JP2009038591A - Color processor, and method thereof - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To convert a spectral image into signal values to be outputted at high speed. <P>SOLUTION: An LUT storage part 16 stores a color conversion table having a plurality of pieces of table data showing a relation between spectral image data and output values. An LUT buffer 17 stores table data used for color conversion. An inputting part 13 inputs the spectral image data. A color conversion processing part 18 performs the color conversion of the spectral image data. In this case, the color conversion processing part 18 uses the table data stored in the LUT buffer 17 to perform color conversion of the spectral image data, and when the spectral image data can not be subjected to the color conversion by using the table data stored in the LUT buffer 17, the spectral image data is subjected to the color conversion by using another table data stored in the LUT storage part 16. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to spectral color reproduction.

  When displaying or printing the image data acquired from the image input device on the image output device, in order to perform faithful color reproduction, based on the three primary color theory, using conditional color, the input tristimulus values and output Colorimetric color reproduction that matches tristimulus values is performed. The CIEXYZ color system and CIELab color system defined by the CIE (International Commission on Illumination) are used as the space where the tristimulus values are quantified.

  FIG. 1 is a block diagram showing the configuration of a general colorimetric color reproduction system.

  An RGB image acquired by an image input device such as a digital camera is subjected to image processing in a device-independent XYZ space or L * a * b * space, and is output to an image output device as an RGB or CMY image.

  Since the XYZ and L * a * b * values are affected by the illumination light source, even if the subject color and the reproduced color match the XYZ and L * a * b * values under a certain illumination light source, the illumination light source Different values result in different values. As a result, the subject color and the reproduced color are perceived as different colors. Therefore, in the field of color management, it is desired to realize spectral color reproduction as a more faithful color reproduction method. According to spectral color reproduction, the spectral reflectance itself of an object that does not include an illumination light source is reproduced as color information. Therefore, the subject color and the reproduced color can be made equal under an arbitrary illumination light source.

  Multiband input / output technology is expected as a technology for realizing spectral color reproduction. Colorimetric color reproduction is based on the three primary colors such as RGB and CMY, whereas multiband input / output technology obtains object color information using four or more primary colors and reproduces the spectral reflectance. Technology. As an image input device, development of a multiband camera having four or more bands is underway (for example, Patent Document 1). In addition, development of multiband output devices has been promoted by the development of multi-primary projectors and the practical use of multi-primary printers (for example, Patent Document 2).

  FIG. 2 is a block diagram showing a spectral color reproduction system using a multiband input / output device.

  The spectral color reproduction system captures a subject with a multiband input device and acquires image data having a spectral reflectance as a value of each pixel (hereinafter, spectral image or spectral image data). Next, image storage, processing, and transmission are performed in a spectral space that does not depend on the device and the illumination light source. The spectral image is reproduced by a multiband output device.

  When the visible light region 380 to 730 nm is sampled every 10 nm, the spectral reflectance becomes one-dimensional 36-dimensional data, and the spectral image has a very large amount of data. In addition, in order to reproduce a spectral image using a multiband output device, an error between the spectral information indicating the relationship between the output signal of the multiband output device and the spectral reflectance and the spectral reflectance of the spectral image data (input). It is necessary to find an output signal that minimizes for all pixels of the input image. However, enormous calculation time is required to search all the output signals of the multiband output device and determine the output signal.

For example, in an inkjet printer that uses six color materials of cyan, magenta, yellow, black, red, and green, and there are 256 output signals corresponding to each color, the combination of output signals is 256 ⁶ ≈ approximately 28 trillion pairs become. In other words, for each pixel, an output signal with the smallest spectral reflectance error must be determined from about 28 trillion sets of output signals. If processing for determining an output signal is performed for each pixel of a spectral image, an extremely long processing time is required.

  Patent Document 3 discloses a method of reducing a data amount and reproducing a spectral image with a smaller processing amount. Patent Document 3 performs principal component analysis on an input spectral image and calculates a principal component vector of spectral reflectance data. Spectral reflectance data is represented using a principal component vector and its addition rate (hereinafter referred to as a principal component coefficient). Further, the principal component coefficients are changed at equal intervals, the corresponding output signals are obtained, and a lookup table (hereinafter referred to as LUT) that associates the principal component coefficients with the output signals is created. If a LUT in which principal component coefficients as input data are arranged at equal intervals is created, the output signal value can be calculated at high speed when the principal component coefficients are input to the LUT. Therefore, the input spectral image can be converted into an output signal at high speed.

