JP2010147586A - Print result predicting method, profile forming method, printer, and survey sheet - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To improve efficiency of prediction of print results. <P>SOLUTION: Ink amount spaces consisting of three or more kinds of ink amounts are mapped on a two-dimensional plane, thereby creating an ink amount map where positions on the two-dimensional plane each have the ink amount, and the ink amount map IM is printed to form a survey sheet PP. Subsequently, the state amount when print is executed based on any ink amount is predicted based on the state amount measured for the position of each lattice point of the survey sheet PP. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention relates to a printing result prediction method, a profile creation method, a printing apparatus, and a survey sheet.

A conventional technique for predicting graininess is to halftone process an ink volume set, estimate the ink dot distribution on the printing paper based on halftone data, and quantify the graininess based on the ink dot distribution Is known (for example, see Patent Document 1). On the other hand, it is also known to quantify the graininess by actually printing a color patch with a printer and analyzing the image obtained by capturing the color patch with a scanner (see, for example, Patent Documents 2 and 3). . In addition, using a neural network in which the result of analyzing an image obtained by scanning a color patch with a scanner is learned as learning data, a method for predicting graininess in printing based on an arbitrary ink amount set has been proposed. .
JP 2005-103921 A Japanese Patent Laying-Open No. 2005-310098 JP 2007-281723 A

When creating a profile that results in an ink volume set with good graininess, it is necessary to grasp the graininess of many ink volume sets that cover the entire ink volume space that can be used by the printer and select the optimal ink set. However, there are problems that it is difficult to perform simulations and to form / evaluate color patches for all these large ink amount sets.
The present invention has been made in view of the above problems, and an object thereof is to improve the prediction of printing results such as graininess.

  In order to achieve the object, first, an ink amount map composed of three or more types of ink amounts is mapped to a two-dimensional plane to create an ink amount map in which each position on the two-dimensional plane has the ink amount. To do. When the ink amount map can be created, a survey sheet is created by printing the ink amount map. The ink amount map can be considered as image data of an ink amount in which each position on the two-dimensional plane has the ink amount, and is based on the ink amount map by performing halftone processing / rasterization processing. The survey sheet reproducing the image can be obtained. Then, the state quantity is measured for each position of the survey sheet, and the state quantity when printing is performed based on the arbitrary ink amount is predicted based on the measurement result. By doing so, it is possible to predict the state quantity for an arbitrary ink quantity set without printing a large number of color patches.

  A self-organizing map obtained by learning the ink amounts of a plurality of units arranged on the ink amount map by a large number of ink amounts distributed in the ink amount space may be adopted as the ink amount map. . For self-organizing maps, see T.W. See Kohonen's revised self-organizing map published on June 7, 2005. In addition, as the number of units increases, the computation processing load and time required for learning also increase significantly, so it is difficult to increase the number of units. When learning, the number of the units constituting the ink amount map is reduced, and if the ink amount map is made high resolution after learning, the ink amount is smoothed without significantly increasing the processing load and time. The ink amount map distributed in the above can be obtained.

  As an example of the state quantity, a graininess index obtained by quantifying the graininess may be predicted. Thereby, the graininess index can be predicted efficiently. As another example, a print color, spectral reflectance, or the like may be predicted as the state quantity. In addition, the ink that can be printed is created by mapping the area inside the ink amount space that is inside the ink usage limit of the printing apparatus that prints the survey sheet to a two-dimensional plane, thereby creating a printable ink. A quantity map can be created.

  Needless to say, the technical idea of the present invention can be embodied not only in a method but also in hardware such as a computer that executes the method or a program that defines a processing procedure in the computer. In addition, the method of the present invention is not limited to a single unit, and may be incorporated as part of a certain method. For example, it goes without saying that the present invention can also be realized in a profile creation method that partially incorporates a method for predicting a printing result by the method of the present invention. Furthermore, it can be said that the features of the present invention are also embodied in a printing apparatus that performs color conversion with reference to a profile created by the profile creation method of the present invention.

  Furthermore, it can be said that the survey sheet created in the above-described printing result prediction method has the following physical characteristics. That is, the survey sheet is a survey sheet formed by coating three or more types of ink on a print medium, and the ink coverage of each ink on the print medium is continuously changed according to each position. And the equal ink coverage line of each ink forms a curve (including a microscopically staircase shape that can be approximated to a curve).

Next, embodiments of the present invention will be described in the following order.
A. Various converters and their preparation:
A-1. Spectral printing model converter:
A-2. Color converter:
A-3. Granularity converter:
B. Create a profile:
C. Variation:

A. Various converters and their preparation A-1. Spectral Printing Model Converter FIG. 1 is a block diagram showing a hardware / software configuration of a computer 10. In the computer 10, a print result prediction unit program P1, an LUT creation program P2, and a printer driver P3 are executed. The print result prediction program P1, the LUT creation program P2, and the printer driver P3 are executed by hardware of the computer 10 and software that is read and executed by the hardware of the computer 10. Specifically, the CPU 12 included in the computer 10 reads the program data 11a stored in the hard disk drive (HDD) 11 or the like, and executes the calculation according to the program data 11a while expanding the program data 11a on the RAM 13. . Then, by controlling the external devices such as the printer 20, the spectral reflectometer 30, and the scanner 40 as the printing apparatus of the present invention through the calculation, the print result prediction program P1, the LUT creation program P2, and the printer are controlled. Various means constituting the driver P3 are realized.

FIG. 2 schematically shows a printing method of the printer 20 of the present embodiment. In the figure, the printer 20 includes a print head 21 having a plurality of nozzles 21a, 21a,... For each ink of CMYKlclm (cyan, magenta, yellow, black, light cyan, light magenta). ... ink amount set as described above the amount of ink in each ink of CMYKlclm to discharge _{φ (d c, d m,} d y, d k, d lc, d lm) control the print control to the amount specified by the The ink droplets ejected by the nozzles 21a, 21a,... Become fine dots on the printing paper, and the ink amount set φ (d _c , d _m , _dy , d _k, d _lc, in. this specification printed image of the ink coverage corresponding is to be formed on the printing paper to the d _lm), ink amount Printing based on the print φ means that an image with an ink coverage corresponding to the ink amount set φ is reproduced on the printing paper, although the printer 20 in this embodiment is an ink jet printer. The present invention can be applied to various printers in addition to the ink jet system, and in this embodiment, each ink amount _dj is given by 8 bits having gradations of 0 to 255. .

  The print result prediction program P1 is mainly composed of a spectral printing model converter RC, a color converter CC, and a graininess converter GC. The graininess converter GC further includes a sample preparation unit GC1, a map creation unit GC2, and an image input unit GC3. The granularity index calculation unit GC4, the data creation unit GC5, and the prediction unit GC6 are included. The spectral printing model converter RC inputs an arbitrary ink amount set φ, and when the ink amount set φ is designated to the printer 20 and printing is performed, the spectral reflectance R (λ) reproduced on the printing paper. The process which calculates is performed.

Predictive model spectral printing model converter RC is used, any ink amount set φ that can be used in the printer 20 of the embodiment _{(d c, d m, d} y, d k, d lc, d lm) printing with This is a prediction model for predicting the spectral reflectance R (λ) when performed as the spectral reflectance R (λ). In the spectral printing model, a spectral reflectance database RDB obtained by actually printing color patches at a plurality of representative points in the ink amount space and measuring the spectral reflectance R (λ) with a spectral reflectance meter is used. prepare. An arbitrary ink amount set φ (d _c , d) is accurately obtained by performing prediction using the Cellular Yule-Nielsen Spectral Neugebauer Model using the spectral reflectance database RDB. _m , d _y , d _k , d _lc , d _lm ), and the spectral reflectance R (λ) when printing is predicted.

