JP2007036747A - 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 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, uses the data of the L*a*b* color space being a color space depending on no device, applies the method of the principal component analysis to the 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. The color prediction apparatus sets a weight in response to the contribution ratio to the space axes to obtain proper prediction data. <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 or a CMYK 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.

  Incidentally, for example, data for outputting a color patch is acquired as a pair of a device-dependent color space (for example, CMYK color space) and a device-independent color space (for example, L * a * b * color space). At this time, in the L * a * b * color space obtained as device-independent data, there is a possibility that the characteristics of the device cannot be sufficiently expressed. For this reason, sufficient accuracy cannot be obtained at the time of LUT lattice point prediction. Thus, there is a problem in accuracy in CMS.

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

  In the proposal of Patent Document 1 described above, there is no data weight for each of the L *, a *, and b * axes, and the parameters are predicted with a uniform weight. It is difficult to predict colors in a color space according to the color.

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 an object thereof is to provide a color prediction method, a color prediction apparatus, and a color prediction program that enable highly accurate color prediction from base data acquired from a device. To do.

  To achieve the above object, the present invention provides a predicted value 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 method for predicting a predicted value output as data in the other color space, the first color space being the device-dependent color space data and the device-independent color space in the device The second color obtained by acquiring base data describing the correspondence relationship with the data of the base data and converting the color space axis using a statistical method on the data of the first color space of the base data A color space axis conversion that obtains a color space axis in the space and describes a correspondence relationship between the device-dependent color space data and the second color space data based on the space axis of the second color space Bae When data is obtained and the predicted value is data in the device-dependent color space, the second color space data is derived from the predicted value based on the color space axis conversion base data. Conversion color space axis prediction data is predicted, and the conversion color space axis prediction data is converted into data of the first color space based on the space axis of the second color space.

  In the present invention, when the predicted value is data in the device-independent color space, the predicted value is converted into data in the second color space, and data in the second color space is obtained. The prediction value is predicted by converting the data converted into the data into the device-dependent color space data based on the color space axis conversion base data.

  Furthermore, the present invention relates to a predicted value of a device-dependent color space output from a device from a predicted value input as device-dependent color space data of a target color in a device to which image data is input or output. A color prediction method for predicting, wherein a target color base describes correspondence between data in the device-dependent color space of a plurality of target colors and data in a first color space which is a device-independent color space Data and base data describing a correspondence relationship of the first color space data to the device-dependent color space data in the device, and obtaining the target color base data and the first of the base data The target color base data is obtained by obtaining a color space axis in a second color space obtained by converting the color space axis using a statistical method on the color space data. Obtaining from the color space axis of the second color space, color space axis conversion target base data describing the correspondence between the target color data of the device-dependent color space and the data of the second color space; Obtaining color space axis conversion base data describing a correspondence relationship between the device-dependent color space data and the color space data of the second color space based on the space axis of the second color space; Based on the color space axis conversion target color base data, after converting the predicted value into the data of the second color space, the data converted into the second color space is converted into the color space axis. The predicted value is predicted by converting the data into the device-dependent color space based on the conversion base data.

  According to the present invention, when predicting a predicted value for a predicted value according to a device between a device-dependent color space and a device-independent color space or on a device-dependent color space, The dependent color space is defined as the first color space, and the second color space is virtually calculated from the data in the first color space of the base data according to the characteristics of the device using statistical methods such as principal component analysis. To set.

  Thus, an appropriate predicted value is predicted from the predicted value based on the device identification.

  At this time, the color space axis conversion base data has color space axis-dependent data weights obtained by a statistical method, and the color space axis-dependent data weights obtained by the statistical method are By giving a value based on the contribution ratio of the principal component vector data to the color space data, a more appropriate predicted value can be obtained.

