WO2016206183A1 - 基于源图像色域的色域匹配方法 - Google Patents

基于源图像色域的色域匹配方法 Download PDF

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WO2016206183A1
WO2016206183A1 PCT/CN2015/088079 CN2015088079W WO2016206183A1 WO 2016206183 A1 WO2016206183 A1 WO 2016206183A1 CN 2015088079 W CN2015088079 W CN 2015088079W WO 2016206183 A1 WO2016206183 A1 WO 2016206183A1
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gray
pixel
source image
gamut
sub
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PCT/CN2015/088079
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English (en)
French (fr)
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陈黎暄
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深圳市华星光电技术有限公司
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Priority to US14/781,400 priority Critical patent/US9661187B1/en
Publication of WO2016206183A1 publication Critical patent/WO2016206183A1/zh

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
    • H04N1/46—Colour picture communication systems
    • H04N1/56—Processing of colour picture signals
    • H04N1/60—Colour correction or control
    • H04N1/6058—Reduction of colour to a range of reproducible colours, e.g. to ink- reproducible colour gamut
    • H04N1/6061—Reduction of colour to a range of reproducible colours, e.g. to ink- reproducible colour gamut involving the consideration or construction of a gamut surface
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K15/00—Arrangements for producing a permanent visual presentation of the output data, e.g. computer output printers
    • G06K15/02—Arrangements for producing a permanent visual presentation of the output data, e.g. computer output printers using printers
    • G06K15/18—Conditioning data for presenting it to the physical printing elements
    • G06K15/1867—Post-processing of the composed and rasterized print image
    • G06K15/1872—Image enhancement
    • G06K15/1878—Adjusting colours
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
    • H04N1/46—Colour picture communication systems
    • H04N1/56—Processing of colour picture signals
    • H04N1/60—Colour correction or control
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
    • H04N1/46—Colour picture communication systems
    • H04N1/56—Processing of colour picture signals
    • H04N1/60—Colour correction or control
    • H04N1/6002—Corrections within particular colour systems
    • H04N1/6008—Corrections within particular colour systems with primary colour signals, e.g. RGB or CMY(K)
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
    • H04N1/46—Colour picture communication systems
    • H04N1/56—Processing of colour picture signals
    • H04N1/60—Colour correction or control
    • H04N1/6058—Reduction of colour to a range of reproducible colours, e.g. to ink- reproducible colour gamut
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
    • H04N1/46—Colour picture communication systems
    • H04N1/56—Processing of colour picture signals
    • H04N1/60—Colour correction or control
    • H04N1/6058—Reduction of colour to a range of reproducible colours, e.g. to ink- reproducible colour gamut
    • H04N1/6063—Reduction of colour to a range of reproducible colours, e.g. to ink- reproducible colour gamut dependent on the contents of the image to be reproduced
    • H04N1/6066—Reduction of colour to a range of reproducible colours, e.g. to ink- reproducible colour gamut dependent on the contents of the image to be reproduced dependent on the gamut of the image to be reproduced

Definitions

  • the present invention generally relates to the field of color gamut matching technology, and more particularly to a color gamut matching method based on source image gamut.
  • the existing gamut mapping method is mostly based on the color gamut of the maximum saturated color of the three color components of R/G/B (ie, red/green/blue) in the chromaticity space. Matching with other color gamuts that display the primary color (eg, C/M/Y (ie, cyan/magenta/yellow) three color components of the color printer) in the chromaticity space.
  • C/M/Y ie, cyan/magenta/yellow
  • the source gamut can be either the gamut of the source device or the gamut of the source image, but the color is based on the gamut of the source device.
  • the loss of the image color during the transfer and reproduction process is often greater than the loss when matching the color gamut based on the source image. Therefore, in the prior art, color gamut matching between different devices is mostly performed based on the color gamut of the source image to obtain a better matching effect.
  • the existing method for determining the color gamut of the source image may include the steps of separately measuring the stimulus value matrix of the three primary color gray scales of the source image (R(X, Y, Z), G(X, Y, Z), B (X) , Y, Z)); a matrix of stimulus values of the three primary color gray scales (R(X, Y, Z), G(X, Y, Z), B (X, Y) measured by the amount according to the color mixing principle , Z)) Calculate the stimulus value matrix S(X, Y, Z) of the source image gray scale; according to the L*, a*, b* in the CIELAB chromaticity space and the tristimulus value matrix S of the source image gray scale ( Conversion relationship between X, Y, Z), Calculating the coordinate values L*, a*, b* of each pixel color in the CIELAB chromaticity space in the source image from the tristimulus value matrix S(X, Y, Z) of the source image gray
  • the above existing method for determining the color gamut of the source image requires measuring the matrix of the stimulus values of the three primary colors of all the pixel points of the source image. This process often takes a long time and is computationally intensive.
