JP2007036746A - Color prediction method, color prediction apparatus, and color prediction program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a color prediction method, a color prediction apparatus, and a color prediction program capable of attaining highly accurate color prediction for pure colors of primary and secondary colors in particular by using base data acquired from a device to/from which image data are inputted/outputted. <P>SOLUTION: The color prediction apparatus receives a printout of a base data patch outputted from a printer adopted as the device, applies colorimetry to the base data patch so as to acquire base data of L*a*b* color space data corresponding to the CMY color space data in response to the characteristic of the device, extracts the data of the L*a*b* color space being a color space depending on no device on the basis of the number of dimensions of prediction data, applies the method of the principal component analysis to the extracted data to set color space axes (e.g., a Z<SB>1</SB>axis, a Z<SB>2</SB>axis, a Z<SB>3</SB>axis) of a second color space with respect to respectively the L* axis, a* axis, and b* axis, and when the color prediction apparatus predicts the prediction data from data to be predicted, the color prediction apparatus carries out data conversion via the second color space so as to accurately predict pure colors. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to a color prediction method, a color prediction apparatus, and a color prediction program in a device such as an input / output color device for forming an equivalent image for the image from an original image.

  Various color devices such as digital cameras, color scanners, color printers, color displays and the like are widespread and widely used, and demands for colors are increasing. In particular, in fields such as DTP (Desk Top Publishing), there is a high demand for reproducibility.

  For this reason, in the DTP system, a CMS (color management system) is indispensable. In the CMS technique, a color profile represented by an ICC (International Color Consortium) profile is used. LUT lattice point data is determined, and various interpolation processes are applied to perform color correction.

  At this time, in the CMS technique, a device-dependent color space (device-dependent color space) and a device-independent color space (device-independent color space) are paired, and the device-dependent color space and the device-independent color space are paired. Color conversion is performed between color spaces.

  For example, in a printer that is one of output devices, a device-dependent color space is a CMY color space, and in a color scanner that is one of input devices, a device-dependent color space is an RGB color space. The device-independent color space includes an L * a * b * color space that is a uniform color space, an L * u * v * color space, and an XYZ color space that is a tristimulus value.

  On the other hand, the output characteristics (or input characteristics of the input device) of the output device can be obtained from the base data by outputting (or inputting) a large number of color patches and acquiring the base data. Further, as a color prediction model for predicting LUT lattice points, a method using a neuro, a polynomial operation, a regression operation, an interpolation operation, or the like from this base data has been proposed (see, for example, Patent Document 1 and Patent Document 2).

  Patent Document 1 proposes color prediction based on regression calculation. When color prediction is performed, the base data is weighted according to the distance between the predicted value and the base data, and set as a parameter. Yes.

  In Patent Document 2, when an arbitrary color signal in the first color space is converted into a color signal in the second color space, a color chart having a plurality of color patches is converted to a color in the first color space. Output as a signal, and at least a primary term of the color signal of the first color space obtained by reading this color chart is obtained as a first explanatory variable group, and a principal component analysis is performed. To the explanatory variable group.

  Subsequently, each colorimetric value of each color patch of the color chart is set as an objective variable group, and the objective variable group and the second explanatory variable group are subjected to multiple regression analysis to obtain a trick between partial regression variables, and the first An arbitrary color signal in the color space is converted into a color signal in the second color space by verifying a matrix of partial regression variables.

  By the way, for example, data for outputting a color patch is acquired as a pair of a device-dependent color space (for example, CMY color space) and a device-independent color space (for example, L * a * b * color space). At this time, depending on the device (for example, a printer), retransfer or the like is affected by the difference between the primary color and the secondary color for each color region, and a considerable bias as data occurs in a device-independent space. The color space data has different sensitivities to the color space axis for each color space. For this reason, in the L * a * b * color space obtained as device-independent data, there is a possibility that the characteristics of the device may not be sufficiently expressed, and a CMS such as insufficient accuracy can be obtained at the time of LUT lattice point prediction. There is a problem with accuracy.

  L *, a *, and b * are given as points on a three-dimensional space, and the weight of data to be predicted is uniform on the L * axis, a * axis, and b * axis. Can be intuitively recognized that the L * -axis data is the most important, and the color difference between the a * -axis and the b * -axis is likely to include noise terms due to various factors. . For this reason, equalizing the weights of the L * axis, the a * axis, and the b * axis may affect the prediction accuracy.

  In the proposal of Patent Document 1 described above, there is no data weight for each of the L * axis, a * axis, and b * axis, and the parameters are predicted with uniform weights. In addition, color prediction in a color space according to the device is difficult.

Further, in Patent Document 2, the amount of base data acquired for each device cannot be fully utilized against the uneven distribution in the device-independent color space that occurs for each device. Also, when calculating partial regression coefficients, an accuracy weight is assigned to each color to improve the prediction accuracy for that color, but in order to increase the accuracy of prediction of gray areas and skin colors that are problematic in accuracy. Although the weighting factor is used for this, the weighting factor largely depends on the empirical value.
JP-A-10-262157 Japanese Patent Laid-Open No. 10-164381

  The present invention has been made in view of the above facts, and is obtained from a device when performing color reproduction with a pure color that is a color on the color space axis in a device color space such as a primary color or a secondary color. Another object of the present invention is to provide a color prediction method, a color prediction device, and a color prediction program that enable highly accurate color prediction from the base data that is generated.

  To achieve the above object, according to the present invention, in a device to which image data is input or output, an object to be predicted is input as data in one color space between a device-dependent color space and a device-independent color space. A color prediction method for predicting a predicted value output as data in another color space from a value, wherein the first color space is the device-independent color space for the device-dependent color space data in the device A database in which the correspondence relationship of the data is acquired, and the color space axis of the device-dependent color space is selected from the predicted value or the predicted value that is the color of the device-dependent color space, and from the base data Extracting the data of the first color space with respect to the data on the selected color space axis, and using a statistical method on the extracted data of the first color space Acquiring a color space axis of a second color space obtained by converting the space axis, and based on the color space axis of the second color space, the device-dependent color space data and the second color space Color space axis conversion base data describing the corresponding relationship with the data of the image data is acquired, and when the predicted value is data of the device-dependent color space, the color space axis conversion base data is used to The conversion color space axis prediction data, which is the data of the second color space, is predicted from the prediction value, and the conversion color space axis prediction data is calculated based on the color space axis of the second color space. It is characterized by converting the data into.

  Further, the present invention provides the second color space data based on the color space axis of the second color space when the predicted value is data of the device-independent color space. Converting the second color space data into the device-dependent color space data based on the color space axis conversion base data, and predicting the predicted value. To do.

