JP2011217222A - Printer, calibration method, and calibration execution program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a printer capable of improving color prediction precision, and to provide a calibration method and a calibration execution program.SOLUTION: A printer reproduces a target primary color by mixing a secondary color or more, and has: a spectral property acquiring means for acquiring the spectral reflectance of a mixed color obtained by mixing different primary color inks for arbitrary hues; a spectral property predicting means for predicting, when predicting a spectral property in an arbitrary ink quantity set while using a color prediction model, the spectral property of the ink quantity set while using spectral properties of primary color inks constituting the ink quantity set and the acquired spectral property of the mixed color; and a correction means for correcting the spectral property in the ink quantity set so as to be made to be closer to the spectral property of the target primary color on the basis of the predicted spectral property.

Description

  The present invention relates to calibration for performing color matching on an ink amount set, and more particularly to calibration for performing color matching by predicting spectral characteristics of an arbitrary ink amount set.

  Conventionally, in a printer, calibration is executed in order to maintain the color accuracy of a color expressed by a certain ink amount set. In this calibration, a printed matter formed by a certain ink amount set is measured, and a correction amount is calculated based on a difference between the color measurement result and a target value.

  In order to improve color accuracy in the calibration described above, it is desirable that there are many colorimetric values, but it is not desirable in terms of cost to measure all ink amount sets that can be output by the printer. For this reason, it has been proposed to reduce the cost associated with calibration by predicting the colorimetric values in other ink amount sets based on the colorimetric values in typical ink amount sets. As an example, a spectral Neugebauer model is known.

  In the spectral Neugebauer model, which is well known as a color prediction model, the reflectance of the primary color ink (for example, each ink of C, M, and Y) and the reflectance of the mixed color obtained by mixing the primary color inks are calculated. Spectral reflectance in a certain ink amount set is predicted by weighting and adding by area ratio (strictly, coverage) (see, for example, Patent Document 1).

JP 2008-263579 A

  In the conventional color prediction model described above, the predicted values are obtained by changing the coverage by using a constant value for the spectral reflectance of the primary color and the mixed color so that color prediction can be performed with an arbitrary ink amount set. ing. Here, since it is impossible to measure the spectral reflectance of all the mixed colors, the value is theoretically determined based on the colorimetric values of the mixed colors that can be printed. Therefore, the prediction accuracy of the obtained spectral reflectance may deteriorate depending on a certain hue.

  SUMMARY An advantage of some aspects of the invention is that it provides a printing apparatus, a calibration method, and a calibration execution program capable of improving color prediction accuracy.

  In order to solve the above problem, in the present invention, first, the spectral characteristic acquisition unit acquires a spectral reflectance of a mixed color obtained by mixing different primary colors for each arbitrary hue. Further, the spectral characteristic predicting means predicts the spectral characteristic of the primary color ink constituting the ink amount set and the acquired spectral characteristic of the mixed color when predicting the spectral characteristic in an arbitrary ink amount set using the color prediction model. Used to predict the spectral characteristics of the same ink amount set. Then, the correcting unit corrects the spectral characteristic in the ink amount set to be close to the spectral characteristic of the target primary color based on the predicted spectral characteristic.

  In the invention configured as described above, when color prediction is performed for an ink amount set belonging to a certain hue, individual values of the spectral characteristics of the mixed colors used in the color prediction model are obtained for each hue, and the spectral characteristics of the mixed colors are obtained. Used for color prediction. Therefore, the prediction accuracy in the color prediction model can be improved.

Further, the spectral characteristic acquisition means acquires spectral characteristics of an ink configured by mixing different primary color inks in an arbitrary hue, and spectral characteristics of the primary color inks. The color mixing spectral characteristics may be obtained based on a spectral Neugebauer color prediction model.
In the invention configured as described above, prediction accuracy can be improved in color prediction using a spectral Neugebauer model.

And as an example of the setting of the primary color ink that acquires the spectral characteristics of the mixed color, it has a color difference acquisition means for acquiring a color difference between the ink amount set and the target data, It is good also as a structure which changes the spectral characteristic of the said mixed color acquired according to a color difference. As an example, the spectral characteristic acquisition unit may acquire a spectral characteristic obtained by mixing primary color inks having similar hues when the color difference is equal to or less than a predetermined threshold.
In the invention configured as described above, the color prediction accuracy can be improved because the spectral reflectance of the mixed color used in the color prediction model is changed according to the color difference.