  • An exemplary embodiment of the present invention provides a color gamut matching method based on a source image color gamut to solve a technical problem of determining a large amount of calculation of a color gamut of a source image in the process of performing gamut matching.
  • a color gamut matching method based on a source image gamut comprising: (a) inputting a source image, and measuring a corresponding pixel point on the source image a grayscale value of each color sub-pixel; (b) determining a plurality of predetermined target pixel points on the source image based on the measured grayscale values of the respective color sub-pixels; (c) calculating the plurality of predetermined targets a plurality of coordinate values corresponding to the pixel points in the uniform chromaticity space; (d) determining a color gamut of the source image based on the plurality of coordinate values; (e) performing gamut boundary extraction on the target device, obtaining a a color gamut of the target device; (f) performing a gamut matching between the source image and the target device.
  • the plurality of predetermined target pixel points may respectively correspond to vertices of the solid colors of the six primary colors in the uniform chromaticity space, wherein the six primary colors may include: red R, green G, blue B, cyan C , magenta M, yellow Y.
  • the plurality of predetermined target pixel points may include six predetermined target pixel points, wherein the step (b) may include: (b1) setting the grayscale value of the red R sub-pixel to a maximum value, and the green G sub-pixel Grayscale value and blue a pixel of a color B sub-pixel having a gray-scale value of 0 as a vertex of the first predetermined target pixel red R; (b2) a gray-scale value of the green G sub-pixel is a maximum value, and a gray-scale value of the red R sub-pixel And the pixel points of the blue B sub-pixels having a grayscale value of 0 respectively as the second predetermined target pixel; (b3) the grayscale value of the blue B sub-pixel is the maximum value, and the grayscale value of the red R sub-pixel and a pixel of a green G sub-pixel having a grayscale value of 0 as a third predetermined target pixel; (b4) calculating a maximum value of the equation Gray(G)+Gray(
  • the step (d) may include: sequentially connecting a plurality of points corresponding to the plurality of coordinate values in a uniform chromaticity space, and using an area formed by the connection line as the source image. Color gamut.
  • the step (f) may include performing color gamut matching between the source image and the target device by using a color gamut clipping method or a color gamut compression method.
  • the mapping from the source image gamut to the target gamut is adopted, so that the loss of image color in the process of transmission and reproduction is reduced, the gamut matching effect is better, and the color of the determined source image is effectively reduced.
  • the amount of calculation of the domain is adopted, so that the loss of image color in the process of transmission and reproduction is reduced, the gamut matching effect is better, and the color of the determined source image is effectively reduced.
  • FIG. 1 illustrates a flowchart of a source image color gamut based color gamut matching method according to an exemplary embodiment of the present invention
  • FIG. 2 illustrates an ab-plane gamut map displayed in a CIELab chromaticity space, in accordance with an exemplary embodiment of the present invention.
  • the gamut matching method in an exemplary embodiment of the present invention since many images do not cover the entire color gamut but only cover a partial color gamut Color gamut matching can be performed depending on the color gamut covered by the source image, which can reduce the loss of image color in the process of transmission and reproduction, and effectively improve the effect of gamut matching.
  • a uniform chromaticity space eg, CIELCh chromaticity space or CIELAB chromaticity space
  • the present invention generally needs to first establish a mapping relationship between a source image and a target device and a uniform chromaticity space, and then perform gamut matching on the source image and the target device in a uniform chromaticity space.
  • FIG. 1 illustrates a flow chart of a color gamut matching method based on a source image gamut, according to an exemplary embodiment of the present invention.
  • step S10 a source image is input, and gray scale values of respective color sub-pixels corresponding to each pixel point on the source image are measured.
  • the gray scale values of the respective color sub-pixels corresponding to each pixel point on the source image can be measured by various existing methods and devices.
  • gray scale values of red R sub-pixels, green G sub-pixels, and blue B sub-pixels corresponding to each pixel point on the source image may be measured.
  • a plurality of predetermined target pixel points on the source image are determined based on the measured grayscale values of the respective color sub-pixels.
