JP5235817B2 - Image processing apparatus, image processing method, and computer program - Google Patents

Description

  The present invention relates to image processing using a basic stimulus value and a spectral auxiliary coefficient corresponding to a spectral error when spectral information is estimated from the basic stimulus value.

  Conventionally, as a color of a recording agent that forms an output image of a color recording apparatus, three subtractive primary colors, cyan (C), magenta (M), and yellow (Y), or black (K ) Is generally added to the four colors. In this case, the three color components of red (R), green (G), and blue (B) in the input image data are converted into the above-described three colors of C, M, and Y, or four colors including K. In addition, image formation is performed with recording agents of each color.

  In recent years, in addition to the four basic color materials of C, M, Y, and K, there is a color recording apparatus in which color materials other than the three primary colors of subtractive color mixing, that is, three color materials of RGB as special colors are added. It has been released. Such a color recording apparatus can realize color reproduction that cannot be achieved by conventional three-color or four-color image formation.

  In addition, with the rapid spread of color recording apparatuses in recent years, there has been an increasing demand for higher image quality, and spectral information in the visible light region has been used as input information given to the color recording apparatus. .

  In a color management system (hereinafter, referred to as “spectral CMS”) that handles such spectral information, an output color that minimizes spectral error from the input spectral information is determined. Thereby, in spectroscopic CMS, it is possible to form a high-accuracy output image that can reduce visual color matching, that is, metamerism, which does not depend on an observation environment such as an environmental light source.

  However, in the spectroscopic CMS, the number of dimensions of data to be handled is remarkably increased as compared with the tristimulus values represented by conventional CIELAB, CIEXYZ, and the like. For example, when sampling from 400 nm to 700 nm in increments of 10 nm, the obtained spectral data has 31 dimensions. In order to construct a simpler spectral CMS, it is important to perform data compression effectively with a small number of dimensions without impairing spectral characteristics.

  Conventionally, a spectral information compression method using a spectral color space called 6-dimensional LabPQR has been proposed as a spectral information data compression method (for example, see Non-Patent Document 1 below). In this compression method, since Lab * QR includes L * a * b * values, color matching equivalent to colorimetric color reproduction that achieves visual matching under a specific viewing condition can be obtained. Further, since the spectral information is included in the PQR of the LabPQR, there is an advantage that metamerism can be reduced.

  Conventionally, as a spectral CMS using the LabPQR, a method of quickly determining an output color using a color look-up table (CLUT) has been proposed (for example, see Non-Patent Document 2 below).

M.M. Derhak, M.C. Rosen, "Spectral Colorimetry using LabPQR-An Interim Connection Space", "Journal of Imaging Science and Technology", USA, Imaging Science 6 S. Tsutsumi, M .; Rosen, R.W. Berns, “Spectral Gamut Mapping using LabPQR”, “Journal of Imaging Science and Technology”, USA, Imaging Science and Technology, pp. 473-48, 2007.

  However, in the method of Non-Patent Document 1, the input spectral information determines the output color using the 6-dimensional spectral color space LabPQR within the color reproduction range of the color recording apparatus, but three outside the color reproduction range. The output color was determined by colorimetric color reproduction using only stimulus values. As described above, in Non-Patent Document 1 in which the output color determination method is discontinuously switched at the boundary of the color reproduction range of the color recording apparatus, the color of the color recording apparatus is obtained under the specific observation conditions used to calculate the tristimulus values. Smooth color change can be realized at the boundary of the reproduction range. On the other hand, when the environmental light source is changed, smooth color change is not compensated, and discontinuous color reproduction occurs near the boundary of the color reproduction range of the color recording apparatus. That is, the technique of Non-Patent Document 1 has a problem that metamerism, which is an advantage of spectroscopic CMS, is insufficiently reduced.

  Further, in the method of Non-Patent Document 2, the determination of the output color using the 6-dimensional LabPQR is performed regardless of whether or not the input spectral information is included in the color reproduction range of the color recording apparatus. The specific gravity of color reproduction and spectral color reproduction was kept constant. For this reason, a region where spectral color reproduction is relatively difficult, such as a color region constituted by a combination of only CMY color materials corresponding to the three primary colors of subtractive color mixing, or a color region greatly deviating from the color reproduction range of the color recording apparatus. Has also attempted to reproduce spectral colors.

  As described above, a color material other than CMY can be added to the color recording apparatus, and spectral color reproduction can be realized in a color region constituted by the additional color material. However, if spectral color reproduction is performed for a color region formed only by a combination of CMY color materials, there will be a detrimental effect that the accuracy of colorimetric color reproduction that should be originally achieved is impaired.

  The present invention has been made in view of such problems, and at the boundary between a color region where spectral color reproduction can be achieved and a color region where spectral color reproduction cannot be achieved, smooth color reproduction can be performed against changes in the observation environment. The purpose is to provide a mechanism to realize.

  An image processing apparatus according to the present invention is an image processing apparatus for processing an input image, and the spectral information at each pixel of the image is obtained by estimating the basic stimulus value and the spectral information from the basic stimulus value. Decomposing means for decomposing into spectral auxiliary coefficients corresponding to spectral errors, and first calculating means for calculating an evaluation value for colorimetric color reproduction based on the basic stimulus value obtained by the decomposing by the decomposing means; Second color calculation means for calculating an evaluation value related to spectral color reproduction based on the spectral auxiliary coefficient obtained by the decomposition by the decomposition means; and a color material provided in the image output apparatus for outputting the image According to color characteristics, weighting factor setting means for setting a weighting factor, calculation using the weighting factor for the evaluation value related to the colorimetric color reproduction and the evaluation value related to the spectral color reproduction , Calculate weighted evaluation value To a third calculating means, using said weighted evaluation values, and a fourth calculating means for calculating an output color at the time of outputting the image by the image output apparatus.

  Another aspect of the image processing apparatus of the present invention is an image processing apparatus for processing an input image, wherein spectral information in each pixel of the image is obtained as a basic stimulus value, and spectral information is obtained from the basic stimulus value. A decomposition unit that decomposes into spectral auxiliary coefficients corresponding to spectral errors at the time of estimation, and a first evaluation value for calculating colorimetric color reproduction based on a basic stimulus value obtained by the decomposition by the decomposition unit , A second calculation unit that calculates an evaluation value related to spectral color reproduction based on the spectral auxiliary coefficient obtained by the decomposition by the decomposition unit, and an image output device that outputs the image. Weighting factor setting means for setting a weighting factor according to the color characteristics of the color material, and the weighting factor for the evaluation value related to the colorimetric color reproduction and the evaluation value related to the spectral color reproduction. Calculate and weight A third calculation means for calculating the evaluated value, a color conversion table for storing a basic stimulus value and a spectral auxiliary coefficient related to the weighted evaluation value, and the image output device using the color conversion table. And fourth calculating means for calculating an output color when outputting the image.

  According to the present invention, it is possible to realize smooth color reproduction with respect to changes in the observation environment at the boundary between a color area where spectral color reproduction can be achieved and a color area where spectral color reproduction cannot be achieved. Furthermore, for color regions where spectral color reproduction cannot be realized, it is possible to output an image giving priority to colorimetric color reproduction.