FIG. 3 shows the spectral reflectance database RDB. As shown in the figure, the spectral reflectance database RDB has an ink amount set φ (d) of a plurality of lattice points in the ink amount space (in this embodiment, it is 6-dimensional, but only the CM plane is shown for simplification of the drawing). _c , d _m , d _y , d _k , d _lc , d _lm ) are look-up tables in which spectral reflectances R (λ) obtained by actually printing / measuring are described. For example, five grid points that divide each ink amount axis are generated. Here 5 ¹³ also lattice points are generated, it is necessary to print / measurement of color patches of huge amount, actually can be discharged simultaneously mountable ink number and simultaneously by the printer 20 ink Since the duty is limited, the number of grid points to be printed / measured is reduced.

  Further, only some of the lattice points are actually printed / measured, and the other lattice points are spectrally reflected R (λ) based on the spectral reflectance R (λ) of the actually printed / measured lattice points. Thus, the number of color patches that are actually printed / measured may be reduced. The spectral reflectance database RDB needs to be prepared for each printing sheet that can be printed by the printer 20. Strictly speaking, the spectral reflectance R (λ) is determined by the spectral transmittance of the ink film (dot) formed on the printing paper and the reflectance of the printing paper, and the surface physical properties of the printing paper (depending on the dot shape). ) And reflectivity. Next, prediction by the cell division Yule-Nielsen spectral Neugebauer model using the spectral reflectance database RDB will be described.

  The spectral printing model converter RC executes prediction based on the cell division Yule-Nielsen spectral Neugebauer model using the spectral reflectance database RDB. In this prediction, various prediction conditions are set. Specifically, the printing paper and the ink amount set φ are set as the prediction conditions. For example, when prediction is performed using glossy paper as a printing paper, a spectral reflectance database RDB created by printing color patches on glossy paper is set.

  When the spectral reflectance database RDB is set, the ink amount set φ is applied to the spectral printing model. The cell splitting Yule-Nielsen spectroscopic Neugebauer model is based on the well-known spectroscopic Neugebauer model and the Yule-Nielsen model. In the following description, for simplification of description, a model in which three types of CMY inks are used will be described. However, a similar model is used as a model using an arbitrary ink set including CMYKlclm of the present embodiment. It is easy to expand. For cell splitting Yule-Nielsen spectral Neugebauer model, Color Res Appl 25, 4-19, 2000 and R Balasubramanian, Optimization of the spectral Neugebauer model for printer characterization, J. Electronic Imaging 8 (2), 156- See 166 (1999).

FIG. 4 is a diagram showing a spectral Neugebauer model. The spectral Neugebauer model, optional ink amount sets _{(d c, d m, d} y) spectral reflectance of the printed matter when printed with R (lambda) is given by the following equation (1).

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 f _y are ratios of areas covered with ink when only one type of CMY ink is ejected (referred to as “ink area coverage”).

The ink coverage ratios f _c , f _m , and _fy are given by the Murray-Davis model shown in FIG. 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（２ａ）式および（２ｂ）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 Yule-Nielsen model for the spectral reflectance is applied, the equation (1) can be rewritten as the following equation (2a) or (2b).

Here, n is a predetermined coefficient of 1 or more, and can be set to n = 10, for example. The above equations (2a) and (2b) are equations representing the Yule-Nielsen Spectral Neugebauer Model.

  The Cellular Yule-Nielsen Spectral Neugebauer Model used in the present embodiment is obtained by dividing the ink amount space of the above-described Yule-Nielsen Spectral Neugebauer Model into a plurality of cells. is there.

FIG. 5A shows an example of cell division in the cell division Yule-Nielsen spectroscopic Neugebauer model. Here, for simplification of description depicts the cell division in a two-dimensional ink amount space including two axes of the ink amount d _c, d _m of the CM inks. Note that it for ink coverage f _c, is f _m with at Murray-Davis model described above the ink amount d _c, a unique relationship with d _m, the ink coverage f _c, also be considered as an axis indicating the f _m . White circles are cell division grid points (called “lattice points”), and a two-dimensional ink amount (coverage) space is divided into nine cells C1 to C9. The ink amount set (d _c , d _m ) corresponding to each lattice point is an ink amount set corresponding to the lattice point defined in the spectral reflectance database RDB. That is, the spectral reflectance R (λ) of each lattice point can be obtained by referring to the above-described spectral reflectance database RDB. Therefore, the spectral reflectances R (λ) ₀₀ , R (λ) ₁₀ , R (λ) ₂₀ ... R (λ) ₃₃ of each lattice point can be acquired from the spectral reflectance database RDB.

In fact, performs six-dimensional ink amount space of even the cell division CMYKlclm In this embodiment, the ink amount set phi (d _c coordinates also the six-dimensional lattice _{points, d m, d y, d} k, d lc , D _lm ). Then, the ink amount set φ for each grid point _{(d c, d m, d} y, d k, d lc, d lm) spectral reflectivity of the grid points corresponding to R (lambda) is the spectral reflectance database RDB (for example, (From glossy paper).

FIG. 5 (B) shows the relationship between the ink area coverage f _c and the ink amount d _c which are used in the cell division model. Here, one kind of the ink amount in the range 0 to D _cmax of ink is also divided into three sections, the virtual used in the cell division model by non-linear curve which increases monotonically from 0 for each section to the 1 A typical ink coverage _fc is determined. For other inks, the ink coverages f _m and f _y are obtained in the same manner.

ここで、（３）式におけるインク被覆率ｆ_c，ｆ_mは図４（Ｂ）のグラフで与えられる値である。また、セルＣ５を囲む４つの格子点に対応する分光反射率Ｒ（λ）₁₁，（λ）₁₂，（λ）₂₁，（λ）₂₂は分光反射率データベースＲＤＢを参照することにより取得することができる。これにより、（３）式の右辺を構成するすべての値を確定することができ、その計算結果として任意のインク量セットφ（ｄ_c，ｄ_m）にて印刷を行った場合の分光反射率Ｒ（λ）を算出することができる。波長λを可視波長域にて順次シフトさせていくことにより、可視波長域における分光反射率Ｒ（λ）を得ることができる。インク量空間を複数のセルに分割すれば、分割しない場合に比べて分光反射率Ｒ（λ）をより精度良く算出することができる。以上のようにして、分光プリンティングモデルコンバータＲＣが分光反射率Ｒ（λ）を予測する。 FIG. 5C shows a method of calculating the spectral reflectance R (λ) when printing is performed with an arbitrary ink amount set (d _c , d _m ) in the center cell C5 of FIG. 5A. Is shown. The spectral reflectance R (λ) when printing is performed with the ink amount set (d _c , d _m ) is given by the following equation (3).

Here, the ink coverages f _c and f _m in the equation (3) are values given in the graph of FIG. The spectral reflectances R (λ) ₁₁ , (λ) ₁₂ , (λ) ₂₁ , and (λ) ₂₂ corresponding to the four lattice points surrounding the cell C5 are acquired by referring to the spectral reflectance database RDB. Can do. Thereby, all values constituting the right side of the equation (3) can be determined, and the spectral reflectance when printing is performed with an arbitrary ink amount set φ (d _c , d _m ) as the calculation result. R (λ) can be calculated. By sequentially shifting the wavelength λ in the visible wavelength range, the spectral reflectance R (λ) in the visible wavelength range can be obtained. If the ink amount space is divided into a plurality of cells, the spectral reflectance R (λ) can be calculated with higher accuracy than when the ink amount space is not divided. As described above, the spectral printing model converter RC predicts the spectral reflectance R (λ).