  In such a color prediction apparatus to which the present invention is applied, in a device to which image data is input or output, a color prediction device that is input as data in one color space between a device-dependent color space and a device-independent color space. A color prediction apparatus for predicting a prediction value output as data of the other color space from a prediction value, wherein the device-dependent color space data and the device-independent color space in the device Base data acquisition means for acquiring the base data describing the correspondence with the data of the color space, and the color space axis is converted using a statistical method on the data of the first color space of the base data A color space axis converting means for acquiring a color space axis in the obtained second color space; and the device-dependent operation based on the space axis of the second color space acquired by the color space axis converting means. Color space axis conversion base data acquisition means for acquiring color space axis conversion base data describing the correspondence between the color space data and the second color space data, and the predicted value is the device-dependent color space Conversion color space axis prediction means for predicting conversion color space axis prediction data that is data of the second color space from the predicted value based on the color space axis conversion base data. Color space axis inverse conversion means for converting the converted color space axis prediction data predicted by the converted color space axis prediction means into data of the first color space based on the space axis of the second color space; When the predicted value is data in the device-independent color space, the color space axis inverse conversion unit is configured to acquire the predicted value by the color space axis conversion unit. Said second color space The color space axis conversion base data is converted into the data of the second color space by the conversion color space axis predicting means and the color space axis conversion means converts the data into the data of the second color space. The prediction value may be predicted by converting the data into the device-dependent color space based on the above.

  In addition, the color prediction apparatus to which the present invention is applied is a device dependent device that is output from a device based on a predicted value that is input as device-dependent color space data for a target color in a device to which image data is input or output. A color prediction apparatus for predicting a predicted value of a color space of a plurality of target colors, and correspondence 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 The target color base data indicating the relationship is acquired, but the target color base data acquisition means and the base data indicating the correspondence relationship of the data of the first color space to the data of the device-dependent color space in the device are acquired. Base data acquisition means that performs the second conversion of the first color space of the base data obtained by converting the color space axis using a statistical method. Color space axis conversion means for acquiring a color space axis in the color space, target color base data acquired by the target color data acquisition means, and the second color space acquired by the color space axis target color acquisition means A color space axis conversion target color base that obtains color space axis conversion target base data describing a correspondence relationship between the target color data of the device-dependent color space and the data of the second color space from the color space axis of the color space Based on data acquisition means, color space axis conversion target color base data, color space axis target value conversion means for converting the predicted value into data of the second color space, and color space axis conversion means A color space axis for acquiring color space axis conversion base data describing a correspondence relationship between the device-dependent color space data and the second color space data based on the acquired space axis of the second color space Conversion base data Based on the color space axis conversion base data obtained by the acquisition means and the color space axis conversion base data acquisition means, the data converted by the color space axis target value conversion means is converted into the color space axis conversion base data. The conversion color space axis prediction means for predicting the data of the device-dependent color space based on the above may be used.

  Furthermore, a color prediction program to which the present invention is applied is a device for inputting or outputting image data to be input as data in one color space between a device-dependent color space and a device-independent color space. A color prediction program for predicting a prediction value output as data of the other color space from a prediction value, wherein the device-dependent color space data and the device-independent color space in the device The color space in the second color space obtained by converting the color space axis using the statistical method with respect to the data in the first color space of the base data describing the correspondence with the data in the color space. A color space axis conversion base data describing a correspondence relationship between the device-dependent color space data and the second color space data based on the color space axis of the second color space; And when the predicted value is data in the device-dependent color space, based on the color space axis conversion base data, from the predicted value to the data in the second color space. Whether to predict certain converted color space axis prediction data; and converting the converted color space axis prediction data into data of the first color space based on the space axis of the second color space. And when the predicted value is data in the device-independent color space, the predicted value is converted into data in the second color space; and converted into data in the second color space. And the step of predicting the predicted value by converting the data into the data of the device-dependent color space based on the color space axis conversion base data.