  However, the method disclosed in Patent Document 3 requires an enormous amount of time to create an LUT that obtains an output signal for each equally spaced principal component coefficient.

  There is also a technique for shortening the time required for the search using a printer model and an optimization technique (see Non-Patent Document 1). However, the search time and the accuracy of the optimal solution are in a trade-off relationship, and if the spectral image is handled and the search time is given priority, the accuracy of the optimal solution decreases.

JP 2001-086383 A JP 2001-054131 A JP 2002-254708 JP D. Tzeng `` Spectral-based color separation algorithm development for multiple-ink color reproduction '' Ph.D. Dissertation, R.I.T., Rochester, NY, 1999

  An object of the present invention is to convert spectral image data into signal values to be output at high speed.

  The present invention has the following configuration as one means for achieving the above object.

  The color processing according to the present invention is color processing for color-converting spectral image data into output values using a color conversion table, and the color conversion having a plurality of table data indicating the relationship between the spectral image data and the output values A table is stored in a storage unit, the table data used for the color conversion is held in a holding unit, spectral image data is input, and the spectral image data is color-converted. If the spectral data is subjected to color conversion using the table data held in the means, and the spectral image data cannot be color converted using the table data held in the holding means, The spectral image data is color-converted using other table data stored in the storage means.

  According to the present invention, spectral image data can be converted to a signal value to be output at high speed.

  Hereinafter, color processing according to an embodiment of the present invention will be described in detail with reference to the drawings.

[Overview]
In the first embodiment, an input spectral image is converted into an output signal using table data of a color conversion table (LUT). From the color distribution characteristics of a photographic image, each pixel of the photographic image does not exist independently, but has a correlation between adjacent pixels. Therefore, once the grid information (table data) in the LUT used to calculate the output signal value is stored in the memory, the output signal value is calculated at high speed by preferentially searching within the solid that the grid constitutes. To do.

[Device configuration]
FIG. 3 is a block diagram illustrating a configuration example of the spectral color reproduction device 10 according to the first embodiment.

  The input unit 13 inputs spectral data, spectral image data, principal component vector data, spectral reflectance data, or the like. The image buffer 14 stores image data to be output and image data being processed. The principal component vector storage unit 15 stores principal component vectors. The LUT storage unit 16 stores LUT data having a plurality of table data. The LUT buffer 17 is a memory for storing grid information of the LUT used for calculation. The data amount reducing unit 18 reduces the data amount of the spectral reflectance data.

  The color conversion processing unit 19 converts the input spectral reflectance data into an output signal. The color gamut processing unit 20 processes colors outside the color gamut of the image output device 24. The output unit 12 outputs the processed image data to a high-speed data bus 28 such as a serial bus.

  Each of the above components is connected to each other via the system bus 11 and is connected to a control unit 29 such as a one-chip microcontroller that controls the entire spectral color reproduction device 10.

  The data bus 28 is an interface (I / F) for an input device 21 such as a multiband camera, a spectral reflectance measuring device 22, a storage device 23 such as a hard disk or a disk drive, an output device 24 such as a printer, a display 25, or a network 27. ) Connect 26, etc.

[processing]
FIG. 4 is a flowchart showing processing of the spectral color reproduction device 10.

  The control unit 29 inputs a spectral image from the input device 21, the storage device 23, the server on the network 27, or the like through the input unit 13 (S201).

  Next, as will be described in detail later, the control unit 29 reduces the data amount of the input spectral image by the data amount reduction unit 18 and stores it in a predetermined area of the image buffer 14 (S202).

  Next, as will be described in detail later, the control unit 29 calculates an LUT for converting the spectral image stored in the image buffer 14 into an output signal for the output device 24, and stores the LUT in the LUT storage unit 16 (S203). ).

  Next, the control unit 29 sets the variable i indicating the target pixel to 1 (S204). Here, the image data is handled as one-dimensional array data. Then, the i-th pixel data is extracted from the spectral image stored in the image buffer 14 (S205).

  Next, as will be described in detail later, the control unit 29 converts the extracted pixel data into an output signal value by the color conversion processing unit 19 (S206), and stores the output signal value in a predetermined area of the image buffer 14 ( S207).

