JP2009071617A - Image processor, image processing method and program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To accurately calibrate a color conversion parameter for other forms having a different characteristic from a form outputting a reference chart even in a color image forming device in which an attachment amount of a color material or a dot area rate greatly varies over time. <P>SOLUTION: When a calibration is designated from a host computer 3, a controller 4 outputs the reference chart into an image forming device 1, and prints out a calibration chart 5 of a form A. The printed chart is subjected to a colorimetry by a chart colorimetry device 2 and colorimetry data is transferred to the controller 4. The controller 4 prepares a new color conversion parameter obtained by correcting a concentration fluctuation of the image forming device 1 by use of the colorimetry data, and replaces the stored color conversion parameter with it. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to an image processing apparatus, an image processing method, and a program for correcting a color conversion parameter in accordance with output characteristics in a color image forming apparatus that can use a plurality of paper types, for example, a digital color copying machine, a color laser, and the like. The present invention relates to a technique suitable for a color image processing system including an image forming apparatus such as a printer and an inkjet color printer, and a computer image processing apparatus.

In an image forming apparatus such as a copying machine or a printer, a recording method such as an electrophotographic method, a thermal method, or an ink jet method is used. In each of these types of image forming apparatuses, various measures are taken in accordance with the characteristics of each recording method in order to satisfy the user's request for high-speed image formation and high image quality.
For example, in an electrophotographic image forming apparatus, since the electrostatic phenomenon of toner, which is a fine particle, is used, the image density fluctuates due to the influence of environmental changes such as temperature and humidity and changes over time, or There is a tendency that the image quality is likely to be deteriorated due to a difference in image density caused by minute variations in the components of the image forming apparatus. Therefore, in order to stably maintain image quality, an electrophotographic image forming apparatus generally has a function of adjusting image density (hereinafter referred to as calibration).

  For example, in Patent Documents 1 and 2, the reference chart is output by a desired image forming apparatus, the output chart is read by a scanner, the colorimetry is performed by the colorimeter, the output characteristics of the image forming apparatus are analyzed, and C, M, The color conversion parameters are corrected according to the deviation from the target value defined in advance for the Y and K gradation values.

  By the way, various papers are often used in the commercial printing field. These papers have different background colors and smoothness of the paper surface, and there is a problem that even if they are output by the same image forming apparatus, the color reproduction characteristics of the output image differ depending on the paper. For this reason, it is necessary to perform calibration for each sheet in applications that require highly accurate color reproduction. For example, in Patent Document 3, an image quality adjustment parameter is switched for each of a plurality of paper types, a chart is output, and adjustment is performed so as to obtain a target density.

However, each time the paper is changed, it is very complicated to output a reference chart, measure a number of color patches, and perform calibration. Therefore, in Patent Document 4, the difference between the spectral density Sd1 (λ) when the color material 1 is printed on the printing paper A and the spectral density Sdw1 (λ) of the printing paper A is multiplied by the adjustment coefficient r1 due to the difference in surface condition. After that, the spectral density SdWd of the printing paper C is added to estimate the color SDM1 (λ) when the color material 1 is printed on the printing paper C. That is, SDM1 (λ) = r1 × (Sd1 (λ) −Sdw1 (λ)) + SdWd (λ)
Is used to estimate color reproduction in consideration of the paper color of the paper and the surface smoothness of the paper.

Japanese Patent No. 2643951 Japanese Patent No. 3520550 JP 2004-341375 A JP 2005-142994 A

  However, in the above-described prior art, it is necessary to perform calibration for every sheet whenever the printer changes. For example, a plurality of media such as plain paper, fine paper, art paper, glossy paper, and recycled paper are recorded. With respect to the target color printer, it is necessary to measure the color patches printed out for each sheet and correct the color conversion parameters such as the gamma correction table and the LUT, resulting in a complicated process.

  On the other hand, in the method proposed in Patent Document 4, color reproduction other than standard paper is estimated using colorimetric data relating to the ground color of the paper, and color conversion parameters corresponding to a plurality of paper types are relatively easily obtained. Can be requested. The color estimation model proposed in this document shows that a proportional density difference occurs between the printing paper A and the printing paper C regardless of the amount of the color material attached. However, when the amount of color material attached is large, the density difference between sheets tends to be small, and when the amount of color material attached is small, the density difference between sheets tends to be large.

  The cause of this phenomenon will be described with reference to FIG. 20. This is because when the amount of color material attached increases, the color material covers the surface of the paper (b), and the influence on the color material surface state is reduced. FIG. 20C is a graph showing the relationship between the color material adhesion amount and the density. As shown in the figure, as the color material adhesion amount increases, the density becomes the same for both art paper and plain paper. For this reason, in the case of the method disclosed in Patent Document 4, sufficient color estimation accuracy cannot be obtained for an image forming apparatus having a large variation range of the amount of adhering color material, and even if calibration is performed, color reproduction as intended is achieved. There is a problem that you can not.

The present invention has been made in view of the above problems,
An object of the present invention is to provide color conversion parameters for other papers having characteristics different from those of the paper that outputs the reference chart, even in a color image forming apparatus in which the amount of color material attached and the dot area ratio vary greatly with time. An object of the present invention is to provide an image processing apparatus, an image processing method, and a program that can be calibrated with high accuracy.

  It is an object of the present invention to provide an image processing apparatus that can calibrate color conversion parameters for other papers having different characteristics from the paper that outputs the reference chart with high accuracy.

  An object of the present invention is to provide an image processing apparatus that can calibrate color conversion parameters for other papers having different surface smoothness from the paper that outputs the reference chart with high accuracy.

  It is an object of the present invention to provide an image processing apparatus capable of estimating a color variation characteristic associated with variation in the amount of color material attached at high speed and with high accuracy.

  An object of the present invention is to provide an image processing apparatus capable of obtaining the adhesion amount with high accuracy from the estimated adhesion amount of the color material.

  An object of the present invention is to provide an image processing apparatus capable of generating appropriate color conversion parameters for another sheet having different characteristics from the sheet that outputs the reference chart.

  An object of the present invention is to provide an image processing apparatus capable of highly accurately calibrating color conversion parameters for other paper having different dot gain characteristics from the paper that outputs the reference chart.

  An object of the present invention is to provide an image processing apparatus capable of estimating the dot area ratio for another sheet different from the sheet that outputs the reference chart.