  As another example of the configuration for changing the spectral characteristics of the mixed color acquired in accordance with the color difference, when the color difference is equal to or greater than a predetermined threshold, the spectral characteristic acquisition means is a primary color ink whose spectral characteristic peak is far. It is also possible to obtain a spectral characteristic obtained by mixing colors.

The first spectral reflectance acquisition unit may acquire the spectral reflectance of mixed colors at different densities while maintaining the color mixing ratio of the primary color ink.
In the invention configured as described above, the prediction accuracy can be further improved by acquiring a plurality of mixed spectral reflectances used for color prediction.

  Further, the present invention can be applied not only to a printing apparatus but also to a calibration method and a calibration execution program using the configuration of the present invention.

It is a figure explaining the method of estimating the spectral reflectance in arbitrary ink amount sets. It is a block block diagram explaining the structure of host PC. It is a block diagram which shows the function of host PC10 at the time of performing calibration. FIG. 2 is a block configuration diagram illustrating a configuration of a printer. It is a figure which shows the relationship of each value recorded on spectral reflectance database RDB. An example of an index table IDT is shown. It is a flowchart explaining the method of the calibration concerning this embodiment. It is a figure for demonstrating a color chart. It is a flowchart explaining the flow at the time of acquiring estimated spectral reflectance Rs ((lambda)). It is a figure which shows the color difference in the arbitrary ink colors on a hue plane. It is a figure which shows the spectral reflectance in arbitrary ink amount sets (PHI). It is a flowchart for demonstrating creation of the spectral reflectance database RDB.

Hereinafter, embodiments of the present invention will be described in the following order.
1. Summary of the invention:
2. Host PC and printer configuration:
3. Calibration flow:
4). Method for creating spectral reflectance database RDB:
5. Other embodiments:

1. Summary of the invention:
FIG. 1 is a diagram for explaining a method for predicting the spectral reflectance in an arbitrary ink amount set. 1A shows a spectral Neugebauer model as a method for predicting the spectral reflectance of a conventional print, and FIG. 1B shows a Murray-Davis model showing the relationship between ink coverage and effective area ratio. Is described.

The spectral Neugebauer model is for predicting the predicted spectral reflectance Rs (λ) when printing is performed with an arbitrary ink amount set φ (in FIG. 1, the models in the ink sets dc and dm are shown for convenience). Model. In this spectral Neugebauer model, a state in which a certain ink amount set Φ is printed on paper is schematically interpreted, and the spectral reflectance in each region is represented by the respective coverage ratios (a _w , a The spectral reflectance in this ink amount set is predicted by linear combination based on _{c 1} , a _m , a _y ).

Here, a _i is the area ratio of the i-th region, and R _i (λ) is the spectral reflectance of the i-th region. The subscript i includes an area without ink (w), an area only with cyan ink (c), an area only with magenta ink (m), an area only with yellow ink (y), magenta ink and yellow ink. A region (r) where yellow ink and cyan ink are ejected, a region (b) where cyan ink and magenta ink are ejected, and a region where three inks CMY are ejected (region) (r) k) respectively. Further, f _c , f _m , and _fy are the proportions of the area covered with only one CMY ink when it is ejected (referred to as “Ink area coverage”).

Ink coverage f _c, f _m, f _y are given by the Murray-Davies model shown in FIG. 1-B. In the Murray-Davies model, for example, the ink area coverage f _c of the cyan ink is a nonlinear function of the ink amount d _c of the cyan, be converted to the ink amount d _c in the ink coverage f _c, for example by one-dimensional lookup table Can do. Ink coverage f _c, f _m, f _y is the ink amount d _c, d _m, reason for the non-linear function of d _y is spread enough ink in the case where a small amount of ink ejected to the unit area, This is because, when a large amount of ink is ejected, the ink is overlapped and the area covered with the ink does not increase so much. The same applies to other types of MY inks.

ここで、ｎは１以上の所定の係数であり、例えばｎ＝１０に設定することができる。前記の（１ａ）式および（１ｂ）式は、ユール・ニールセン分光ノイゲバウアモデル(Yule-Nielsen Spectral Neugebauer Model)を表す式である。 When the spectral Neugebauer model relating to the spectral reflectance is applied, the equation (1) can be rewritten as the following equation (1a) or (1b).

Here, n is a predetermined coefficient of 1 or more, and can be set to n = 10, for example. The above equations (1a) and (1b) are equations representing the Yule-Nielsen Spectral Neugebauer Model.