  Then, the control unit 29 determines whether or not all pixel data of the spectral image has been converted into an output signal value (S208), and if not completed, increments the variable i (S209), and the process proceeds to step S205. return. When the conversion is completed, the output signal value stored in the image buffer 14 is output as output image data via the output unit 12 (S210). The output destination of the output image data is the storage device 23, the output device 14, the display 25, the server on the network 27, or the like.

[Principal component vector]
Here, the principal component vectors stored in the principal component vector storage unit 15 will be described.

  The principal component vector is calculated by performing principal component analysis on the spectral reflectance data. Spectral reflectance data for principal component analysis may include typical spectral reflectances of color charts such as GretagMacbeth's ColorChecker, input spectral reflectance, or spectral reflectance of patches output by an output device. However, the present invention is not limited to these, and any spectral reflectance data corresponding to the application may be used.

  In this embodiment, principal component analysis is performed on typical spectral reflectance data of 24 ColorChecker colors and stored in the principal component vector storage unit 15. FIG. 5 is a diagram showing first to sixth principal component vectors among principal component vectors calculated by principal component analysis in the present embodiment.

[Reduction of data volume]
The process (S202) of the data amount reduction unit 18 will be described. The data amount reducing unit 18 uses the principal component vectors stored in the principal component vector storage unit 15 to reduce the spectral reflectance data of each pixel of the spectral image. Spectral reflectance is expressed by equation (1).
o = Σ _{i = 1} ⁿ a _i v _i + m (1)
Where i = 1 to n, n is the number of dimensions used (equal to the number of input dimensions),
a _i is the i-th principal component coefficient,
v _i is the i-th principal component vector stored in the principal component vector storage unit 15; v _i = [ν _{380, i} ν _{390, i} … ν _{730, i} ] ^T ,
m is the average spectral reflectance of spectral reflectance data, m = [m ₃₈₀ m ₃₉₀ … m ₇₃₀ ] ^T ,
o is the spectral reflectance, o = [o ₃₈₀ o ₃₉₀ … o ₇₃₀ ] ^T ,
T represents a transposed matrix.

  FIG. 6 is a diagram showing the relationship between the number of dimensions and the contribution rate when principal component analysis is performed on the spectral reflectance data of ColorChecker 24 colors.

  The contribution rate is a ratio indicating how much the principal component vector calculated by the principal component analysis has an information amount with respect to the spectral reflectance data. As shown in FIG. 6, the contribution ratio of the first principal component vector is the largest, and the contribution ratio decreases as it becomes the second and third. In other words, the input spectral reflectance can be approximated with high accuracy without losing much information even when the principal component vector is lower than the dimension number n of the input. Further, as shown in FIG. 6, since the contribution ratio after the seventh principal component vector is substantially zero, the spectral reflectance of 24 ColorChecker colors can be reproduced by using up to the sixth principal component vector.

Next, a method for calculating the principal component coefficient a _i will be described. As an example, the case where the input spectral reflectance is approximated using the first to sixth principal component vectors will be described. In this case, Expression (1) can be expressed as Expression (2) using a matrix.
┌ ┐ ┌ ┐┌ ┐ ┌ ┐
│o ₃₈₀ │ │ν _{380, 1} ν _{380, 2} … ν _{380, 6} ││a ₁ │ │m ₁ │
│o ₃₉₀ │ │ν _{390, 1} ν _{390, 2} … ν _{390, 6} ││a ₂ │ │m ₂ │
│ │ = │ │ ： ： ││ ： │ + │ ： │… (2)
│o ₇₃₀ │ │ν _{730, 1} ν _{730, 2} … ν _{730, 6} ││a ₆ │ │m ₆ │
└ ┘ └ ┘└ ┘ └ ┘
Where o = [o ₃₈₀ o ₃₉₀ … o ₇₃₀ ] ^T ,
a = [a ₁ a ₂ … a ₆ ] ^T ,
┌ ┐
│ν _{380, 1} ν _{380, 2} … ν _{380, 6} │
│ν _{390, 1} ν _{390, 2} … ν _{390, 6} │
V = │::: │
│ν _{730, 1} ν _{730, 2} … ν _{730, 6} │
└ ┘
Then, it can be expressed as equation (3).
0 = Va + m (3)

The principal component coefficient a can be obtained from Equation (4) using a pseudo inverse matrix.
a = (V ^T V) ^-1 V ^T (o-m) (4)

Therefore, the principal component coefficient a = [a ₁ a ₂ ... A ₆ ] ^T can be calculated. By using this principal component coefficient a, the data amount can be reduced from 36-dimensional data spectral reflectance o to 6-dimensional principal component coefficient. Although an example using a six-dimensional principal component vector has been described above, an arbitrary-dimensional principal component coefficient a can be calculated by the same processing for an arbitrary number of dimensions.