  An object of the present invention is to provide an image processing apparatus capable of highly accurately calibrating a gamma correction table for another sheet having different characteristics from the sheet that outputs the reference chart.

  An object of the present invention is to provide an image processing apparatus that can calibrate interpolation calculation parameters for other papers having different characteristics from the paper that outputs the reference chart with high accuracy.

  An object of the present invention is to provide an image processing apparatus capable of color estimation even in an output apparatus such as an electrophotographic printer in which the dot area ratio changes in accordance with ink overlap.

  An object of an eleventh aspect is to provide an image processing apparatus capable of estimating a solid color with a small load in the case of a secondary color or more with a large amount of color material attached.

  An object of the twelfth aspect is to provide an image processing method capable of calibrating color conversion parameters for other paper having different characteristics from those of the paper that outputs the reference chart with high accuracy.

  The object of the thirteenth aspect is to provide a program that can calibrate the color conversion parameters for other papers having different characteristics from the paper that outputs the reference chart with high accuracy.

  The present invention relates to a reference chart using a first recording medium in an image processing apparatus that generates a color conversion parameter for converting an input color image signal into an output signal for an image forming apparatus according to the type of the recording medium. Chart output means for outputting color measurement data acquisition means for acquiring color measurement data of the output reference chart, and fluctuation characteristic estimation means for estimating a fluctuation characteristic value of the image forming apparatus from the acquired color measurement data; The main feature is that it comprises a parameter creating means for creating a color conversion parameter corresponding to a second recording medium different from the first recording medium based on the estimated variation characteristic value.

  Claims 1, 12, and 13: When a color conversion parameter for converting an input color image signal into an output signal for an image forming apparatus is generated according to the type of the recording medium, the first recording medium is used. A chart output for outputting a reference chart, obtaining colorimetric data of the outputted chart, estimating a fluctuation characteristic value of the image forming apparatus from the obtained colorimetry data, and based on the estimated fluctuation characteristic value, Since the color conversion parameter corresponding to the second recording medium different from the first recording medium is created, the color conversion parameter of the second recording medium is output without outputting the reference chart on the second recording medium. Can be calibrated.

  Claim 2: Since the color material adhesion amount is estimated from the colorimetric data of the first recording medium, even if the same adhesion amount fluctuation occurs, the recording medium with different color fluctuation behavior is calibrated with high accuracy. It can be performed.

  According to the third aspect of the present invention, since the variation characteristic table in which the variation characteristic value is associated with the colorimetric data is provided, the color variation characteristic for each recording medium can be estimated at high speed and with high accuracy.

  Claim 4: Since the color material adhesion amount is estimated using spectral colorimetric data of a plurality of wavelengths, even if the color material adhesion amount is large and the influence of surface scattering is large, the color material adhesion The quantity can be estimated with high accuracy.

  Claim 5: Since the output color of the solid patch when the second recording medium is used is estimated based on the estimated color material adhesion amount, the reference chart is not output on the second recording medium. The output color of a solid patch can be estimated with high accuracy.

  [6] Since the area ratio or the rate of change of the area ratio with respect to the output gradation value is estimated from the colorimetric data of the first recording medium, even when a recording medium having different dot gain characteristics is used Accurate calibration can be performed.

  Claim 7: Multiply the estimated change rate of the area ratio by the reference area ratio of the second storage medium to obtain the dot area ratio corresponding to the output gradation value when printing is output using the second recording medium. Therefore, it is possible to estimate the gradation characteristics when a halftone dot is output on the second recording medium without outputting the reference chart on the second recording medium.

  Claim 8: Since the color estimation means for estimating the output color when the second recording medium is used based on the estimated variation characteristic value is provided, the gamma correction capable of faithfully reproducing the target gradation characteristic A table can be created.

  Claim 9: Since there is provided a color estimation means for estimating the output color when the second recording medium is used based on the estimated variation characteristic value, a three-dimensional look capable of faithfully reproducing the target output color You can create uptables.

  Claim 10: Since an area ratio correcting means for correcting the dot area ratio in accordance with the overlap of the ink is provided, even in an image forming apparatus in which the area ratio changes in accordance with the overlap of the ink, like an electrophotographic printer. Color prediction can be performed with high accuracy.

  Claim 11: Since the spectral reflectance of the solid patch is estimated using different estimation methods depending on the number of colors of the ink to be superimposed, it is easy for secondary to quaternary colors where the surface smoothness of the paper is less likely to be affected. The output color can be estimated.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

Example 1:
1. Image Processing System FIG. 1 shows the overall configuration of the image processing system of the present invention. In FIG. 1, 1 is an image forming apparatus, 2 is a chart colorimetric apparatus, 3 is a host computer, and 4 is a controller.
Further, the controller 4 is provided with processing functions such as the image processing unit 10 and the calibration processing 20 and can execute basic print processing, calibration processing, and the like.

  When printing is instructed from an application running on the host computer 3, the host computer 3 operates a printer driver to transmit image information to be printed to the controller 4. In the controller 4, the image information input by operating the image processing unit 10 is subjected to various image processing such as color conversion processing, rasterization processing, and halftone processing, and converted into printer output data. Output.

  The image forming apparatus 1 is an output device for printing out image data. For example, an image forming apparatus such as an electrophotographic or inkjet color printer or a color facsimile can be used.

On the other hand, when calibration is instructed from the host computer 3, the controller 4 outputs a built-in calibration reference chart to the image forming apparatus 1 to calibrate the paper A (first recording medium). The chart 5 is printed out. Here, the reference chart is image data stored in the image processing unit 10, and the calibration chart 5 of the paper A means an output product output on the paper A by the image forming apparatus 1. Then, the operator measures the color of the printed calibration chart 5 with the chart colorimetric device 2 and transfers the colorimetric data to the controller 4. When the color measurement is completed, the controller 4 uses the color measurement data to create a new color conversion parameter in which the density variation of the image forming apparatus 1 is corrected, and realizes calibration by replacing the stored color conversion parameter. is doing.
In FIG. 1, the chart colorimetric device 2 is configured as an apparatus independent of the image forming apparatus 1, but the chart colorimetric device 2 may be built in the paper conveyance path of the image forming apparatus. Further, a display may be connected to the host computer 3 to display the result of calibration or to display a screen for the operator to set a calibration operation.