  In the Yule-Nielsen spectral Neugebauer model, the spectral reflectance R1 (λ) of the primary color ink (for example, dc, dm, dy) (in FIG. 1, Rc (λ), Rm (λ), Ry (λ )), Spectral reflectance Rw (λ) of paper white, and spectral reflectance R2 (λ) of the region where the primary color ink is mixed (in FIG. 1, Rr (λ), Rg (λ), Rb (λ ), Rk (λ)) must be acquired in advance. On the other hand, the spectral reflectance R1 (λ) of the primary color and the spectral reflectance Rw (λ) of the paper white can be easily obtained with high accuracy, but the spectral reflectance R2 (λ) of the mixed color is easily obtained. In some cases, measurement cannot be performed, and the spectral reflectance of mixed colors may be obtained with high accuracy. This leads to accurate determination of the predicted spectral reflectance Rs (λ) in the ink amount set Φ.

  Therefore, in the present embodiment, based on the spectral Neugebauer model, the spectral reflectance R2 (λ) of the mixed color for each hue is calculated in advance from the spectral reflectance R1 (λ) of the primary ink color that can be obtained with high accuracy. The obtained spectral reflectance R2 (λ) of the mixed color is used for color prediction when color prediction is performed for the ink amount set Φ in a certain hue. That is, in the present embodiment, the spectral reflectance R2 (λ) of the mixed colors is not set to a common value for all the hues as in the prior art, but different color mixing is performed for each hue to which the ink amount set Φ for which color prediction is performed belongs. Color prediction is performed using the spectral reflectance R2 (λ).

2. Host PC and printer configuration:
FIG. 2 is a block diagram illustrating the configuration of the host PC. FIG. 3 is a block diagram showing functions of the host PC 10 when executing calibration. FIG. 4 is a block diagram illustrating the configuration of the printer.

  The host PC 10 includes a CPU 11, a RAM 12, a ROM 13, an HDD 14, various interfaces (GIF, VIF, IIF) 15 to 17, and a bus 18, and integrated control via the bus 18 by the CPU 11. Is done.

  The GIF (general-purpose interface) 15 is an interface conforming to the USB standard, for example, and transmits data output from the host PC 10 to the printer 20 by connecting to the printer 20. A VIF (video interface) 16 is connected to the display 40 to display an image on the display 40. The IIF (input device interface) 17 is connected to the keyboard 50a and the mouse 50b, and causes the host PC 10 to input operation inputs obtained by operating the keyboard 50a and the mouse 50b.

  In addition to the program data PD, the HDD 14 records a color conversion table LUT, an index table IDT, a spectral reflectance database RDB, and patch data CPD1 and CPD2.

  By executing the program data PD, the host PC 10 executes a driver module M1, a database creation module M2, a spectral prediction module M3, a spectral characteristic acquisition module M4, a correction amount calculation module M5, a table correction module M6, It is configured with.

  The driver module M1 prints a color chart based on the color conversion table LUT and the patch data CPD. The database creation module M2 creates a spectral reflectance database RDB used by the spectral prediction module M3. The spectral prediction module M3 refers to the index table IDT and the spectral reflectance database RDB to perform spectral reflectance prediction using a spectral Neugebauer model. The spectral characteristic acquisition module M4 acquires the spectral reflectance obtained by measuring the color chart as the correcting spectral reflectance Rc (λ). The correction amount calculation module M5 calculates a correction amount for bringing the ink amount set Φ closer to the target value based on the target spectral reflectance Rt (λ) and the correction spectral reflectance Rc (λ) recorded in the index table IDT. calculate. The table correction module M6 corrects each ink amount set Φ recorded in the index table IDT based on the correction amount calculated by the correction amount calculation module M5.

  FIG. 5 is a diagram showing the relationship between the values recorded in the spectral reflectance database RDB. In the spectral reflectance database RDB, data used for predicting the spectral reflectance in an arbitrary ink amount set Φ is recorded. In the spectral reflectance database RDB according to the present embodiment, the spectral reflectance R1 (λ) (hereinafter referred to as first spectral data Rd1) of the primary color ink (dc, dm, dy), and the white spectral spectrum of paper. The reflectance Rw (λ) and the spectral reflectance R2 (λ) of the mixed color (secondary color) (r, g, b) obtained by mixing the primary color inks (hereinafter referred to as second spectral data Rd2). Is recorded).

As the first spectral data Rd1, values corresponding to the values of C, M, and Y inks are recorded on the hue plane (a ^* b ^* ) shown in FIG. The second spectral data Rd2 is a value existing in each of the first to fourth planes in the hue plane, and a plurality of points are recorded in each plane (indicated by hatching in the drawing).