  • the plurality of predetermined target pixel points may include six predetermined target pixel points, and the plurality of predetermined target pixel points may respectively correspond to vertices of the respective solid colors of the six primary colors in the uniform chromaticity space.
  • the six primary colors may include: red R, green G, blue B, cyan C, magenta M, and yellow Y.
  • the method of the exemplary embodiment of the present invention only needs to reduce the calculation amount of the color gamut of the determination source image. Determining a plurality of predetermined target pixel points on the source image, and then calculating only a plurality of coordinate values corresponding to the plurality of predetermined target pixel points in the uniform chromaticity space, so that the amount of calculation can be effectively reduced.
  • the step of determining a plurality of predetermined target pixel points on the source image based on the measured gray scale values of the color sub-pixels may include: setting a gray scale value of the red R sub-pixel to a maximum value, and a green color a gray point value of the G sub-pixel and a gray point value of the blue B sub-pixel are 0 as the first predetermined target pixel (corresponding to the vertex of the red R in the uniform chromaticity space); the green G sub-pixel a pixel whose gray scale value is the maximum value, the gray scale value of the red R sub-pixel, and the gray scale value of the blue B sub-pixel are 0 respectively as the second predetermined target pixel point (corresponding to the vertex of the green G in the uniform chroma space) a pixel point having a gray-scale value of a blue B sub-pixel, a gray-scale value of a red R sub-pixel, and a gray-scale value of a green G sub-pixel of 0 as a third predetermined
  • the grayscale value of the red R sub-pixel when the grayscale value of the red R sub-pixel is the maximum value, the grayscale value of the green G sub-pixel and the grayscale of the blue B-subpixel The value may not be equal to 0 as long as the grayscale value of the green G sub-pixel and the grayscale value of the blue B sub-pixel are approximately zero.
  • the maximum value of the equation Gray(G)+Gray(B)-Gray(R) may be calculated and will correspond to the maximum value of the equation Gray(G)+Gray(B)-Gray(R).
  • the pixel is taken as a fourth predetermined target pixel (corresponding to the apex of cyan C in the uniform chromaticity space).
  • Gray(G) is the grayscale value of the green G sub-pixel
  • Gray(B) is the grayscale value of the blue B sub-pixel
  • Gray(R) is the grayscale value of the red R sub-pixel
  • the calculation equation Gray ( R)+Gray(B)-Gray(G), and the pixel corresponding to the maximum value of the equation Gray(R)+Gray(B)-Gray(G) is taken as the fifth predetermined target pixel ( Corresponds to the vertices of magenta M in the uniform chromaticity space); calculates the maximum value of the equation Gray(R)+Gray(G)-Gray(B), and will be related to the equation Gray(R)+Gray(G)- The pixel corresponding to the maximum value of Gray (B) is taken as the sixth predetermined target pixel (corresponding The vertices of yellow Y in a uniform chromaticity space).
  • step S30 a plurality of coordinate values L*, a*, b* corresponding to the plurality of predetermined target pixel points in the uniform chromaticity space are respectively calculated.
  • L* represents the brightness index
  • a* and b* represent the chromaticity index.
  • the existing various methods can be used to calculate the coordinate values corresponding to the pixel points in the uniform chromaticity space, and the content of this part of the present invention will not be described again.
  • step S40 a color gamut of the source image is determined based on the plurality of coordinate values.
  • the step of determining a color gamut of the source image based on the plurality of coordinate values may include: sequentially connecting a plurality of points corresponding to the plurality of coordinate values in a uniform chromaticity space, and The area surrounded by the line is used as the color gamut of the source image.
  • FIG. 2 illustrates an ab-plane gamut map displayed in a CIELab chromaticity space, in accordance with an exemplary embodiment of the present invention.
  • the six marked points in the figure are the vertices of the solid colors of the six primary colors in the uniform chromaticity space.
  • the area formed by the connection of the six vertices is the color gamut of the source image. Since only a plurality of predetermined target pixel points need to be determined by simply calculating the R/G/B gray scale values of the pixels on the source image, then only the determined plurality of predetermined target pixel points are in the uniform chromaticity space. It is described above, and it is not necessary to describe each pixel of the source image in a uniform chromaticity space, which greatly reduces the amount of calculation, and improves the matching efficiency for the subsequent matching of the source image gamut to the target device gamut.