  Furthermore, the present invention provides a device that receives or outputs a device-dependent color space that is output from a device when a predicted value that is device-dependent color space data for a target color is input to a device that receives or outputs image data. A color prediction method for predicting a predicted value, the target describing a correspondence relationship between data of the device-dependent color space of a plurality of target colors and data of a first color space which is a device-independent color space Obtaining base data describing a correspondence relationship between the color base data and the data of the first color space with respect to the data of the device-dependent color space in the device, and the device-dependent color space from the predicted value or the predicted The first color space for the data on the selected color space axis from the target color base data and the base data. To obtain the color space axis of the second color space obtained by converting the color space axis using a statistical method with respect to the extracted data of the first color space, and the target Obtain color space axis conversion target base data describing the correspondence between the target color of the device-dependent color space and the data of the second color space from the color base data and the color space axis of the second color space. In addition, based on the color space axis of the second color space, color space axis conversion base data describing the correspondence between the device-dependent color space data and the second color space data is acquired. Then, based on the color space axis target base data, the predicted value is converted into data of the second color space, and then the converted data of the second color space is converted into the color space axis conversion base. Based on the data, the device-dependent color space Predicting the prediction value by converting the data, and wherein the.

  According to the present invention, a predicted value for a predicted value of a primary color or a secondary color depending on a device between a device-dependent color space and a device-independent color space or on a device-dependent color space. Predict. At this time, the device-independent color space is set as the first color space, the color space axis that is the dimensionality of the predicted value or the data of the predicted value is recognized, and the characteristics of the device on the recognized color space axis are obtained. Based on the data of the first color space of the corresponding base data, a second color space is virtually set using a statistical method such as a principal component analysis method.

  Thus, it is possible to set a second color space that is not affected by noise or disturbance when acquiring the base data, and by performing data conversion via this second color space, An appropriate predicted value based on the device characteristics is predicted from the predicted value.

  At this time, the number of dimensions of the predicted value is set based on the number of dimensions of the predicted value, and data conversion to a different color space is performed using data of the set number of dimensions. Also, the color space axis-dependent data weight obtained by the statistical method is given, and the color space axis-dependent data weight obtained by the statistical method is used as data of the principal component vector for the data of the first color space. A more appropriate predicted value can be obtained by giving a value based on the contribution ratio.

  Such a color prediction apparatus to which the present invention is applied is input as data in one color space between a device-dependent color space and a device-independent color space in a device to which image data is input or output. A color prediction apparatus that predicts a predicted value output as data in another color space from a predicted value to be generated, and is a device-independent color space for the device-dependent color space data in the device Base data acquisition means for acquiring a database describing the correspondence of the data of the color space, and the color space axis of the device-dependent color space from the predicted value or the predicted value that is the color of the device-dependent color space The limited color recognition means to be selected, and the first color space data for the data on the color space axis recognized by the recognition means is extracted from the base data. A color space axis of a second color space obtained by converting a color space axis using a statistical method with respect to the data of the first color space extracted by the constant color extracting means and the limited color extracting means. Color space axis conversion means for describing the correspondence between the device-dependent color space data and the second color space data based on the color space axis conversion means to be acquired and the color space axis of the second color space Color space axis conversion base data acquisition means for acquiring base data, and when the predicted value is data of the device-dependent color space, based on the color space axis conversion base data, from the predicted value, Prediction means for predicting converted color space axis prediction data that is data of the second color space; and the converted color space axis prediction data of the first color space based on the color space axis of the second color space. Color space axis reverse conversion method to convert to data And when the predicted value is data of the device-independent color space, the predicting means sets the predicted value to the color space axis of the second color space. Based on the color space axis conversion base data, the data converted into the data of the second color space, and the color space axis inverse conversion means converts the data converted into the data of the second color space based on the color space axis conversion base data. Anything can be used as long as it is converted into color space data.

  In addition, a color prediction apparatus to which the present invention is applied is output from a device when a predicted value, which is device-dependent color space data for a target color, is input to a device to which image data is input or output. A device for predicting a predicted value of a device-dependent color space, the device-dependent color space data of a plurality of target colors, and a first color space data which is a device-independent color space, Target color data acquisition means for acquiring target color base data describing the correspondence relationship between the first color space and the base data describing the correspondence relationship of the data in the first color space with respect to the device-dependent color space data in the device Base data acquisition means, limited color recognition means for recognizing a color space axis of the device-dependent color space from the predicted value or the prediction target, and the target color base data A limited color extracting means for extracting data of the first color space with respect to data on the color space axis recognized by the limited color recognizing means from the data and the base data, and the second color extracted by the limited color extracting means. Color space axis conversion means for acquiring a color space axis of a second color space obtained by converting a color space axis using a statistical method for data of one color space; the target color base data; and Color space axis conversion for obtaining color space axis conversion target base data describing the correspondence between the target color of the device-dependent color space and the data of the second color space from the color space axis of the second color space A color space axis conversion base describing a correspondence relationship between the device-dependent color space data and the second color space data based on the target base data acquisition unit and the color space axis of the second color space; Get data Color space axis conversion target base data acquisition means, color space axis reverse conversion means for converting the predicted value into data of the second color space based on the color space axis conversion target base data, and the second And a prediction unit that predicts the predicted value by converting the converted data of the color space into the data of the device-dependent color space based on the color space axis conversion base data. Good.

  The color prediction program of the present invention is a predicted value input as data of one color space between a device-dependent color space and a device-independent color space in a device to which image data is input or output. A color prediction program for predicting a predicted value output as data of another color space from the predicted value that is a color of the device-dependent color space or a color space of a device-dependent color space from the predicted value Selecting an axis, and on the selected color space axis from a database describing a correspondence relationship between the data of the first color space which is the device-independent color space with respect to the data of the device-dependent color space in the device Extracting the data of the first color space for the data of the first color space, and using a statistical method for the extracted data of the first color space Obtaining a color space axis of a second color space obtained by converting the axis, and based on the color space axis of the second color space, the device-dependent color space data and the second color space Obtaining the color space axis conversion base data describing the corresponding relationship with the data, and when the predicted value is data of the device-dependent color space, based on the color space axis conversion base data, Predicting converted color space axis prediction data that is data of the second color space from the predicted values; and converting the converted color space axis prediction data based on the color space axis of the second color space. Converting the data into one color space data, or when the predicted value is data in the device-independent color space, the predicted value is converted into the color space axis of the second color space. Based on the second color space data Converting the converted second color space data into the device-dependent color space data based on the color space axis conversion base data, and predicting the predicted value. Can do.