  Further, the second spectral data Rd2 is obtained by measuring the color reflectance of a patch group formed by mixing an arbitrary primary color ink with ink adjacent to both sides of the hue of the primary color ink ( Hereinafter, it is referred to as first color mixing data.) And spectral reflectance obtained by measuring colors of a patch group formed by mixing primary color ink and primary color ink whose spectral reflectance peak is far away. (Hereinafter referred to as second color mixture data). A method for creating the spectral reflectance database RDB will be described later.

  FIG. 6 shows an example of the index table IDT. The index table IDT defines the correspondence between the machine number, the paint number, the index, and the primary color ink of the printer 20. Each paint number is associated with a target spectral reflectance Rt (λ). The target spectral reflectance Rt (λ) is a spectral reflectance obtained by measuring a sample actually coated with each paint with a spectral reflectance meter. Note that both the paint number and the index are unique.

  In FIG. 4, the printer 20 includes an ASIC 21, a print head 22, an ejection control circuit 24, a print head drive control circuit 25, a paper transport mechanism 27, a GIF 28, and a bus 29. Integrated control is performed. In addition to the above configuration, the printer 20 according to the present embodiment includes a color measurement head 23 and a color measurement head drive control circuit 26, and performs color measurement on each patch P of the color chart printed on paper. Color measurement can be performed by the head 23.

  The print head 22 is a mechanism that reciprocates in the main scanning direction under the control of the print head drive control circuit 25. The print head 22 is controlled by the discharge control circuit 24 to control each ink cartridge (C, M, Y, K, lc, not shown). , Lm, ly) is ejected onto the paper. The paper transport mechanism 27 is a mechanism for transporting print paper in the sub-scanning direction. Further, the GIF 28 establishes communication with the host PC 10 by connecting to the GIF 15 of the host PC 10.

  The color measuring head 23 measures the color of a printed image (color chart) printed on paper while reciprocating in the main scanning direction under the control of the color measuring head drive control circuit 26. The color measurement head 23 includes an optical sensor (not shown), and detects the spectral reflectance R (λ) of each color indicated by the print image printed on the paper.

3. Calibration flow:
FIG. 7 is a flowchart for explaining a calibration method according to the present embodiment. In this calibration, the case where the target primary color is reproduced with an ink amount set of secondary colors or more will be described as an example. Hereinafter, calibration according to the present embodiment will be described with reference to FIG.

  When the conditions for executing calibration are satisfied in the host PC 10, in step S1, the driver module M1 prints a color chart on a sheet using the patch data CPD1. FIG. 8 is a diagram for explaining a color chart. In the color chart, a large number of rectangular patches P are arranged in a matrix. Each patch P corresponds to each paint, and a paint number is printed near each patch P. The paint spectral number is associated with the target spectral reflectance Rt (λ) through the index table IDT, and the target spectral reflectance Rt (λ) of each patch P can be identified.

  In step S2, the spectral characteristic acquisition module M4 selects a patch P to be measured from now on. In step S3, the spectral characteristic acquisition module M4 acquires the target reflectance Rt (λ) corresponding to the selected patch P with reference to the index table IDT. In step S <b> 4, the spectral characteristic acquisition module M <b> 4 acquires the spectral reflectance (correcting spectral reflectance Rc (λ)) of the selected patch P via the colorimetric head 23.

  In step S5, the spectral prediction module M3 acquires a predicted spectral reflectance Rs (λ) in the vicinity of the ink amount set Φ corresponding to the correction spectral reflectance Rc (λ) acquired in step S4. Here, the spectral prediction module M3 acquires the predicted spectral reflectance Rs (λ) based on the spectral reflectance database RDB and the spectral prediction model shown in the above formula (1a) or (1b). The predicted spectral reflectance Rs (λ) is a value that is changed every time the value of the ink amount set Φ in step S6 described later is changed.

  FIG. 9 is a flowchart for explaining the flow in obtaining the predicted spectral reflectance Rs (λ). In step S20, the spectral prediction module M3 acquires a white spectral reflectance Rw (λ) from the spectral reflectance database RDB. In step S21, the spectral prediction module M3 refers to the spectral reflectance database RDB and determines the spectral reflectance R1 (λ) of the primary color ink in the ink amount set Φ to be predicted from the first spectral data Rd1. Get from.