It is a block diagram which shows an example of a function structure of the image processing apparatus which concerns on 1st Embodiment. It is a block diagram which shows an example of an internal structure of the spectral information decomposition | disassembly part shown in FIG. It is a flowchart which shows an example of the process sequence in operation | movement of the spectral information decomposition | disassembly part shown in FIG. It is a schematic diagram which shows an example of the memory array in which the spectral image data input into the spectral information decomposition | disassembly part shown in FIG. 2 are stored. 3 is a flowchart illustrating an example of a processing procedure of a method for determining a spectral fundamental stimulus calculation function and a spectral auxiliary coefficient calculation function illustrated in FIG. 2. It is a schematic diagram which shows an example of the coefficient (X, Y, Z) corresponding to each wavelength of the matrix T (spectral fundamental stimulus calculation function) derived | led-out from the print patch of 729 colors. It is a schematic diagram which shows an example of the plot of the spectral error in a 729 color print patch. It is a schematic diagram which shows an example of the coefficient (P, Q, R) corresponding to each wavelength of the matrix V (spectral auxiliary coefficient calculation function) derived | led-out from the print patch of 729 colors. It is a block diagram which shows an example of an internal structure of the color conversion table preparation part shown in FIG. 10 is a flowchart illustrating an example of a processing procedure in the operation of the color conversion table creation unit (and further the spectral information decomposition unit) illustrated in FIG. 9. It is a block diagram which shows an example of an internal structure of the evaluation value calculating part shown in FIG. It is a flowchart which shows an example of the process sequence in operation | movement of the evaluation value calculating part shown in FIG. It is a schematic diagram which shows an example of the distribution position of a special color material area | region and a gray area | region in CIELAB space. It is a schematic diagram which shows an example of distribution of the weighting coefficient (alpha) in CIELAB space. FIG. 6 is a schematic diagram illustrating an example in which a distribution of a weighting coefficient α is plotted in a CIELAB space in a color recording apparatus including four color materials of CMYK. FIG. 2 is a schematic diagram illustrating an example of fluctuations in stimulus values output from a color recording apparatus in the image forming unit illustrated in FIG. 1. FIG. 2 is a schematic diagram illustrating an example of how input stimulus values located outside the color reproduction range of the color recording apparatus in the image forming unit illustrated in FIG. 1 are mapped within the color reproduction range of the color recording apparatus with different weighting factors α. is there. It is a schematic diagram which shows an example of the relationship between the average color difference of the input stimulus value in a different weighting coefficient, and a corresponding stimulus value, and the distribution of a corresponding stimulus value. FIG. 2 is a schematic diagram illustrating an example of a distribution range of corresponding stimulus values within a color reproduction range of a color recording apparatus in the image forming unit illustrated in FIG. 1. It is a block diagram which shows an example of the hardware constitutions of the image processing apparatus which concerns on embodiment. It is a block diagram which shows an example of a function structure of the image processing apparatus which concerns on 2nd Embodiment. It is a block diagram which shows an example of an internal structure of the output color calculation part shown in FIG. It is a flowchart which shows an example of the process sequence in operation | movement of the output color calculation part (further spectral information decomposition | disassembly part) shown in FIG.

  Hereinafter, embodiments (embodiments) for carrying out the present invention will be described with reference to the drawings.

(First embodiment)
First, a first embodiment of the present invention will be described.

<Functional Configuration of Image Processing Device (Color Processing Device)>
FIG. 1 is a block diagram illustrating an example of a functional configuration of the image processing apparatus according to the first embodiment of the present invention. Here, the image processing apparatus illustrated in FIG. 1 will be described as “image processing apparatus 100-1”.

  As shown in FIG. 1, the image processing apparatus 100-1 includes functional components of a spectral image data input unit 110, a spectral information decomposition unit 120, a color conversion table creation unit 130, a color processing unit 140, and an image forming unit 150. It is comprised.

  The spectral image data input unit 110 inputs spectral image data to be imaged from the outside into the image processing apparatus 100-1. Here, the input spectral image data is spectral image data having spectral information at each pixel position.

  The spectral information decomposition unit 120 decomposes spectral information at each pixel position of spectral image data input from the spectral image data input unit 110 into basic stimulus values and spectral auxiliary coefficients. Here, the basic stimulus value is a tristimulus value under a specific environmental light source represented by CIELAB, CIEXYZ, or the like, or a color value (for example, RGB) calculated based on the tristimulus value. The spectral auxiliary coefficient corresponds to the spectral error when the input spectral information is estimated from the basic stimulus value, and is a value used when the spectral error is compensated.

  The color conversion table creation unit 130 creates a color conversion table that associates output colors from basic stimulus values and spectral auxiliary coefficients. The color conversion table created by the color conversion table creation unit 130 is stored as a color conversion table 143 inside the color processing unit 140.

  The color processing unit 140 performs color processing such as determining an output color value provided in the image forming unit 150. As shown in FIG. 1, the color processing unit 140 includes a basic stimulus value input terminal 141, a spectral auxiliary coefficient input terminal 142, a color conversion table 143, an output color calculation unit 144, and an output terminal 145. .

  The basic stimulus value input terminal 141 of the color processing unit 140 is an input terminal for inputting the basic stimulus value decomposed and output by the spectral information decomposing unit 120 to the output color calculating unit 144. The spectral auxiliary coefficient input terminal 142 of the color processing unit 140 is an input terminal for inputting the spectral auxiliary coefficient decomposed and output by the spectral information decomposing unit 120 to the output color calculating unit 144. The color conversion table 143 is a color conversion table created by the color conversion table creation unit 130 and stored inside the color processing unit 140. The output color calculation unit 144 calculates the output color using the input basic stimulus value and the spectral auxiliary coefficient and using the color conversion table 143. The output terminal 145 of the color processing unit 140 is an output terminal for outputting the output color calculated by the output color calculation unit 144 to the image forming unit 150.

  The image forming unit 150 forms an image (a print image in the example shown in FIG. 1) by a color recording apparatus. In the present embodiment, the color recording apparatus in the image forming unit 150 includes color materials of RGB special colors as color materials of CMYK.

  Hereinafter, details of each functional configuration of the image processing apparatus 100-1 illustrated in FIG. 1 will be described.

<Internal configuration of spectral information decomposition unit>
First, the internal configuration of the spectral information decomposition unit 120 shown in FIG. 1 will be described.
FIG. 2 is a block diagram showing an example of the internal configuration of the spectral information decomposition unit 120 shown in FIG.

  As shown in FIG. 2, the spectral information decomposition unit 120 includes a color matching function / light source information 121, a tristimulus value calculation unit 122, and a conversion unit 123 for converting to a uniform color space. Further, as shown in FIG. 2, the spectral information decomposition unit 120 includes a spectral fundamental stimulus calculation function 124, a spectral fundamental stimulus calculation unit 125, a spectral error calculation unit 126, a spectral auxiliary coefficient calculation function 127, and a spectral auxiliary coefficient calculation unit. 128.

  The color matching function / light source information 121 is specifically information including information on the CIE color matching function and information on the environmental light source. The tristimulus value calculation unit 122 uses the color matching function / light source information 121 (specifically, information on the CIE color matching function and the environmental light source) for each spectral image data input from the spectral image data input unit 110. Tristimulus values are calculated from the spectral information at the pixel positions. The uniform color space conversion unit 123 performs conversion from the tristimulus value calculated by the tristimulus value calculation unit 122 to the uniform color space, and outputs a basic stimulus value (L * a * b * value).

  The spectral fundamental stimulus calculation function 124 is a function for calculating a spectral fundamental stimulus from tristimulus values. The spectral fundamental stimulus calculator 125 calculates a spectral fundamental stimulus from the tristimulus values calculated by the tristimulus value calculator 122 using the spectral fundamental stimulus calculation function 124.

  The spectral error calculator 126 calculates a spectral error between the spectral fundamental stimulus value calculated by the spectral fundamental stimulus calculator 125 and the spectral information at each pixel position of the spectral image data input from the spectral image data input unit 110. .

  The spectral auxiliary coefficient calculation function 127 is a function for calculating a spectral auxiliary coefficient from the spectral error between the spectral basic stimulus value and the spectral information. The spectral auxiliary coefficient calculation unit 128 calculates a spectral auxiliary coefficient (PQR) from the spectral error calculated by the spectral error calculation unit 126 using the spectral auxiliary coefficient calculation function 127, and outputs this.

Hereinafter, the operation of the spectral information decomposition unit 120 shown in FIG. 2 will be described.
FIG. 3 is a flowchart illustrating an example of a processing procedure in the operation of the spectral information decomposition unit 120 illustrated in FIG.

  First, when spectral image data is sequentially scanned by the spectral image data input unit 110 and spectral information at each pixel position of the spectral image data is input from the spectral image data input unit 110, in step S101, tristimulus values are obtained. The calculation unit 122 accepts this. At this time, similarly, the spectral error calculation unit 126 receives the spectral information. Here, the spectral information input from the spectral image data input unit 110 is described as spectral information R (i, j, λ).

  FIG. 4 is a schematic diagram illustrating an example of a memory array in which spectral image data input to the spectral information decomposition unit 120 illustrated in FIG. 2 is stored. As shown in FIG. 4, the spectral image data used in the present embodiment has a horizontal pixel number W and a vertical pixel number H, and each pixel position has a spectral wavelength in increments of 10 nm in a visible light region having a wavelength λ of 380 nm to 730 nm. The reflectance is stored. That is, each pixel is given a 36-dimensional spectral reflectance, and each reflectance varies between 0 and 1.

Here, it returns to description of FIG. 3 again.
Subsequently, in step S102, the tristimulus value calculator 122 calculates the following (1) from the spectral reflectance at the pixel position (i, j) of the spectral information R (i, j, λ) input in step S101. Processing for calculating the tristimulus value CIEXYZ is performed using the equations (4) to (4).

  Here, in the formulas (1) to (4), S (λ) is the spectral radiance of the environmental light source, and in this embodiment, a D50 light source defined by the CIE is used.