[Calculation of LUT]
FIG. 7 is a flowchart for explaining calculation of the LUT (S203) by the control unit 29.

  As will be described in detail later, the control unit 29 reads the spectral reflectance data output from the output device 24 (S301), and reduces the data amount of the read spectral reflectance data (S302). Then, the spectral reflectance data with the reduced data amount is stored in the LUT storage unit 16 in a format to be described later (S303).

Spectral Reflectance Data The spectral reflectance data read in step S301 is, for example, spectral reflectance data that can be output by the output device 24. Assume that the output device 24 is an inkjet printer that uses six color materials of cyan C, magenta M, yellow Y, black K, red R, and green G. The output signal level of each color is set to 256 levels of 0, 1,. In this case, there are about 28 trillion combinations of output signals as described above. However, in practice, there is an upper limit of the amount of ink that can be driven onto the paper, and not all combinations are possible, and it is not practical to measure the spectral reflectance of all these combinations. Therefore, C, M, select Y, K, R, the 0,64,128,192,255 respectively from the output signal values of G, to prepare the data for 5 ⁶ = 15,625 pairs minute. Then, 15,625 sets of color patches are printed on the output device 24, and the spectral reflectance data of each color patch is measured using the spectral reflectance measuring device 22.

Saving to LUT FIG. 8 is a diagram showing an example of an LUT format for saving spectral reflectance data. That is, 15,625 sets of LUTs describing correspondence relationships between C, M, Y, K, R, and G output signal values and principal component coefficient values up to six dimensions are stored in the LUT storage unit 16.

  In addition, although the example which calculates LUT was demonstrated, you may read LUT calculated beforehand. The LUT format may be any format as long as the correspondence between the principal component coefficients and the output signal values can be shown.

[LUT buffer]
In the present embodiment, tetrahedral interpolation in a multidimensional space is used as an interpolation method when calculating output signal values between lattice points of the LUT. The tetrahedral interpolation in the multidimensional space is an extension of the tetrahedral interpolation in the three-dimensional space to multidimensional. A solid used for tetrahedral interpolation in a multidimensional space is called a multidimensional tetrahedron.

  As described above, particularly in a photographic image, adjacent pixels have a correlation with each other. Therefore, the multidimensional tetrahedron once used for calculating the output signal value is likely to be reused. Therefore, the grid information of the multidimensional tetrahedron used once for the calculation is held in the LUT buffer 17, and the multidimensional tetrahedron in the LUT buffer 17 is searched preferentially in the subsequent processing. In this way, the output signal value calculation (interpolation process) can be speeded up.

  For the multi-dimensional tetrahedron stored in the LUT buffer 17, first use the multi-dimensional tetrahedron used for the previous calculation (calculation of the previous pixel), and then calculate the surrounding pixels. The search is performed in the order of the multidimensional tetrahedrons.

  Alternatively, grid information of a multidimensional tetrahedron considered to be frequently used (high priority) can be held in the LUT buffer 17 in advance. For example, grid information of the multidimensional tetrahedron used when inputting the spectral reflectance of 24 ColorChecker colors representing typical colors in the natural world is stored in advance in the LUT buffer 17 as grid information of the multidimensional tetrahedron having a high priority. Just keep it. Also, as a pre-processing, a histogram of spectral reflectance of each pixel of the spectral image is created, and the grid information of the multidimensional tetrahedron used when the high frequency spectral reflectance is input, the multidimensional tetrahedron with high priority The grid information may be stored in the LUT buffer 17 in advance.

[Color conversion processing]
FIG. 9 is a flowchart showing the color conversion processing (S206) of the color conversion processing unit 19.

  As will be described in detail later, the color conversion processing unit 19 performs interpolation processing using the grid information of the multidimensional tetrahedron stored in the LUT buffer 17 (S401), and the spectral reflectance data of the pixels of the input spectral image It is determined whether or not the output signal value corresponding to can be calculated (S402). If the output signal value can be calculated, the process ends.

  If the output signal value cannot be calculated, the color conversion processing unit 19 performs an interpolation process using the LUT stored in the LUT storage unit 16 (details will be described later) (S404). It is determined whether the output signal value corresponding to the rate data has been calculated (S405). If the output signal value can be calculated, the process ends.