  In step S22, the spectral prediction module M3 calculates a color difference ΔE between the ink amount set Φ output from the printer 20 and the target spectral reflectance Rt (λ) corresponding to the ink amount set Φ. The color difference ΔE calculated in step S22 is, for example, the correction spectral reflectance Rc (λ) acquired in step S4 and the target spectral reflectance Rt (λ) corresponding to the correction spectral reflectance Rc (λ). Although it is calculated by the difference, the value does not need to be exact and may be calculated from a representative value. Therefore, the color difference acquisition unit of the present invention is realized by the process of step S22.

  When the color difference ΔE acquired in step S22 is equal to or smaller than the threshold Th (step S23: YES), in step S24, the spectral prediction module M3 uses the second spectral data Rd2 used for predicting the ink amount set Φ. A plurality of colors are acquired from the first color mixture data in the spectral reflectance database RDB.

FIG. 10 is a diagram illustrating a color difference in an arbitrary ink color on the hue plane. FIG. 10 shows the color difference ΔE on the a ^* b ^* plane in the CIELAB uniform color space. For example, when the color difference ΔE from the target value Tc (value defined by the target spectral reflectance Rt (λ)) is small in cyan (C) ink, the other primary color inks are mixed with cyan ink in small amounts. As a result, the hue can be moved in the target direction (magenta direction in the figure).

  Therefore, in this embodiment, when the color difference ΔE obtained in step S22 is small, when the ink amount set Φ is moved in the target direction by the above-described method, the spectral reflectance R2 (λ) of the mixed color that is the component is primary. The value is the second spectral data Rd2 formed by mixing a small amount of primary color ink with color ink. Therefore, the predicted spectral reflectance of the ink amount set Φ is calculated using the second spectral data Rd2 recorded in the first color mixture data for calculating the predicted spectral reflectance Rs (λ).

  When the color difference ΔE obtained in step S22 is larger than the threshold Th (step S23: NO), in step S25, the spectral prediction module M3 uses the second spectral data Rd2 used for predicting the ink amount set Φ as spectral reflection. A plurality of pieces of color mixture data are acquired from the second color mixture data in the rate database RDB.

  FIG. 11 is a diagram showing the spectral reflectance in an arbitrary ink amount set Φ. For example, it is assumed that the ink amount set Φ having the predicted spectral reflectance Rs (λ) shown in A is made closer to the target spectral reflectance Rt (λ) shown in B having a large color difference ΔE. Note that the two spectral reflectances are greatly different in waveform for each wavelength component other than the wavelength peak. Here, the spectral reflectance obtained by mixing arbitrary inks can be obtained by the integral sum of the spectral reflectances of the respective inks. However, when inks having close spectral reflectance peaks are mixed, The waveform change is small and it is difficult to approach the target value Rt (λ) shown in FIG. That is, when the color difference ΔE is large, it is desirable to mix inks having different peak wavelengths in order to bring the predicted spectral reflectance Rc (λ) of an arbitrary ink amount set Φ closer to the target spectral reflectance Rt (λ).

  For this reason, in the present embodiment, when the color difference ΔE obtained in step S22 is large, when the ink amount set Φ is moved in the target direction by the above method, the mixed color spectral reflectance R2 (λ), which is the component, is first-order. This is the value in the second spectral data Rd2 formed by mixing the primary color inks having different peak wavelengths with the color inks. Therefore, the predicted spectral reflectance of the ink amount set Φ is calculated using the second spectral data Rd2 recorded in the second color mixture data for calculating the predicted spectral reflectance Rs (λ). As described above, the spectral characteristic acquisition means of the present invention is realized by the processing of steps S24 and S25.

  Here, the second spectral data Rd2 used for obtaining the predicted spectral reflectance Rs (λ) is obtained when changing the color mixture ratio or obtaining values corresponding to different ink densities while maintaining the color mixture ratio. It is desirable to obtain a wide range of values. For example, changing the color mixture ratio of the primary color ink on the hue plane corresponds to changing the hue direction with respect to the ink after color mixing, and changing the ink density changes the saturation direction. Correspond. Therefore, by obtaining a plurality of second spectral data Rd2 having different color mixture ratios and densities on a certain hue plane, the prediction accuracy of the predicted spectral reflectance Rs (λ) can be improved.