Subsequently, in step S103, the spectral fundamental stimulus calculation unit 125 uses the spectral fundamental stimulus calculation function 124 to convert the spectral fundamental stimulus from the tristimulus values calculated by the tristimulus value calculation unit 122 into the following equation (5). And the process which calculates like (6) Formula is performed.
N (i, j, λ) = T × Nc (i, j) (5)
Nc (i, j) = [X (i, j), Y (i, j), Z (i, j)] ^T (6)

  Here, in Equations (5) and (6), N is a matrix representing a spectral fundamental stimulus of size 36 × 1, T is a matrix representing a spectral fundamental stimulus calculation function 124 of size 36 × 3, Nc (i , J) is a 3 × 1 matrix composed of tristimulus values at pixel position (i, j). The superscript “T” written on the right side of the equation (6) means a transposed matrix. As is clear from the equations (5) and (6), a unique spectral fundamental stimulus N is obtained for all spectral reflectance combinations (metameric pairs) with the same tristimulus value Nc.

Subsequently, in step S104, the spectral error calculation unit 126 includes the spectral information R (i, j, λ) input in step S101 and the spectral fundamental stimulus N (i, j, λ) calculated in step S103. Is processed as shown in the following equation (7). Here, it is assumed that the spectral error B (i, j, λ).
B (i, j, λ) = R (i, j, λ) −N (i, j, λ) (7)

Subsequently, in step S105, the spectral auxiliary coefficient calculation unit 128 uses the spectral auxiliary coefficient calculation function 127 to calculate the spectral auxiliary coefficient Np (i, j) at the pixel position (i, j) from the spectral error calculated in step S104. j) is calculated as shown in the following equation (8).
Np (i, j) = V ^T × B (i, j, λ) (8)

  Here, in Equation (8), V is a matrix representing the spectral auxiliary coefficient calculation function 127 of size 36 × 3. In the present embodiment, the spectral auxiliary coefficient Np (i, j) is composed of three coefficients, and each coefficient is referred to as P, Q, and R. Then, the spectral auxiliary coefficient calculation unit 128 outputs the spectral auxiliary coefficient calculated in step S105 to the color processing unit 140.

Further, while the spectral auxiliary coefficient Np is calculated from the tristimulus values (Nc) calculated in step S102, in parallel, in step S106, the conversion unit 123 for converting to the uniform color space converts the tristimulus values into the uniform color. A process of calculating a basic stimulus value by performing conversion to space is performed. In this embodiment, CIELAB is used as a uniform color space. Then, the conversion from the tristimulus value (Nc) to the L * a * b * value is performed, for example, according to the following formulas (9) to (11).
L * = 116 (f (Y / Yn) -16/116) (9)
a * = 500 (f (X / Xn) -f (Y / Yn)) (10)
b * = 200 (f (Y / Yn) -f (Z / Zn)) (11)

Here, the function f (x) in the equations (9) to (11) is
f (x) = x ^ (1/3) (x> 0.008856)
f (x) = 7.787x + 16/116 (x ≦ 0.008856)
Is defined as follows.
In the equations (9) to (11), Xn, Yn, and Zn are tristimulus values in reference white under the measurement light source.

  Then, the uniform color space converting unit 123 outputs the basic stimulus value calculated in step S106 to the color processing unit 140.

When the processes of steps S105 and S106 are completed, the process proceeds to step S107.
In step S107, the spectral information decomposition unit 120 has performed the spectral auxiliary coefficient calculation processing and the basic stimulus value calculation processing for all the pixels of the spectral image data input from the spectral image data input unit 110. Judge whether or not.

  As a result of the determination in step S107, when the spectral auxiliary coefficient calculation process and the basic stimulus value calculation process are not performed for all the pixels of the spectral image data (that is, there are unprocessed pixels), Proceed to step S108.

  In step S108, the spectral information decomposing unit 120 performs a process of updating the processing target pixel position in the spectral image data input from the spectral image data input unit 110. Thereafter, the process returns to step S101, and the tristimulus value calculation unit 122 receives the input of the spectral information at the pixel position updated in step S108, and the processes in and after step S102 are performed again.

  On the other hand, if it is determined in step S107 that the spectral auxiliary coefficient calculation process and the basic stimulus value calculation process have been performed for all the pixels of the spectral image data, the process in the flowchart of FIG. 3 ends. .

  In this example, the spectral auxiliary coefficient and the basic stimulus value are output to the color processing unit 140 in the processing timing of each pixel of the spectral image data, that is, in steps S105 and S106, respectively. Is not limited to this mode. For example, a mode in which the processing results of all the pixels are collectively output to the color processing unit 140 after it is determined in step S107 that the processing has been completed for all the pixels of the spectral image data is also applicable.

  In the present embodiment, a six-dimensional color space having both L * a * b * values and PQR values is referred to as LabPQR.

<Spectral basic stimulus calculation function, spectral auxiliary coefficient calculation function>
Next, a method for determining the spectral fundamental stimulus calculation function 124 and the spectral auxiliary coefficient calculation function 127 shown in FIG. 2 will be described in detail with reference to the flowchart of FIG.

  FIG. 5 is a flowchart showing an example of the processing procedure of the method for determining the spectral fundamental stimulus calculation function 124 and the spectral auxiliary coefficient calculation function 127 shown in FIG.

  First, in step S201, the spectral information decomposition unit 120 determines a data group (spectral reflectance group) including a spectral reflectance of a printed matter, etc., in determining the spectral fundamental stimulus calculation function 124 and the spectral auxiliary coefficient calculation function 127. get. Here, as the data group to be acquired, for example, ColorChecker, ColorCheckerDC, or Munsell Book of Color manufactured by GretagMacbeth can be used. Note that the data group to be acquired is applicable as long as it is a data group randomly distributed in the color space, and is not limited to the data group described above. In the present embodiment, print patches having different colors of 729 colors formed by the image forming unit 150 and randomly distributed in the CIELAB space are used. Note that a patch is a color chart having a uniform color distribution.

  Subsequently, in step S202, the spectral information decomposing unit 120 calculates tristimulus values (Nc) of each print patch from the spectral reflectance group acquired in step S201. Here, the calculation method for calculating the tristimulus value (Nc) from the spectral reflectance is as described above.

  Subsequently, in step S203, the spectral information decomposition unit 120 calculates a pseudo inverse matrix for estimating the input spectral reflectance from the tristimulus values calculated in step S202.

  Subsequently, in step S204, the spectral information decomposition unit 120 holds the pseudo inverse matrix calculated in step S203 as a spectral basic stimulus calculation function T (spectral basic stimulus calculation function 124).

The following equation (12) shows an image of the processing in steps S203 and S204.
T = R × pinv (Nc) (12)

  Here, in Equation (12), R is a matrix composed of a spectral reflectance group of size 36 × 729 that stores spectral reflectances in the row direction, Nc is a matrix composed of a tristimulus value group of size 3 × 729, pinv () Is a function for obtaining a pseudo inverse matrix of an input matrix.

FIG. 6 is a schematic diagram illustrating an example of coefficients (X, Y, Z) corresponding to respective wavelengths of a matrix T (spectral fundamental stimulus calculation function) derived from 729 color print patches. By using this spectral fundamental stimulus calculation function T (spectral fundamental stimulus calculation function 124) and tristimulus value (Nc), the spectral information decomposing unit 120 shows the input spectral reflectance in the following equation (13). Can be estimated as follows.
N = T × Nc (13)

  Here, in the equation (13), N corresponds to the above-described spectral basic stimulus. However, as described above, since the spectral reflectance is uniquely determined for one set of tristimulus values, the spectral estimation that can distinguish the metameric pair cannot be performed by the equation (13). And in order to identify a metameric pair, the auxiliary | assistant parameter | index which considered spectral information other than the tristimulus value is newly needed.

Therefore, in step S205 of FIG. 5, the spectral information decomposing unit 120 calculates a spectral error between the input spectral information R and the spectral fundamental stimulus as shown in the following equation (14).
B = R−N (14)

  Here, in the equation (13), the matrix B stores all the spectral errors of the 729 color print patches, and the matrix size is 36 × 729. By calculating the above spectral error, a spectral difference can be derived even for a metameric pair treated as the same stimulus value in the conventional colorimetric color reproduction.

  FIG. 7 is a schematic diagram illustrating an example of a spectral error plot in a 729 color print patch. In the present embodiment, since the spectral fundamental stimulus calculation function T is determined so as to minimize the spectral error of the entire print patch, it can be confirmed that the error is distributed around zero.