A-2. Color Converter FIG. 6 schematically shows a process in which the color converter CC specifies a color based on the spectral reflectance R (λ). In the figure, the spectrum of the reflected light from the printed material is predicted by multiplying the spectrum of the desired light source at each wavelength λ of the spectral reflectance R (λ) predicted by the spectral printing converter RC. Further, the tristimulus values XYZ are calculated by convolving and normalizing the sensitivity functions x (λ), y (λ), and z (λ) under the desired observation conditions with respect to the spectrum of the reflected light. In this embodiment, unless otherwise indicated, the tristimulus values XYZ are calculated under the observation conditions of the CIE 1931 2 ° observer. As the light source, CIE standard D50 light, D65 light, F-system light, A-system light, or the like can be input. Further, the color converter CC calculates the L ^* a ^* b ^* value of the CIELAB color system by applying the conversion formula of the CIE standard to the tristimulus values XYZ. In this way, by using the spectral printing converter RC and the color converter CC sequentially, the L ^* a ^* b ^* value when printing is performed with an arbitrary ink amount set can be obtained.

Furthermore, the color converter CC can perform chromatic adaptation conversion on the tristimulus values XYZ. For example, by applying a chromatic adaptation conversion formula based on CIECAT02 to the tristimulus values XYZ calculated with D50 light, for example, L ^* a ^* expressing the appearance of color under D50 light with the corresponding color of D65 light b ^* value can be converted. Regarding CIECAT02, for example, “The CIECAM02 Color Appearance Model”, Nathan Moroney et al., IS & T / SID Tenth Color Imaging Conference, pp.23-27, and “The performance of CIECAM02”, Changjun Li et al., IS & T / SID Tenth Color Imaging Conference, pp.28-31. However, as the chromatic adaptation conversion, any other chromatic adaptation conversion such as von Kries's chromatic adaptation prediction formula may be used. The L ^* a ^* b ^* value obtained by this chromatic adaptation conversion is expressed as CV _{L1 → Ls} . This subscript “L1 → Ls” means that the color appearance under the light source L1 is an L ^* a ^* b ^* value expressed by the corresponding color of the standard light source Ls. The color converter CC obtains color values CV _{L1 → Ls} and CV _{L2 → Ls} representing the appearance under at least two comparative light sources L1 and L2 with colors corresponding to the standard light source Ls, and based on these values. A color constancy index CII is calculated. The color constancy index CII can be calculated by, for example, the following equation (4).

For color constancy index CII, see Billmeyer and Saltzman's Principles of Color Technology, 3rd edition, John Wiley & Sons, Inc, 2000, p. 129, p. 213-215. The right side of the equation (4) is the color difference ΔE ^* 94 (2) in the CIE 1994 color difference equation in which the values of the lightness and saturation coefficients kL and kC are set to 2 and the value of the hue coefficient kH is set to 1. : Corresponds to 2). In the CIE 1994 color difference equation, the denominator coefficients SL, Sc, SH on the right side of the equation (4) are given by the following equation (5).

In addition, as a color difference formula used for calculation of the color constancy index CII, other formulas can be used. The color constancy index CII is defined as the difference in color appearance when a color patch is observed under first and second different observation conditions. Therefore, an ink amount set that reduces the color constancy index CII when printed is preferable in that the difference in color appearance under different viewing conditions is small. In addition, since the color values CV _{L1 → Ls} and CV _{L2 → Ls} are the colorimetric values of the corresponding colors under the same standard observation conditions, the color constancy index CII, which is the color difference between them, is quite accurate in the difference in color appearance. It becomes the value expressed in. Next, the granularity converter GC and its preparation will be described.

A-3. Granularity Converter FIG. 7 shows the flow of the granularity converter preparation process. As shown in FIG. 1, the granularity converter GC includes a sample preparation unit GC1, a map creation unit GC2, an image input unit GC3, a graininess index calculation unit GC4, a data creation unit GC5, and a prediction unit GC6. Among them, the graininess converter preparation process is executed by the sample preparation unit GC1, the map creation unit GC2, the image input unit GC3, the graininess index calculation unit GC4, and the data creation unit GC5. First, in step S200, the sample preparation unit GC1 specifies the number and type of ink to be used. As described above, CMYKlclm ink is used in this embodiment. Sample preparation unit GC1 is, the ink amount sets of 0 to 255 gradations _{φ (d c, d m,} d y, d k, d lc, d lm) to create an ink amount space existing combinations.

Figure 8 shows the CM ink amount d _c d _m plane when the ink amount d _y of ink amount _{space, d k, d lc, d} lm is a predetermined constant value. In step S210, the sample preparation unit GC1 sets a duty limit (ink use limit) for the ink amount space created in step S200. 8 shows by hatching the area outside the duty limit, are set duty limit the region where the sum of the CM ink amount d _c d _m is greater than a predetermined reference. The duty limit defines a region of the ink amount set φ that exceeds the ink amount set φ that can be printed by the printer 20. For example, the duty limit is set for an upper limit ink amount set φ that does not cause ink bleeding. In step S220, the sample preparation unit GC1 extracts a sample from the ink amount space inside the duty limit. Here, the samples are a large number of ink amount sets φ existing in the ink amount space inside the duty limit, and are extracted uniformly or randomly so as to cover the ink amount space inside the duty limit. The ink amount set φ of each sample is extracted, hereinafter, the input vector _{s (d c, d m,} d y, d k, d lc, d lm) shall be denoted as. Many extracted input vectors s are stored in the HDD 11 as sample data SD. In step S230, the map creation unit GC2 executes a process of creating the ink amount map IM.

FIG. 9 shows an ink amount map IM (part) as a self-organizing map of the present invention. In the ink amount map IM, a total of 10,000 units U of 100 rows × 100 columns are arranged in an orthogonal lattice pattern on a two-dimensional plane. Each unit U has an ink amount set φ. It is assumed that representation vector ink amount set φ for each unit U has a reference vector _{m (d c, d m,} d y, d k, d lc, d lm) and. The number of units U is not limited to 100 rows × 100 columns, and other numbers may be adopted. Further, the units U are not limited to being arranged in an orthogonal lattice shape, and the units U may be arranged in a honeycomb shape.

  FIG. 10 shows a detailed flow of the ink amount map creating process executed by the map creating unit GC2. In step S231, the reference vector m of each unit U is initialized when the ink amount map IM is created. In initialization of the reference vector m of each unit U, the reference vector m of each unit U is determined at random. Note that the average vector of all input vectors s may be the reference vector m of the unit U located at the center of the ink amount map IM. Further, a vector obtained by multiplying a scalar whose absolute value gradually increases from the center of the ink amount map IM toward the periphery by the first principal component and the second principal component of each input vector s is added to the average vector. Thus, the reference vector m of the unit U may be initialized. The first principal component is a unit vector in a direction in which the deviation between the input vectors s is maximum, and the second principal component is the deviation between the input vectors s among the unit vectors orthogonal to the first principal component. Is.

