JPH11275376A - Color data holding method and color managing method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To realize a general purpose color data holding method and a color managing method, by which the relation between a picture equipment signal value and a spectroscopic distribution is estimated highly precisely with the min. data. SOLUTION: Data showing a color characteristic when the signal value of a reproducing equipment is calculated from the signal value of a target equipment includes basic spectroscopic distribution data which means the spectroscopic distribution to be a base and spectroscopic distribution contributing data which relate an input signal to the contribution of the spectroscopic distribution. Thus, a spectroscopic feature is not lost and also a smaller quantity of data is adequate as compared with a case when spectroscopic data is simply provided in the combination of equipment signal values so that a picture is reproduced highly precisely even when a light source is changed at a reproducing destination.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a color data holding method and a color management method, and more particularly, to a method capable of handling colors with high precision with minimum data in an image apparatus while considering metamerism.

[0002]

2. Description of the Related Art (1) Image data characteristic holding means has input data of an image device and tristimulus values under a specific light source, or data having a color space coordinate relationship derived therefrom. I was

So far, as a table for determining the relationship between the halftone pattern of printing and L * a * b *, an ANSI color target IT-8 / 7.3, "Development of color management system" for performing color management using relationships (Hirohiro Satoshi, KONICA TECHNICAL
REPORTF Vol.8 (1995)) ・ As an ICC profile, "ICC, ICC Profile for
mat v3.3 http://www.color.org/ (1996) ". In each case, the relationship between the system value of the imaging device and the tristimulus value under a specific light source is basically provided as a table.

[0004] (2) As for the spectral data prediction of the image equipment, the following method is used: "A color conversion method for matching a color appearance using a neural network" (as a method using a neural network) (Yoshifumi Arai, Shigeki Nakauchi, Shiro Usui) , Proceedings of the 98th Spring Meeting of the Printing Society of Japan, pp161-164
(1997)), "Colorimetric characterization of a desktop"
drum scanner using aspectral model "RoyS. Berns,
MJ Shyu, Journal of Electronic Imaging 4 (4), 360
-372 (1995).

[0005] (3) As a color management technique, there is US Pat. No. 4,500,919. Tristimulus values are obtained from a spectral distribution, and three values (typically, tristimulus values) calculated thereby are used. Some of them perform color reproduction.

This is shown in FIG. Here, tristimulus values are obtained by forward conversion based on colorimetric device characteristics, target perceived colors are obtained by forward conversion of color appearance models, and color appearance is corrected for perceived colors corrected by color gamut mapping. Tristimulus values are obtained by inverse transformation of the model, and further, reproduced device signals are obtained by inverse transformation based on colorimetric device characteristics.

[0007]

The prior arts described above have the following problems, respectively. There is a problem that it is not possible to cope with a change in the light source because the relationship with the tristimulus value is specified instead of the spectral data. This is because spectral data is data of about 35 dimensions, whereas tristimulus values are only three dimensions, and spectral characteristics have already been lost.

[0008] As a simple extension, it is also possible to provide spectral data for each output with respect to the signal value of an image device, and there is a case where data of a colorimeter is held as it is.

In this case, assuming that the combination of image device signal values is N and the number of dimensions is M, N ³ * M, and the tristimulus value is 17 ³ * 3 = 14739 (word). Meanwhile, 3
In the case of five-dimensional spectral data, 17 ³ * 35 = 171
955 (words), requiring data that is one or more digits larger.

Generally, one word requires 4 bytes or 2 bytes. If it is 2 bytes, the data is 29478 bytes for the tristimulus value and 343910 bytes for the spectral data.

As described above, there is a problem that very large data is required in order to leave spectral characteristics by spectral data. The method using a neural network as the spectral data prediction of the device has a problem that a learning operation is required in advance. Further, similarly to the above (1), there is a problem that accuracy is not good when inferring from data of a small number of dimensions.

Further, in obtaining the color characteristics of a specific device, the above-mentioned Berns et al. Paper discloses a method of once obtaining a spectral distribution by using principal component analysis. Can be determined with high accuracy from the spectral distribution of. However, this paper was limited to slides and could not be applied to other media.