2. Configuration and Operation of Image Processing Unit FIG. 2 shows the configuration of the image processing unit of the first embodiment. As shown in FIG. 2, the image processing unit 10 includes a color conversion processing unit 100, a rasterization processing unit 200, a halftone processing unit 300, and the like. Hereinafter, the operation of the image processing unit 10 will be described with reference to FIG.
The image information sent from the application is drawing command format data represented by RGB color signals for normal display display. However, since the color reproduction range and color reproduction characteristics differ greatly between the display and the printer, the color conversion processing unit 100 converts the input RGB data in units of drawing objects into CMYK ( 8 bits).
Next, the rasterization processing unit 200 interprets the CMYK 8-bit drawing data after the color conversion processing and develops it into 8-bit bitmap data of each CMYK color.
The halftone processing unit 300 receives CMYK image data (8 bits) developed into a bitmap image, and converts it into CMYK image data with a small number of bits (for example, 1 bit) that can be output by the image forming apparatus 1. I do. By the image processing as described above, CMYK image data that can be output by the image forming apparatus 1 is created.

3. Configuration / Operation of Color Conversion Processing Unit FIG. 3 illustrates a configuration of the color conversion processing unit 100 according to the first embodiment. As shown in FIG. 3, the color conversion processing unit 100 includes an interpolation calculation unit 101, a gamma correction unit 102, a color conversion parameter memory 103, a selector 104, a parameter storage unit 105, and the like.

  The parameter storage unit 105 stores color conversion parameters corresponding to various types of paper usable in the image forming apparatus 1, and color conversion parameters corresponding to the paper type designated by the operator via the paper type designation unit. Is selected by the selector 104 and loaded into the color conversion parameter memory 103.

  Here, the color conversion parameters include a three-dimensional lookup table (3D-LUT) used in the interpolation calculation unit 101, a gamma correction table used in the gamma correction unit 102, and the like.

  The interpolation calculation unit 101 refers to the 3D-LUT stored in the color conversion parameter memory 103 and converts the input image data into CMYK 8-bit color data for the printer. As the color conversion algorithm, a memory map interpolation method such as tetrahedral interpolation is used. In the memory map interpolation method, R, G, and B color signal levels are each divided into n, and output CMYK values corresponding to the respective grid points are created in advance as a three-dimensional lookup table, and the input located between the grid points is performed. For RGB values, CMYK gradation values corresponding to neighboring grid points are read from the 3D-LUT and output CMYK values are calculated by interpolation.

  The above-described memory map interpolation calculation provides higher conversion accuracy when the input / output characteristics are linear. However, since the image forming apparatus 1 generally has non-linear distortion, the gamma correction unit 102 uses the non-linearity of the image forming apparatus 1. Has the role of correcting sex. The gamma correction unit 102 performs a simple process of converting the data values of C, M, Y, and K colors into a table using a one-dimensional lookup table.

  Furthermore, by defining the target output density as a standard value for the C, M, Y, and K gradation values after the interpolation calculation, even when density fluctuation occurs, only this gamma correction table adjustment is performed. And there is an advantage that the color can be generally stabilized.

  Through the processing described above, the image information input from the host computer 3 can be output to the image forming apparatus 1.

4). Description of Calibration Processing Unit (1) Calibration Chart Output / Colorimetry Next, a calibration processing method will be described with reference to the flowchart of FIG. In this embodiment, a case where only the gamma correction table is calibrated will be described.

When calibration is instructed by the operator, the calibration processing unit 20 outputs a reference chart stored in advance (or input externally) to the image forming apparatus 1 via the image processing unit 10, and The calibration chart 5 is printed out (step S11).
The reference chart is a CMYK format image data file specially designed exclusively for calibration, and expresses a sample chart in which color patches of various colors are arranged. When outputting the reference chart, the interpolation calculation unit 101 and the gamma correction unit 102 are set to through. Accordingly, the input / output characteristics as the engine characteristics of the image forming apparatus 1 appear in the printed calibration chart 5.

  FIG. 5 is an example of a calibration chart to be printed out. This chart shows the entire gradation range that can be output for each of the primary colors C (cyan), M (magenta), Y (yellow), and K (black) of the image forming apparatus 1 in order to calculate the gamma table. A rectangular region (hereinafter referred to as a patch) printed with eight gradation values (32, 64,..., 224, 255) selected from (0 to 255) at 32 gradation intervals is arranged in one direction. Yes. However, the creation of the gamma table need not be limited to eight stages, and there is no problem even if the number of gradations is larger.

  In terms of printing, the primary color is the color of the ink itself, that is, CMY (K). Incidentally, the secondary color is a color created using two of the primary colors, and RGB may be referred to as a secondary color. Tertiary colors are similar and are made using three colors. K may be called a tertiary color. Also, a patch in which uniform ink is applied to all pixels in the rectangular area is called a solid patch, and a patch in which ink is applied or not applied by pixels is called a halftone patch. To do. In general, a solid patch is output when the gradation value = 255.

  Next, the output calibration chart 5 is measured by the chart colorimetric device 2 (step S12). The chart colorimetric device 2 is a device for measuring the color value of each color patch included in the output calibration chart 5 and can use, for example, a spectral reflectometer. An example illustrating the color measurement data of the read color patch is shown in FIG. FIG. 6 shows spectral reflectance data of solid patches of C, M, Y, and K. In the example of FIG. 6, the colorimetric data is reflectance data at intervals of 10 nm with respect to the wavelength range of 400 to 700 nm, but the wavelength range and wavelength interval may be different.

  The read colorimetric data of the calibration chart is transferred to the controller 4 (step S13). After acquiring the transferred color measurement data, the calibration processing unit 20 temporarily stores it in a storage device such as an HDD in the controller 4.

  When the colorimetric data is acquired, the calibration processing unit 20 creates a gamma correction table (color conversion parameter) for the paper A based on the reference chart data held in the controller 4 (step S14).

(2) Generation of Gamma Correction Table for Paper A A method for creating this gamma correction table will be described with reference to FIGS. FIG. 8 shows the flow of the gamma correction table calculation process in detail.

First, in step S21, an output density ID is obtained from the colorimetric data of each color patch. For example, when the colorimetric data is given by the spectral reflectance as shown in FIG. 6, the output density ID is determined after converting the spectral reflectance into the spectral density. The spectral density D (λ) is the spectral reflectance R (λ),
D (λ) = log (1 / R (λ))
This is the value obtained by density conversion using the formula The output density ID is obtained as the maximum value of the spectral density D (λ) for each wavelength obtained by the above equation.