  In step S26, the spectral prediction module M3 uses the plurality of second spectral data Rd2 acquired from the spectral reflectance database RDB according to the proximity of the mixed color spectral reflectance R2 (λ) in the ink amount set Φ to be predicted. Weighted and added together to calculate the spectral reflectance R′d2 (λ) of the mixed color to be substituted into the spectral prediction model. In other words, when acquiring a plurality of points of the second spectral data Rd2, if the ink amount set Φ is changed so as to approach the target spectral reflectance Rt (λ), the mixed spectral reflectance R2 (λ) and the step S24 or S25 are performed. The distance on the hue plane with the plurality of acquired second spectral data Rd2 varies. Therefore, in a certain predicted spectral reflectance Rs (λ), the second spectral data Rd2 that is closest to the spectral reflectance R2 (λ) of the color mixture that is the predicted component (for example, the density or the color mixing ratio of the constituent inks is close). Each value is weighted so that the value is reflected.

  In step S27, the spectral prediction module M3 substitutes the acquired spectral reflectances Rd1 (λ), Rw (λ), and R′d2 (λ) into the formula (1a) or the formula (1b) indicating the spectral prediction model. Thus, the predicted spectral reflectance Rs (λ) in an arbitrary ink amount set Φ is calculated. Therefore, a spectral characteristic predicting means is realized by the processing in step S27.

  If the predicted spectral reflectance Rs (λ) has not been acquired for all ink amount sets Φ to be predicted (step S28: NO), step S21 is performed until all predicted spectral reflectances Rs (λ) are acquired. The processes up to S27 are repeated. If the predicted spectral reflectance Rs (λ) is acquired for all prediction targets, the process proceeds to step S6 in FIG.

In step S6 of FIG. 7, the correction amount calculation module M5 calculates a difference D (λ) between the target spectral reflectance Rt (λ) and the predicted spectral reflectance Rs (λ) acquired in step S5 for each wavelength λ. The difference D (λ) is multiplied by a weight function w (λ) weighted for each wavelength λ. The square root of the mean square of this value is calculated as the evaluation value E (φ). The above calculation can be expressed by the following equation (2).

  In the above equation (2), N means the number of divisions of the wavelength λ. In the above equation (2), the smaller the evaluation value E (φ), the smaller the difference between the target spectral reflectance Rt (λ) and the predicted spectral reflectance Rs (λ) at each wavelength λ. That is, the smaller the evaluation value E (φ), the more the spectral reflectance R (λ) reproduced on the glossy paper when the printer 20 prints with the input ink amount set φ and the corresponding paint sample. It can be said that the obtained target spectral reflectance Rt (λ) is approximate.

  Furthermore, the reproduction color of the printer 20 based on the ink amount set φ and the absolute color indicated by the corresponding paint sample vary depending on the variation of the light source, but the evaluation value E (φ) is reduced. Thus, the colors of both can be relatively matched. Therefore, according to the ink amount set φ in which the evaluation value E (φ) is small, it can be said that a print result perceived by the color indicated by the paint can be obtained with any light source.

前記の式（３）においては、等色関数ｘ（λ），ｙ（λ），ｚ（λ）を加算することにより、重み関数ｗ（λ）が定義されている。なお、前記の（３）式の右辺全体に所定の係数を乗算して、重み関数ｗ（λ）の値の範囲を正規化してもよい。等色関数ｘ（λ），ｙ（λ），ｚ（λ）は、人間の視覚感度に応じたスペクトルを有しており、人間の視覚感度が敏感な波長域での分光反射率Ｒ（λ）を重視させることができる。例えば、人間の目に知覚されない近紫外波長域においてはｗ（λ）が０となり、当該波長域における差分Ｄ（λ）は評価値Ｅ（φ）の増大に寄与しないこととなる。 In the present embodiment, the weighting function w (λ) uses the following equation (3).

In the above equation (3), the weighting function w (λ) is defined by adding the color matching functions x (λ), y (λ), and z (λ). The range of the value of the weighting function w (λ) may be normalized by multiplying the entire right side of the equation (3) by a predetermined coefficient. The color matching functions x (λ), y (λ), and z (λ) have a spectrum corresponding to the human visual sensitivity, and the spectral reflectance R (λ in the wavelength region where the human visual sensitivity is sensitive. ). For example, w (λ) is 0 in the near ultraviolet wavelength region that is not perceived by human eyes, and the difference D (λ) in the wavelength region does not contribute to the increase in the evaluation value E (φ).

  In other words, even if the difference between the target spectral reflectance Rt (λ) and the predicted spectral reflectance Rs (λ) is not necessarily small in the entire visible wavelength range, the target spectral reflectance in the wavelength range that is particularly strongly perceived by the human eye. If Rt (λ) and the predicted spectral reflectance Rs (λ) are similar, a small evaluation value E (φ) can be obtained, and the spectral reflectance R (λ) conforming to the perception of the human eye. The evaluation value E (φ) can be used as an index of closeness.