  In addition, the color prediction program is a device-dependent color space that is output from a device when a predicted value that is device-dependent color space data for a target color is input to a device to which image data is input or output. A color prediction program for predicting a predicted value of the device, the step of selecting a color space axis of the device-dependent color space from the predicted value or the predicted value, and the device-dependent color space of a plurality of target colors Extracting the data of the selected color space axis from target color base data describing the correspondence between the data and the data of the first color space which is a device-independent color space; and the device in the device From the base data describing the correspondence of the first color space data to the dependent color space data, the data on the selected color space axis Extracting the data of the first color space; and converting the color space axis using a statistical method on the data of the first color space extracted from the target color base data and the base data. Obtaining the color space axis of the second color space obtained in the above, the data extracted from the target color base data and the color space axis of the second color space, and the target color of the device-dependent color space Obtaining the color space axis conversion target base data describing the correspondence between the data and the data of the second color space, and based on the color space axis of the second color space, Obtaining a color space axis conversion base data describing a correspondence relationship between the data and the data of the second color space; and based on the color space axis target base data, the predicted value is converted to the second color space. Data of Converting the data converted from the second color space into the data of the device-dependent color space based on the color space axis conversion base data, and predicting the predicted value And can be included.

  The present invention can also include various storage media for storing the color prediction program and a transmission medium used for transmission of the color prediction program.

  The present invention provides a statistical method such as principal component analysis from device-independent color space data corresponding to device-dependent color space data when improving the reproducibility of primary colors and secondary colors. Is used to set the second color space, and the predicted value is predicted from the predicted value via the second color space. At this time, by limiting the data to be used based on the number of dimensions of the color to be predicted, an excellent effect that an appropriate prediction of a pure color is possible can be obtained.

Embodiments of the present invention will be described below with reference to the drawings.
[First Embodiment]
FIG. 1 shows a schematic configuration of a main part of the color prediction apparatus 10 according to the first embodiment. The color prediction apparatus 10 is a so-called forward model that converts from a device-dependent color space to a device-independent color space. As an example of a device, the color prediction apparatus 10 is a printer (color) in which a device-dependent color space is a CMY color space and a device-independent color space that is a first color space is an L * a * b * color space. Printer) to convert from the CMY color space to the L * a * b * color space. At this time, color data in the CMY color space is set as a predicted value, and color data in the L * a * b * color space is set as a predicted value.

  Although a printer will be described as an example of a device, a device to which the present invention is applied is not limited to this, such as a digital camera such as a digital still camera, an input device such as a color scanner, an output device such as a color display, or the like. Any device such as a device whose device-dependent color space is an RGB color space can be applied.

  The color prediction apparatus 10 is provided with a base data storage unit 12 as a base data acquisition unit, a limited color selection unit 14, a color space axis conversion unit 16, a conversion color space axis color prediction unit 18, and a color space axis. The reverse conversion unit 20 is included.

  The base data storage unit 12 stores base data acquired in advance. The base data is acquired by printing out a base data patch including a number of preset patches as a color chart using a printer provided with the color predicting device 10 and printing each patch (base Measure the color of the data patch. For patch colorimetry, a contact colorimeter, a scanner with known device characteristics, or the like can be used, and data in the L * a * b * color space is acquired by this colorimetry.

  Thereby, base data describing the correspondence relationship between the input data in the CMY color space specified by each base data patch and the output data in the L * a * b * color space is obtained. This base data is in accordance with the output characteristics of the device, and this base data is stored in the base data storage unit 12.

  The base data patch used at this time is set so as to cover substantially the entire color gamut that can be processed by the printer and to obtain input data that is subdivided in the CMY color space. Base data that can accurately represent the characteristics (output characteristics) of the printer is obtained.

  The limited color selection unit 14 selects data belonging to the color space axis based on the prediction data as the prediction value from the base data storage unit 12. That is, the limited color selection unit 14 extracts pure color data.

  FIG. 2 shows a schematic configuration of a main part of the limited color selection unit 14. The limited color selection unit 14 includes a limited color recognition unit 22 and a limited color extraction unit 24. The limited color recognition unit 22 recognizes a limited color that is a pure color from the predicted data when the predicted data is input.

  For example, when the predicted data is a primary color of (C, M, Y) = (50, 0, 0) and a single cyan color, the limited color recognition unit 22 recognizes cyan as the limited color. Further, when the predicted data is blue with a secondary color of (C, M, Y) = (50, 10, 0) (secondary color of CM), blue is recognized as a limited color.

  The limited color extraction unit 24 extracts data for the limited color recognized by the limited color recognition unit 22 from the base data storage unit 12. For example, when the limited color is cyan, data in the CMY color space (C, 0, 0) and data in the first color space (L * a * b * color space) corresponding to this data are extracted. The

  The color space axis conversion unit 16 illustrated in FIG. 1 acquires the space axis of the second color space to be applied to color prediction from the data extracted from the base data storage unit 12 by the limited color selection unit 14.

  FIG. 3 shows an example of a schematic configuration of the color space axis conversion unit 16 applied to the present embodiment. The color space axis conversion unit 16 includes a principal component vector acquisition unit 26, a principal component contribution rate acquisition unit 28, and a color space conversion unit 30, and the base data extracted by the limited color selection unit 14 is the principal component vector. Input to the acquisition unit 26 and the principal component contribution rate acquisition unit 28.

  The principal component vector acquisition unit 26 performs statistical analysis from data (L * value, a * value, and b * value) of the L * a * b * color space that is the first color space included in the extracted base data. As an example of the method, a principal component vector is obtained using a principal component analysis method. As a principal component analysis method, a general configuration described in Japanese Patent Laid-Open No. 10-164381 can be applied.

Here, three variables are defined as variables X ₁ , X ₂ , and X _3, and a weight L is assigned to these variables X _{1 to} X ₃ .
When the weight synthetic _{variate ij} has been applied to the synthesis variables _{Z 1, Z 2, Z 3} , synthetic variables Z ₁ to Z ₃ from a variable X ₁ to X ₃ are shown below.

Z ₁ = L ₁₁ · X ₁ + L ₁₂ · X ₂ + L ₁₃ · X ₃
Z ₂ = L ₂₁ · X ₁ + L ₂₂ · X ₂ + L ₂₃ · X ₃
Z ₃ = L ₃₁ · X ₁ + L ₃₂ · X ₂ + L ₃₃ · X ₃
However, (L _i1 ) ² + (L _i2 ) ² + (L _i3 ) ² = 1 (i = 1, 2, 3)
With such synthetic variables Z _{1 to} Z ₃ , synthetic variables Z ₁ , Z ₂ , and Z ₃ whose correlation is 0 are obtained.

In fact, three variables X _1, eigenvalues of X _2, X ₃ of the correlation coefficient matrix _{(λ 1, λ 2, λ} 3) seek, the eigenvalues _{(λ 1, λ 2, λ} 3) eigenvectors for The combined variables Z ₁ , Z ₂ , and Z ₃ are weights. At this time, the eigenvectors are orthogonal to each other and have a correlation of 0, and the combined variables Z ₁ , Z ₂ , and Z _{3 at} this time are the principal component vectors acquired by the principal component vector acquisition unit 26. Hereinafter, the principal component vectors Z ₁ , Z ₂ , and Z ₃ are used. Note that eigenvalues and eigenvectors can be easily calculated by a power method or the like, but the description thereof is omitted here.