Since only the output density corresponding to the discrete gradation value can be obtained from the colorimetric data, the gradation value of the color patch included in the reference chart is X, the actual printer output density is Y, and Y = f (X) Are subjected to linear interpolation or spline approximation to obtain output densities for all 256 gradations (step S22). At this time, the output density of the paper white of the paper A may be read out by storing data measured in advance in a storage device in the controller. The thin line in the graph of FIG. 7A is an example of the output density corresponding to the gradation values 0 to 255 obtained above.
When the output density for the printer gradation value for 256 gradations is obtained, the target output density (target output density) TD is read from the storage device in step S23. The target output density TD is the density defined as the standard value for the CMYK gradation values before gamma correction as described above, and corresponds to the thick line (target output density table) in the graph of FIG. ing.
Finally, the process proceeds to step S24, and a gamma correction table is created from the target output density TD and the output density obtained in step S22. In the graph of FIG. 7A, the actual printer density is higher than the ideal output density characteristic due to fluctuations. In such a case, the gamma correction table is set so as to lower the density so as to cancel out the density fluctuation.
Specifically, as shown in FIG. 9, CMYK gradation values 0 to 255 with varying density are input to the gamma correction table 110, and the reference gamma correction table 110 for the paper A is applied to obtain C′M ′. Convert to Y'K '. Next, a target printer output density characteristic 111 (that is, a target output density table indicated by a thick line in FIG. 7A) with respect to C′M′Y′K ′ is applied to obtain a target output density TD for CMYK gradation values. Obtain (convert tone value to density by characteristic 111). The target output density TD is subjected to the inverse mapping conversion 112 of the output density characteristic created in step S22 (the density is converted back to the gradation value) to obtain C ″, M ″, Y ″, K ″. . In this way, CMYK => C ″, M ″, Y ″, K ″ are created as new gamma correction tables.

  An example of the gamma correction table created by the above method is shown in FIG. The standard gamma correction table and the standard printer output density characteristic are tables set at the time of product shipment, and are recorded in advance on a hard disk or ROM.

  When the creation of the gamma correction table is completed, the color conversion parameters of paper A stored in the parameter storage unit 105 are rewritten to the created gamma correction table (step S25).

In the above processing, the case where the gamma correction table is created using the density has been described. However, the present invention is not particularly limited, and there is no particular influence even if the brightness and other color values are used. Further, instead of obtaining the output density from the spectral reflectance, a read value obtained from a reading device such as a scanner or an RGB sensor may be converted into a density.
(3) Creation of Gamma Correction Table for Paper X (i) Returning to FIG. If it is determined (step S15) and a mode for executing calibration is selected for sheets other than the sheet A, the process proceeds to step S16 to continue the calibration.

  In this embodiment, a variation characteristic table for each sheet is set in advance, and a gamma correction table for a sheet X (i) (second recording medium) different from the sheet A is created by referring to the variation characteristic table. ing.

  First, the variation characteristic table that is a feature of the present invention will be described. The variation characteristic table is a data file in which the relationship between the adhesion amount of the color material to the paper X (i) and the spectral density is tabulated, and is set for each primary color of the image forming apparatus 1. FIG. 10 shows a specific example of a variation characteristic table for M (magenta). In the example of FIG. 10, the spectral density with respect to the wavelength obtained by dividing the wavelength range of 400 to 700 nm at intervals of 50 nm and the spectral density with respect to 570 nm are described in the variation characteristic table for each adhesion amount of the coloring material. Here, the adhesion amount of the coloring material in FIG. 10 is a normalized value assuming that the adhesion amount in the ideal state (state in which there is no density fluctuation) is 1.0. It may be the weight of the material.

  However, the wavelength interval need not be limited to the 50 nm interval, and may be finer or coarser. When the wavelength interval is coarse, it is better to include the spectral density of the wavelength (peak wavelength) that most easily detects the density change.

  The spectral density described in the variation characteristic table is measured by actually forming a patch on the paper X (i) with the color material adhering amount changed using the image forming apparatus 1 and measuring the colorimetric data. The spectral density obtained by the conversion is set as a table value. Alternatively, if it is difficult to control the amount of color material attached, build a prediction model that predicts the spectral density from the color material transmission characteristics and paper white characteristics, and use the built prediction model to determine the amount of color material adhesion. You may make it obtain | require the spectral density when changing.

  The model for predicting the spectral density of the solid patch used here is a model that can predict the spectral density based on the adhesion amount of the coloring material. For example, Kubelka-Munk theory (P. Kubelka & F. Munk: “Ein Beitrag”). zur Optik der Ferbanstrich ", Z. tech Physik, 12, p. 593 (1931)) and Williams-Clappa theory (FC Williams and Clapper:" Multiple internal reflections in pharmacology. .. Am.43, p.595 (1953)) can be used.

Here, the Kubelka-Munk theory is a method of calculating a reflectance with respect to incident light by constructing a differential equation from the light absorptance and light diffusivity of the color material and the reflectance of paper. The Williams-Clapper theory is a method of calculating the reflectance by tracing the state of incident light that is reflected multiple times inside the color material layer when the color material is on the paper.
In step S16, the fluctuation characteristic of the image forming apparatus 1 is estimated using the fluctuation characteristic table or the like. Hereinafter, the fluctuation characteristic estimation processing will be described with reference to the flowchart of FIG.

Estimation of variation characteristics using colorimetric data of paper A First, the color material adhesion amount is estimated using the above-described variation characteristic table and the color measurement data corresponding to the primary color solid patch (step S31). . FIG. 12 shows changes in the spectral density of M (magenta) when the amount of color material attached varies. As shown in FIG. 12, the change in the spectral density varies depending on the wavelength, and the higher the spectral density, the larger the change due to the variation in the amount of adhering colorant.
Therefore, a wavelength having a large change width of the spectral density accompanying the variation in the color material adhesion amount is determined for each color material, and the color material adhesion amount is estimated by analyzing the spectral density of the determined wavelength. For example, in the case of M (magenta), by using a spectral density of 570 nm, it is possible to estimate the color material adhesion amount with high accuracy. If there is no color material adhesion amount that matches the measured spectral density value, the color material adhesion amount is calculated by linear interpolation or the like.