  The correction amount calculation module M5 causes the spectral prediction module M3 to calculate the predicted spectral reflectance Rs (λ) according to the flowchart shown in FIG. 9 while sequentially shifting the ink amount set φ, and the evaluation value E ( φ) is calculated. Then, the optimum solution of the ink amount set φ that minimizes the evaluation value E (φ) is calculated. As a method for calculating the optimum solution, various optimization methods can be used. For example, it is desirable to use a nonlinear optimization method such as a gradient method. As described above, the correcting means of the present invention is realized by the processing of step S6.

  As described above, when the ink amount set φ that can reproduce the target spectral reflectance Rt (λ) can be calculated in step S6, the table correction module M6 calculates the target spectral reflectance Rt (λ) in step S7. The measured paint number of the sample, the target spectral reflectance Rt (λ), and the calculated ink amount set φ are stored in the index table IDT in association with each other. In step S8, it is determined whether or not all patches P have been selected. If not selected (step S8: NO), the process returns to step S2 to select the next ink amount set. This makes it possible to sequentially match the ink amount set Φ to the target value. The calibration according to the present embodiment has been described above.

4). Method for creating spectral reflectance database RDB:
FIG. 12 is a flowchart for explaining the creation of the spectral reflectance database RDB. Hereinafter, a process of creating the spectral reflectance database RDB according to the present embodiment will be described with reference to FIG.

  In step S31, the database creation module M2 acquires the white spectral reflectance Rw (λ) of the paper. In step S32, the database creation module M2 acquires the spectral reflectance R1 (λ) of the primary color ink as the first spectral data Rd1. The above-described spectral reflectance Rw (λ) of paper white and the spectral reflectance Rd1 (λ) of the primary color ink do not need to be obtained by actual measurement, but it is desirable that the color accuracy is high.

  In step S33, the database creation module M2 prints a color chart using the patch data CPD2. The patch data CPD2 is for printing the patch P necessary for obtaining the second spectral data Rd2 recorded in the first and second color mixture data. Therefore, the patch P forming the color chart is formed by mixing the primary color ink with another primary color ink at an arbitrary color mixture ratio. Here, each patch P is configured by setting the color mixture ratio of the primary color ink so that the colorimetric values exist at a plurality of points on all hue planes.

  In step S34, the database creation module M2 causes the colorimetric head 23 to measure the colors of the printed color charts and acquires the respective spectral reflectances Rp (λ).

  In step S35, the spectral reflectance Rw (λ) of the paper white acquired in step S31, the spectral reflectance Rd1 (λ) of the primary color ink acquired in S32, and the color chart are measured in step S34. From the obtained spectral reflectance Rp (λ), second spectral data Rd2 when an arbitrary primary color is mixed is acquired.

Here, the relationship between the spectral reflectance Rp (λ) obtained by mixing an arbitrary ink amount set Φ and each spectral reflectance (Rd1 (λ), Rd2 (λ), Rw (λ)) is expressed by the following equation. It can be defined from the Yule-Nielsen spectroscopic Neugebauer model shown in (1a). Therefore, the second spectral data Rd2 can be predicted by substituting the reflectances acquired in steps S31, 32, and 34 into (Equation 4) shown below. In the following formula (4), cyan ink (C) and magenta ink (M) are described as examples of primary color ink. Further, _ab is a mixed color coverage.

  If the second spectral data Rd2 is not acquired for all the color charts by the process of step S35 (step S36: NO), the processes of steps S32 to S35 are repeated. When the second spectral data Rd2 is acquired for all the color charts (step S36: YES), in step S37, the acquired first spectral data Rd1, paper white spectral reflectance Rw (λ), second The spectral data Rd2 is recorded in the spectral reflectance database RDB. The method for creating the spectral reflectance database RDB has been described above.

5. Other embodiments:
There are various embodiments of the present invention.
Although the description has been given based on the system configured by the host PC 10 and the printer 20 as an apparatus for executing calibration according to the present embodiment, the configuration of the apparatus is not limited to this. For example, the calibration according to the present embodiment may be executed only by the printer 20.

  Further, (Equation 2) used to approximate the predicted spectral reflectance Rs (λ) to the target spectral reflectance Rt (λ) in the calibration process is an example, and a known technique using, for example, a Jacobian matrix is applied. It may be a thing.