  In step S232, a counter t indicating the number of learning is initialized to 1. When the initial setting is completed as described above, the reference vector m of each unit U is learned using each input vector s. In learning the reference vector m, an input vector s to be used for the current learning is first selected, for example, at random from among a large number of input vectors s (step S233). Then, the highest unit U (high evaluation unit BMU: shown by ● in FIG. 9) is searched from all the units U (step S234). In the present embodiment, the unit U having the reference vector m having the smallest Euclidean distance from the current input vector s is set as the high evaluation unit BMU. That is, the unit U having the ink amount set φ most similar to the ink amount set φ indicated by the current input vector s is evaluated highly. When the high evaluation unit BMU can be specified, a unit U (neighboring unit NU: illustrated by ◎ in FIG. 9) in the vicinity of the high evaluation unit BMU is specified (step S235). The neighboring unit NU is a unit U within the neighboring range centered on the high evaluation unit BMU on the ink amount map IM. In the present embodiment, a square area having a side length h (t) with the high evaluation unit BMU as the center is set as the vicinity range. The neighborhood range width h (t) is a function of the counter t indicating the number of learnings.

前記の（６）式におけるα（ｔ）は、０〜１の値をとる学習率係数であり、カウンタｔの関数である。前記の（６）式においては、現在の入力ベクトルｓと参照ベクトルｍ₀との偏差を、もとの参照ベクトルｍ₀に加算することにより、更新後の参照ベクトルｍ₁を算出している。参照ベクトルｍを更新すると、カウンタｔが所定の学習回数ｃに達したか否かを判定し（ステップＳ２３７）、達していない場合にはステップＳ２３８にてカウンタｔに１を加算し、ステップＳ２３３に戻る。このようにすることにより、デューティ制限よりも内側のインク量空間から抽出した多数の入力ベクトルｓに応じて参照ベクトルｍを更新する処理を学習回数ｃだけ繰り返すことができる。 As described above, when the high evaluation unit BMU and the neighboring unit NU can be specified, the reference vector m of the high evaluation unit BMU and the neighboring unit NU is updated based on the current input vector s. When the reference vector m before update is m ₀ and the reference vector m after update is m ₁ , the reference vector m is updated by the following equation (6) (step S236).

In the above equation (6), α (t) is a learning rate coefficient that takes a value of 0 to 1, and is a function of the counter t. In the above equation (6), the updated reference vector m ₁ is calculated by adding the deviation between the current input vector s and the reference vector m ₀ to the original reference vector m ₀ . When the reference vector m is updated, it is determined whether or not the counter t has reached the predetermined learning count c (step S237). If not, 1 is added to the counter t in step S238, and the process proceeds to step S233. Return. By doing in this way, the process of updating the reference vector m according to many input vectors s extracted from the ink amount space inside the duty limit can be repeated as many times as the learning count c.

  FIG. 11 shows the learning rate coefficient α (t) and the neighborhood range width h (t). The width h (t) of the neighborhood range is expressed by a linear function having a negative slope, and is half the width of the entire ink amount map IM at the initial stage of learning (t = 1), and at the end of learning (t = c). When it becomes 1. By doing in this way, the influence range of learning can be narrowed gradually. On the other hand, the learning rate coefficient α (t) is represented by a monotonically decreasing function that decreases as the counter t increases, and converges to 0 at the end of learning (t = c). In this way, the degree to which the reference vector m is corrected by the input vector s can be gradually weakened. By repeatedly performing the learning in steps S233 to S238, the reference vector m of each unit U is updated to a value close to one of the many input vectors s extracted from the ink amount space inside the duty limit. be able to. That is, the input vector s in the ink amount space can be sequentially mapped to each unit U (BMU) on the two-dimensional plane. Since the neighborhood range is set, the ink amount map IM maintaining the local relative positional relationship in the ink amount space can be created.

  In the learning process of steps S233 to S238, it can be considered that each unit U self-organizes the ink amount set φ based on the relative positional relationship with each other. Various methods can be employed as this self-organization method, and for example, batch learning may be employed. In this batch learning, each input vector s is grouped by the unit U closest to the reference vector m, and an average vector of the reference vectors m is calculated in the group corresponding to each unit U. Then, the reference vector m is updated with the corresponding unit U as the high evaluation unit BMU by the average vector. By doing in this way, it can suppress that the selection (step S233) order of the input vector s influences the ink amount map IM finally produced.

  In step S240, the created ink amount map IM is increased in resolution. The ink amount map IM is composed of unit U of 100 rows × 100 columns each having a reference vector m of the ink amount set φ, and is image data of the ink amount set φ having a size of 100 × 100 pixels. . Here, the resolution of the ink amount map IM is increased to 2000 × 2000 pixels. Specifically, the resolution of the ink amount map IM is increased by interpolating pixels having the ink amount set φ interpolated by the reference vector m of the surrounding units U between the units U.

  In step S250, the data creation unit GC5 creates the first data D1 that stores the correspondence between the position of each pixel constituting the high-resolution ink amount map IM and the ink amount set φ, and stores it in the HDD 11. In step S255, the ink amount map IM having a higher resolution is output to the printer driver P3. The printer driver P3 includes a resolution conversion unit P3a, a color conversion unit P3b, a halftone processing unit P3c, and a rasterization unit P3d. When the printer driver P3 acquires the ink amount map IM, the printer driver P3 further increases the resolution so that the ink amount map IM matches the print size and the print resolution. Next, the halftone processing unit P3b and the rasterization processing unit P3c sequentially perform halftone processing such as an error diffusion method and a dither method on the ink amount map IM and rasterization processing, thereby generating print control data CD. By outputting the generated print control data CD to the printer 20, printing based on the ink amount map IM is executed. As a result, a survey sheet PP in which the ink amount map IM itself is reproduced on the printing paper is formed.

  FIG. 12 is a diagram schematically illustrating the survey sheet PP. As described above, since the local relative positional relationship in the ink amount space is maintained in the ink amount map IM, a continuous and smooth gradation pattern is formed on the survey sheet PP. If an arbitrary point on the survey sheet PP is printed based on a predetermined ink amount set φ, the area around the point (entire periphery) is printed with an ink amount set φ similar to the ink amount set φ. Will be. Unlike a general color chart, the direction of gradation of ink amount is not limited to a specific direction (for example, a row direction, a column direction, a radial direction, or a circumferential direction). That is, when an equal ink coverage line in which the ink coverage corresponding to the ink amount is equal to each other is drawn on the investigation sheet PP, the equal ink amount line of each ink is an indefinite continuous as shown in FIG. It becomes a curve. FIG. 12 shows the ink coverage distribution on the survey sheet PP independently for each ink. Actually, the illustrated ink coverage distribution of each ink is synthesized (microscopically a plurality of ink coverage distributions). The survey sheet PP in a state where the ink dots of the adjacent inks are adjacent or overlapped is printed. According to such a survey sheet PP, it is possible to efficiently arrange the reproduction results with many ink amount sets φ on the survey sheet PP. When the survey sheet PP can be printed, in step S260, the image input unit GC3 scans the survey sheet PP with the scanner 40, and generates scan data SD (for example, RGB image data) that is image data obtained by scanning the survey sheet PP. And stored in the HDD 11. Here, scanning is performed at a resolution higher than the resolution when the printer 20 prints the survey sheet PP. In this way, it is possible to obtain scan data SD that can grasp in detail the distribution state of ink dots on the survey sheet PP.