For example, when the color density of the magenta patch of the output chart is measured, and the spectral density at a wavelength of 570 nm is 1.6, the color material adhesion amount Pm is Pm = ((1.1−1.0) × ( 1.6-1.5) / (1.65-1.5)) + 1.0
= 1.067
It becomes.
However, if the adhesion amount of the coloring material is too large, the scattered light on the surface of the coloring material layer becomes dominant, so that the density change at a wavelength having a high spectral density tends to be small. As a result, when applying even when the amount of color material attached is very large, the prediction accuracy of the amount of color material attached decreases only with the reflection density of 570 nm, and therefore other wavelengths (for example, 450 nm and 500 nm). ) Is also used together to estimate the color material adhesion amount (for example, the maximum color material adhesion amount obtained from three types of wavelengths), thereby further improving the color material adhesion amount prediction accuracy. be able to.

Next, the process proceeds to step S32, and the fluctuation characteristics in the halftone are calculated based on the colorimetric data of the halftone dot patch of the paper A. In this embodiment, the change rate of the dot area rate is obtained as a variation characteristic in halftone, and the change rate of the area rate is treated as being constant regardless of the paper.

  First, a color reproduction model of a halftone dot patch will be described. As a color reproduction model of a halftone dot patch, for example, Clapper-Yul theory (FR Clipper and JAC Yule: “The Effect of Multiple Internals of the Densities of Half-Prints” .Opt.Soc.Am.43, p.600 (1953)). The clapper-Yule theory predicts the spectral reflectance of the halftone from the reflectance of the color material and the area ratio. Based on the Williams-Clappa theory, multiple reflections inside the paper are considered.

  The model formula of the Clapper-Yule theory is expressed by the following formula.

上式において、ａｉは面積率、Ｒ（λ）は分光反射率、を表している。ｎはユールニールセンのｎ値と呼ばれるもので、スクリーン線数や記録媒体の内部構造によって変わる定数なので、プリンタ出荷前に用紙の種類ごとに求めておく。一般に、ｎ≧１のときはスクリーン線数が大きいほど、コート紙より上質紙の方が大きな値をとる傾向がある。

In the above equation, ai represents the area ratio, and R (λ) represents the spectral reflectance. Since n is called Yule Nielsen's n value and is a constant that varies depending on the number of screen lines and the internal structure of the recording medium, it is obtained for each type of paper before shipping the printer. In general, when n ≧ 1, the higher the number of screen lines, the larger the quality of the high quality paper than the coated paper.

  When correcting the variation characteristic table, only the color mixture of the color material color and the paper color needs to be considered, so the above equation can be simplified as shown in Equation 2.

  In the present invention, when color material adhesion amount fluctuation occurs, color prediction is performed assuming that the n value is constant and the area ratio am and the spectral reflectance Rm (λ) change. The spectral reflectances Rm (λ) and Rw (λ) coincide with the spectral reflectances obtained by measuring solid patches and paper white.

  By transforming Equation 2, the area ratio am can be obtained by the following equation.

  Looking at Equation 3, since the spectral reflectances Rm (λ), Rw (λ), and R (λ) are all colorimetric values and the n value is also fixed, the area ratio am is Required for each halftone patch.

  However, since am varies from wavelength to wavelength, in this embodiment, calculation is performed in the wavelength region where the change in reflectance is greatest, but other methods such as averaging the area ratio am obtained at all wavelengths are used. It doesn't matter.

  When the area ratio is obtained for each gradation value, the area ratio change rate Ar = am / at is obtained for each gradation value by comparing with the reference area ratio data “at” set in advance, and the storage medium is used as the fluctuation characteristic value. Alternatively, it is stored in the memory (step S33). Here, the reference area ratio at is a reference area ratio set in advance before the shipment of the product, and corresponds to the area ratio obtained by the same method as in step S32 described above.

  In the above description, Equation 3 is calculated and obtained. However, it is calculated in advance, and a table in which the relationship between the peak reflectance and the change rate Ar is prepared for each gradation value is prepared. The change rate Ar may be obtained by referring to the table as in the case of the color material adhesion amount.

Creating a gamma correction table for X (i) based on the fluctuation characteristics Returning to FIG. 4, when the fluctuation characteristics can be estimated in step S16, the process proceeds to step S17 to create a gamma correction table for the paper X (i).
As a procedure for creating the gamma correction table, the output color of the solid patch for the paper X (i) is estimated, and then the tone characteristics are estimated to create the gamma correction table.

  For the output color of the solid patch, first, the spectral density of each wavelength from 400 nm to 700 nm is read based on the variation characteristic table of the paper X (i). For example, in the case of FIG. 13, in order to estimate the spectral density of the adhesion amount 1.067, the spectral densities of the adhesion amounts 1.0 and 1.1 are read from the table, and the spectral density at the adhesion amount 1.067 is obtained by linear interpolation. Obtain (lower figure in FIG. 13).

  When the spectral density D (λ) is obtained,

の式により分光反射率Ｒ（λ）に変換する。

Is converted into a spectral reflectance R (λ).

  Next, the output density characteristic for the paper X (i) is calculated based on the area ratio change rate Ar obtained in step S33. First, reference dot area ratio data of the preset paper X (i) is read. Next, Ar ′ is calculated by multiplying the reference dot area ratio by the area ratio change rate Ar.

  Next, using the estimated spectral reflectance of the solid patch X (i), the spectral reflectance of the X (i) paper, and the corrected dot area ratio Ar ′, the prediction model equation of Equation 2 is used to calculate the scale. Calculate the reflectance for each key value. Here, the required reflectance does not have to be all wavelengths, and only the peak wavelength may be used.

  The output density characteristic of the paper X (i) can be obtained by converting the reflectance obtained for each gradation value into a density value and approximating it with a spline function or the like.

(4) Creation of Gamma Correction Table When the density characteristics of X (i) are obtained, the gamma correction table for X (i) is created by the procedure of FIG. 9 in the same manner as when the gamma correction table for paper A is created.

  That is, the reference gamma correction table 110 for the paper X (i) is applied to the CMYK gradation values 0 to 255 to convert it to C′M′Y′K ′, and for C′M′Y′K ′. The target printer output density characteristic 111 is applied to obtain the target output density for the CMYK gradation value, and the calculated target output density is subjected to inverse mapping transformation 112 of the output density characteristic of X (i) to obtain C ″. , M ″, Y ″, K ″.