  Needless to say, the present invention is not limited to the above embodiments. That is, the mutually replaceable members and configurations disclosed in the above embodiments are applied by changing their combinations as appropriate. The members and configurations disclosed in the examples can be replaced with the members and configurations interchangeable with each other as appropriate, and the combination thereof is changed and applied. It is one of the present invention that a person skilled in the art can appropriately substitute the members and configurations that can be assumed as substitutes for the members and configurations disclosed in the above-described embodiments based on the above, and change the combination to apply. It is disclosed as an example.

  DESCRIPTION OF SYMBOLS 10 ... Host PC, 11 ... CPU, 12 ... RAM, 13 ... ROM, 14 ... HDD, 15 ... GIF, 16 ... VIF, 17 ... IIF, 18 ... Bus, 20 ... Printer, 21 ... ASIC, 22 ... Print head, DESCRIPTION OF SYMBOLS 23 ... Color measurement head, 24 ... Discharge control circuit, 25 ... Print head drive control circuit, 26 ... Color measurement head drive control circuit, 27 ... Paper conveyance mechanism, 28 ... GIF, 29 ... Bus, 40 ... Display, 50a ... Keyboard 50b ... mouse, M1 ... driver module, M2 ... database creation module, M3 ... spectral prediction module, M4 ... spectral characteristic acquisition module, M5 ... correction amount calculation module, M6 ... table correction module

Claims (8)

A printing apparatus that reproduces a target primary color with a mixed color of secondary colors or more,
Spectral characteristic acquisition means for acquiring a spectral reflectance of a mixed color obtained by mixing different primary color inks for each arbitrary hue;
When predicting the spectral characteristics in an arbitrary ink amount set using the color prediction model, the same ink amount is obtained using the spectral characteristics of the primary color ink constituting the ink amount set and the acquired spectral characteristics of the mixed colors. Spectral characteristic prediction means for predicting the spectral characteristics of the set;
And a correction unit configured to correct the spectral characteristic in the ink amount set to be close to the spectral characteristic of the target primary color based on the predicted spectral characteristic. The spectral characteristic acquisition means includes:
Obtaining spectral characteristics of ink composed by mixing different primary color inks in arbitrary hues and spectral characteristics of each primary color ink;
The printing apparatus according to claim 1, wherein the color mixing spectral characteristic is obtained based on each spectral characteristic and a spectral Neugebauer color prediction model. A color difference acquisition means for acquiring a color difference between the ink amount set and the target data;
The printing apparatus according to claim 1, wherein the spectral characteristic acquisition unit changes the spectral characteristic of the mixed color acquired in accordance with the color difference.   4. The printing apparatus according to claim 3, wherein the spectral characteristic acquisition unit acquires a spectral characteristic obtained by mixing primary color inks having similar hues when the color difference is equal to or less than a predetermined threshold value.   4. The printing apparatus according to claim 3, wherein when the color difference is equal to or greater than a predetermined threshold, the spectral characteristic acquisition unit acquires the spectral characteristic obtained by mixing the primary color ink having a distant peak in the spectral characteristic. .   6. The spectral characteristic acquisition unit acquires spectral reflectances of mixed colors at different densities while maintaining a color mixing ratio of the primary color ink. 6. The printing apparatus as described. A calibration method for reproducing a target primary color with a mixed color of secondary colors or more,
A spectral characteristic acquisition step of acquiring a spectral reflectance of a mixed color obtained by mixing different primary colors for each arbitrary hue;
When predicting the spectral characteristics in an arbitrary ink amount set using the color prediction model, the same ink amount is obtained using the spectral characteristics of the primary color ink constituting the ink amount set and the acquired spectral characteristics of the mixed colors. A spectral characteristic prediction step for predicting the spectral characteristics of the set;
And a correction step of correcting the spectral characteristic in the ink amount set so as to approach the spectral characteristic of the target primary color based on the predicted spectral characteristic. A calibration execution program for causing a computer to reproduce a target primary color with a mixed color of secondary colors or more,
A spectral characteristic acquisition function for acquiring a spectral reflectance of a mixed color obtained by mixing different primary colors for each arbitrary hue;
When predicting the spectral characteristics in an arbitrary ink amount set using the color prediction model, the same ink amount is obtained using the spectral characteristics of the primary color ink constituting the ink amount set and the acquired spectral characteristics of the mixed colors. Spectral characteristics prediction function to predict the spectral characteristics of the set;
A calibration execution program for causing a computer to realize a correction function for correcting the spectral characteristic in the ink amount set so as to approach the spectral characteristic of the target primary color based on the predicted spectral characteristic.

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