  If the output signal value cannot be calculated, the color conversion processing unit 19 passes the spectral reflectance data input to the color gamut processing unit 20 and outputs the output signal value closest to the spectral reflectance data in the output color gamut. Out-of-gamut processing is performed to acquire (S405).

Interpolation Process FIG. 10 is a flowchart showing the interpolation process (S401, S403).

  The color conversion processing unit 19 selects a multidimensional tetrahedron (S501), and details will be described later, but determines whether the input spectral reflectance data exists in the selected multidimensional tetrahedron (internal / external determination) (S502). If there is no spectral reflectance data in the selected multidimensional tetrahedron, it is determined whether or not the determination in step S502 has been performed for all the multidimensional tetrahedrons stored in the LUT buffer 17 or the LUT storage unit 16 (S503). If there is an undetermined multidimensional tetrahedron, the process returns to step S501. If there is no undetermined multidimensional tetrahedron, the process is terminated.

  When a multidimensional tetrahedron including spectral reflectance data is found, the output signal value is calculated by interpolation processing and output signal value is output (S504), as will be described in detail later. Then, in order to increase the priority of the multidimensional tetrahedron used for the interpolation process, the grid information of the multidimensional tetrahedron stored in the LUT buffer 17 is updated (S505), and the process ends.

Inside / Outside Determination In this embodiment, the input to the LUT is the principal component coefficient a, and the output from the LUT is the output signal value. FIG. 11 is a diagram showing the arrangement of LUT lattice points in the principal component coefficient space, and FIG. 12 is a diagram showing LUT lattice points in the output signal space. In the principal component coefficient space, the lattices are arranged at unequal intervals. Therefore, it cannot be easily determined in which multidimensional tetrahedron the principal component coefficient a is included. The value (input point) of the principal component coefficient a can be expressed by Equation (5).
↑ OP = a ₀・ ↑ Q ₀ Q ₁
+ a ₁・ ↑ Q ₀ Q ₂
+…
+ a _n-1・ ↑ Q ₀ Q _n-1
+ ↑ OQ ₀ … (5)
Where Q ₀ (q _{0, 0} , q _{0, 1} ,…, q _{0, n-1} ),…, Q _n-1 (q _{n-1, 0} , q _{n-1, 1} ,…, q _{n-1, n-1} ) is each vertex of the multidimensional tetrahedron,
O (0, 0,…, 0) is the origin,
P (p ₀ , p ₁ ,…, p _n-1 ) is the input point,
↑ represents a vector

Further, the necessary condition for the input point P to be present in the multidimensional tetrahedron is expressed by Expression (6).
0 ≤ a ₀ + a ₁ +… + a _n-1 ≤ 1
And
a ₀ ≥ 0, a ₁ ≥ 0, ..., a _n-1 ≥ 0 ... (6)

Expression (5) can be represented by a matrix like Expression (7).
[p ₀ p ₁ … p _n-1 ]
= [q _{1, 0} -q _{0, 0} q _{1, 1} -q _{0, 1} … q _{1, n-1} -q _{0, n-1} ] [a ₀ a ₁ … a _n-1 ] ^T
+ [q _{0, 0} q _{0, 1} … q _{0, n-1} ]… (7)

Next, [a ₀ a ₁ ... A _n−1 ] ^T can be calculated from the equation (8) by regression analysis.
[a ₀ a ₁ … a _n-1 ] ^T
_{= ([Q 1, 0 -q} 0, 0 q 1, 1 -q 0, 1 ... q 1, n-1 -q 0, n-1] T
[q _{1, 0} -q _{0, 0} q _{1, 1} -q _{0, 1} … q _{1, n-1} -q _{0, n-1} ]) ^-1
[q _{1, 0} -q _{0, 0} q _{1, 1} -q _{0, 1} … q _{1, n-1} -q _{0, n-1} ] ^T
[p ₀ -q ₀ p ₁ -q ₁ … p _n-1 q _n-1 ]… (8)

The inside / outside determination of the multidimensional tetrahedron can be made depending on whether or not [a ₀ a ₁ ... A _n−1 ] calculated by Expression (8) satisfies the condition of Expression (6). That is, if the expression (6) is satisfied, the input point P is in the multidimensional tetrahedron, and if not satisfied, the input point P is outside the multidimensional tetrahedron.