  In addition, the color prediction program to which the present invention is applied is a device-dependent output that is output from a device based on a predicted value that is input as device-dependent color space data for a target color in a device to which image data is input or output. A color prediction program for predicting a predicted value of a color space of the device, wherein the device-dependent color space data of a plurality of target colors and the data in the first color space which is a device-independent color space Extracting target color base data describing the relationship; 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; and the target The color base data and the first color space data of the base data are obtained by converting the color space axis using a statistical method. A color space axis conversion step for obtaining a color space axis in a second color space; and data on a target color in the device-dependent color space from the target color base data and the color space axis in the second color space. And obtaining the color space axis conversion target base data describing the correspondence between the data and the data of the second color space, and data of the device-dependent color space based on the color space axis of the second color space Obtaining the color space axis conversion base data describing the correspondence between the second color space data and the second color space data, and based on the color space axis conversion target color base data, Converting to the color space data; and converting the data converted to the second color space into the device-dependent color space data based on the color space axis conversion base data. A step of predicting the value, as long as it contains a.

  Furthermore, the present invention can 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 sets a second color space from data in a device-independent color space corresponding to data in a device-dependent color space using a statistical method such as principal component analysis. Thus, by predicting the predicted value from the predicted value, it is possible to obtain an excellent effect that it is possible to predict an appropriate predicted value according to device identification.

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, an input device such as a digital camera such as a digital still camera, a color scanner, an output such as a color printer, a color display, or the like. Any device such as a device can be applied.

  Here, the device-dependent color space is a three-dimensional CMY color space. However, the device-dependent color space is not limited to this, and can be applied to a four-dimensional CMYK color space.

  The color prediction apparatus 10 includes a base data storage unit 12 as a base data acquisition unit, and includes a color space axis conversion unit 14, a conversion color space axis color prediction unit 16, and a color space axis reverse conversion unit 18. It is formed with.

  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 Data of the L * a * b * color space is acquired by measuring the color of the data patch.

  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. The base data storage unit 12 stores this base data.

  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 of the printer is obtained.

  The color space axis conversion unit 14 acquires from the base data stored in the base data storage unit 12 a second color space axis (coordinate axis) to be applied to color prediction.

  FIG. 2 shows an example of a schematic configuration of the color space axis conversion unit 14 applied to the present embodiment. The color space axis conversion unit 14 includes a principal component vector acquisition unit 20, a principal component contribution rate acquisition unit 22, and a color space conversion unit 24. The base data described above includes the principal component vector acquisition unit 20 and the principal component contribution. Input to the rate acquisition unit 22.

  The principal component vector acquisition unit 20 is an example of a statistical method based on data (L * value, a * value, and b * value) of the L * a * b * color space that is the first color space included in the base data. The principal component vector is obtained using the principal component analysis method as follows. As a principal component analysis method, a general configuration described in Japanese Patent Laid-Open No. 10-164381 can be applied.

Here, assuming that three variables are variables X ₁ , X ₂ , and X _3, and these weighted composite variables are composite variables Z ₁ , Z ₂ , and Z ₃ , the variables X _{1 to} X _{3 are} combined with the composite variables Z ₁ to Z _3. Z ₃ is 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
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 20. Hereinafter, the principal component vectors Z ₁ , Z ₂ , and Z ₃ are used.

Further, among the principal component vectors Z ₁ , Z ₂ , Z ₃ , the first principal component, the second principal component, and the third principal component are arranged in the order of the largest dispersion. In the present embodiment, the first principal component, The second principal component and the third principal component become three axes (coordinate 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 20. In other words, by using the principal component analysis method, as shown in FIG. 3, for example, the principal component vector Z ₁ becomes the first principal component, principal component vector from the dispersion of data in the L * a * b * color space. If Z ₂ is the second principal component and 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.

In FIG. 3, the L * axis, a * axis, and b * axis of the L * a * b * color space are indicated by solid lines, and the Z ₁ axis, Z ₂ axis, and Z ₃ axis forming the second color space are shown. Is indicated by a broken line, and plotting of data is omitted.