  FIG. 13 illustrates scan data SD (pattern not shown). The survey sheet PP is printed by further increasing (enlarging) the ink amount map IM, which has been increased in resolution to 2000 × 2000 pixels, to the print resolution. For this reason, the ink amount set φ of each pixel of the ink amount map IM is in the vicinity of lattice points (illustrated by ●) where lattices (illustrated by broken lines) that divide the scan data SD into 1999 × 1999 equal square regions intersect. It can be said that the print result printed based on the image is input as an image. That is, in the vicinity of each grid point, a print result printed based on 2000 × 2000 ink amount sets φ defined in the first data D1 is input as an image. Since the scan data SD and the ink amount map IM are similar to each other, the position of each grid point in the scan data SD is determined based on the position of each pixel of the ink amount map IM defined in the first data D1. Can be identified. In step S270, the graininess index calculation unit GC4 executes a graininess index calculation process for calculating the graininess index GI (state quantity of the present invention) for each lattice point on the scan data SD.

ＧＩについては、例えば、Makoto Fujino,Image Quality Evaluation of Inkjet Prints, Japan Hardcopy '99, p.291-294を参照。なお、（７）式のａ_Lは明度補正項、ＷＳ（ｕ）は画像のウイナースペクトラム、ＶＴＦは視覚の空間周波数特性、ｕは空間周波数である。 FIG. 14 shows the flow of the graininess index calculation process. First, in step S271, (x, y) for converting the scan data SD into image data L (x, y) of lightness L ^* distribution means the horizontal and vertical coordinates in the survey sheet PP, and is specified by x, y. This pixel is referred to as a sub pixel. ). In step S272, one grid point shown in FIG. 13 is selected. Here, selecting one grid point means selecting one ink amount set φ stored in the first data D1. In step S273, a rectangular lattice point area W (shown by a broken line in FIG. 13) centered on the selected lattice point is formed, and the graininess index calculation unit GC4 displays an image in the lattice point area W. To get. From the next step S274, the graininess index calculation unit GC4 starts a process of calculating the graininess index GI for the image in the lattice point area W. The graininess index GI is the graininess (or the degree of noise) felt by an observer when an observer visually recognizes a certain printed matter. The smaller the graininess index GI, the smaller the graininess felt by the observer. . In this embodiment, the graininess index GI is defined by the following equation (7).

Regarding GI, see, for example, Makoto Fujino, Image Quality Evaluation of Inkjet Prints, Japan Hardcopy '99, p.291-294. In Equation (7), a _L is a brightness correction term, WS (u) is a winner spectrum of an image, VTF is a visual spatial frequency characteristic, and u is a spatial frequency.

  FIG. 15 illustrates how the graininess index GI is calculated. In this embodiment, the graininess index GI evaluates the graininess of a printed image by the spatial frequency (cycle / mm) characteristic of the brightness of the image. For this purpose, first, FFT (Fast Fourier Transformation) is performed on the spatial distribution L (x, y) in the brightness sub-pixel plane shown at the left end of FIG. 15 (step S274). In FIG. 15, the spectrum of the obtained spatial frequency is shown as S (u, v). The spectrum S (u, v) is composed of a real part Re (u, v) and an imaginary part Im (u, v), and S (u, v) = Re (u, v) + jIm (u, v). It is. This spectrum S (u, v) corresponds to the above-described winner spectrum.

Here, (u, v) has a dimension of the inverse space of (x, y). In this embodiment, (x, y) is defined as a coordinate, and in order to correspond to an actual length dimension, a scanner is used. For example, 40 scan resolutions must be considered. Therefore, even when evaluating S (u, v) in the spatial frequency dimension, dimension conversion is required. Therefore, first, the spatial frequency magnitude f (u, v) corresponding to the coordinates (u, v) is calculated. That is, the lowest frequency e _u in the main scanning direction X resolution per 25.4, the lowest frequency e _v in the sub-scanning direction is defined as Y resolution per 25.4. The X resolution and Y resolution are resolutions when the scanner 40 scans. Here, 1 inch is 25.4 mm. Minimum frequency e _u in each scanning direction, if e _v is calculated, the spatial frequency in arbitrary coordinates (u, v) size f (u, v) is ^{_{((e u · u) 2}} + (e v・ V) ² )) It can be calculated as ^1/2 .

  On the other hand, human eyes have different sensitivities to lightness depending on the magnitude f (u, v) of the spatial frequency, and the visual spatial frequency characteristic is, for example, VTF (f) shown in the lower center of FIG. It is a characteristic. VTF (f) in FIG. 15 is VTF (f) = 5.05 × exp (−0.138 · d · π · f / 180) × (1−exp (−0.1 · d · π · f / 180)). Here, d is the distance between the printed matter and the eyes, and f is the magnitude of the spatial frequency. Since f is expressed as a function of (u, v) described above, the visual spatial frequency characteristic VTF can be a function VTF (u, v) of (u, v).

  By multiplying the above-mentioned spectrum S (u, v) by this VTF (u, v), the spectrum S (u, v) can be evaluated in a state in which the visual spatial frequency characteristic is taken into consideration. If this evaluation is integrated, the spatial frequency can be evaluated for the entire lattice point region W. Therefore, in this embodiment, the processing up to integration is performed in the processing of steps S276 to S280. First, both (u, v) are initialized to “0” (step S276), and a certain coordinate (u, The spatial frequency f (u, v) at v) is calculated (step S277). Further, the VTF at the spatial frequency f is calculated (step S278).

  When the VTF is obtained, the sum of the square of the VTF and the square of the spectrum S (u, v) and the variable Pow for substituting the integration result are calculated (step S279). That is, since the spectrum S (u, v) includes a real part Re (u, v) and an imaginary part Im (u, v), in order to evaluate the magnitude, first, the square of the VTF and the spectrum S ( Integration is performed by the square of u, v). Then, it is determined whether or not the above processing has been performed for all the coordinates (u, v) in the lattice point area W (step S280), and it is determined that the processing has been completed for all coordinates (u, v). If not, unprocessed coordinates (u, v) in the lattice point area W are extracted, and the processes after step S277 are repeated. Note that, as shown in FIG. 15, the VTF suddenly decreases and becomes almost “0” as the spatial frequency increases, so that the range of coordinates (u, v) is limited to a predetermined value or less in advance. Calculation can be performed within a necessary and sufficient range.

When the integration is completed, Pow ^1/2 / total number of pixels is calculated (step S281). That is, the dimension is returned to the dimension of the spectrum S (u, v) by the square root of the variable Pow, and is normalized by dividing by the total number of pixels. By this normalization, an objective index (Int in FIG. 15) that does not depend on the number of pixels of the input image is calculated. In the present embodiment, the graininess index GI is further corrected by considering the influence of the brightness of the entire lattice point region W. That is, in the present embodiment, even when the spatial frequency spectrum is the same, a different impression is given to the human eye when the entire lattice point region W is bright and when the entire lattice point region W is dark. Correction is performed to make it easy to feel graininess. For this reason, first, the average brightness Ave of the entire image is calculated by adding the lightness L (x, y) for all the pixels in the lattice point region W and dividing the sum by all the pixels (step S282).

Then, the correction coefficient a (L) based on the brightness of the entire lattice point area W is defined as a (L) = ((Ave + 16) / 116) ^0.8, and the correction coefficient a (L) is calculated (step S283). The Int is multiplied to obtain the graininess index GI (step S284). The correction coefficient a (L) corresponds to the brightness correction term a _L described above. Through the above processing, the graininess index calculation unit GC4 specifically calculates the above expression (7). The correction coefficient may be a function that increases or decreases the coefficient value according to the average brightness, and various other functions may be employed. Of course, the component for evaluating the graininess index GI is not limited to the lightness component, and the spatial frequency may be evaluated in consideration of the hue and the saturation component, and the lightness component, red-green component, yellow- After calculating the blue component and Fourier transforming each, the graininess index GI may be calculated by multiplying the visual spatial frequency characteristic defined in advance for each color component.