  • the two-dimensional equal brightness gamut refers to the gamut range of an image or device under fixed brightness conditions, which is generally described in CIELAB space, specifying the gamut boundary range of the a*b* plane under L* values.
  • step S50 gamut boundary extraction is performed on the target device to obtain a color gamut of the target device.
  • various methods can be utilized to perform gamut boundary extraction on the target device.
  • the color gamut of the present invention refers to the color of the a*b* plane under the condition of specifying the L* value in the uniform chromaticity space. Domain boundary range.
  • step S60 color gamut matching between the source image and the target device is performed.
  • the source image can be color-matched with the target device by using various existing color gamut matching methods.
  • color gamut matching between the source image and the target device may be performed using a color gamut clipping method or a color gamut compression method. It should be understood that color gamut matching using the color gamut clipping method and the gamut compression method is common knowledge in the art, and the content of this part of the present invention will not be described again.
  • the color gamut matching method based on the source image color gamut can use the mapping of the source image color gamut to the target color gamut without depending on the color gamut of the source device, so that the image color is in the process of transmission and reproduction. The loss is reduced, improving the effect of gamut matching.
  • the color gamut matching method is adopted, and the color gamut matching is performed depending on the color gamut of the source image, and the tristimulus value of each gray scale value of all the pixel points on the source image is not required, so that the color gamut of the source image can be quickly determined. , greatly reducing the amount of calculations.
  • the source image color gamut-based color gamut matching method of the exemplary embodiment of the present invention does not require measurement of a tristimulus value of each grayscale value of all pixel points on the source image, and does not require each pixel of the source image.