  On the other hand, the principal component contribution rate acquisition unit 22 acquires the principal component contribution rate from the data of the L * a * b * color space acquired as the base data 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, and thus is preferable as a data weight for each axis. In the principal component contribution rate acquisition unit 22, the first principal component, The contribution ratio of the second principal component and the third principal component, that is, the contribution ratio of each axis of the second color space is acquired.

On the other hand, as shown in FIG. 2, the color space conversion unit 24 receives data of the L * a * b * color space included in the base data, and the principal component acquired by the principal component vector acquisition unit 20. A vector (for example, principal component vectors Z ₁ , Z ₂ , Z ₃ ) is input. From here, the color space conversion unit 24 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 Base data between the space data and the second color space data is acquired.

  As shown in FIG. 1, the conversion data for the L * a * b * color space of the base data acquired by the color space axis conversion unit 14 and the principal component contribution rate are input to the conversion color space axis prediction unit 16. .

  FIG. 4 shows an example of a schematic configuration of the conversion color space axis prediction unit 16. The conversion color space axis prediction unit 16 includes a color space axis conversion base data storage unit 26, an axis weight setting unit 28, a predicted data storage unit 30, and a color prediction unit 32.

  The color space axis conversion base data storage unit 26 receives the conversion data acquired by the color space conversion unit 24 of the color space axis conversion unit 14. The conversion data is stored in order. As a result, the color space axis conversion base data storage unit 26 stores the CMY color space data and the color space axis conversion base data based on the second color space data (conversion data) corresponding to the CMY color space data. Is done.

  The axis weight setting unit 28 receives the contribution rate for each axis of the second color space acquired by the principal component contribution rate acquisition unit 22 of the color space axis conversion unit 14. Thereby, the axis weight setting unit 28 sets a weight for each axis in the second color space.

  On the other hand, as shown in FIG. 1, the converted color space axis predicting unit 16 is input with a predicted value (predicted data) that is device-dependent color space data. As shown in FIG. 4, the predicted data is stored in order in the predicted data storage unit 30.

  Predicted data is input from the predicted data storage unit 30 to the color prediction unit 32. The color prediction unit 32 receives data of the second color space corresponding to the data to be predicted from the color space axis conversion base data storage unit 26 when the data to be predicted is input, By performing the weighting set by the weight setting unit 28 for each axis, the predicted data is converted into data of the second color space and output.

  At this time, weighting is performed by using a known method such as a regression method in which a weighting coefficient is added to the axis of the color space axis conversion base data. In addition, weighting is not restricted to this, For example, general methods, such as a regression method currently disclosed by Unexamined-Japanese-Patent No. 10-262175 etc., can be used.

  As shown in FIG. 1, the data converted to the second color space output from the conversion color space axis prediction unit 16 is input to the color space axis inverse conversion unit 18. The principal component vector is input from the color space axis conversion unit 14 to the color space axis inverse conversion unit 18.

  The color space axis reverse conversion unit 18 performs reverse conversion for converting data in the second color space into data in the first color space. At this time, for example, the above-described equation (2) can be applied.

  Thereby, the data converted into the L * a * b * color space which is the first color space is output as the prediction 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. 5 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.

  On the other hand, the color prediction apparatus 10 acquires the principal component vector as the axis of the second color space and the principal component contribution rate from the data of the first color space included in the base data, and obtains the base data Based on the principal component vector and the principal component contribution rate, prediction data in the device-independent color space is acquired from the predicted data in the device-dependent color space.

  At this time, the principal component vector and the principal component contribution ratio of the second color space can be acquired in advance. FIG. 6 shows the flow of this process as a second color space axis setting process.