Here, it returns to description of FIG. 5 again.
In step S206, the spectral information decomposition unit 120 calculates the principal component vector vi of the spectral error matrix B. Next, in step S207, the first to third low-order principal component vectors are used as spectral auxiliary coefficients. The calculation function V is held inside as shown in the following equation (15).
V = (v1, v2, v3) (15)

  Here, in Equation (15), v1 is a first principal component vector, v2 is a second principal component vector, and v3 is a second principal component vector. Each principal component vector is 36 × 1 in size.

FIG. 8 is a schematic diagram showing an example of coefficients (P, Q, R) corresponding to each wavelength of the matrix V (spectral auxiliary coefficient calculation function) derived from the 729 color print patches. In calculating the principal component vector in step S206, first, the covariance matrix of the spectral error matrix B needs to be calculated as in the following equation (16).
W = B × B ^T (16)

Thereafter, the following equation (17) is solved to obtain the eigenvalue λi and eigenvector vi (where i is the number of dimensions of the vector) of the covariance matrix in equation (16), and the eigenvector is handled as the spectral auxiliary coefficient calculation function.
W × vi = λi × vi (17)

  When the process of step S207 in FIG. 5 ends, the process in the flowchart shown in FIG. 5 ends. Through the above processing, the spectral fundamental stimulus calculation function T (spectral fundamental stimulus calculation function 124) and spectral auxiliary coefficient calculation function V (spectral auxiliary coefficient calculation function 127) are determined.

Note that the process of deriving the basic stimulus value Nc and the spectral auxiliary coefficient Np from the spectral reflectance is performed by the spectral information decomposition unit 120 described above. It is also possible to construct. At this time, the conversion formula used for the spectral reconstruction is as the following formula (18).
R ′ = N + B
= T × Nc + V × Np (18)

  Here, in the equation (18), R ′ is a reconstructed spectral reflectance. The right side of the equation (18), the second term represents the spectral difference in the metameric pair.

<Internal configuration of color conversion table creation unit>
Next, the internal configuration of the color conversion table creation unit 130 shown in FIG. 1 will be described.
FIG. 9 is a block diagram showing an example of the internal configuration of the color conversion table creation unit 130 shown in FIG.

  As shown in FIG. 9, the color conversion table creation unit 130 includes an initial value setting unit 131, a loop processing unit 132, a printer color prediction unit 133, a grid point setting unit 134, an evaluation value calculation unit 136, an end determination unit 137, and , An optimization unit 138 is provided. The evaluation value calculation unit 136 is configured to calculate an evaluation value using the evaluation function 135.

  The initial value setting unit 131 performs a process of setting initial values of parameters that are necessary when the color conversion table 143 is created. The loop processing unit 132 performs processing for sequentially updating the parameters. The printer color prediction unit 133 performs processing for predicting the color formed by the image forming unit 150.

  The lattice point setting unit 134 performs processing of setting lattice points of the color conversion table 143 and outputting a basic stimulus value (L * a * b *) and a spectral auxiliary coefficient (PQR).

  The evaluation function 135 is used when the evaluation value calculation unit 136 calculates an evaluation value. The evaluation value calculation unit 136 compares the basic stimulus value and the spectral auxiliary coefficient input from the lattice point setting unit 134 with the basic stimulus value and the spectral auxiliary coefficient given from the spectral information decomposition unit 120, and uses the evaluation function 135. Then, a process of calculating the comparison result as an evaluation value is performed.

  The end determination unit 137 performs a process for determining whether or not to end the loop process. The optimization unit 138 performs processing for searching for the next output color to be given to the printer color prediction unit 133.

The operation of the color conversion table creation unit 130 shown in FIG. 9 will be described below.
FIG. 10 is a flowchart illustrating an example of a processing procedure in the operation of the color conversion table creation unit 130 (and also the spectral information decomposition unit 120) illustrated in FIG.

  First, in step S <b> 301, the grid point setting unit 134 sets the positions of grid points to be incorporated in the color conversion table 143. In this embodiment, L * a * b * values are used as basic stimulus values, and PQR values are used as spectral auxiliary coefficients. At this time, the color conversion table 143 to be formed is composed of the above-described LabPQR spectral space, and the number of dimensions is six.

Further, the number of lattices in the L * a * b * space is 17 × 17 × 17, and sampling is performed equally for each axis as shown in the following equations (19) to (21).
L * = 0, 6.25, 12.5, ..., 93.75, 100, ... (19)
a * =-128, -112, -96, ..., 112, 128, ... (20)
b * =-128, -112, -96, ..., 112, 128, ... (21)

In addition, the lattice points in the PQR space are set to 7 × 7 × 7, and sampling is performed uniformly as shown in the following equations (22) to (24).
P = −1.2, −0.8, −0.4, 0, 0.4, 0.8, 1.2, (22)
Q = −1.2, −0.8, −0.4, 0, 0.4, 0.8, 1.2, (23)
R = −1.2, −0.8, −0.4, 0, 0.4, 0.8, 1.2, (24)

  Subsequently, in step S302, the initial value setting unit 131 first performs a process of designating a lattice point LabPQR_in (n) (where n is a target lattice point) to be calculated. For example, the lattice point LabPQR_in (1) = (0, −128, −128, −1.2, −0.8, −0.6) is set as an initial value.

  Subsequently, in step S303, the initial value setting unit 131 sets an appropriate output color as an initial value when performing the optimum processing. Here, for example, a result of generating a random number by a random number generator (not shown) is set as an initial value.

  Subsequently, in step S304, the printer color prediction unit 133 performs processing for estimating the spectral reflectance of the print corresponding to the output color. For the color prediction of the printer, for example, a Spectral Neuebauer model is used.

  Subsequently, in step S305, the spectral information decomposing unit 120 receives the spectral reflectance of the print predicted by the printer color predicting unit 133, and decomposes the spectral reflectance of the print into basic stimulus values and spectral auxiliary coefficients. I do.

  Subsequently, in step S306, the evaluation value calculation unit 136 determines the LabPQR_in (n) value of the target lattice point supplied from the lattice point setting unit 134 and the basic stimulus value (L * a) supplied from the spectral information decomposition unit 120. * B * value) and a spectral auxiliary coefficient (PQR value) are compared, and an evaluation value Eval indicating the degree of coincidence between the two sets of LabPQR values is calculated using the evaluation function 135 for evaluation.

  Subsequently, in step S307, the end determination unit 137 determines whether or not the evaluation value Eval satisfies the end condition (the evaluation value is equal to or less than a predetermined value, or the evaluation value Eval has converged after repeating the loop process). To do.

  If the end condition is not satisfied as a result of the determination in step S307, the process proceeds to step S308.

  In step S308, the optimization unit 138 performs processing for setting an optimum next output color to be given to the printer color prediction unit 133 and the like and updating the output color. Thereafter, the process returns to step S304, and the processes after step S304 are performed again based on the updated output color. For example, the quasi-Newton method is used as a search method for the next output color by the optimization unit 138.

  On the other hand, if the end condition is satisfied as a result of the determination in step S307, the process proceeds to step S309.

  In step S309, for example, the end determination unit 137 performs processing for saving (storing) the output color in the color conversion table 143 as the optimum value in LabPQR_in (n).

  Subsequently, in step S310, for example, the end determination unit 137 determines whether or not optimum output values have been determined for all grid points.

  As a result of the determination in step S310, if optimum output values have not been determined for all grid points, the process proceeds to step S311.

  In step S311, the color conversion table creation unit 130 (for example, the initial value setting unit 131) performs a process of updating to an unprocessed LabPQR value. Then, it returns to step S303 and performs the process after step S303 again based on the updated LabPQR value.

  On the other hand, as a result of the determination in step S310, when optimum output values are determined for all the grid points, the processing of the flowchart shown in FIG. The color conversion table 143 is created by the processing of the flowchart shown in FIG.

<Internal configuration of evaluation value calculation unit>
Next, the internal configuration of the evaluation value calculation unit 136 shown in FIG. 9 will be described.
FIG. 11 is a block diagram illustrating an example of an internal configuration of the evaluation value calculation unit 136 illustrated in FIG. 9.

  As shown in FIG. 11, the evaluation value calculation unit 136 includes a colorimetric color reproduction evaluation value calculation unit 1361, a spectral color reproduction evaluation value calculation unit 1362, a color material color characteristic 1363, a weighting factor database 1364, and a weighting factor setting. A unit 1365 and an evaluation value calculation unit 1366 are included.

  The colorimetric color reproduction evaluation value calculation unit 1361 has two sets of basics, a basic stimulus value (1) input from the grid point setting unit 134 and a basic stimulus value (2) input from the spectral information decomposition unit 120. Based on the stimulus value, a process for calculating an evaluation value related to colorimetric color reproduction is performed.