  • the points are all described in the uniform chromaticity space, thereby greatly reducing the calculation amount of determining the color gamut of the source image, and realizing the color gamut of the source image quickly.

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Abstract

提供一种基于源图像色域的色域匹配方法，所述方法包括：(a)输入源图像，并测量所述源图像上每个像素点对应的各颜色子像素的灰阶值；(b)基于测量得到的各颜色子像素的灰阶值，确定出所述源图像上的多个预定目标像素点；(c)计算所述多个预定目标像素点分别在均匀色度空间中对应的多个坐标值；(d)基于所述多个坐标值确定出所述源图像的色域；(e)对目标设备进行色域边界提取，得到所述目标设备的色域；(f)进行所述源图像与所述目标设备之间的色域匹配。采用上述色域匹配方法，采用源图像色域到目标色域的映射，使得图像色彩在传递、再现过程中的损失减小，色域匹配效果较好，而且有效减小了确定源图像的色域的计算量。

Description

基于源图像色域的色域匹配方法 技术领域
本发明总体说来涉及色域匹配技术领域，更具体地讲，涉及一种基于源图像色域的色域匹配方法。
背景技术
现有的色域匹配方法(gamut mapping)，大多是基于R/G/B(即，红色/绿色/蓝色)三种颜色分量的最大饱和色彩在色度空间的连线构成的色域，与其他显示基色(例如，彩色打印机的C/M/Y(即，青色/品红色/黄色)三种颜色分量)的最大饱和色彩在色度空间的连线构成的色域之间的匹配。
一般在进行色域匹配时，需要考虑源色域和目标色域，源色域既可以是源设备的色域，也可以是源图像的色域，但是，由于基于源设备的色域进行色域匹配时图像色彩在传递、再现过程中的损失往往比基于源图像的色域进行匹配时的损失大。因此，现有技术中，大多基于源图像的色域进行不同设备之间的色域匹配，以获得更好的匹配效果。
现有的确定源图像的色域的方法可包括以下步骤：分别量测源图像三原色灰阶的刺激值矩阵(R(X，Y，Z)、G(X，Y，Z)、B(X，Y，Z))；根据颜色混合原理，由所述量测出的三原色灰阶的刺激值矩阵(R(X，Y，Z)、G(X，Y，Z)、B(X，Y，Z))计算出源图像灰阶的刺激值矩阵S(X，Y，Z)；根据CIELAB色度空间中的L*、a*、b*与源图像灰阶的三刺激值矩阵S(X，Y，Z)之间的换算关系， 由所述源图像灰阶的三刺激值矩阵S(X，Y，Z)计算出源图像中每个像素颜色在CIELAB色度空间中的坐标值L*、a*、b*；基于计算的坐标值L*、a*、b*确定出源图像的色域。这里，X表示红色刺激量、Y表示绿色刺激量、Z表示蓝色刺激量、L*表示明度指数、a*和b*表示色度指数。
但是，上述现有的确定源图像的色域的方法需量测源图像所有像素点的三原色灰阶的刺激值矩阵，这个过程往往需要很长的时间，且计算量很大。
发明内容
本发明的示例性实施例在于提供一种基于源图像色域的色域匹配方法，以解决在进行色域匹配的过程中，确定源图像的色域的计算量较大的技术问题。
根据本发明示例性实施例的一方面，提供一种基于源图像色域的色域匹配方法，所述方法包括:(a)输入源图像，并测量所述源图像上每个像素点对应的各颜色子像素的灰阶值；(b)基于测量得到的各颜色子像素的灰阶值，确定出所述源图像上的多个预定目标像素点；(c)计算所述多个预定目标像素点分别在均匀色度空间中对应的多个坐标值；(d)基于所述多个坐标值确定出所述源图像的色域；(e)对目标设备进行色域边界提取，得到所述目标设备的色域；(f)进行所述源图像与所述目标设备之间的色域匹配。
可选地，所述多个预定目标像素点可分别对应于均匀色度空间中六基色的各纯色的顶点，其中，所述六基色可包括：红色R、绿色G、蓝色B、青色C、品红色M、黄色Y。
可选地，所述多个预定目标像素点可包括六个预定目标像素点，其中，步骤(b)可包括：(b1)将红色R子像素的灰阶值为最大值、绿色G子像素的灰阶值和蓝 色B子像素的灰阶值分别为0的像素点作为第一预定目标像素点红色R的顶点；(b2)将绿色G子像素的灰阶值为最大值、红色R子像素的灰阶值和蓝色B子像素的灰阶值分别为0的像素点作为第二预定目标像素点；(b3)将蓝色B子像素的灰阶值为最大值、红色R子像素的灰阶值和绿色G子像素的灰阶值分别为0的像素点作为第三预定目标像素点；(b4)计算等式Gray(G)+Gray(B)-Gray(R)的最大值，并将与等式Gray(G)+Gray(B)-Gray(R)的最大值对应的像素点作为第四预定目标像素点，其中，Gray(G)为绿色G子像素的灰阶值，Gray(B)为蓝色B子像素的灰阶值，Gray(R)为红色R子像素的灰阶值；(b5)计算等式Gray(R)+Gray(B)-Gray(G)的最大值，并将与等式Gray(R)+Gray(B)-Gray(G)的最大值对应的像素点作为第五预定目标像素点；(b6)计算等式Gray(R)+Gray(G)-Gray(B)的最大值，并将与等式Gray(R)+Gray(G)-Gray(B)的最大值对应的像素点作为第六预定目标像素点。