  In the flowchart of FIG. 6, in the first step 110, the data of the first color space stored in the base data storage unit 12 is read, and in the next step 112, the principal component analysis method is based on the read data. To obtain a principal component vector that becomes the axis of the second color space. Thereby, the second color space based on the data of the first color space is set. In step 114, the principal component contribution rate is acquired from the principal component vector.

  Thereafter, in step 116, based on the principal component vector and the base data, color space axis conversion base data that enables conversion of the device-dependent color space data into the second color space data is generated and generated. The color space axis conversion base data thus stored is stored in the color space axis conversion base data storage unit 26.

  In step 118, the weight of each axis in the second color space is set based on the principal component contribution rate.

  In this manner, by setting the principal component vector, the color space axis conversion base data, and the axis weight in advance, it is possible to smoothly perform the operation of obtaining the prediction data from the predicted data.

  FIG. 7 shows an outline of the color prediction process at this time. This flowchart is executed while storing the predicted data in the predicted data storage unit 30 by inputting the predicted data in the device-dependent color space, for example. In the first step 120, the predicted data storage unit The data to be predicted stored in 30 is read as a prediction target, and the process proceeds to step 122.

  In this step 122, data of the second color space corresponding to the read predicted data is acquired based on the base data stored in the color space axis conversion base data storage unit 26, and the acquired data is By adding the axis weight, the data to be predicted is converted into the data of the second color space. As a result, the predicted data is converted into data depending on the axis of the second color space.

  In the next step 124, the data in the second color space is converted into data in the first color space by performing inverse conversion based on the principal component vector, and the converted data is converted into the device-independent color space. It outputs as prediction data (step 126).

  As described above, in the color prediction apparatus 10, when the predicted data in the first color space is converted into the prediction data in the device-independent color space, the data in the device-independent color space with respect to the device-dependent color space. Is used to set a second color space by a statistical method and convert it to data of that color space.

At this time, by adding a weight according to the contribution ratio of the axis of the second color space, it is possible to obtain prediction data that enables appropriate color correction.
[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 40 applied to the second embodiment. The color prediction apparatus 40 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 device 40, a printer is applied as an example of a device, similar to the color prediction device 10 described above. Further, the color prediction apparatus 40 uses data in the Lab color space, which is a device-independent color space, as predicted data, and predicts data in the CMY color space, which is a device-dependent color space (prediction data).

  As shown in FIG. 8, the color prediction apparatus 40 includes a color space axis color prediction unit 42 instead of the color space axis color prediction unit 16, and a color space axis reverse conversion unit instead of the color space axis reverse conversion unit 18. 44 is formed, and data in the L * a * b * color space, which is a device-independent color space, is input to the color space axis inverse transform unit 44 as predicted data, and is a CMY, which is a device-dependent color space. Color space data is output from the color space axis prediction unit 42 as prediction data.

  The color space axis inverse conversion unit 44 converts the predicted data in the L * a * b * color space into data in the second color space. Further, the color space axis color prediction unit 42 converts the data of the second color space into device-dependent CMY color space data based on the color space axis conversion base data.

  At this time, the conversion color space axis prediction unit 42 obtains prediction data of the CMY color space for the input data by weighting the principal component contribution rate with respect to the base data.

  In the color prediction device 40, the predicted data storage unit 30 provided in the conversion color space axis color prediction unit 16 of the color prediction device 10 is provided in the color space axis inverse conversion unit 44. The prediction data is processed while being sequentially stored in the color space axis inverse transform unit 44.

  FIG. 9 shows an outline of color prediction processing in the color prediction apparatus. The acquisition of base data and the setting of the second color space (acquisition of principal component vector, principal component contribution rate and color space axis conversion base data) in the color prediction device 40 are the same as those of the color prediction device 10 described above. Processing can be applied, and the description thereof is omitted here.

  This flowchart is executed while storing the predicted data in the predicted data storage unit 30 by inputting the predicted data of the device-dependent color space, and in the first step 130, the predicted data The predicted data stored in the storage unit 30 is read as a prediction target, and the process proceeds to step 132.