  Through the processes in steps S272 to S284 described above, the graininess of the lattice points (ink amount set φ) on the scan data SD selected in step S272 can be quantified as the graininess index GI. In step S285, the data creation unit GC5 stores the correspondence between the ink amount set and the graininess index GI as the second data D2 in the HDD 11. In step S286, it is determined whether or not all grid points (ink amount set φ) on the scan data SD (first data D1) have been selected. If not all are selected, the process returns to step S272. That is, another grid point shown in FIG. 13 is selected, and the graininess index GI for the other grid point (ink amount set φ) is calculated. By repeatedly executing the above processing, the graininess index GI for each ink quantity set φ stored in the first data D1 is sequentially calculated, and the correspondence between each ink quantity set φ and the graininess index GI. The relationship can be stored in the second data D2. When the second data D2 that defines the correspondence between the ink amount set φ and the graininess index GI for all grid points (2000 × 2000) can be created, the graininess converter preparation process (graininess index calculation process) is completed. Let When the second data D2 can be created by the graininess converter preparation process, the graininess index GI is predicted when the prediction unit GC6 performs printing based on an arbitrary ink amount set φ by using the second data D2. Is possible.

  FIG. 16 shows the flow of graininess prediction processing. In step S100, the prediction unit GC6 acquires the ink amount set φ for which the graininess index GI should be predicted. In particular, in the profile creation process described later, the ink amount set φ designated by the optimization unit P2d at the time of optimization is sequentially acquired. In step S110, the second data D2 is searched for the ink amount set φ closest to the acquired ink amount set φ. That is, the 2000 × 2000 ink amount sets φ defined in the second data D2 are searched for the most similar to the prediction target ink amount set φ. For example, it may be determined whether or not the distance is close based on the Euclidean distance in the ink amount space. When the closest ink amount set φ can be searched, the granularity index GI associated with the ink amount set φ is acquired in the second data D2, and the granularity index GI is used as a prediction result (step S120). In the present exemplary embodiment, the second data D2 is exemplified by using the granularity index GI associated with the ink amount set φ closest to the prediction target ink amount set φ as a prediction result. The prediction result may be determined. For example, the prediction result is determined by performing an interpolation operation using a plurality of ink amount sets φ close to the prediction target ink amount set φ among the ink amount sets φ defined in the second data D2. Also good.

B. Creation of Profiles In the above, the various converters RC, CC, GC constituting the print result prediction unit program P1 and the preparation thereof have been described. In the following, the various converters RC, CC, GC are used to make the profile of the present invention. A profile creation apparatus and a print control apparatus that create a lookup table and execute color conversion using the created lookup table will be described. Specifically, the LUT creation program P2 and the printer driver P3 executed by the computer 10 embody a profile creation device and a print control device. Note that the lookup table may be abbreviated as LUT.

  FIG. 17 shows the configuration of the LUT creation program P2. The LUT creation program P2 includes an initial LUT generation unit P2a, an evaluation function setting unit P2b, a smoothness degree calculation unit P2c, an optimization unit P2d, and an LUT generation unit P2e. Hereinafter, the details of the profile creation processing executed by each of the modules P2a to P2d will be described based on the flow.

FIG. 18 shows the flow of profile creation processing. In step S400, an initial ink profile IP is created. The ink profile IP is compatible with an absolute color space CIELAB color space (L ^* a ^* b ^* space) and an ink amount space CMYKlclm space _{(d c d m d y d} k d lc d lm space) This is an LUT that defines the relationship for a plurality of representative grid points. In the creation of the initial ink profile IP, for example 17 ^three sets of random ink amount set phi = from among the effective region as described above _{(d c, d m, d} y, d k, d lc, d lm) generating an To do. The 17 ^three sets of the ink amount set is the initial value of the ink amount set for LUT of the present invention, it will be optimized by processing described later. Since the initial LUT ink amount set is finally optimized, it may be generated in any way in the initial stage.

  Next, in step S410, the evaluation function setting unit P2b sets an evaluation function. That is, an optimization guideline described later is set. Since the ink profile IP defines the correspondence between the CIELAB color space, which is an absolute color space, and the ink amount space with respect to a finite number of lattice points, correspondence relationships other than the lattice points are predicted in the future by interpolation processing. The Rukoto. In general, when a grid point arranged in order in each color space calculates a color positioned between them by interpolation calculation, interpolation can be performed without greatly changing the interpolation accuracy depending on the local position of the space. Therefore, by adopting an index for smoothing the grid point arrangement in the CIELAB color space as an optimization guideline in the present embodiment, interpolation calculation is performed with high accuracy at the time of ink profile IP creation and color conversion after creation. It becomes possible to do. As a result, it is possible to create an ink profile IP that can suppress the occurrence of tone jump and can obtain a printed matter in which gradation changes smoothly.

  In the ink profile IP, in order to realize as wide a color reproducibility as possible, the grid points should be distributed over the entire color reproduction gamut that the printer 20 can reproduce with the ink set (CMYKlclm). Therefore, by adopting an index that secures the color reproduction gamut in the CIELAB color space as an optimization guideline in the present embodiment, an ink profile IP capable of realizing a wide color reproducibility can be realized. With the above guidelines, an optimal distribution of lattice points in the CIELAB color space can be specified.

However, even determine the optimal grid point in the CIELAB color space, the ink amount set φ = (d _c corresponding to the lattice point by the ink profile _{IP, d m, d y,} d k, d lc, d lm ) Cannot be determined uniquely. The ink profile IP defines the correspondence between the CIELAB color space, which is an absolute color space, and the CMYKlclm space, which is an ink amount space, but the correspondence between the CIELAB color space and the CMYKlclm space has a unique relationship. It is not. That is, even if one L ^* a ^* b ^* value is determined in the CIELAB color space, an ink amount set φ that realizes a print result that can reproduce the L ^* a ^* b ^* value under a certain light source is uniquely set. It cannot be determined. For example, since K ink and CMY ink have a relationship capable of color separation, the same L ^* a ^* b ^* can be reproduced even if the color separation ratio is changed in a certain light source. The same applies to the relationship between C ink and lc ink and between M ink and lm ink.

Therefore, it is necessary to determine the optimal grid point in the CIELAB color space and to optimize the ink amount set φ corresponding to the grid point. For example, the separation ratio between K ink and CMY ink cannot be determined uniquely even if the L ^* a ^* b ^* value in the CIELAB color space is determined. However, if dark K ink is generated in the highlight area, the graininess is reduced. It will stand out. Therefore, if the improvement in graininess is used as an optimization guideline, the ink amount set φ where d _k = 0 can be optimized for the L ^* a ^* b ^* value in the highlight area. Conversely, the color separation ratio between K ink and CMY ink cannot be determined uniquely even if the L ^* a ^* b ^* value in the CIELAB color space is determined, but a lot of composite gray is used with CMY ink whose spectral reflectance is not flat. In this case, the light source dependency of color becomes a problem. Therefore, the grid points of the ink profile IP should be optimized using the improvement of color constancy as a guideline for optimization. Furthermore, by applying a guideline for reducing the size of the ink amount set φ as a whole, it is possible to create an ink profile IP that is optimal in terms of ink running cost.