Among the principal component vectors Z ₁ , Z ₂ , and Z ₃ , the first principal component, the second principal component, and the third principal component are arranged in the order of the largest variance. In the present embodiment, the first principal component, the second principal component, and the like. The main component and the third main component become three axes (color space axes) in the second color space.

Thereby, the axis of the second color space is obtained from the principal component vector acquired by the principal component vector acquisition unit 26. That is, by using the principal component analysis method, for example, the principal component vector Z ₁ is the first principal component and the principal component vector Z ₂ is the second principal component from the data distribution in the L * a * b * color space. If the principal component vector Z ₃ is the third principal component, the color space of the axes Z ₁ , Z ₂ , and Z ₃ can be acquired.

  On the other hand, the principal component contribution rate acquisition unit 28 acquires the principal component contribution rate from the data of the L * a * b * color space acquired as the base data by using a principal component analysis method.

In general, in the principal component analysis, the correlation coefficient between the original variables is called factor loading, and is obtained by multiplying the square root of the eigenvalue corresponding to the weights L _i1 , L _i2 , and L _i3 of the i-th principal component. is there.

From here, the correlation coefficient a _1i of the i-th principal component Z _i and the variable X ₁ is
a _1i = L _i1 · (λ _i ) ^1/2
It becomes. The correlation coefficient a _2i between Z _i and the variable X ₂ is
a _2i = L _i2 · (λ _i ) ^1/2
It becomes.

That is, the contribution rate represents how much each principal component reflects the original information. For example, the total of the information amount having p variables is p. The contribution ratio C ₁ is expressed by the equation (1) in Equation 1.

  Such a contribution rate can be said to indicate the information amount of data for each of the three axes to be converted. Therefore, it is preferable as a weight of data for each axis. In the principal component contribution rate acquisition unit 28, the first principal component, The contribution ratio of the second principal component and the third principal component, that is, the contribution ratio of each color space axis of the second color space is acquired.

On the other hand, as shown in FIG. 3, the color space conversion unit 30 receives data of the L * a * b * color space included in the extracted base data and is acquired by the principal component vector acquisition unit 26. The principal component vectors (for example, principal component vectors Z ₁ , Z ₂ , Z ₃ ) are input. From here, the color space conversion unit 30 converts each data in the L * a * b * color space into data in the second color space using the principal component vector. That is, L * a * b * color space data is data (L *, a *, b *), principal component vectors are principal component vectors Z ₁ (Z ₁₁ , Z ₁₂ , Z ₁₃ ), Z ₂ (Z ₂₁ , Z ₂₂ , Z ₂₃ ), Z ₃ (Z ₃₁ , Z ₃₂ , Z ₃₃ ), conversion data as conversion data α (α ₁ , α ₂ , α ₃ ), average vector as average vector m (m ₁ , m ₂₎ , M ₃ ), the data (L *, a *, b *) in the L * a * b * color space is expressed by the equation (2) in Equation 2.

By developing this, conversion data α (α ₁ , α ₂ , α ₃ ) is obtained as shown in equation (3).

  The original L * a * b * color space data corresponds to each data in the CMY color space, which is a device-dependent color space stored in the base data storage unit 12, and thus, the CMY color Limited color space axis conversion base data describing the correspondence between the space data and the second color space data is acquired.

  Here, the color prediction apparatus 10 performs principal component analysis using the limited color base data selected by the limited color selection unit 14. Thus, for example, if the data to be predicted is a single cyan color such as (C, M, Y) = (50, 0, 0), the data from the base data to the cyan single color (primary color) (C, 0, Data of the first color space corresponding to 0) is extracted and principal component analysis is performed.

The data in the L * a * b * color space, which is the first color space corresponding to the primary color data, is distributed on a substantially straight line along the L * axis, as shown in FIG. As a result, a second color space in which the Z ₁ axis corresponding to the first principal component is substantially along the L * axis is acquired.

If the predicted data is a secondary color such as (C, M, Y) = (50, 10, 0), the extracted base data is Lab color as shown in FIG. It is distributed two-dimensionally in space. As a result, the Z ₁ axis and the Z ₂ axis are obtained as a second space along this plane.

  Limited color space axis conversion base data indicating the correspondence between the data of the second color space acquired in this way and the data of the CMY color space is obtained.

  As illustrated in FIG. 1, the limited color space axis conversion base data and the principal component contribution rate acquired by the color space axis conversion unit 16 are input to the conversion color space axis limited color prediction unit 18.

  FIG. 5 shows an example of a schematic configuration of the conversion color space axis limited color prediction unit 18. The converted color space axis limited color prediction unit 18 includes a limited color space axis conversion base data storage unit 32, an axis weight setting unit 34, a predicted data storage unit 36, and a color prediction unit 38, and a predicted data dimension number setting unit 40. The base data dimension number setting unit 42 is included.

  The limited color space axis conversion base data storage unit 32 is input with the limited color space axis conversion base data acquired by the color space conversion unit 30 of the color space axis conversion unit 16. The color space axis conversion base data storage unit 32 stores limited color space axis conversion base data describing the correspondence between the limited colors in the device-dependent color space (CMY color space) and the data in the second color space.

  The axis weight setting unit 34 receives the contribution rate for each color space axis of the second color space acquired by the principal component contribution rate acquisition unit 28 of the color space axis conversion unit 16. Accordingly, the axis weight setting unit 32 sets a weight for each color space axis of the second color space.

  On the other hand, as shown in FIG. 1, the converted color space axis limited color predicting unit 18 is input with a predicted value (predicted data) that is data in a device-dependent color space. As shown in FIG. 5, the predicted data is stored in order in the predicted data storage unit 36.

  The color prediction unit 38 reads the predicted data stored in the predicted data storage unit 36. At this time, the predicted data dimension number setting unit 40 sets the number of dimensions of the predicted data from the number of dimensions of the predicted data.

  At this time, for example, when the predicted data is cyan (50, 0, 0), it is recognized that the predicted data is a primary color of cyan, and from here, the number of dimensions of the predicted data is 1 (1 Dimension). When the predicted data is blue (50, 10, 0) which is a secondary color of cyan and magenta, the number of dimensions of the predicted data is set to 2 (two dimensions).

  When the number of dimensions of the prediction data set by the prediction data dimension number setting unit 40 is read, the base data dimension number setting unit 42 sets the number of dimensions of the limited color space lexical conversion base data based on the number of dimensions.