  In step 132, the data is converted into data of the second color space based on the principal component vector.

  Thereafter, in step 134, the second color space data is converted into CMY color space data based on the base data and axis weight settings stored in the color space axis conversion base data storage unit 26. At this time, by using the base data to which the axis weight is added, data in the CMY color space (device-dependent color space) corresponding to the principal component contribution rate is obtained.

The data thus converted is output as prediction data for the data to be predicted (step 136). Thereby, prediction data to which appropriate weights are added can be obtained.
[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 and second embodiments 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 50 according to the third embodiment. Also in this color prediction apparatus 50, as in the color prediction apparatuses 10 and 40 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 device 50 is a so-called calibration model that can output prediction data that is not affected by the color characteristics of devices (individual differences between devices).

  The color prediction device 50 is provided with a target color data storage unit 52 in addition to the base data storage unit 12. In the target color data storage unit 52, target color base data is formed 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 50 may be data of a target color on the printed matter, or may be a reference color in the device.

  The color prediction apparatus 50 is provided with a color space axis conversion unit 54. Similar to the color space axis conversion unit 14 described above, the color space axis conversion unit 54 applies a second method based on a statistical method such as principal component analysis to the device-independent color space data included in the base data. The principal component vector and principal component contribution rate for specifying the color space are acquired.

  In addition, the color space axis conversion unit 54 reads the first color space data of the target color base data from the target color data storage unit 52, and uses the principal component vector for each data, thereby converting the data into the color space axis. Perform conversion. That is, color space axis conversion target base data that enables data conversion between the CMY color space data of the target color and the data of the second color space is generated.

  As a result, the color prediction device 50 acquires color space axis conversion target base data that is data in the second color space for the target color and device color space axis conversion base data.

On the other hand, the color predicting device 50 includes conversion color space axis color predicting units 56 and 58. The conversion color space axis color prediction unit 56 generates data (target data) corresponding to the target color data. For example, when a grid point of a DLUT (direct lookup table) is input as predicted data, the conversion color space axis color prediction unit 56 receives data (C, M, Y) in the CMY color space of the target color and the target. Using the conversion data (α ₁ , α ₂ , α ₃ ), which is data after color axis conversion, as base data, data conversion is performed 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. In such a conversion color space axis prediction unit 56, a method equivalent to that of the conversion color space axis prediction unit 16 described above can be applied.

  On the other hand, the converted color space axis predicting unit 58 receives the data obtained by the converted color space axis predicting unit 56 to obtain coverage data in the device for this data.

  That is, the converted color space axis predicting unit 58 uses the same method as the converted color space axis predicting unit 42 described as the inverse model as the base data for the CMY color space data and the converted data, and converts the converted color space axis. The axis conversion data input from the prediction unit 56 is converted into CMY color space data. Thereby, prediction data in the CMY color space, which is device coverage data for the target color data, is obtained.

  Here, an outline of color prediction processing in the color prediction device 50 is shown with reference to FIGS. 11 and 12. In the color predicting apparatus 50, 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 52.

  FIG. 11 schematically shows the setting of the second color space and the process associated with the setting. In this flowchart, in steps 110 to 118, based on the base data stored in the base data storage unit 12. When the acquisition of the principal component vector, the acquisition of the principal component contribution rate, the setting of the axis weight based on the principal component contribution rate, and the acquisition of the color space axis conversion base data are performed, the process proceeds to step 140.

  In step 140, when the target color base data for the target color stored in the target color data storage unit 152 is read, the process proceeds to step 142, where the target color base data is based on the principal component vector of the second color space. Is subjected to color space axis conversion to generate color space axis conversion target base data. This color space axis conversion target base data is used for converting data in the CMY color space into data in the second color space.