可选地，步骤(d)可包括：在均匀色度空间中将所述多个坐标值对应的多个点依次进行连线，并将由所述连线包围形成的区域作为所述源图像的色域。
可选地，步骤(f)可包括：利用色域剪裁方法或色域压缩方法进行所述源图像与所述目标设备之间的色域匹配。
采用上述色域匹配方法，采用源图像色域到目标色域的映射，使得图像色彩在传递、再现过程中的损失减小，色域匹配效果较好，而且有效减小了确定源图像的色域的计算量。
附图说明
图1示出根据本发明示例性实施例的基于源图像色域的色域匹配方法的流程图；
图2示出根据本发明示例性实施例的在CIELab色度空间中显示的ab平面色域图。
具体实施方式
现将详细描述本发明的示例性实施例，所述实施例的示例在附图中示出，其中，相同的标号始终指的是相同的部件。
在均匀色度空间(例如，CIELCh色度空间或CIELAB色度空间)，由于很多图像并不是覆盖全部色域，而是仅仅覆盖部分色域，所以在本发明示例性实施例的色域匹配方法中可依赖于源图像覆盖的色域进行色域匹配，这样可以使得图像色彩在传递、再现过程中的损失减小，有效改善色域匹配的效果。
本发明总体说来，需首先分别建立源图像和目标设备与均匀色度空间的映射关系，然后在均匀色度空间中对所述源图像和所述目标设备进行色域匹配。
图1示出根据本发明示例性实施例的基于源图像色域的色域匹配方法的流程图。
参照图1，在步骤S10中，输入源图像，并测量所述源图像上每个像素点对应的各颜色子像素的灰阶值。这里，可利用现有的各种方法和装置测量出所述源图像上每个像素点对应的各颜色子像素的灰阶值。优选地，可测量所述源图像上每个像素点对应的红色R子像素、绿色G子像素、蓝色B子像素的灰阶值。
在步骤S20中，基于测量得到的各颜色子像素的灰阶值，确定出所述源图像上的多个预定目标像素点。可选地，所述多个预定目标像素点可包括六个预定目标像素点，且所述多个预定目标像素点可分别对应于均匀色度空间中六基色的各纯色的顶点。这里，所述六基色可包括：红色R、绿色G、蓝色B、青色C、品红色M、黄色Y。
这里，由于源图像本身并不会含有R/G/B的0～255灰阶的所有组合，因此，为减小确定源图像的色域的计算量，本发明示例性实施例的方法仅需确定所述源图像上的多个预定目标像素点，然后仅计算所述多个预定目标像素点在均匀色度空间中对应的多个坐标值，这样可有效减小计算量。
具体说来，基于测量得到的各颜色子像素的灰阶值，确定出所述源图像上的多个预定目标像素点的步骤可包括：将红色R子像素的灰阶值为最大值、绿色G子像素的灰阶值和蓝色B子像素的灰阶值分别为0的像素点作为第一预定目标像素点(对应于均匀色度空间中红色R的顶点)；将绿色G子像素的灰阶值为最大值、红色R子像素的灰阶值和蓝色B子像素的灰阶值分别为0的像素点作为第二预定目标像素点(对应于均匀色度空间中绿色G的顶点)；将蓝色B子像素的灰阶值为最大值、红色R子像素的灰阶值和绿色G子像素的灰阶值分别为0的像素点作为第三预定目标像素点(对应于均匀色度空间中蓝色B的顶点)。这里，本领域的技术人员应理解，以第一预定目标像素点为例，红色R子像素的灰阶值为最大值时，绿色G子像素的灰阶值和蓝色B子像素的灰阶值可不等于0，只要绿色G子像素的灰阶值和蓝色B子像素的灰阶值近似于0即可。
可选地，可计算等式Gray(G)+Gray(B)-Gray(R)的最大值，并将与等式Gray(G)+Gray(B)-Gray(R)的最大值对应的像素点作为第四预定目标像素点(对应于均匀色度空间中青色C的顶点)。这里，Gray(G)为绿色G子像素的灰阶值，Gray(B)为蓝色B子像素的灰阶值，Gray(R)为红色R子像素的灰阶值；计算等式Gray(R)+Gray(B)-Gray(G)的最大值，并将与等式Gray(R)+Gray(B)-Gray(G)的最大值对应的像素点作为第五预定目标像素点(对应于均匀色度空间中品红色M的顶点)；计算等式Gray(R)+Gray(G)-Gray(B)的最大值，并将与等式Gray(R)+Gray(G)-Gray(B)的最大值对应的像素点作为第六预定目标像素点(对应 于均匀色度空间中黄色Y的顶点)。
在步骤S30中，计算所述多个预定目标像素点分别在均匀色度空间中对应的多个坐标值L*、a*、b*。这里，L*表示明度指数、a*和b*表示色度指数。这里，可利用现有的各种方法来计算像素点在均匀色度空间中对应的坐标值，本发明对此部分的内容不再赘述。
在步骤S40中，基于所述多个坐标值确定出所述源图像的色域。