That is, when the number of dimensions of the prediction data is set to one dimension, among the data (second color space data) stored as the limited color space axis conversion base data, the first principal component axis (for example, FIG. The dimension number of the base data is set so that the value (α ₁ ) for 4 (A) (Z ₁ axis) is selected as the data (base data) at the time of prediction by the color prediction unit 38. When the number of dimensions of the prediction data is set to two dimensions, a value (α ₁ ) for the first principal component axis (for example, the Z ₁ axis shown in FIG. 4B) from the limited color space axis conversion base data and The number of dimensions of the base data is set so that the value (α ₂ ) for the second principal component axis (for example, the Z ₂ axis shown in FIG. 4B) is selected.

  Thereby, the base data corresponding to the number of dimensions of the predicted data and the setting of the axis weight are input to the color predicting unit 38 together with the predicted data, and the color predicting unit 38 receives the second data based on these inputs. Predict data in the color space.

Here, for example, when the predicted data is a cyan single color of (50, 0, 0), since the number of dimensions is set to one dimension, the first principal component data (α ₁ ) and the axis weight setting of the first principal component are input.

That is, by setting the number of dimensions of the limited color space axis conversion base data to one dimension, a pair of i base data extracted as limited colors in the color space of the predicted data (C) _i = (α ₁ ) Data α ₁ of the second color space corresponding to C = 50 ((C, M, Y) = (50, 0, 0)) is obtained from _i . In the case of one dimension, the substantial axis weight is set to “1.0”, but in the case of two dimensions, the set weight is added to the values of the first principal component axis and the second principal component axis. Data is required.

  In this way, by using a known method such as a regression method in which a weighting coefficient is added to the color space axis of the limited color space axis conversion base data, weighting is performed, thereby predicting data depending on the color space axis. Is possible. For weighting, a general method such as a regression method disclosed in, for example, Japanese Patent Laid-Open No. 10-262175 can be used.

  As shown in FIG. 1, the data converted to the second color space output from the conversion color space axis limited color prediction unit 18 is input to the color space axis inverse conversion unit 20. Further, the principal component vector is input from the color space axis conversion unit 16 to the color space axis inverse conversion unit 20.

  As a result, the color space axis inverse conversion unit 20 performs reverse conversion for converting the data in the second color space into the data in the first color space. At this time, for example, the above-described equation (2) can be applied.

  As a result, data converted from the predicted data in the device-dependent color space into the L * a * b * color space, which is the first color space (device-independent color space), is output as predicted data.

  Here, processing using the color prediction apparatus 10 will be described. When this color prediction apparatus 10 is used, data in a CMY color space (device-dependent color space) depending on a printer as a device and an L * a * b * color space (device-independent color space independent of the printer) are used. ) Needs to be acquired in advance.

  FIG. 6 shows an outline of the base data acquisition process. When acquiring the base data, first, the base data patch is printed out using a printer. At this time, data of the CMY color space of each patch is set in advance, and printing processing based on the set data is performed.

  In the next step 102, each printed base data patch is colorimetrically measured to obtain data in the L * a * b * color space for each base data patch.

  In step 104, the CMY color space data thus obtained and the L * a * b * color space data are associated with each other and stored in the base data storage unit 12 as base data. Thereby, the base data applied by the color prediction apparatus 10 is acquired.

  The color prediction apparatus 10 performs color prediction processing using this base data. FIG. 7 shows an outline of this color prediction process. This color prediction process is executed by inputting predicted data in a device-dependent color space. Note that as the predicted data, a pure color that is a primary color or a secondary color is used.

  In this flowchart, when predicted data is input, first, in step 110, a limited color is recognized from the predicted data, and in the next step 112, data corresponding to the recognized limited color is extracted from the base data. As a result, for example, if the predicted data is a cyan primary color, cyan monochrome data and L * a * b * color space data corresponding to this data are extracted from the base data storage unit 12. When the predicted data is a secondary color of CM such as blue, (C, M, 0) data and L * a * b * color space data corresponding to this data are extracted.

  When the data extraction for predicting the limited color is completed in this way, in step 114, the second color space is extracted from the extracted data by the principal component analysis based on the data of the first color space. The principal component vector that becomes the axis of is obtained. Thereby, the principal component vector which becomes the color space axis of the second color space based on the data of the first color space is acquired. In step 116, the principal component contribution rate is acquired from the acquired principal component vector.

  Thereafter, in step 118, limited color space axis conversion base data that enables conversion of device-dependent color space data into second color space data based on the principal component vector and data extracted from the base data. In step 120, the weight of the axis of each color space axis (first principal component, second principal component) in the second color space is set based on the principal component contribution rate.

  In this way, when the principal component vector, the limited color space axis conversion base data, and the axis weight are set, in step 122, the number of dimensions of the prediction data is set from the number of dimensions of the predicted data, and in step 124, the set prediction is set. The number of dimensions of the limited color space axis conversion base data is set from the number of dimensions of the data.

  Thereafter, in step 126, data of the set number of dimensions is read from the limited color space axis conversion base data storage unit, and in step 128, based on the read data of limited color space axis conversion base data and the setting of the axis weight. The data to be predicted is converted into data in the second color space.

  Thereby, for example, if the predicted data is a cyan primary color, the data of the first principal component is read, and a value corresponding to the predicted data is set from the read data. When the predicted data is secondary color data, the data of the first principal component and the second principal component are read and set to values corresponding to the prediction data. At this time, an axis weight is added and predicted as data depending on the space axis of the second color space.

  Thereafter, in step 130, the predicted second color space data is subjected to inverse transformation based on the principal component vector to convert it into the first color space data. Output as prediction data of the dependent color space (step 132).

  In this way, it is possible to predict an appropriate pure color by predicting pure colors such as a primary color and a secondary color. That is, the Lab color space data acquired as the base data may include a noise component, and the noise component causes an error in the prediction data, making it difficult to perform proper color prediction.

  At this time, when predicting the primary color or the secondary color, the color prediction apparatus 10 extracts only the data of the corresponding dimension number from the base data, and performs the principal component analysis to accurately obtain the pure color. It becomes possible to predict.

In addition, since the axis weight is set according to the contribution ratio of the axis of the second color space, it is possible to perform appropriate color prediction independent of the space axis.
[Second Embodiment]
Next, a second embodiment of the present invention will be described. The basic configuration of the second embodiment is the same as that of the first embodiment described above, and the same components as those in the first embodiment are denoted by the same reference numerals and the description thereof is omitted. Omitted.

  FIG. 8 shows a schematic configuration of a main part of the color prediction apparatus 50 applied to the second embodiment. The color prediction apparatus 50 is a so-called inverse model that converts from a device-independent color space to a device-dependent color space. Also, in the color prediction apparatus 50, as in the color prediction apparatus 10, a printer is applied as an example of a device. In the color prediction apparatus 50, an L * a * b * color space which is a device-independent color space. The data of CMY color space, which is a device-dependent color space, is predicted (predicted data).