  The spectral color reproduction evaluation value calculation unit 1362 includes two sets of spectral assistance, that is, the spectral auxiliary coefficient (1) input from the lattice point setting unit 134 and the spectral auxiliary coefficient (2) input from the spectral information decomposition unit 120. Based on the coefficient, processing for calculating an evaluation value related to spectral color reproduction is performed.

  A color material color characteristic 1363 is a color characteristic of the color material included in the color recording apparatus in the image forming unit 150. For example, the color characteristic 1363 of the color material is a tristimulus value under a specific environmental light source represented by CIELAB, CIEXYZ, or the like, or a color value derived from the tristimulus value. The weighting coefficient database 1364 is a database that stores weighting coefficients for spectral color reproduction and colorimetric color reproduction, which are constructed in consideration of basic stimulus values and color material color characteristics 1363.

  The weighting factor setting unit 1365 refers to the weighting factor database 1364 and sets the weighting factors for spectral color reproduction and colorimetric color reproduction according to the basic stimulus value (1) input from the grid point setting unit 134. Perform the process.

  The evaluation value calculation unit 1366 uses the weighting coefficient set by the weighting coefficient setting unit 1365 for both evaluation values calculated by the colorimetric color reproduction evaluation value calculation unit 1361 and the spectral color reproduction evaluation value calculation unit 1362. And an evaluation value is calculated using the evaluation function 135.

Hereinafter, the operation of the evaluation value calculation unit 136 illustrated in FIG. 11 will be described.
FIG. 12 is a flowchart illustrating an example of a processing procedure in the operation of the evaluation value calculation unit 136 illustrated in FIG.

  First, in step S401, the colorimetric color reproduction evaluation value calculation unit 1361 performs a process of calculating a colorimetric color reproduction evaluation value related to colorimetric color reproduction (first calculation step). Here, the colorimetric color reproduction evaluation value calculation unit 1361 that performs the process of step S401 constitutes a first calculation unit.

Specifically, the colorimetric color reproduction evaluation value calculation unit 1361 has two sets of basics, the basic stimulus value (1) from the grid point setting unit 134 and the basic stimulus value (2) from the spectral information decomposition unit 120. The degree of coincidence (evaluation value) of the stimulus values is calculated by the following color difference formula (25).
Eval_Lab = ΔEab (25)

  In Equation (25), ΔEab is used in the colorimetric color reproduction evaluation value, but the color difference equation that can be used in Equation (25) is not limited to this. For example, ΔE94, ΔE00, or means similar to color difference may be used. That is, the colorimetric color reproduction evaluation value calculated by the colorimetric color reproduction evaluation value calculation unit 1361 is, for example, a color difference or a value derived from the color difference.

  Subsequently, in step S402, the spectral color reproduction evaluation value calculation unit 1362 performs a process of calculating a spectral color reproduction evaluation value related to the spectral color reproduction (second calculation step). Here, the spectral color reproduction evaluation value calculation unit 1362 that performs the processing of step S402 constitutes a second calculation unit.

Specifically, the degree of coincidence (evaluation value) of the two sets of spectral auxiliary coefficients of the spectral auxiliary coefficient (1) from the lattice point setting unit 134 and the spectral auxiliary coefficient (2) from the spectral information decomposition unit 120 is as follows. (26).
Eval_PQR = ΔPQR (26)

  In the equation (26), ΔPQR represents the Euclidean distance in the PQR space. In other words, the colorimetric color reproduction evaluation value calculated by the colorimetric color reproduction evaluation value calculation unit 1361 is, for example, a distance in a space constituted by spectral auxiliary coefficients or a value derived from the distance.

  Subsequently, in step S403, the weighting factor setting unit 1365 refers to the weighting factor database 1364, and according to the basic stimulus value (1) input from the lattice point setting unit 134, the colorimetric color reproduction and the spectral measurement are performed. A weighting coefficient α for controlling color reproduction weighting is set. Here, in the present embodiment, the weighting factor α is determined according to the color characteristics 1363 of the color material provided in the color recording apparatus (image output apparatus) in the image forming unit 150. Details of the method of setting the weighting factor α will be described later.

  Subsequently, in step S404, the evaluation value calculation unit 1366 calculates a weighting coefficient α set with the basic stimulus value as an input value for each of the colorimetric color reproduction evaluation value and the spectral color reproduction evaluation value. The used calculation is performed to calculate a weighted evaluation value (third calculation step). Here, the evaluation value calculation unit 1366 that performs the process of step S404 constitutes a third calculation unit.

Specifically, the evaluation value calculation unit 1366 calculates a weighted evaluation value by the following equation (27).
Eval = Eval_Lab + α (L * a * b *) × Eval_PQR (27)

  Here, the right side of the equation (27) corresponds to the evaluation function 135. In other words, the evaluation function 135 is set by the weighting coefficient α set with the colorimetric color reproduction evaluation value and the spectral color reproduction evaluation value, and the basic stimulus value as input values.

  When the process of step S404 is completed, the process of the flowchart of FIG. 12 ends, and the evaluation value Eval (Equation (27)) when the output color calculation unit 144 calculates the output color by the process of the flowchart of FIG. It is determined.

  Thereafter, the output color calculation unit 144 uses the weighted evaluation value to perform a process of calculating an output color when the color recording device (image output device) in the image forming unit 150 outputs an image (first). 4 calculation steps). The output color calculation unit 144 that performs this process constitutes a fourth calculation unit.

<Setting the weighting factor>
Next, the setting of the weighting factor α performed by the weighting factor setting unit 1365 in step S403 in FIG. 12 will be described.

  The color recording apparatus in the image forming unit 150 handled in the present embodiment includes RGB special color materials as CMYK color materials. Therefore, in the color region in which these color materials are distributed, a color region that can be reproduced by the RGB color material is generated in addition to the color realized by the combination of the CMY color materials. For example, in order to reproduce the blue region, it can be realized by mixing C and M color materials, or by using a relatively large amount of B color material.

  FIG. 13 is a schematic diagram illustrating an example of distribution positions of the spot color material region and the gray region in the CIELAB space. Specifically, FIG. 13 shows special color material regions 1301 to 1303 as special color material regions, and a gray region (achromatic color region) 1304 that can be reproduced with K color material as a gray region. It is shown.

  In these color regions shown in FIG. 13, since a plurality of combinations of color materials expressing tristimulus values in a specific observation environment are generated, a plurality of metameric pairs exist, which is suitable for performing spectral color reproduction. Color area. On the other hand, areas that are not included in the above color areas can be reproduced with only a combination of color materials, so that the degree of color matching when the observation environment is changed is low. In this embodiment, the weight coefficient α is set in consideration of the color characteristics of the color material.

  FIG. 14 is a schematic diagram illustrating an example of the distribution of the weighting factor α in the CIELAB space. Specifically, FIG. 14 shows an example of the distribution of weighting factors in a color recording apparatus having a special color material. Further, in FIG. 14, the distribution of the weighting factor at L * = 50 is shown in FIG. 14A as an a * b * cross-sectional view, and the weighting factor at L * = 20 is shown in FIG. Is shown as an a * b * cross-sectional view.

  The special color material regions 1301 to 1303 and the gray region 1304 shown in FIG. 13 have a plurality of metameric pairs and are relatively easy to realize spectral color reproduction. Therefore, the weight coefficient value is set to be relatively high. . On the other hand, for color regions where it is relatively difficult to realize spectral color reproduction, the specific gravity of colorimetric color reproduction is increased and the weight coefficient value is set relatively low. Further, the weight coefficient outside the color reproduction range is set not to be uniformly zero, but gradually changed so as not to cause discontinuous color reproduction near the boundary of the color reproduction range of the color recording apparatus.

  In the present embodiment, the weight coefficient distribution in the color recording apparatus including the RGB special color materials has been described. However, it is preferable that the weight coefficients are set differently depending on the color materials used. Also, a color recording apparatus having four color materials of CMYK that does not have a special color material can set a weighting factor based on the same technical idea.

  FIG. 15 is a schematic diagram showing an example in which the distribution of the weighting coefficient α is plotted in the CIELAB space in a color recording apparatus having four color materials of CMYK. Specifically, FIG. 15 shows an example of the distribution of weighting factors in a color recording apparatus that does not include a special color material. In FIG. 15, the distribution of the weighting factor at L * = 50 is shown in FIG. 15A as an a * b * cross-sectional view, and the weighting factor at L * = 20 is shown in FIG. Is shown as an a * b * cross-sectional view.

  Here, the weighting coefficient in the vicinity of the gray area is set relatively high, and the weighting coefficient value is gradually decreased toward the outside of the color reproduction range of the color recording apparatus.