  When the gamma correction table for X (i) can be created by the above method, the calibration is completed by replacing the color conversion parameters stored in the parameter storage unit 105 of the color conversion processing unit 100.

  Further, in the above description, an electrophotographic printer mainly having a large variation in the amount of adhering color material has been described as an example, but the same effect can also be obtained in the case of an IJ printer. In the case of the IJ method, although the fluctuation with time is small, there is a problem that the amount of ink droplets discharged from the nozzles is easily changed between lots because a high technique is required for manufacturing the head. Therefore, by making the droplet amount a variable characteristic instead of the color material adhesion amount described above, it is possible to efficiently create color conversion parameters for various papers.

Example 2: Calibration of color conversion parameters by color mixture estimation In Example 1, a method for calibrating a gamma correction table has been described. In this embodiment, a case will be described in which a gamma correction table and a 3D-LUT used in an interpolation calculation are calibrated.

  As shown in FIG. 14, the color conversion unit 100 according to the present embodiment includes an interpolation calculation unit 201, a color separation unit 202, and a gamma correction unit 203. The interpolation calculation unit 201 converts an input signal into CMY gradation values. It is assumed that the color separation unit 202 performs black generation and undercolor removal to convert it into CMYK gradation values, and then the gamma correction unit 203 performs gamma correction. Here, the color separation unit 202 is a fixed color conversion process and does not need to be calibrated. In this manner, by including the color separation unit 202, the interpolation calculation unit 201 only needs to have a three-input three-output three-dimensional LUT, so that the calibration process can be simplified. Needless to say, in the case of a CMY three-color printer, the color separation unit 202 is unnecessary.

  Next, a calibration method will be described. The overall operation of calibration is the same as that in the flowchart of FIG. 4 and will be described with reference to FIG.

(1) Calibration chart output / colorimetry First, calibration chart output (step S11) and colorimetry (step S12) are performed. In the first embodiment, the reference chart including only the primary color patch is used. However, in this embodiment, the reference chart including the secondary color and the tertiary color in addition to the primary color patch is used. FIG. 15 is an example of a calibration chart to be printed out. In this example, the left half of the chart is the entire gradation range (0 to 0) that can be output for the primary colors C (cyan), M (magenta), Y (yellow), and K (black) of the image forming apparatus 1. 255) Patches printed with eight gradation values (32, 64,..., 224, 255) selected at intervals of 32 gradations are arranged in one direction.

  In the right half of the chart, solid patches of secondary to quaternary colors are arranged. For example, six types of solid patches of C + M, C + Y, M + Y, C + K, M + K, and Y + K are arranged as the secondary color solid patches. The arrangement of the charts is not particularly specified, and each patch may be arranged at any position on the chart.

  As in the first embodiment, the reference chart is printed out through the interpolation calculation 201 and the gamma correction 203, and the chart colorimetric device 2 performs colorimetry.

(2) Creation of color conversion parameters for paper A When the colorimetric data of the calibration chart is acquired (step S13), in step S14, the calibration processing unit 20 creates a gamma correction table and a three-dimensional LUT. .

  Here, the method for creating the gamma correction table is the same as that in the first embodiment, and therefore the description thereof will be omitted. Only the method for creating the three-dimensional LUT will be described.

  An outline of a method for creating a three-dimensional LUT will be described with reference to the flowchart of FIG. First, in step S41, a color prediction module reflecting color variations is constructed with reference to the color patch data measured. As will be described later, this color prediction module has a function of estimating Lab colorimetric values from CMY gradation values after interpolation calculation by a three-dimensional LUT.

  Next, in step S42, data relating to the target output color (Lt, at, bt) corresponding to the lattice points of the three-dimensional LUT is acquired. Data regarding the target output color may be calculated at any time, or may be recorded in advance on a hard disk or a ROM and read out at any time.

  When the target output color (Lt, at, bt) is calculated, the grid point output value C′M′Y ′ of the reference three-dimensional LUT for the paper A is used for the RGB gradation values corresponding to the grid points. It is read and converted into C′M′Y′K ′ by performing color separation processing 202 and gamma correction 203 in FIG. As the gamma correction table applied in the gamma correction 203, a reference gamma correction table set at the time of product shipment is used. Then, a target output color (Lt, at, bt) is obtained by using a standard colorimetric value estimation process for the C′M′Y′K ′ gradation value.

  The standard colorimetric value estimation process is an estimation process based on printer characteristics when no variation is included, and uses a color prediction module built at the time of product shipment.

  Once the target output color data is acquired, the CMY gradation value for outputting the target output color (Lt, at, bt) is then recalculated using the color prediction module reflecting the color variation (step) S43). The CMY tone value is searched for a combination of CMY that minimizes the color difference between the target output color and the estimated Lab value by an optimization algorithm. As an optimization algorithm, there are many methods such as a simplex method, a hill-climbing method, and a Newton method, and any of them may be used.

  Here, when the color reproduction range of the image forming apparatus 1 is narrowed due to temporal variation, the target output color (Lt, at, bt) may not be reproduced. However, even in such a case, since the minimum point of color difference is searched, it is possible to obtain a combination of CMY tone values that are closest to the target color among the reproducible output colors.

  When the gamma correction table and the three-dimensional LUT for paper A are created by the above processing, the color conversion parameters stored in the parameter storage unit 105 of the color processing unit 100 are replaced (step S44).

Next, the method for creating the color prediction module in step S41 will be described in detail. FIG. 17 is a configuration example of the color prediction module in this embodiment.
As shown in FIG. 17, the color prediction module includes a color separation processing unit 210 that separates CMY gradation values into C′M′Y′K ′ gradation values, and converts the CMY gradation values into C′M′Y′K ′ gradation values. A gamma correction unit 211 that performs gamma correction, a colorimetric value estimation unit 212 that estimates Lab values from the C ″ M ″ Y ″ K ″ gradation values after gamma correction, and a storage buffer for a gamma correction table It comprises a memory 213, a buffer memory 214 for storing colorimetric value data, a colorimetric value estimation parameter creating unit 215 for calculating a colorimetric estimation parameter based on the colorimetric value data, and the like.

  Here, the color separation processing unit 210 and the gamma correction unit 211 have the same algorithms as the color separation unit 202 and the gamma correction unit 203 in the color conversion unit 100. Further, since the purpose is to estimate the color reproduction characteristics at the time of calibration execution, the gamma correction table storage buffer memory 213 includes a gamma correction table calibrated based on the above-described primary color measurement data. Load and set.