  As shown in FIG. 8, the color prediction apparatus 50 includes a conversion color space axis limited color prediction unit 52 and a color space axis inverse conversion unit 54, and is a device-independent color space L as predicted data. By inputting * a * b * color space data to the color space axis inverse transform unit 54, data in the CMY color space, which is a device-dependent color space, is output as prediction data.

  The color space axis inverse conversion unit 54 converts the predicted data in the L * a * b * color space into data of the second color space based on the principal component vector acquired by the color space axis conversion unit 16. Also, the conversion color space axis limited color prediction unit 52 converts the second color space data into device-dependent CMY color space data based on the limited color space axis conversion base data, and outputs the converted data as prediction data. .

  FIG. 9 shows an outline of color prediction processing in the color prediction device 50. Acquisition of base data in the color prediction device 50, extraction of limited colors from the base data, and setting of the second color space (acquisition of principal component vector, principal component contribution rate and limited color space axis conversion base data) Can apply the same processing as the color prediction apparatus 10 described above, and the description thereof is omitted here.

  This flowchart is executed, for example, by inputting predicted data in a device-dependent color space. In the first step 110, a limited color is recognized from the input predicted data, and the recognized limited color is obtained. When the corresponding data is extracted from the base data storage unit 12 (step 112), a principal component vector as a color space axis of the second color space is obtained by principal component analysis based on the extracted data (step 114). .

  Next, a principal component contribution rate is extracted from the principal component vector (step 116), limited color space axis conversion base data is acquired (step 118), and axis weights are set (step 120).

  Thereafter, in step 140, the predicted data is converted into data of the second color space based on the principal component vector.

  Thereafter, in step 142, the number of dimensions of the prediction data is set. Since the prediction data is pure color, it is one-dimensional or two-dimensional. Accordingly, the data converted into the second color space is also mainly the first principal component data or the first principal component and the second principal component. It can be determined whether the data is principal component data.

  Next, in step 144, the number of dimensions of the limited color space axis conversion base data is set from the number of dimensions of the prediction data, and in step 146, the limited color space axis conversion base data of the set number of dimensions is read.

  Thereafter, in step 148, data in the CMY color space predicted from the data in the second color space is obtained by converting the data in the second color space into data in the CMY color space.

The data thus converted is output as prediction data for the data to be predicted (step 132). As a result, it is possible to obtain prediction data to which appropriate weights are added, and it is possible to appropriately predict primary colors and secondary colors.
[Third Embodiment]
Next, a third embodiment of the present invention will be described. The basic configuration of the third embodiment is the same as that of the first or second embodiment described above, and the same reference numerals are used for the same parts as those of the first or second embodiment. The explanation is omitted.

  FIG. 10 shows a schematic configuration of a main part of the color prediction apparatus 60 according to the third embodiment. In this color predicting apparatus 60, as in the color predicting apparatuses 10 and 50 described above, a printer is applied as a device, a device-dependent color space is a CMY color space, and a device-independent color space is L * a. * b * color space.

  The color prediction apparatus 60 applied to the third embodiment is a so-called calibration model that can output prediction data that is not affected by the color characteristics of the device (individual differences between devices). By inputting the predicted data in the dependent color space, the predicted data in the device-dependent color space can be predicted.

  The color prediction device 60 includes a target color data storage unit 62 in addition to the base data storage unit 12. The target color data storage unit 62 stores target color base data by storing data in a CMY color space of a target color and data in a corresponding L * a * b * color space. At this time, the target color data used in the color prediction device 60 may be data of a target color on the printed matter, or may be a reference color in the device.

  The color prediction device 60 is provided with a limited color selection unit 64. The limited color selection unit 64 recognizes the color (pure color) as a limited color as well as whether the predicted data is a primary color or a secondary color, as in the limited color selection unit 14 described above. Yes.

  Further, the limited color selection unit 64 extracts data corresponding to the recognized limited color from the base data storage unit 12 and the target color data storage unit 62 and outputs the data to the color space axis conversion unit 66.

  Similar to the color space axis conversion unit 14 described above, the color space axis conversion unit 66 performs a principal component analysis or the like from the device-independent color space data included in the limited color data extracted from the base data storage unit 12. Based on the statistical method, a principal component vector and a principal component contribution rate that specify the second color space based on the characteristics of the device are acquired.

  Further, the color space axis conversion unit 66 reads the data of the first color space of the target color base data of the limited color extracted from the target color data storage unit 62, and based on the limited color data for each data. Color space axis conversion target base data for a limited color that can be converted between CMY color space data of the target color and data of the second color space by performing color space axis conversion using the principal component vector. Generate.

  As a result, the color prediction device 60 acquires color space axis conversion target base data, which is data in the second color space having the limited color as the target color, and device color space axis conversion base data.

  On the other hand, conversion color space axis limited color prediction units 68 and 70 are formed in the color prediction device 60, and data to be predicted is input to the conversion color space axis limited color prediction unit 68. The converted color space axis limited color prediction unit 68 predicts the data of the second color space axis for the predicted data based on the color space axis target base data of the limited color, and the data corresponding to the target color data (Target data) is generated.

As a result, the conversion color space axis limited color predicting unit 68 receives the data (C, M, Y) in the CMY color space of the target color when the grid point of the DLUT (direct lookup table) is input as the predicted data. And conversion data (α ₁ , α ₂ , α ₃ ), which is data after target color axis conversion, is converted into data from the device-dependent color space to the second color space ((C, M, Y)). → (α ₁ , α ₂ , α ₃ )).

  As a result, the conversion color space axis color prediction unit 56 generates target data for the DLUT lattice points.

  In other words, the data for the DLUT lattice point is originally given as data in the L * a * b * color space, and the predicted value is also data in the L * a * b * color space. The axis-converted data is predicted. Note that, in such a conversion color space axis limited color prediction unit 68, a method equivalent to the above-described conversion color space axis limited color prediction unit 16 can be applied.

  On the other hand, the conversion color space axis limited color prediction unit 70 receives the data obtained by the conversion color space axis limited color prediction unit 68 so that device coverage data for this data can be obtained. ing.

  That is, the conversion color space axis limited color prediction unit 70 uses the same method as the conversion color space axis limited color prediction unit 52 described as the inverse model as the base data for the data in the CMY color space and the data after axis conversion. The axis conversion data input from the conversion color space axis limited color prediction unit 68 is converted into data in the CMY color space. Thereby, prediction data in the CMY color space, which is device coverage data for the target color data, is obtained.

  Here, an outline of the color prediction processing in the color prediction device 60 will be described with reference to FIG. In the color predicting device 60, base data of a printer as a device is acquired in advance together with target color base data, and stored in the base data storage unit 12 and the target color data storage unit 62. In FIG. 11, the same step numbers are written together in the processes equivalent to those in FIGS. 7 to 9 described above.