前記の（８）式において、評価関数Ｅ_pは５個の項をｗ₁〜ｗ₅の重み係数によって加算した値であり、各項がそれぞれ上述した格子点を選択する指針に基づいて設定されている。（８）式の第１項は、上述した粒状性コンバータＧＣによる粒状性予測処理によって得られる粒状性指数ＧＩを最大値で除算することによって正規化し、所定の重み係数ｗ₁を乗算したものとなっている。なお、評価関数Ｅ_pの添え字ｐ（ｐ＝１〜１７³）は注目する格子点の識別符号を示している。粒状性指数ＧＩは小さい方が良好な画質となるため、（８）式の第１項が小さくなるほど最適であるといえる。 As described above, in order to create the optimum ink profile IP, it is preferable to optimize the grid points in consideration of various factors, and to set the optimum color separation rule in consideration of all factors. It is difficult. Therefore, in the present embodiment, an evaluation function E _p that can simultaneously evaluate these elements is set based on the above-described guidelines (step S410). Specifically, the evaluation function E _p (E _p (φ)) is set by the following equation (8). In addition, the light source used when calculating the evaluation function E _p is also set.

In the above equation (8), the evaluation function E _p is a value obtained by adding _five terms by weighting factors w ₁ to w ₅ , and each term is set based on the above-described pointers for selecting the lattice points. ing. The first term of the equation (8) is obtained by normalizing by dividing the graininess index GI obtained by the graininess prediction process by the graininess converter GC described above by the maximum value and multiplying by a predetermined weighting factor w _1. It has become. Note that the subscript p (p = 1 to 17 ³ ) of the evaluation function E _p indicates the identification code of the lattice point of interest. The smaller the graininess index GI, the better the image quality. Therefore, the smaller the first term in equation (8), the more optimal it is.

(8) The second term of the color constancy index CII obtained by the color converter CC normalized by dividing by the maximum value, and is obtained by multiplying a predetermined weight coefficient w _2. The color constancy index CII is obtained by further converting the spectral reflectance R (λ) obtained by inputting an arbitrary ink amount set φ into the spectral printing model converter RC by the color converter CC. It can be said that it is a function of the quantity set φ. (8) the third term in is made shall smoothness degree evaluation index SI obtained by smoothness calculating unit P2c normalized by dividing by the maximum value, obtained by multiplying a predetermined weighting coefficient w _3. The smaller the color constancy index CII, the better the image quality. Therefore, the smaller the second term in equation (8), the more optimal. The light source from which the color converter CC calculates the color constancy index CII is set in step S410. For example, the comparison light sources L1 and L2 are set to D50 light, the F11 light is set, and the standard light source Ls is set to D65 light.

平滑程度評価指数ＳＩは、注目する格子点から互いに逆向きのベクトルの距離が等しく、方向が正反対に近いほど値が小さくなるようにしてある。 FIG. 19 schematically illustrates the smoothness degree evaluation index SI. In the figure, ◯ indicates the position of a plurality of lattice points in the CIELAB space, and ● indicates the lattice point of interest (the lattice point for which the evaluation function _Ep is calculated) among the lattice points. The position vector of the grid point of interest as Lp, the position vector of the six grid points adjacent to the grid point and L _a1 ~L _a6, smoothness degree evaluation index SI is represented by the following formula (9) .

The smoothness degree evaluation index SI is set such that the distance between vectors in the opposite direction from the lattice point of interest is equal, and the value becomes smaller as the direction is closer to the opposite direction.

As shown in FIG. 19B, lines connecting adjacent lattice points (such as a line passing through the lattice points indicated by the vector L _a1 to the vector L _p to the vector L _a2 ) are close to a straight line, and the lattice points are evenly arranged. Since the arrangement of the grid points in the CIELAB color space tends to be smoothed as the degree of smoothness increases, the degree of smoothness increases as the smoothness degree evaluation index SI shown in the equation (9) decreases. L ^* a ^* b ^* values in the CIELAB color space, the ink amount set phi = sequentially converted by _{(d c, d m, d} y, d k, d lc, d lm) the spectral printing model converter RC and color converter CC Can be obtained. The light source for which the color converter CC calculates the L ^* a ^* b ^* value uses the D65 light set as the standard light source Ls in step S410. Therefore, it can be said that the smoothness degree evaluation index SI specified by the position vector in the CIELAB color space in the equation (9) is a function of the ink amount set φ. The smaller the smoothness degree evaluation index SI, the higher the interpolation accuracy can be expected. Therefore, it can be said that the smaller the third term of the equation (8), the more optimal. Next, the fourth term of the equation (8) indicates whether or not the position vector L _p of the target lattice point is close to a specific color.

  FIG. 20 shows the color reproduction gamut of the printer 20 in the CIELAB color space. As shown in the figure, the color reproduction gamut of the printer 20 is determined in advance by the hardware specifications of the printer 20 and the ink set, and colors can be reproduced within this range. Accordingly, the grid points of the ink profile IP must be present in the entire color reproduction gamut in the CIELAB color space. For this reason, some grid points need to be constrained to the outer surface, ridgeline, or vertex of the color reproduction gamut. By adding the color difference ΔE between the color of the color reproduction gamut on the outer surface, the ridgeline, and the vertex and the lattice point as the fourth term of the equation (8), the lattice point is present in the entire color reproduction gamut. Can do. Note that the fourth term of the equation (8) is also specified by the position vector in the CIELAB color space, and thus can be said to be a function of the ink amount set φ. As the lattice point showing the same color as the outermost color of the color reproduction gamut is included, the widest color reproducibility can be realized. Therefore, it can be said that the smaller the fourth term in the equation (8), the more optimal.

The fifth term of the equation (8) is obtained by normalizing the total value of the ink amount set φ. Thereby, the grid point can be optimized by the evaluation function E _p taking into account the total consumption of ink. The fifth term of the equation (8) also depends on the ink amount set φ and can be said to be a function of the ink amount set φ. Note that T _{Duty in} equation (8) is a value corresponding to the limit of the amount of ink that can adhere to the recording medium. The smaller the amount of ink, the better the running cost. Therefore, the smaller the fifth term in equation (8), the more optimal.

As described above, all the terms constituting the evaluation function E _p are expressed by the function of the ink amount set φ, and the lattice point becomes optimal as the value decreases. Accordingly, in step S400, only the initial grid points are appropriately determined. Therefore, even if the evaluation function E _p is calculated by paying attention to each grid point, the evaluation function E _p does not become a small value. Accordingly, in step S420, the optimization unit P2d optimizes each lattice point so that the evaluation function E _p is minimized. Which term is to be optimized is determined by the above-described weighting factors w _{1 to} w ₅ . Therefore, it is desirable to set which item should be emphasized when creating the ink profile IP, and to set the weighting factors w _{1 to} w ₅ based on it. For example, if it is desired to create an ink profile IP with good running cost even at the expense of image quality, the weight coefficient w ₅ should be set large. In addition, it is desirable to increase the weighting factor w ₄ for the grid points that should be located on the outer surface of the color reproduction gamut to increase the constraint on the outer surface.

Specifically, in step S420, an ink amount set φ that minimizes the evaluation function E _p is sequentially calculated for each lattice point. For example, the ink amount set φ is locally moved from the position of the initial ink amount set in the ink amount space, and an ink amount set that minimizes the evaluation function E _p at that time is calculated for each lattice point. Thus, the position of the lattice points in the ink amount space is modified in a direction that minimizes the evaluation function E _p. Furthermore, likewise locally moving the ink amount set φ from the position of the corrected, it will calculated for each grid point an ink amount sets which minimize the evaluation function E _p at that time. By repeating the aforementioned processing (for example, 200 times), and finally it is possible to optimize the lattice point evaluation function E _p becomes extremely small for each grid point. It should be noted that the optimization of the grid point may be completed by performing the above process a specified number of times, or the optimization of the grid point may be completed when the value of the evaluation function E _p falls below a predetermined threshold value.