● Multidimensional tetrahedral interpolation In step S402, when it is determined that spectral reflectance data (principal component coefficient a) exists in the multidimensional tetrahedron, the multidimensional tetrahedron including the input point P and the equation (8) [a ₀ a ₁ ... a _n-1 ] can be calculated. The output signal value can be interpolated by equation (9).
[s ₀ s ₁ … s _n-1 ]
= [r _{1, 0} -r _{0, 0} r _{1, 1} -r _{0, 1} … r _{1, n-1} -r _{0, n-1} ] [a ₀ a ₁ … a _n-1 ] ^T
+ [r _{0, 0} r _{0, 1} … r _{0, n-1} ]… (9)
Where R ₀ (r _{0, 0} , r _{0, 1} ,…, r _{0, n-1} ),…, R _n-1 (r _{n-1, 0} , r _{n-1, 1} ,…, r _{n-1, n-1} ) is Q ₀ (q _{0, 0} , q _{0, 1} ,…, q _{0, n-1} ),…, Q _n-1 (q _{n-1, 0} , q _{n- 1, 1} ,…, q _{n-1, n-1} ), each vertex of the output multidimensional tetrahedron,
S (s ₀ , s ₁ ,…, s _n-1 ) is the output value

  In this way, in the spectral color reproduction device 10 that reproduces the input spectral image using the output device 24, the output signal is buffered by grid information of the multi-dimensional tetrahedron of the LUT used to calculate the output signal value. The time required to calculate the value is shortened, and high-speed conversion processing becomes possible.

  The color processing according to the second embodiment of the present invention will be described below. Note that the same reference numerals in the second embodiment denote the same parts as in the first embodiment, and a detailed description thereof will be omitted.

  In the second embodiment, a spectral color reproduction device 10 having a user interface for reading spectral images, principal component vectors, LUTs, and LUT buffers will be described.

  FIG. 13 is a block diagram illustrating a configuration example of the spectral color reproduction device 10 according to the second embodiment. 3 is different from the configuration shown in FIG. 3 in that the spectral color reproduction device 10 according to the second embodiment includes an RGB conversion unit 30 that converts spectral reflectance into device RGB, and a UI unit 31 that displays a user interface (UI). Is to have.

[UI]
FIG. 14 is a diagram showing an example of a UI displayed on the display 617 or the display unit (not shown) of the spectral color reproduction device 10 by the UI unit 31.

  The input image display unit 701 displays the input spectral image converted into the RGB image by the RGB conversion unit 30.

  An input image edit box 702 is a box for describing a path name of a spectral image to be input. A principal component vector edit box 703 is a box for describing a path name of principal component vector data.

  The LUT edit box 704 is a box for writing a path name of LUT data. The LUT buffer edit box 705 is a box for writing a path name of data to be stored in the LUT buffer 17.

  A read button 706 is a button for instructing a process of reading each data. A print button 707 is a button for instructing printing of the input spectral image.

[processing]
FIG. 15 is a flowchart showing the processing of the spectral color reproduction device 10.

  The control unit 29 determines whether or not the read button 706 has been pressed (S801), and waits until the read button 706 is pressed.

  When the read button 706 is pressed, the control unit 29 inputs the principal component vector data according to the path name input in the principal component vector edit box 703 and stores it in the principal component vector storage unit 15 (S802). Subsequently, a spectral image is input according to the path name input in the input image edit box 702 (S803). Then, the input spectral image is converted into an RGB image by the RGB conversion unit 30 and displayed on the input image display unit 701 of the UI unit 612 (S804), and the data amount of the input spectral image is reduced by the data amount reduction unit 18. Then, it is stored in a predetermined area of the image buffer 14 (S805).

  Next, the control unit 29 inputs LUT data according to the path name described in the LUT edit box 704 and stores it in the LUT storage unit 16 (S806). Subsequently, the LUT buffer data is input according to the path name described in the LUT buffer edit box 705 and stored in the LUT buffer 17 (S807).

  Next, the control unit 29 determines whether or not the print button 707 has been pressed (S808), and waits until the print button 707 is pressed.

  When the print button is pressed, the control unit 29 causes the color conversion processing unit 19 to convert the spectral image stored in the image buffer 14 into an output signal and store it in a predetermined area of the image buffer 14 (S809). Then, the output signal stored in the image buffer 604 is output as image data to, for example, the output device 24 (S810), and the spectral image is reproduced.