  The colorimetric value estimation unit 212 has a function of predicting the Lab value of the print output color from the CMYK gradation value. For example, the spectral reflectance of the halftone patch is estimated from the CMYK tone value using the improved clapper-Yule model described in detail below, and the Lab value is obtained from the estimated spectral reflectance using the standard conversion formula of CIE. Like that. Hereinafter, the improved clapper-Yule model formula will be described in detail. However, the present invention is not limited to this, and the present invention can be implemented using other halftone dot models.

Description of Improved Clapper-Yule Model Equation In the clapper-Yule model equation (Equation 1) used in Example 1, the color mixture is obtained by substituting the monochrome area ratio obtained from the halftone dot data of the primary color into the Demishel formula. The area ratio of each reference color of the halftone dot is calculated.

  As an example, FIG. 18 shows an enlarged image of a part of a halftone dot patch in which the area ratio of Y = 30% and the area ratio of M = 30%. In this example, for example, the area ratio of the area where Y dots and M dots overlap is 0.3 × 0.3 = 0.09, which is an area ratio of 9%.

Similarly, White (white) = 49%, Y = 21%, M = 21%, Y + M = 9%
Is calculated as

  The Demishel formula is based on the assumption that the area ratio of the same ink does not change between a single color halftone dot and a mixed color halftone dot. In the case where there is, there is a characteristic that the adhesion amount of the coloring material changes. In other words, if it is assumed that the dot area ratio of a single color is equal to the dot area ratio of each ink color in the second and third colors as in the clapper-Eule equation, sufficient prediction accuracy cannot be obtained. Therefore, the model formula is expanded so that the dot area ratio during color mixing can be estimated with high accuracy.

  That is, the area ratio of each color of C, M, Y, K in the single color patch

とすると、インクの重なりを考慮した面積率

Then, the area ratio considering the overlap of ink

は式５で表すことができる。ここで、Ｃ、Ｍ、Ｙ、Ｋ各色の面積率

Can be represented by Equation 5. Here, the area ratio of each color of C, M, Y, K

は実施例１と同様に、キャリブレーション・チャートに含まれる１次色の網点パッチの測色データを（式３）に適用することにより求めることができる。

Can be obtained by applying the colorimetric data of the halftone dot patch of the primary color included in the calibration chart to (Equation 3) as in the first embodiment.

  In the above equation, f () is a function for correcting the area ratio when the inks overlap. As this correction function, a function such as a multi-degree polynomial or a neural network is used. Since optimization of this function requires measurement of a large number of color patches, a large number of color patches are measured and constructed before shipment from the factory, and are not changed when calibration is performed. Moreover, what is necessary is just to determine according to prediction accuracy whether a separate function is produced for every paper type, or it is set as the same function irrespective of a paper type.

  Correction area ratio

が求まると、クラッパ−ユール式に代入して網点パッチの色推定を行う。即ち、（式６）のＤｅｍｉｃｈｅｌ式を用いて混色パッチにおける基準色Ｍａの面積率を計算し、（式７）により分光反射率Ｒ（λ）を計算する。

Is obtained, the halftone patch color estimation is performed by substituting it into the Clapper-Yule equation. That is, the area ratio of the reference color Ma in the mixed color patch is calculated using the Demichelle expression of (Expression 6), and the spectral reflectance R (λ) is calculated according to (Expression 7).

Further, the spectral reflectance R _i (λ) of the reference solid color in (Expression 7) corresponds to the colorimetric data of the solid patch included in the calibration chart.

As described above, the color prediction module for the paper A can be constructed by obtaining the spectral reflectance R _i (λ) and the area ratio Ma based on the calorimetric data of the calibration chart. A color prediction module can be used to calibrate a three-dimensional LUT for reproducing a target output color.

(3) Creation of color conversion parameters for paper X (i) When the creation of color conversion parameters for paper A is completed, color conversion parameters for paper X (i) are created in steps S16 to S18.

  In order to create a color conversion parameter for the paper X (i), a variation characteristic is estimated based on the colorimetric data of the sheet A, and a color prediction module for the sheet X (i) is constructed based on the estimated variation characteristic.

  Also for this color prediction module, the above-described improved clapper-Yule model formulas (formulas 5 to 7) are used.

  In order to construct the improved Clapper-Yule model formula for the paper X (i), the spectral reflectance data Ri (λ) and the area ratio Ma in (Formula 7) must be obtained.

The spectral reflectance data Ri (λ) needs to be estimated for a total of 2 ⁴ = 16 reference solid colors in the case of a four-color printer. Since the spectral reflectance of white is the spectral reflectance itself of the paper, it may be measured in advance.

  As for the spectral reflectance of the primary colors (C, M, Y, K), the color material adhesion amount is estimated from the colorimetric data of the paper A in the same manner as in the first embodiment, and the paper X is determined from the color material adhesion amount. The spectral density of the solid color in (i) is obtained.

  In the case of secondary to quaternary colors, a method of having a table associated with the adhesion amount of the color material may be used, but in that case, many experiments are required to create the table, which is not very realistic. On the other hand, in the secondary to quaternary colors, the amount of color material attached is 2, 3, and 4 times that of the primary color, so that it is less affected by the smoothness of the paper surface, and color reproduction on paper A and paper X The difference in color reproduction in (i) is small. Therefore, sufficient prediction accuracy can be obtained by correcting only the paper color difference of the paper for the spectral data of the secondary and tertiary colors measured on the paper A.

For example, the spectral reflectance of the secondary color solid on the paper A is Ra (λ), the spectral reflectance of the secondary color solid on the paper X (i) is Rx (λ), and the spectral reflectance of the paper A is Pa ( λ) and the spectral reflectance of the paper X (i) is Px (λ), Rx (λ) = (Ra (λ) * Px (λ)) / Pa (λ)
Can be obtained with relatively high accuracy.

  For the area ratio Ma, the change rate of the area ratio for each gradation value is obtained from the area ratio obtained for the paper A as in the first embodiment, and the area ratio of the primary color on the paper X (i) is obtained from the obtained change ratio.

を求める。

Ask for.

  And the primary color area ratio

から、式５および式６を計算して、式７で用いる面積率Ｍａを求める。

From Equation 5, Equation 5 and Equation 6 are calculated to obtain the area ratio Ma used in Equation 7.