  In this flowchart, for example, data such as a DLUT lattice point is input as predicted data, so that in step 150, a limited color is recognized from the predicted data and the first color space corresponding to the recognized limited color is displayed. Data is extracted from the base data storage unit 12 (step 152). In step 154, target color data for the recognized limited color is extracted from the target color base data.

  Next, in step 156, a principal component analysis is performed on the data of the first color space extracted from the base data storage unit 12, thereby obtaining a principal component vector for specifying the color space axis of the second color space. In step 158, the principal component contribution rate is obtained from the principal component vector.

  Thereafter, in step 160, limited color space axis conversion base data is generated based on the extracted base data and principal component vector, and an axis weight is set from the principal component contribution rate (step 162). In step 164, color space axis conversion target base data is generated based on the data extracted from the target color base data and the limited color space axis conversion base data.

  When the color space axis conversion target base data, the limited color space axis conversion base data, and the axis weight are acquired in this way, in step 166, the predicted data is read, and the limited color space axis conversion is performed from the number of dimensions of the predicted data. The number of dimensions of the base data is set (step 168).

  Next, in step 170, the predicted data is converted into data of the second color space based on the color space conversion target base data.

  In step 172, data corresponding to the previously set number of dimensions is read from the limited color space axis conversion base data, and in step 174, weights are added to the data based on the setting of the axis weights. Prediction data is predicted by converting the data of the second color space axis converted from the data to be predicted into data of the CMY color space which is a device-dependent color space.

  Thereafter, the converted device-dependent color space data is output as prediction data (step 176).

  Thus, the color prediction device 60 suppresses the influence of disturbances such as noise when performing patch density measurement and the like, and makes the predicted data of the primary color or secondary color (pure color) the target color that is the standard color. A matching DLUT can be obtained, calibration that absorbs individual differences between printers is possible, and printing colors can be simulated using the printer.

  In addition, this Embodiment demonstrated above does not limit the structure of this invention. For example, in this embodiment, a printer is applied as an example of a device, and prediction data is predicted from predicted data between the CMY color space and the L * a * b * color space or between the CMY color spaces. Although described as an example, the present invention is not limited to this, and the device is not limited to a printer, and an output device such as a color display, and an input device such as a digital camera or a color scanner can be applied.

  Further, the device-dependent color space is not limited to the CMY color space, and a color space according to the device such as a four-dimensional color space such as the CMYK color space or an RGB color space can be applied.

  Furthermore, the device-independent color space is not limited to the L * a * b * color space, and any color space can be applied because it is an L * u * v * color space or an XYZ color space.

It is a functional block diagram which shows schematic structure of the principal part of the color prediction apparatus which concerns on 1st Embodiment. It is a schematic block diagram which shows an example of a limited color selection part. It is a schematic block diagram which shows an example of a color space axis conversion part. (A) And (B) is the schematic which shows an example of the color space axis of L * a * b * color space used as 1st color space, and the color space axis of 2nd color space, (A) A primary color is shown, and (B) shows a secondary color. It is a schematic block diagram which shows an example of a conversion color space axis limited color prediction part. It is a flowchart which shows an example of acquisition of base data. It is a flowchart which shows the outline of the color prediction process which concerns on 1st Embodiment. It is a functional block diagram which shows schematic structure of the principal part of the color prediction apparatus which concerns on 2nd Embodiment. It is a flowchart which shows the outline of the color prediction process which concerns on 2nd Embodiment. It is a functional block diagram which shows schematic structure of the principal part of the color prediction apparatus which concerns on 3rd Embodiment. It is a flowchart which shows the outline of the color prediction process which concerns on 3rd Embodiment.

Explanation of symbols

10, 50, 60 color prediction device 12 base data storage unit 14, 64 limited color selection unit 16, 66 color space axis conversion unit 18, 52, 68, 70 conversion color space axis limited color prediction unit 20, 54 color space axis reverse Conversion unit 22 Limited color recognition unit 24 Limited color extraction unit 26 Principal component vector acquisition unit 28 Principal component contribution rate acquisition unit 30 Color space conversion unit 32 Limited color space axis conversion unit 34 Axis weight setting unit 38 Prediction unit 40 Number of prediction data dimensions Setting unit 42 Base data dimension number setting unit 62 Target color data storage unit

Claims (17)