  On the other hand, FIG. 12 shows an outline of color prediction processing in the color prediction device 50. This flowchart is executed by inputting, for example, a DLUT lattice point as predicted data. In the first step 150, when predicted data that is device-dependent color space data is read, in step 152, the read data is read. The predicted data is converted into data (conversion data) on the second color space based on the color space axis conversion target base data.

  In the next step 154, the conversion data, which is data in the second color space, is converted into data in the device-dependent color space (CMY color space) based on the color space axis conversion base data and the axis weight settings. And output as prediction data (step 156).

  As a result, the color predicting device 50 can obtain a DLUT that matches the data to be predicted with the target color, which is a standard color, and enables calibration to absorb individual differences between printers, as well as print colors using the printer. Simulation is possible.

  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.

  Further, 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 a Luv 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 color space axis conversion part. It is the schematic which shows an example of the space axis of the Lab color space used as the 1st color space, and the space axis of the 2nd color space. It is a schematic block diagram which shows an example of a conversion color space axis color prediction part. It is a flowchart which shows an example of base data acquisition. It is a flowchart which shows an example of the setting process of a 2nd color space and a color space axis. It is a flowchart which shows an example 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 an example 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 an example of the setting process of the 2nd color space and color space axis which concerns on 3rd Embodiment. It is a flowchart which shows an example of the color prediction process which concerns on 3rd Embodiment.

Explanation of symbols

10, 40, 50 color prediction device 12 base data storage unit 14, 54 color space axis conversion unit 16, 42, 56, 58 conversion color space axis prediction unit 18, 44 color space axis inverse conversion unit 20 principal component vector acquisition unit 22 Principal component contribution rate acquisition unit 24 color space conversion unit 26 color space axis conversion base data storage unit 28 axis weight setting unit 32 color prediction unit 52 target color data storage unit

Claims (15)

