage space and make the determination of the intersection point between a boundary and a "mapping line" easier and faster. In this article, a form of Zernike polynomial expression is suggested to be used as the expression of gamut boundary surface. For instance, if CIE1976L*a*b* is adopted as the color space for gamut mapping, then each color(point) on the boundary can be expressed as L*=L*(a*,b*) and the boundary can be expanded into a series of Zernike polynomials with an appropriate coefficient for each of which. These coefficients can be obtained with sufficient experimental data of boundary points and existing algorithms. Experiments have been executed for a color printer with(R,G,B) as its input. The 6 boundaries in RGB space would consist of (0,G,B),(R,0,B),(R,G,0),(255,G,B),(R,255,B) and (R,G,255) where each of R,G,B varies from 0 to 255. Then 6 corresponding sets of Zernike coefficients are calculated, based on about half of the measured L*a*b*'s for each boundary. A comparison between original measured data and the data predicted by Zernike polynomials shows that, not only for the data that have been used to calculate the coefficients, but also for those not used, the differences are acceptably small even negligible with only a few exceptions.
机译:在跨媒体彩色图像再现中,由于不同媒体之间的色域差异,因此需要色域映射。为了实现色域映射,应首先确定所涉及的每种介质的色域边界。可以预期,对于边界的分析表达式比一组离散数据更可取,因为它会占用较少的存储空间,并使边界和“映射线”之间的交点的确定更加容易和快捷。在本文中,建议使用一种Zernike多项式表达式作为色域边界面的表达式。例如,如果采用CIE1976L * a * b *作为色域映射的色彩空间,则边界上的每个颜色(点)都可以表示为L * = L *(a *,b *),并且边界可以是扩展为一系列Zernike多项式,每个系数都有适当的系数。这些系数可以通过足够的边界点实验数据和现有算法来获得。已针对以(R,G,B)作为输入的彩色打印机执行了实验。 RGB空间中的6个边界将由(0,G,B),(R,0,B),(R,G,0),(255,G,B),(R,255,B)和( R,G,255),其中每个R,G,B在0到255之间变化。然后,基于每个边界的L * a * b *的一半,计算6组相应的Zernike系数。原始测量数据与Zernike多项式预测的数据之间的比较表明,不仅对于已用于计算系数的数据,而且对于未使用的数据,差异也很小,甚至可以忽略不计,只有少数例外。

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