  With the configuration as described above, smooth color reproduction is possible even with a change in the observation environment at the boundary between a color region where spectral color reproduction can be realized and a color region where spectral color reproduction cannot be realized.

<Output color calculation unit>
Next, processing of the output color calculation unit 144 in FIG. 1 will be described.
In the output color calculation unit 144 of FIG. 1, with reference to the color conversion table 143, the basic stimulus value (L * a * b * value) supplied from the basic stimulus value input terminal 141 and the spectral auxiliary coefficient input terminal 142. An output color for the supplied spectral auxiliary coefficient (PQR value) is calculated. More specifically, the output color is calculated by multidimensional interpolation processing using the output color stored in the grid point of the color conversion table 143 in the vicinity of the point of interest. In this case, multidimensional interpolation processing includes linear interpolation, cubic interpolation, and the like. In this embodiment, for example, the output color is calculated and determined using linear interpolation.

  In this embodiment, the color conversion table 143 having a size of 17 × 17 × 17 × 7 × 7 × 7 is mentioned as an example. However, the interval between lattice points, the definition area of the lattice points, and the number of lattice points are necessary. It can be changed accordingly. Further, the lattice point setting unit 134 may have a user interface and arbitrarily set the lattice point interval, the defined region of the lattice points, and the lattice points according to the user's specification.

<Relationship between weight coefficient change and color reproduction>
Next, the relationship between the change in weighting factor and color reproduction will be described.

  In order to verify the effectiveness of the image processing apparatus 100-1 in the present embodiment, the change in the weighting factor α for controlling the specific gravity of the colorimetric color reproduction and the spectral color reproduction, and the stimulus value output from the color recording apparatus Was evaluated by the method shown in FIG.

  FIG. 16 is a schematic diagram showing an example of fluctuations in stimulus values output from the color recording apparatus in the image forming unit 150 shown in FIG. Specifically, in FIG. 16, the stimulus values existing within the color reproduction range corresponding to the input stimulus value I located outside the color reproduction range of the color recording apparatus are plotted in a black dot group. The input stimulus value I has five LabPQR stimulus values having different PQR values, and each stimulus value holds the same CIELAB value. FIG. 16 illustrates the case where the weighting factor α is non-zero.

  FIG. 17 shows an example of how the input stimulus values located outside the color reproduction range of the color recording apparatus in the image forming unit 150 shown in FIG. 1 are mapped within the color reproduction range of the color recording apparatus at different weighting factors α. It is a schematic diagram which shows.

  In this example, all input stimulus values have CIELAB values of (L *, a *, b *) = (83, 0, 128), and each input stimulus value holds a different PQR value. Specifically, in FIG. 17, the distribution of the corresponding stimulus value when the weighting factor α is 0 is shown in FIG. 17A, and the corresponding stimulus when the weighting factor α is 1.8 is shown in FIG. The distribution of values is shown. Further, in FIG. 17, the distribution of the corresponding stimulus value when the weighting factor α is 5.4 is shown in FIG. 17C, and the corresponding stimulus when the weighting factor α is 9.0 is shown in FIG. The distribution of values is shown.

  As can be seen from FIG. 17, it can be confirmed that the distribution range of the corresponding stimulus value is increased as the weighting factor α is increased. If the weighting coefficient α = 0, all the corresponding stimulus values are mapped to the same point, and the same result as the conventional colorimetric color reproduction is obtained. Here, the distribution of the corresponding stimulus value is quantitatively expressed using the following equation (28).

  According to the equation (28), the combination of the corresponding stimulus values showing the largest color difference (CIEDE2000) is extracted from the combinations of the corresponding stimulus values, and the color difference in the combination is made to correspond to the distribution of the corresponding stimulus values. Here, the distributions of the corresponding stimuli when the weighting coefficient α is 0, 1.8, 5.4, and 9.0 are 0, 1.5, 3.2, and 4.6, respectively. When the weighting coefficient α is increased to increase the specific gravity of the spectral color reproduction, it can also be confirmed from this numerical value that the distribution range of the corresponding stimulus values is proportionally increased.

  If the distribution range of the corresponding stimulus value is small, the influence is small, but if the distribution range of the corresponding stimulus value becomes large, the adverse effect on the image quality cannot be ignored. In other words, when two sets of input stimulus values that are perceived as the same color under a specific viewing condition but have spectrally different properties are output by the color recording device, different colors are displayed in spite of the same viewing condition. It brings about the harmful effects perceived as

  Also, a plurality of input stimuli positioned outside the color reproduction range of the color recording apparatus using input stimulus values other than (L *, a *, b *) = (83, 0, 128) used in the above description. The same verification was performed for.

  FIG. 18 is a schematic diagram illustrating an example of the relationship between the average color difference between the input stimulus value and the corresponding stimulus value at different weighting factors, and the distribution of the corresponding stimulus value. In FIG. 18, the relationship between the average color difference between the input stimulus value and the corresponding stimulus value (see FIG. 16) and the distribution of the corresponding stimulus value at the six-stage weighting coefficient α is plotted.

  Specifically, 0, 1.8, 3.6, 5.4, 7.2, and 9.0 are used for the six-stage weight coefficient α. As can be seen from FIG. 18, even when the average color difference between the input stimulus value and the corresponding stimulus value is large, that is, when the input stimulus value is gradually away from the color reproduction range of the color recording apparatus, even with the same weighting factor. The distribution range of the corresponding stimulus value tends to increase. In order to alleviate the adverse effects of color reproduction that occur under the same observation conditions as described above, it is effective to set the distribution range of the corresponding stimulus values within a certain value. It is preferable that the weighting factor α is gradually set lower as the distance from the color reproduction range increases.

  So far, the case where the input stimulus value is located outside the color reproduction range of the color recording apparatus has been mentioned, but the same applies to the case where the input stimulus value is located within the color reproduction range of the color recording apparatus. I can say that.

  FIG. 19 is a schematic diagram illustrating an example of a distribution range of corresponding stimulus values within the color reproduction range of the color recording apparatus in the image forming unit 150 illustrated in FIG. In FIG. 19, the distribution range of the corresponding stimulus value within the color reproduction range of the color recording apparatus is shown by shading, and the bright region indicates that the distribution of the corresponding stimulus value is large. The white area is outside the color reproduction range of the color recording apparatus, and the distribution range of the corresponding stimulus value is not calculated. In FIG. 19, FIG. 19A shows a case where the weighting factor α is α = 5.4, and FIG. 19B shows a case where the weighting factor α is α = 9.0.

  From FIG. 19, it can be confirmed that the distribution range of the corresponding stimulus value varies greatly depending on the color region, even though the weighting factors are the same. In particular, it is known that the region having a large variation range corresponds to the color region where the special color material shown in FIG. 13 is not distributed. That is, in a color area where spot color materials are distributed, spectral color reproduction is actively performed by setting a relatively high weight coefficient, but in other areas where spot color materials are not distributed, the weight coefficient is set. Is set relatively low. In this way, by switching the specific gravity of spectral color reproduction and colorimetric color reproduction according to the color characteristics of the color material, image quality detriment that has occurred in regions where spectral color reproduction is difficult, that is, under the same observation conditions However, it is possible to reduce harmful effects perceived as different colors.

<Hardware configuration of image processing apparatus>
Next, the hardware configuration of the image processing apparatus 100-1 shown in FIG. 1 will be described.
FIG. 20 is a block diagram illustrating an example of a hardware configuration of the image processing apparatus according to the embodiment of the present invention.

  As illustrated in FIG. 20, the image processing apparatus 100 (the image processing apparatus 100-1) includes the hardware configurations of a CPU 2001, a ROM 2002, a RAM 2003, a storage device 2004, an interface (I / F) 2005, and an input device 2006. It is configured.

  The CPU 2001 performs various controls related to the image processing method performed in the image processing apparatus in accordance with a control program stored in the ROM 2002 or a control program loaded from the storage device 2004 to the RAM 2003.

  The ROM 2002 stores various parameters, a control program executed by the CPU 2001, and the like. The RAM 2003 provides a work area when the CPU 2001 executes various controls, and stores a loaded control program executed by the CPU 2001.

  The storage device 2004 is formed by, for example, a hard disk, a floppy (registered trademark) disk, a CD-ROM, a DVD-ROM, a memory card, or the like. When the storage device 2004 is a hard disk, various programs installed from a CD-ROM, a floppy (registered trademark) disk, and various data to be processed (image data, color conversion table, etc.) are stored.

  The I / F 2005 is connected to various image output devices such as a printer, for example. The input device 2006 is various input devices such as a mouse and a keyboard, and plays a role of a user interface.