  Since the color prediction module suitable for the paper X (i) can be constructed as described above, a new three-dimensional LUT for the paper X (i) can be created. The three-dimensional LUT creation method is basically the same as the three-dimensional LUT creation method for paper A. The CMY floor that reads the target output color data corresponding to paper X (i) and reproduces the read output color. A key value is calculated using an optimization algorithm.

  When the X (i) gamma correction table and the three-dimensional LUT can be created by the above method, the calibration is completed by replacing the color conversion parameters stored in the parameter storage unit 105 of the color processing unit 100.

Example 3:
FIG. 19 shows a system configuration example when the present invention is realized by software. In this image processing system, a workstation 401 is connected to an image forming apparatus (printer) 403 and a color measuring device 402. The workstation 401 implements functions such as the above-described calibration processing and color conversion processing, and includes a display 406, a keyboard 404, a program reading device 412, and an arithmetic processing device. In the arithmetic processing unit, a ROM 409 and a RAM 408 are connected to a CPU 407 capable of executing various commands via a bus. Also connected to the bus are an HDD 411 which is a mass storage device and a NIC 410 which communicates with devices on the network.

  The program reading device 412 is a storage medium storing various program codes, that is, a floppy disk, a hard disk, an optical disk (CD-ROM, CD-R, CD-R / W, DVD-ROM, DVD-RAM, etc.), optical An apparatus for reading a program code stored in a magnetic disk, a memory card, or the like, such as a floppy disk drive, an optical disk drive, or a magneto-optical disk drive.

  The program code stored in the storage medium is read by the program reader 412 and stored in the HDD 411 or the like, and the program code stored in the HDD 411 or the like is executed by the CPU 407 to realize the image processing method or the like. Will be able to. In addition, by executing the program code read by the computer, an OS (operating system) or a device driver running on the computer performs part or all of the actual processing based on the instruction of the program code. The case where the above-described function is achieved by the processing is also included.

1 shows an overall configuration of an image processing system of the present invention. 1 illustrates a configuration of an image processing unit according to a first exemplary embodiment. 1 illustrates a configuration of a color conversion processing unit according to a first exemplary embodiment. It is a flowchart of a calibration process. An example of a calibration chart of the first embodiment is shown. An example of colorimetric data of Example 1 is shown. A correction example of the gamma table for the paper A will be shown. 6 is a flowchart of a process for creating a gamma correction table according to the first embodiment. A method for creating a gamma correction table for paper A will be described. A specific example of the fluctuation characteristic table is shown. It is a flowchart of the calculation process of a fluctuation characteristic value. The change in spectral density due to the variation in the amount of color material attached is shown. It is a figure explaining estimation of the solid spectral density of the paper X (i). 2 shows a configuration of a color conversion processing unit according to a second embodiment. An example of a calibration chart of Example 2 is shown. 10 is a flowchart of a process for creating a three-dimensional LUT for paper A in the second embodiment. 3 shows a configuration example of a color prediction module according to a second embodiment. It is a figure explaining a Demishel formula. The structural example of Example 3 is shown. It is a figure explaining the relationship between the adhesion amount of a coloring material, and a surface state.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Image forming apparatus 2 Chart colorimetry apparatus 3 Host computer 4 Controller 5 Calibration chart 10 Image processing part 20 Calibration processing part

Claims (13)

  A chart for outputting a reference chart using a first recording medium in an image processing apparatus for generating a color conversion parameter for converting an input color image signal into an output signal for an image forming apparatus according to the type of the recording medium An output unit; a calorimetric data acquisition unit that acquires the calorimetric data of the output reference chart; a variation characteristic estimation unit that estimates a variation characteristic value of the image forming apparatus from the acquired colorimetric data; An image processing apparatus comprising: a parameter creating unit that creates a color conversion parameter corresponding to a second recording medium different from the first recording medium based on a variation characteristic value.   The image processing apparatus according to claim 1, wherein the variation characteristic value is a characteristic value associated with a color material adhesion amount or a color material adhesion amount.   Storage means for storing a variation characteristic table in which the variation characteristic value and the colorimetric data are associated with each other is provided for each recording medium, and the variation characteristic estimation unit varies based on the colorimetric data and the variation characteristic table. The image processing apparatus according to claim 1, wherein the characteristic value is estimated.   The variation characteristic table describes a spectral density with respect to the adhesion amount of the color material at a plurality of wavelengths, and estimates the adhesion amount of the color material using spectral colorimetric data at a plurality of wavelengths. The image processing apparatus according to claim 3.   The image processing apparatus according to claim 4, wherein an output color of the solid patch when printed using the second storage medium is estimated using the estimated color material adhesion amount.   The image processing apparatus according to claim 1, wherein the estimated variation characteristic value is a dot area rate or a change rate of the dot area rate.   Means for multiplying the estimated change rate of the area ratio by a reference area ratio of the second storage medium to obtain a dot area ratio corresponding to an output gradation value when printing is output using the second recording medium; The image processing apparatus according to claim 6, further comprising:   The parameter creation means includes color estimation means for estimating an output color when the second recording medium is used based on the estimated variation characteristic value, and creates a gamma correction table using the color estimation means. The image processing apparatus according to claim 1.   The parameter creating means includes color estimation means for estimating an output color when using the second recording medium based on the estimated variation characteristic value, and using the color estimation means, a three-dimensional lookup table is obtained. The image processing apparatus according to claim 1, wherein the image processing apparatus is created.   The color estimation unit includes an area ratio correction unit that corrects a dot area ratio according to ink overlap, and a color mixture when a plurality of inks are mixed based on the corrected area ratio and the spectral reflectance of the solid patch. The image processing apparatus according to claim 9, wherein a three-dimensional lookup table is created based on a means for obtaining a spectral reflectance and the mixed color spectral reflectance.   The image processing apparatus according to claim 10, wherein the estimation of the spectral reflectance of the solid patch uses a different estimation method according to the number of colors of ink to be superimposed.   In an image processing method for generating a color conversion parameter for converting an input color image signal into an output signal for an image forming apparatus according to the type of the recording medium, a reference chart is output using the first recording medium, The color measurement data of the output reference chart is acquired, the fluctuation characteristic value of the image forming apparatus is estimated from the acquired color measurement data, and the first recording medium is based on the estimated fluctuation characteristic value An image processing method, wherein color conversion parameters corresponding to different second recording media are created.   A program for causing a computer to realize the image processing method according to claim 12.

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