In a device to which image data is input or output, a predicted value input as data in one color space between a device-dependent color space and a device-independent color space is output as data in the other color space. A color prediction method for predicting a predicted value,
Obtaining base data describing a correspondence relationship between data of the device-dependent color space in the device and data of the first color space which is the device-independent color space;
Obtaining the color space axis in the second color space obtained by converting the color space axis using a statistical method on the data of the first color space of the base data;
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 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 Predict
Converting the converted color space axis prediction data into data of the first color space based on the space axis of the second color space;
A color prediction method characterized by that. When the predicted value is data in the device-independent color space, the predicted value is converted into data in the second color space;
Predicting the predicted value by converting the data converted to the second color space data into the device-dependent color space data based on the color space axis conversion base data;
The color prediction method according to claim 1, wherein: A color prediction method for predicting a predicted value of a device-dependent color space output from a device from a predicted value input as device-dependent color space data of a target color in a device to which image data is input or output There,
Target color base data describing the correspondence between the device-dependent color space data of a plurality of target colors and the data in the first color space which is a device-independent color space;
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;
Obtaining a color space axis in a second color space obtained by converting a color space axis using a statistical method on the target color base data and the data of the first color space of the base data; ,
From the target color base data and the color space axis of the second color space, a color space axis conversion target describing the correspondence between the target color data of the device-dependent color space and the data of the second color space Get the base data,
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 color space of the second color space based on the space axis of the second color space;
Based on the color space axis conversion target color base data, after converting the predicted value into data of the second color space,
Predicting the predicted value by converting the data converted to 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.   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 4, 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 5, wherein the calculated value is given. In a device to which image data is input or output, a predicted value input as data in one color space between a device-dependent color space and a device-independent 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 base data describing a correspondence relationship between data of the device-dependent color space in the device and data of the first color space which is the device-independent color space;
Color space axis conversion means for acquiring a color space axis in 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 of the base data;
A color space axis conversion base that describes the correspondence between the device-dependent color space data and the second color space data based on the space axis of the second color space acquired by the color space axis conversion means Color space axis conversion base data acquisition means for acquiring 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 Conversion color space axis prediction means for predicting,
Color space axis inverse conversion means for converting the converted color space axis prediction data predicted by the converted color space axis prediction means into data of the first color space based on the 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 color space axis inverse conversion unit is configured to output the predicted value of the second color space obtained by the color space axis conversion unit. Along with conversion to color space axis data,
The converted color space axis prediction means converts the data converted into the data of the second color space by the color space axis inverse conversion means, based on the color space axis conversion base data, in the device-dependent color space. Predicting the predicted value by converting to data,
The color prediction apparatus according to claim 7. A color prediction apparatus that predicts a predicted value of a device-dependent color space output from a device from a predicted value input as data of the device-dependent color space of a target color in a device to which image data is input or output There,
The target color base data for acquiring the target color base data indicating the correspondence relationship between the data of the device-dependent color space of the plurality of target colors and the data in the first color space which is a device-independent color space is also obtained. Acquisition means;
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;
A color for obtaining a color space axis in a second color space obtained by converting a color space axis using a statistical method with respect to the target color base data and the data of the first color space of the base data Spatial axis conversion means;
The target color data of the device-dependent color space from the target color base data acquired by the target color data acquisition unit and the color space axis of the second color space acquired by the color space axis target color acquisition unit Color space axis conversion target color base data acquisition means for acquiring color space axis conversion target base data describing the correspondence between the second color space and the data of the second color space;
Color space axis target value conversion means for converting the predicted value into data of the second color space based on the color space axis conversion target color base data;
A color space axis conversion base that describes the correspondence between the device-dependent color space data and the second color space data based on the space axis of the second color space acquired by the color space axis conversion means Color space axis conversion base data acquisition means for acquiring data;
Based on the color space axis conversion base data obtained by the color space axis conversion base data acquisition means, the device converts the data converted by the color space axis target value conversion means based on the color space axis conversion base data. A conversion color space axis prediction means for predicting data of a dependent color space;
A color predicting device comprising:   The color prediction apparatus according to claim 7, wherein the statistical method obtained by the color space axis conversion unit is a principal component analysis method.   The color space axis conversion base data used in the conversion color space axis color prediction means has a data weight dependent on a color space axis obtained by a statistical method obtained by the color space axis conversion means. The color prediction device according to any one of claims 7 to 10.   The data weight depending on the data axis obtained by the statistical method obtained by the color space axis conversion means is a principal component vector for the output data in the first color space independent of the device of the device forming the image. The color prediction apparatus according to claim 11, wherein a value based on a contribution ratio of the data is given. In a device to which image data is input or output, a predicted value input as data in one color space between a device-dependent color space and a device-independent color space is output as data in the other color space. A color prediction program for predicting predicted values,
For the data of the first color space of the base data describing the correspondence between the data of the device-dependent color space in the device and the data of the first color space which is the device-independent color space, Obtaining a color space axis in the second color space obtained by converting the color space axis using a genetic technique;
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 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 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 the second color space;
Predicting the predicted value by converting the data converted to the second color space data into the device-dependent color space data based on the color space axis conversion base data;
The color prediction program according to claim 13, comprising: A color prediction program for predicting a predicted value of a device-dependent color space output from a device from a predicted value input as data of the device-dependent color space of a target color in a device to which image data is input or output There,
Extracting target color base data describing a correspondence relationship between data in the device-dependent color space of a plurality of target colors and data in the first color space which is a device-independent color space;
Obtaining base data describing the correspondence of the first color space data to the device-dependent color space data in the device;
A color for obtaining a color space axis in a second color space obtained by converting a color space axis using a statistical method with respect to the target color base data and the data of the first color space of the base data Converting the spatial axis;
From the target color base data and the color space axis of the second color space, a color space axis conversion target describing the correspondence between the target color data of the device-dependent color space and the data of the second color space Along with the step of acquiring base data
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 conversion target color base data;
Predicting the predicted value by converting the data converted to 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:

Images