  Here, the spectral image data input unit 110 in FIG. 1 includes, for example, a control program stored in the CPU 2001 and the ROM 2002 or the storage device 2004 in FIG. The spectral information decomposition unit 120, the color conversion table creation unit 130, and the color processing unit 140 in FIG. 1 are, for example, a control program stored in the CPU 2001 and the ROM 2002 or the storage device 2004 in FIG. It is composed of Further, the image forming unit 150 in FIG. 1 includes, for example, a control program, an I / F 2005, a storage device 2004, and the like stored in the CPU 2001 and the ROM 2002 or the storage device 2004 in FIG.

  In the present embodiment, the 6-dimensional LabPQR has been described as an example of the color space. However, the number of dimensions of the spectral space associated with CIELAB is not limited to 3 dimensions. For example, a four-dimensional color space composed of P values and CIELAB corresponding to the first principal component vector, and a five-dimensional color composed of PQ values and CIELAB corresponding to the first principal component vector and the second principal component vector. Applicable even in space. Furthermore, the present embodiment can be executed even when a 7-dimensional or higher color space including the fourth and subsequent principal component vectors is used.

  As described above, in the image processing apparatus 100-1 according to the first embodiment, the specific gravity between the spectral color reproduction and the colorimetric color reproduction in consideration of the color characteristics 1363 of the color material mounted on the color recording apparatus. To switch. According to this configuration, it is possible to realize smooth color reproduction even with respect to changes in the observation environment at the boundary between the color region where spectral color reproduction can be achieved and the color region where spectral color reproduction cannot be achieved. Furthermore, for color regions where spectral color reproduction cannot be realized, it is possible to output an image giving priority to colorimetric color reproduction.

  In addition, by using the color conversion table 143 based on the multidimensional spectral space LabPQR to calculate and determine the output color when the image is output by the color recording apparatus, it is possible to obtain an output image at high speed. is there.

(Second Embodiment)
Next, a second embodiment of the present invention will be described.

  In the first embodiment of the present invention, the output color calculation unit 144 uses the color conversion table 143 to determine the output color. On the other hand, in the second embodiment, the output color is determined by calculation without using the color conversion table.

<Functional Configuration of Image Processing Device (Color Processing Device)>
FIG. 21 is a block diagram illustrating an example of a functional configuration of an image processing apparatus according to the second embodiment of the present invention. Here, the image processing apparatus illustrated in FIG. 21 will be described as “image processing apparatus 100-2”. In FIG. 21, the same components as those in FIG. 1 are denoted by the same reference numerals, and detailed description thereof is omitted.

  As shown in FIG. 21, the image processing apparatus 100-2 is configured to have each functional configuration of a spectral image data input unit 110, a spectral information decomposition unit 120, a color processing unit 240, and an image forming unit 150. Yes. That is, the image processing apparatus 100-2 changes the function of the output color calculation unit 144 of the color processing unit 140 of the image processing apparatus 100-1 shown in FIG. 1 to become the output color calculation unit 241 of the color processing unit 240. The color conversion table 143 and the color conversion table creation unit 130 shown in FIG.

  The output color calculation unit 241 calculates and determines the output color provided in the image forming unit 150 based on the basic stimulus value and the spectral auxiliary coefficient input from the spectral information decomposition unit 120.

<Internal configuration of output color calculation unit>
Next, the internal configuration of the output color calculation unit 241 shown in FIG. 21 will be described.
FIG. 22 is a block diagram illustrating an example of an internal configuration of the output color calculation unit 241 illustrated in FIG. Here, in FIG. 22, the same reference numerals are given to the same configurations as the internal configuration of the color conversion table creating unit 130 illustrated in FIG. 9, and the detailed description thereof is omitted.

  As shown in FIG. 22, the output color calculation unit 241 includes an initial value setting unit 131, a loop processing unit 132, a printer color prediction unit 133, a grid point setting unit 134, an evaluation value calculation unit 2411, an end determination unit 2412, and An optimization unit 138 is included. The evaluation value calculation unit 2411 is configured to calculate an evaluation value using the evaluation function 135. Further, the output color calculation unit 241 is provided with a basic stimulus value input terminal 141, a spectral auxiliary coefficient input terminal 142, and an output terminal 145 shown in FIG.

  That is, the output color calculation unit 241 changes the functions of the evaluation value calculation unit 136 and the end determination unit 137 of the color conversion table creation unit 130 shown in FIG. 9 to obtain an evaluation value calculation unit 2411 and an end determination unit 2412, respectively. 9 is a form in which the grid point setting unit 134 shown in FIG. Also, the internal configuration of the evaluation value calculation unit 2411 is the same as the internal configuration of the evaluation value calculation unit 136 shown in FIG.

  Furthermore, the output color calculation unit 241 has a configuration in which a basic stimulus value input terminal 141, a spectral auxiliary coefficient input terminal 142, and an output terminal 145 are added to the color conversion table creation unit 130 shown in FIG.

Hereinafter, the operation of the output color calculation unit 241 illustrated in FIG. 22 will be described.
FIG. 23 is a flowchart illustrating an example of a processing procedure in the operation of the output color calculation unit 241 (further, the spectral information decomposition unit 120) illustrated in FIG.

  First, in step S501, the evaluation value calculation unit 2411 inputs a basic stimulus value (L * a * b * value) from the basic stimulus value input terminal 141, and a spectral auxiliary coefficient (PQR value) from the spectral auxiliary coefficient input terminal 142. I do. Specifically, the basic stimulus value and the spectral auxiliary coefficient input here are values that are first input from the spectral information decomposition unit 120 in the processing target pixel.

  Subsequently, in step S502, as in step S302 in FIG. 10, the initial value setting unit 131 first performs processing for designating a lattice point LabPQR_in (n) (where n is a target lattice point) to be calculated.

  Subsequently, in step S503, the initial value setting unit 131 sets an appropriate output color as an initial value when performing the optimum processing, similarly to step S303 in FIG.

  Subsequently, in step S504, the printer color prediction unit 133 performs processing for estimating the spectral reflectance of the print corresponding to the output color, as in step S304 of FIG.

  Subsequently, in step S505, the spectral information decomposing unit 120 receives the spectral reflectance of the print predicted by the printer color predicting unit 133 as in step S305 of FIG. Processing to decompose into values and spectral auxiliary coefficients.

  Subsequently, in step S506, the evaluation value calculation unit 136 first receives the basic stimulus value and the spectral auxiliary coefficient obtained in the process of step S505 from the spectral information decomposition unit 120. Then, the evaluation value calculation unit 136 compares the basic stimulus value and the spectral auxiliary coefficient input in step S501 with the received basic stimulus value and spectral auxiliary coefficient, respectively, and uses the evaluation function 135 to set two sets of LabPQR. Evaluation is performed by calculating an evaluation value Eval indicating the degree of coincidence of the values. This specific evaluation method is the same as in the first embodiment.

  Subsequently, in step S507, as in step S307 of FIG. 10, the end determination unit 137 determines that the evaluation value Eval is an end condition (the evaluation value is equal to or less than a predetermined value, or the evaluation value Eval converges after repeating the loop process). Judgment is satisfied.

  If the end condition is not satisfied as a result of the determination in step S507, the process proceeds to step S508.

  When the processing proceeds to step S508, the optimization unit 138 performs processing for setting the optimum next output color to be given to the printer color prediction unit 133 and the like and updating the output color, as in step S303 of FIG. Thereafter, the process returns to step S504, and the processes after step S504 are performed again based on the updated output color.

  On the other hand, if it is determined in step S507 that the end condition is satisfied, the process proceeds to step S509.

  In step S509, for example, the optimization unit 138 outputs the output color to the image forming unit 150 via the output terminal 145 as the optimum value in LabPQR_in (n).

  Subsequently, in step S510, for example, the end determination unit 137 determines whether or not the output color calculation processing has ended for all the pixels.

  If the result of determination in step S510 is that output color calculation processing has not been completed for all pixels (ie, there is an unprocessed image), processing proceeds to step S511.

  In step S511, the output color calculation unit 241 performs a process of updating the output pixel position based on the unprocessed pixel. Thereafter, the process returns to step S501, and the processes after step S501 are performed again based on the updated pixel position.

  On the other hand, as a result of the determination in step S510, when the output color calculation processing is completed for all pixels, the processing of the flowchart shown in FIG.