  In this way, the user can reproduce the spectral image by designating the spectral image, principal component vector data, and LUT data to be reproduced using the UI. At that time, since LUT buffer data can be designated, when an image that is the same as or similar to the spectral image to be reproduced has been processed in the past, the grid information used for color conversion of the image can be used.

[Modification]
Although the example using multidimensional tetrahedral interpolation has been described above, for example, a method in which prism interpolation, pyramid interpolation, or the like is extended in multiple dimensions may be used.

[Other embodiments]
Note that the present invention can be applied to a system constituted by a plurality of devices (for example, a computer, an interface device, a reader, a printer, etc.), but an apparatus (for example, a copier, a facsimile machine, a control device) composed of a single device. Etc.).

  Another object of the present invention is to supply a storage medium storing a computer program for realizing the functions of the above-described embodiments to a system or apparatus, and the computer (CPU or MPU) of the system or apparatus executes the computer program. But it is achieved. In this case, the software read from the storage medium itself realizes the functions of the above embodiments, and the computer program and the computer-readable storage medium storing the computer program constitute the present invention. .

  Further, the above functions are not only realized by the execution of the computer program. That is, according to the instruction of the computer program, the operating system (OS) and / or the first, second, third,... This includes the case where the above function is realized.

  The computer program may be written in a memory of a device such as a function expansion card or unit connected to the computer. That is, it includes the case where the CPU of the first, second, third,... Device performs part or all of the actual processing according to the instructions of the computer program, thereby realizing the above functions.

  When the present invention is applied to the storage medium, the storage medium stores a computer program corresponding to or related to the flowchart described above.

Block diagram showing the configuration of a general colorimetric color reproduction system, Block diagram showing a spectral color reproduction system using a multiband input / output device, Block diagram showing a configuration example of the spectral color reproduction device of Example 1, A flowchart showing processing of the spectral color reproduction device; The figure which shows the 1st-6th principal component vector among the principal component vectors computed by principal component analysis in this example, A diagram showing the relationship between the number of dimensions and the contribution ratio when performing principal component analysis on the spectral reflectance data of 24 ColorChecker colors, A flowchart for explaining the calculation of the LUT by the control unit; The figure which shows an example of the LUT format which preserve | saves spectral reflectance data, A flowchart showing color conversion processing of a color conversion processing unit; A flowchart showing interpolation processing; A diagram showing the arrangement of LUT grid points in the principal component coefficient space, A diagram showing LUT grid points in the output signal space, Block diagram showing a configuration example of the spectral color reproduction device of Example 2, The figure which shows an example of UI which UI part displays, It is a flowchart which shows the process of a spectral color reproduction apparatus.

Claims (7)

A color processing device that converts spectral image data into an output value using a color conversion table,
Storage means for storing the color conversion table having a plurality of table data indicating the relationship between spectral image data and output values;
Holding means for holding table data used for the color conversion;
Input means for inputting spectral image data;
Color conversion means for color-converting the spectral image data,
The color conversion unit performs color conversion on the spectral image data using the table data held in the holding unit, and performs color conversion on the spectral image data using the table data held in the holding unit. If not, the color processing apparatus performs color conversion on the spectral image data using other table data stored in the storage means.   The spectral image data is data indicating spectral reflectance, and further includes data amount reduction means for converting the spectral image data into a principal component coefficient representing an addition rate of principal component vectors of the spectral reflectance. The color processing apparatus according to claim 1.   3. The color processing apparatus according to claim 1, wherein the holding unit stores table data used by the color conversion unit for color conversion.   4. The color processing apparatus according to claim 1, wherein the color conversion unit performs an interpolation process. A color processing method for color-converting spectral image data into an output value using a color conversion table,
Storing the color conversion table having a plurality of table data indicating the relationship between the spectral image data and the output value in a storage means;
Holding the table data used for the color conversion in a holding means;
Enter spectral image data,
Each step of color-converting the spectral image data,
In the color conversion, color conversion is performed on the spectral image data using the table data held in the holding unit, and the spectral image data is color-converted using the table data held in the holding unit. If the color image cannot be obtained, the color processing method is characterized in that the spectral image data is color-converted using other table data stored in the storage means.   5. A computer program for controlling a computer device to function as each unit of the color processing device according to claim 1.   7. A computer-readable storage medium in which the computer program according to claim 6 is recorded.

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