In this optimization process, it is necessary to calculate the evaluation function E _p for the ink amount set φ that is sequentially updated. At this time, the converters RC, CC, GC, and the smoothing degree calculation unit P2c described above are used. Thus, the spectral reflectance R (λ), the graininess index GI, the color constancy index CII, and the smoothness evaluation index SI corresponding to each ink amount set φ are sequentially calculated. According to the above optimization, the evaluation function E _p can obtain the grid points of the ink amount set φ excellent in graininess, color constancy, and running cost, and the distribution of the grid points in the CIELAB color space is also optimal. It becomes. This is because the evaluation in the CIELAB color space is also incorporated as part of the evaluation function E _p in the third and fourth terms of Equation (8). In the present embodiment, since the optimization of the grid points in the CIELAB color space and the optimization of the grid points in the ink amount space can be performed simultaneously, the processing efficiency is high. In the present embodiment, the grid point optimization technique disclosed in Japanese Patent Application Laid-Open No. 2006-197080 can also be applied. In this case, a virtual force in the direction in which the evaluation function E _p is 0 is applied to each lattice point in the ink amount space, and the position of the lattice point in the ink amount space is converged to a steady state by the force. .

When each grid point is optimized as described above, the L ^* a ^* b ^* value corresponding to the ink amount set φ of the grid point optimized in step S430 is converted into the spectral printing model converter RC and the color converter CC. (D65 light). Then, the LUT generation unit P2e creates an ink profile IP describing the correspondence between the L ^* a ^* b ^* values corresponding to each other and the ink amount set φ. It should be noted that the ink amount set φ described in the ink profile IP is an ink amount set φ that satisfies the condition that at least the graininess is good (to the extent that the value of the evaluation function E _p is minimized). it can.

In step S440, a color conversion profile CP is created based on the ink profile IP. The color conversion profile CP is an LUT that converts, for example, image data in which the color of each pixel is expressed in the sRGB color space into image data in the ink amount space in the printer 20. Since the sRGB color space has a corresponding relationship (sRGB profile SP) with the CIELAB color space based on the CIE standard, the sRGB color space is determined by the L ^* a ^* b ^* values of each grid point defined in the ink profile IP. The correspondence between the RGB values of the ink and the ink amount set φ can be specified, and can be converted into an LUT. At that time, interpolation processing is performed, but since the distribution in the CIELAB color space of the grid points defined by the ink profile IP is smoothed by the above-described optimization, high interpolation accuracy can be realized.

  It is desirable that the sRGB profile SP is also optimized by the above-described smoothness degree evaluation index SI (see Japanese Patent Application Laid-Open No. 2006-197080). Since the gamut of the sRGB color space in the CIELAB color space and the color reproduction gamut of the printer 20 are different, gamut mapping is appropriately performed. Here, an example in which an sRGB color space, which is another absolute color space, is combined with the ink profile IP is taken as an example, but a device-dependent source color space that depends on the input device and the ink profile IP are combined. A device link profile may be created. When the color conversion profile CP can be created, the color conversion unit P3a of the printer driver P3 thereafter performs interpolation processing with reference to the color conversion profile CP, whereby print image data representing the color of each pixel in the sRGB color space. Can be converted into image data of the ink amount set φ. Further, the halftone processing unit P3b and the rasterization processing unit P3c sequentially perform halftone processing such as an error diffusion method and a dither method and rasterization processing on the image data of the ink amount set φ, thereby generating the print control data CD. be able to. By outputting the generated print control data CD to the printer 20, printing based on the print image data can be executed.

C. Modified Example In the above, an example in which the graininess index GI is measured from each position of the survey sheet PP is illustrated, but the state quantity that can be measured from the survey sheet PP is not limited to the graininess index GI. For example, it is possible to measure the spectral reflectance R (λ) at each position of the survey sheet PP, and it is also possible to measure colorimetric values under a predetermined light source. By creating data in which the measured spectral reflectance R (λ) and colorimetric values are associated with the position of the survey sheet PP (that is, the ink amount set φ), an arbitrary ink amount set is referred to the data. Spectral reflectance R (λ) and colorimetric values when printing is performed based on φ can be obtained. In the above description, the print result prediction program P1, the LUT creation program P2, and the printer driver P3 are executed by the single computer 10, but these may be executed by another computer. Further, a function equivalent to that of the printer driver P3 may be executed in a so-called direct printer.

It is a block diagram which shows the structure of a computer. It is a schematic diagram explaining a printing system. It is a figure which shows a spectral reflectance database. It is a figure which shows a spectral Neugebauer model. It is a figure which shows a cell division | segmentation Yule-Nielsen spectroscopic Neugebauer model. It is a figure which shows a mode that a color is specified from a spectral reflectance. It is a flowchart of a granularity converter preparation process. It is a figure which shows an ink amount plane. It is a figure which shows an ink amount map. It is a flowchart of an ink amount map creation process. It is a graph which shows the learning rate coefficient and the width | variety of a vicinity range. It is a figure which shows the equal ink coverage line of each ink in a survey sheet. It is a figure which shows scan data. It is a flowchart of a graininess index calculation process. It is a figure which shows the process which calculates a granularity index | exponent. It is a flowchart of a graininess prediction process. It is a block diagram which shows the structure of a LUT creation program. It is a flowchart of a profile creation process. It is a figure explaining a smoothness degree evaluation index. It is a graph which shows the color reproduction gamut of a printer.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 10 ... Computer, 11 ... HDD, 12 ... CPU, 20 ... Printer, 30 ... Spectral reflectance meter, 40 ... Scanner, RC ... Spectral printing converter, CC ... Color converter, GC ... Granularity converter, RDB ... Spectral reflectance database , D1 ... first data, D2 ... second data.

Claims (8)

By mapping an ink amount space composed of three or more types of ink amounts to a two-dimensional plane, an ink amount map in which each position on the two-dimensional plane has the ink amount is created,
Create a survey sheet by printing the ink amount map,
A printing result prediction method characterized by predicting the state quantity when printing is performed based on an arbitrary ink amount based on the state quantity measured for each position of the survey sheet. The ink amount map is
The self-organizing map created by learning the ink amounts of a plurality of units arranged on the ink amount map from a large number of ink amounts distributed in the ink amount space. Item 4. The printing result prediction method according to Item 1.   The printing result prediction method according to claim 2, wherein the survey sheet is printed after the ink amount map has a high resolution.   The print result prediction method according to claim 1, wherein the state quantity is a graininess index obtained by quantifying graininess.   2. The ink amount map is created by mapping a region inside the ink amount space inside an ink usage limit of a printing apparatus that prints the survey sheet on a two-dimensional plane. Item 5. The printing result prediction method according to any one of Items4.   The state quantity predicted by the printing result prediction method according to any one of claims 1 to 5 is reproduced when the plurality of ink quantities satisfying a predetermined condition and printing is performed using the ink quantities. A profile creation method characterized by creating a profile that defines a correspondence relationship with a specific color. Color conversion means for acquiring the ink amount by referring to the profile created by the profile creation method according to claim 6;
And a printing unit that executes printing based on the acquired ink amount. A survey sheet formed by coating three or more types of ink on a print medium,
An investigation sheet, wherein the ink coverage of each ink on the printing medium continuously varies depending on each position, and the equal ink coverage line of each ink forms a curve.

Images