  In the present embodiment, the 6-dimensional LabPQR has been described as an example of the color space. However, the number of dimensions of the spectral space associated with CIELAB is not limited to 3 dimensions. For example, a four-dimensional color space composed of P values and CIELAB corresponding to the first principal component vector, and a five-dimensional color composed of PQ values and CIELAB corresponding to the first principal component vector and the second principal component vector. Applicable even in space. Furthermore, the second embodiment can be executed even when a 7-dimensional or higher color space including the fourth and subsequent principal component vectors is used.

  As described above, according to the image processing apparatus 100-2 according to the second embodiment, since the output color is calculated and determined by calculation without using the color conversion table 143, interpolation is performed when calculating the output color. No processing occurs and it is possible to obtain a more accurate output value. Furthermore, since the color conversion table 143 is not held, there is an advantage that a memory required for the color conversion table can be deleted.

(Third embodiment)
Next, a third embodiment of the present invention will be described.
In the first embodiment (the same applies to the second embodiment), the distribution of weighting factors as shown in FIGS. 14 and 15 is shown as an example. However, the distribution of weighting factors is not limited to this. Absent. For example, when the combination of color materials used in the color recording apparatus differs depending on the recording mode, a mode in which the setting of the weighting factor is varied depending on each recording mode can achieve the object of the present invention and can be applied. It is. Furthermore, the weight coefficient setting unit 1365 may have a user interface, and the weight coefficient distribution may be arbitrarily set according to the user designation.

(Fourth embodiment)
Next, a fourth embodiment of the present invention will be described.
In the first embodiment (the same applies to the second and third embodiments), the spectral basic stimulus calculation function 124 for calculating the spectral basic stimulus from the basic stimulus value is determined, including the spectral reflectance of a printed matter or the like. Although the data group was used, it is not limited to this. For example, as a method of determining the spectral fundamental stimulus calculation function 124, a form in which calculation is performed using a color matching function and an environmental light source without using a specific data group may be used.

  In the first embodiment (the same applies to the second and third embodiments), an example in which CIELAB is used as a basic stimulus value has been described, but other basic stimuli may be used. For example, it is possible to use tristimulus values under a specific environmental light source typified by CIELV, CIEXYZ, etc., or color values (for example, RGB) calculated derived therefrom. Furthermore, the present invention is also applicable when a color appearance model such as CIECAM97, CIECAM02 or the like that takes the influence of appearance into account is adopted as the basic stimulus value.

(Other embodiments of the present invention)
For example, the CPU (2001) of the computer stores the respective units related to the functional configurations of FIG. 1 and FIG. 21 constituting the image processing apparatus 100 according to each embodiment of the present invention in the storage medium (2002 or 2004). This can be realized by executing the program. 3, FIG. 5, FIG. 10, FIG. 12, and FIG. 23, which show the image processing method by the image processing apparatus 100, are stored in a storage medium (2002 or 2004) by a CPU (2001) of a computer, for example. This can be realized by executing the program. This program (computer program) and a computer-readable recording medium storing the program are included in the present invention.

Claims (16)

Decomposition means for decomposing spectral information in each pixel of the image into a basic stimulus value and a spectral auxiliary coefficient according to a spectral error when spectral information is estimated from the basic stimulus value;
First calculation means for calculating an evaluation value relating to colorimetric color reproduction based on the basic stimulus value obtained by the decomposition by the decomposition means;
Second calculation means for calculating an evaluation value relating to spectral color reproduction based on the spectral auxiliary coefficient obtained by the decomposition by the decomposition means;
Weighting coefficient setting means for setting a weighting coefficient in accordance with the color characteristics of the color material provided in the image output apparatus for outputting the image;
Third calculation means for calculating a weighted evaluation value by performing an operation using the weighting coefficient on the evaluation value related to the colorimetric color reproduction and the evaluation value related to the spectral color reproduction;
An image processing apparatus comprising: a fourth calculation unit configured to calculate an output color when the image output apparatus outputs the image using the weighted evaluation value. Decomposition means for decomposing spectral information in each pixel of the image into a basic stimulus value and a spectral auxiliary coefficient according to a spectral error when spectral information is estimated from the basic stimulus value;
First calculation means for calculating an evaluation value relating to colorimetric color reproduction based on the basic stimulus value obtained by the decomposition by the decomposition means;
Second calculation means for calculating an evaluation value relating to spectral color reproduction based on the spectral auxiliary coefficient obtained by the decomposition by the decomposition means;
Weighting coefficient setting means for setting a weighting coefficient in accordance with the color characteristics of the color material provided in the image output apparatus for outputting the image;
Third calculation means for calculating a weighted evaluation value by performing an operation using the weighting coefficient on the evaluation value related to the colorimetric color reproduction and the evaluation value related to the spectral color reproduction;
A color conversion table storing basic stimulus values and spectral auxiliary coefficients related to the weighted evaluation values;
An image processing apparatus comprising: a fourth calculation unit configured to calculate an output color when the image output apparatus outputs the image using the color conversion table.   The image processing apparatus according to claim 1, wherein the evaluation value related to the colorimetric color reproduction is a color difference or a value derived from the color difference.   The image processing apparatus according to claim 1, wherein the evaluation value related to the spectral color reproduction is a distance in a space constituted by the spectral auxiliary coefficient or a value derived from the distance.   5. The image processing apparatus according to claim 1, wherein the color characteristic of the color material is a tristimulus value under a specific environmental light source or a color value derived from the tristimulus value. 6. .   The weighting factor setting unit sets the weight of the spectral color reproduction relatively higher than the colorimetric color reproduction for the color region corresponding to the color characteristic of the color material when setting the weighting factor. The image processing apparatus according to claim 1, wherein the image processing apparatus is an image processing apparatus.   The weight coefficient setting means sets the weight of the spectral color reproduction relative to the colorimetric color reproduction relative to the color area corresponding to the achromatic color of the color material when setting the weight coefficient. The image processing apparatus according to claim 1, wherein the image processing apparatus is set high.   The weighting factor setting means sets the weight of the spectral color reproduction relatively lower than the colorimetric color reproduction near the boundary of the color reproduction range of the image output device when setting the weighting factor. The image processing apparatus according to claim 1, wherein the image processing apparatus is an image processing apparatus.   9. The weight coefficient setting unit, when setting the weight coefficient, performs setting according to an input basic stimulus value in addition to the color characteristics of the color material. The image processing apparatus according to claim 1.   The image processing apparatus according to claim 1, wherein the weighting factor setting unit varies the weighting factor according to color characteristics of the color material.   The image processing apparatus according to claim 1, wherein the weighting factor setting unit sets the weighting factor via a user interface.   The image processing apparatus according to claim 1, wherein the spectral auxiliary coefficient corresponds to a main component of the spectral error.   The image processing apparatus according to claim 1, wherein the basic stimulus value is a tristimulus value under a specific environmental light source or a color value derived from the tristimulus value. A decomposition step for decomposing the spectral information in each pixel of the image into a basic stimulus value and a spectral auxiliary coefficient corresponding to the spectral error when the spectral information is estimated from the basic stimulus value;
A first calculation step of calculating an evaluation value related to colorimetric color reproduction based on the basic stimulus value obtained by the decomposition in the decomposition step;
A second calculation step of calculating an evaluation value related to spectral color reproduction based on the spectral auxiliary coefficient obtained by the decomposition in the decomposition step;
A weighting factor setting step for setting a weighting factor according to the color characteristics of the color material provided in the image output apparatus that outputs the image;
A third calculation step of calculating a weighted evaluation value by performing an operation using the weighting coefficient on the evaluation value related to the colorimetric color reproduction and the evaluation value related to the spectral color reproduction;
And a fourth calculation step of calculating an output color when the image output device outputs the image using the weighted evaluation value. A decomposition step for decomposing the spectral information in each pixel of the image into a basic stimulus value and a spectral auxiliary coefficient corresponding to the spectral error when the spectral information is estimated from the basic stimulus value;
A first calculation step of calculating an evaluation value related to colorimetric color reproduction based on the basic stimulus value obtained by the decomposition in the decomposition step;
A second calculation step of calculating an evaluation value related to spectral color reproduction based on the spectral auxiliary coefficient obtained by the decomposition in the decomposition step;
A weighting factor setting step for setting a weighting factor according to the color characteristics of the color material provided in the image output apparatus that outputs the image;
A third calculation step of calculating a weighted evaluation value by performing an operation using the weighting coefficient on the evaluation value related to the colorimetric color reproduction and the evaluation value related to the spectral color reproduction;
A storage step of storing the basic stimulus value and spectral auxiliary coefficient relating to the weighted evaluation value as a color conversion table;
And a fourth calculation step of calculating an output color when the image output device outputs the image using the color conversion table.   A computer program for causing a computer to function as each unit included in the image processing apparatus according to any one of claims 1 to 13.

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