可选地，基于所述多个坐标值确定出所述源图像的色域的步骤可包括：在均匀色度空间中将所述多个坐标值对应的多个点依次进行连线，并将由所述连线包围形成的区域作为所述源图像的色域。
图2示出根据本发明示例性实施例的在CIELab色度空间中显示的ab平面色域图。
如图2所示，图中的六个标记点即为均匀色度空间中六基色的各纯色的顶点。由六个顶点的连接包围形成的区域即为源图像的色域。由于只需要对源图像上各像素的R/G/B灰阶值进行简单就可以确定出多个预定目标像素点，然后仅将确定出的所述多个预定目标像素点在均匀色度空间上描述出来，而不必将源图像的每个像素点均在均匀色度空间上描述出来，大大减少了计算量，为之后的源图像色域到目标设备色域的匹配提高了匹配效率。
二维等明度色域指的是在固定亮度条件下，图像或设备的色域范围，其一般被描述在CIELAB空间中，指定L*值条件下的a*b*平面的色域边界范围。
返回图1，在步骤S50中，对目标设备进行色域边界提取，得到所述目标设备的色域。这里，可利用现有的各种方法来对目标设备进行色域边界提取。这里，本发明的色域是指在均匀色度空间中，指定L*值条件下的a*b*平面的色 域边界范围。
在步骤S60中，进行所述源图像与所述目标设备之间的色域匹配。
这里，可利用现有的各种色域匹配方法将源图像与目标设备进行色域匹配。作为示例，可利用色域剪裁方法或色域压缩方法进行所述源图像与所述目标设备之间的色域匹配。应理解，利用色域剪裁方法和色域压缩方法进行色域匹配为本领域的公知常识，本发明对此部分的内容不再赘述。
采用本发明示例性实施例的基于源图像色域的色域匹配方法，可不依赖于源设备的色域，而采用源图像色域到目标色域的映射，使得图像色彩在传递、再现过程中的损失减小，改善了色域匹配的效果。
采用上述色域匹配方法，而依赖于源图像的色域进行色域匹配，且不需要测量源图像上所有像素点的每个灰阶值的三刺激值，从而能够快速确定源图像的色域，大大减少计算量。
此外，本发明示例性实施例的基于源图像色域的色域匹配方法，不需要测量源图像上所有像素点的每个灰阶值的三刺激值，也不需要将源图像的每个像素点均在均匀色度空间上描述出来，从而大大减小了确定源图像色域的计算量，实现快速确定源图像的色域。
上面已经结合具体示例性实施例描述了本发明，但是本发明的实施不限于此。在本发明的精神和范围内，本领域技术人员可以进行各种修改和变型，这些修改和变型将落入权利要求限定的保护范围之内。

Claims (5)

  1. 一种基于源图像色域的色域匹配方法，所述方法包括：
    (a)输入源图像，并测量所述源图像上每个像素点对应的各颜色子像素的灰阶值；
    (b)基于测量得到的各颜色子像素的灰阶值，确定出所述源图像上的多个预定目标像素点；
    (c)计算所述多个预定目标像素点分别在均匀色度空间中对应的多个坐标值；
    (d)基于所述多个坐标值确定出所述源图像的色域；
    (e)对目标设备进行色域边界提取，得到所述目标设备的色域；
    (f)进行所述源图像与所述目标设备之间的色域匹配。
  2. 根据权利要求1所述的方法，其中，所述多个预定目标像素点分别对应于均匀色度空间中六基色的各纯色的顶点，其中，所述六基色包括：红色R、绿色G、蓝色B、青色C、品红色M、黄色Y。
  3. 根据权利要求1所述的方法，其中，所述多个预定目标像素点包括六个预定目标像素点，
    其中，步骤(b)包括：
    (b1)将红色R子像素的灰阶值为最大值、绿色G子像素的灰阶值和蓝色B子像素的灰阶值分别为0的像素点作为第一预定目标像素点红色R的顶点；
    (b2)将绿色G子像素的灰阶值为最大值、红色R子像素的灰阶值和蓝色B子像素的灰阶值分别为0的像素点作为第二预定目标像素点；
    (b3)将蓝色B子像素的灰阶值为最大值、红色R子像素的灰阶值和绿色G子像素的灰阶值分别为0的像素点作为第三预定目标像素点；
    (b4)计算等式Gray(G)+Gray(B)-Gray(R)的最大值，并将与等式Gray(G)+Gray(B)-Gray(R)的最大值对应的像素点作为第四预定目标像素点，其中，Gray(G)为绿色G子像素的灰阶值，Gray(B)为蓝色B子像素的灰阶值，Gray(R)为红色R子像素的灰阶值；
    (b5)计算等式Gray(R)+Gray(B)-Gray(G)的最大值，并将与等式Gray(R)+Gray(B)-Gray(G)的最大值对应的像素点作为第五预定目标像素点；
    (b6)计算等式Gray(R)+Gray(G)-Gray(B)的最大值，并将与等式Gray(R)+Gray(G)-Gray(B)的最大值对应的像素点作为第六预定目标像素点。
  4. 根据权利要求1所述的方法，其中，步骤(d)包括：在均匀色度空间中将所述多个坐标值对应的多个点依次进行连线，并将由所述连线包围形成的区域作为所述源图像的色域。
  5. 根据权利要求1所述的方法，其中，步骤(f)包括：利用色域剪裁方法或色域压缩方法进行所述源图像与所述目标设备之间的色域匹配。
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