In a device to which image data is input or output, between a device-dependent color space and a device-independent color space, the predicted value input as data in one color space is output as data in the other color space. A color prediction method for predicting a predicted value,
Obtaining a database describing a correspondence relationship of data of the first color space which is the device-independent color space with respect to data of the device-dependent color space in the device;
Selecting a color space axis of a device-dependent color space from the predicted value or the predicted value that is a color of the device-dependent color space;
Extracting the data of the first color space for the data on the selected color space axis from the base data;
While obtaining the color space axis of the second color space obtained by converting the color space axis using a statistical method on the extracted data of the first color space,
Based on the color space axis of the second color space, obtain color space axis conversion base data describing the correspondence between the device-dependent color space data and the second color space data,
When the predicted value is data in the device-dependent color space, converted color space axis predicted data that is data of the second color space from the predicted value based on the color space axis conversion base data Predict
Converting the converted color space axis prediction data into data of the first color space based on the color space axis of the second color space;
A color prediction method characterized by that. When the predicted value is data of the device-independent color space, the predicted value is converted into data of the second color space based on the color space axis of the second color space;
Predicting the predicted value by converting the converted data of the second color space into data of the device-dependent color space based on the color space axis conversion base data;
The color prediction method according to claim 1, wherein: A color that predicts a predicted value in a device-dependent color space that is output from a device when a predicted value that is data in the device-dependent color space in a target color is input to a device that receives or outputs image data. A prediction method,
Target color base data describing the correspondence between the device-dependent color space data of a plurality of target colors and the first color space data which is a device-independent color space; and
Obtaining base data describing a correspondence relationship of the data of the first color space to the data of the device-dependent color space in the device;
Selecting a color space axis of the device-dependent color space from the predicted value or the predicted value;
Extracting the data of the first color space for the data on the selected color space axis from the target color base data and the base data;
Obtaining a color space axis of a second color space obtained by converting the color space axis using a statistical method on the extracted data of the first color space;
Color space axis conversion target base data describing a correspondence relationship between the target color of the device-dependent color space and the data of the second color space from the target color base data and the color space axis of the second color space. And get
Based on the color space axis of the second color space, obtaining color space axis conversion base data describing the correspondence between the device-dependent color space data and the second color space data;
Based on the color space axis target base data, after converting the predicted value to data of the second color space,
Predicting the predicted value by converting the converted data of the second color space into data of the device-dependent color space based on the color space axis conversion base data;
A color prediction method characterized by that. Set the number of dimensions of the predicted value based on the number of dimensions of the predicted value,
Perform data conversion to a different color space using data of the set number of dimensions.
The color prediction method according to claim 1, wherein the color prediction method is a color prediction method.   The color prediction method according to claim 1, wherein the statistical method is a principal component analysis method.   The color prediction method according to any one of claims 1 to 5, wherein the color space axis conversion base data has a data weight depending on a color space axis obtained by a statistical method.   The color space axis-dependent data weight obtained by the statistical method is based on a contribution ratio of principal component vector data to the first color space data which is the device-independent color space of the base data. The color prediction method according to claim 6, wherein the calculated value is given. In a device to which image data is input or output, between a device-dependent color space and a device-independent color space, the predicted value input as data in one color space is output as data in the other color space. A color prediction device for predicting a predicted value,
Base data acquisition means for acquiring a database describing a correspondence relationship of data of a first color space that is the device-independent color space with respect to data of the device-dependent color space in the device;
Limited color recognition means for selecting a color space axis of a device-dependent color space from the predicted value or the predicted value that is a color of the device-dependent color space;
Limited color extraction means for extracting data of the first color space for data on the color space axis recognized by the recognition means from the base data;
Color space axis conversion for obtaining the color space axis of the second color space obtained by converting the color space axis using the statistical method on the data of the first color space extracted by the limited color extracting means Means,
Based on the color space axis of the second color space, color space axis conversion for obtaining color space axis conversion base data describing the correspondence between the device-dependent color space data and the second color space data Base data acquisition means;
When the predicted value is data in the device-dependent color space, converted color space axis predicted data that is data of the second color space from the predicted value based on the color space axis conversion base data A prediction means for predicting
Color space axis inverse conversion means for converting the converted color space axis prediction data into data of the first color space based on the color space axis of the second color space;
A color predicting device comprising: When the predicted value is data of the device-independent color space, the prediction means converts the predicted value into data of the second color space based on the color space axis of the second color space. Converted,
The color space axis inverse conversion means converts the data converted into the data of the second color space into data of the device-dependent color space based on the color space axis conversion base data.
The color prediction apparatus according to claim 8. A color that predicts a predicted value in a device-dependent color space that is output from a device when a predicted value that is data in the device-dependent color space in a target color is input to a device that receives or outputs image data. A prediction device,
Target color data acquisition means for acquiring target color base data describing a correspondence relationship between data of the device-dependent color space of a plurality of target colors and data of a first color space which is a device-independent color space; ,
Base data acquisition means for acquiring base data describing a correspondence relationship of the data of the first color space to the data of the device-dependent color space in the device;
Limited color recognition means for recognizing a color space axis of the device-dependent color space from the predicted value or the predicted;
Limited color extraction means for extracting data of the first color space for the data on the color space axis recognized by the limited color recognition means from the target color base data and the base data;
Color space axis conversion for obtaining the color space axis of the second color space obtained by converting the color space axis using the statistical method on the data of the first color space extracted by the limited color extracting means Means,
Color space axis conversion target base data describing a correspondence relationship between the target color of the device-dependent color space and the data of the second color space from the target color base data and the color space axis of the second color space. A color space axis conversion target base data acquisition unit for acquiring
Based on the color space axis of the second color space, the color space axis for obtaining color space axis conversion base data describing the correspondence between the device-dependent color space data and the second color space data A conversion target base data acquisition means;
Based on the color space axis conversion target base data, color space axis inverse conversion means for converting the predicted value into data of the second color space;
Predicting means for predicting the predicted value by converting the converted data of the second color space into data of the device-dependent color space based on the color space axis conversion base data;
A color predicting device comprising:   Including dimension number setting means for setting the dimension number of the predicted value based on the dimension number of the predicted value, and performing data conversion to a different color space using data of the set dimension number The color prediction apparatus according to any one of claims 8 to 10.   The color prediction apparatus according to claim 8, wherein the statistical method is a principal component analysis method.   The color prediction apparatus according to any one of claims 8 to 12, wherein the color space axis conversion base data has a color weight depending on a color space axis obtained by a statistical method.   The color space axis-dependent data weight obtained by the statistical method is based on a contribution ratio of principal component vector data to the first color space data which is the device-independent color space of the base data. The color prediction apparatus according to claim 13, wherein the calculated value is given. In a device to which image data is input or output, between a device-dependent color space and a device-independent color space, the predicted value input as data in one color space is output as data in the other color space. A color prediction program for predicting predicted values,
Selecting a color space axis of a device-dependent color space from the predicted value or the predicted value that is a color of the device-dependent color space;
The first to the data on the color space axis selected from the database describing the correspondence of the data of the first color space which is the device-independent color space to the data of the device-dependent color space in the device Extracting color space data; and
Obtaining a color space axis of a second color space obtained by converting the color space axis using a statistical method on the extracted data of the first color space;
Obtaining color space axis conversion base data describing a correspondence relationship between the data of the device-dependent color space and the data of the second color space based on the color space axis of the second color space;
When the predicted value is data in the device-dependent color space, converted color space axis predicted data that is data of the second color space from the predicted value based on the color space axis conversion base data Predicting
Converting the converted color space axis prediction data into data of the first color space based on the color space axis of the second color space;
A color prediction program comprising: When the predicted value is data in the device-independent color space, converting the predicted value into data in a second color space based on a color space axis of the second color space;
Predicting the predicted value by converting the converted second color space data to the device-dependent color space data based on the color space axis conversion base data;
The color prediction program according to claim 15. A color that predicts a predicted value in a device-dependent color space that is output from a device when a predicted value that is data in the device-dependent color space in a target color is input to a device that receives or outputs image data. A prediction program,
Selecting a color space axis of the device-dependent color space from the predicted value or the predicted value;
From the target color base data indicating the correspondence between the device-dependent color space data of a plurality of target colors and the first color space data which is a device-independent color space, the selected color space axis Extracting the data;
Extracting the data of the first color space for the data on the selected color space axis from the base data describing the correspondence relationship of the data of the first color space to the data of the device-dependent color space in the device. When,
Obtaining the color space axis of the second color space obtained by converting the color space axis using the statistical method on the target color base data and the data of the first color space extracted from the base data And steps to
A color space describing the correspondence between the target color of the device-dependent color space and the data of the second color space from the data extracted from the target color base data and the color space axis of the second color space Obtaining axis conversion target base data; and
Obtaining color space axis conversion base data describing a correspondence relationship between the data of the device-dependent color space and the data of the second color space based on the color space axis of the second color space;
Converting the predicted value into data of the second color space based on the color space axis target base data;
Predicting the predicted value by converting the converted data of the second color space into data of the device-dependent color space based on the color space axis conversion base data;
A color prediction program comprising:

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