In the conventional color management system, the spectral distribution is obtained, multiplied by the spectral distribution of the light source, and then temporarily converted into tristimulus values. In this case, conversion to tristimulus values causes a problem of metamerism (conditionally equal colors). Therefore, there is no single solution for a printer using four or more primary colors.
In the case of color, other conditions (such as the total amount of ink amount, whether to reproduce using black as much as possible, to reproduce without using black as much as possible, or to generate black smoothly) were given. .

Further, there is an apparatus which performs color reproduction using colors of so-called HiFi printing (6 to 8 primary colors). Color reproduction is performed based on tristimulus values (or color values derived from tristimulus values). Since there are innumerable combinations of primary colors expressing the same color value, there has been no means for rationally selecting them.

As described above, in the conventional method, the relationship between the spectral distribution with respect to the image device signal value cannot be estimated with high accuracy and minimum data. In addition, since the media is limited, there has been no general-purpose method. Furthermore, in a system using more than three primary colors using spectral data, there is no method for determining not only colorimetrically but also spectrally, and metamerism cannot be eliminated.

An object of the present invention is to realize a general-purpose color data holding method and a color management method capable of inferring a relationship between a spectral distribution with respect to an image device signal value with high accuracy and minimum data.

[0017]

The present invention for solving the above problems is as described below. (1) According to the first aspect of the invention, there is provided a spectral distribution which relates basic color distribution data indicating a basic spectral distribution to color characteristic data indicating a color characteristic of an image device, and an input signal and a contribution of the spectral distribution. A color data holding method characterized by including contribution data.

According to the present invention, by including the basic spectral distribution data and the spectral distribution contribution data in the color characteristic data, the spectral characteristics are not lost, and the spectral data is simply stored in the combination of the device signal values. It is possible to use less data as compared with the case where it is performed. And since it has a spectral feature, an image can be reproduced with high accuracy even if the light source is changed at the reproduction destination.

(2) According to a second aspect of the present invention, in the color data holding method according to the first aspect, the spectral distribution contribution data is subjected to a gradation change for linearizing a color mixing characteristic of an image device. And

According to the present invention, by linearizing the color mixing characteristics, the size of the multidimensional matrix can be reduced and the number of basic spectral distribution data can be reduced while maintaining the accuracy. As a result, the data capacity and the processing amount can be reduced. Will be reduced.

According to a third aspect of the present invention, in the color data holding method according to the first aspect, the number of data of the basic spectral distribution data is N to 2 when the number of colors to be used is N. It is one of the N-th power.

In the present invention, since ^2N mixed colors become primary colors, it is possible to transmit these without deteriorating the accuracy of spectral distribution characteristics. (4) In the color data holding method according to the first aspect, when the image device is an input device,
The basic spectral distribution data can be changed according to the type of subject.

According to the present invention, it is possible to transmit optimal data according to the type of the subject. (5) In the color data holding method according to the first aspect, when the image device is an input device,
The basic spectral distribution data is selected from any of a plurality of media-related data.

According to the present invention, the symbol is transmitted as a representative symbol in the spectral distribution according to the type of the subject, so that the symbol is transmitted without sending all the data, so that the interpretation on the receiving side can be made. And the amount of data to be transmitted can be reduced.

(6) In the color data holding method according to the first aspect, when the basic spectral distribution data does not exist, the predetermined spectral data is used as the basic spectral distribution data. It is characterized by using.

According to the present invention, the data amount can be significantly reduced by omitting the basic spectral distribution data and using predetermined data in the spectral distribution generally used.

(7) According to a seventh aspect of the present invention, in the color data holding method according to the first aspect, the color characteristic data is incorporated in a part of the image data. According to the present invention, the spectral characteristics are incorporated into the image data, so that even if the light source changes, it is possible to reproduce the apparent color on the reproduction side.

(8) In the color data holding method according to the first aspect of the present invention, the tristimulus value under a specific condition from a signal value of an image device, or It is characterized in that color values derived from these tristimulus values are converted.

According to the present invention, a signal value of an image device is converted into a tristimulus value or a color value derived from the tristimulus value, thereby achieving matching with a color management system based on colorimetry. .

(9) According to a ninth aspect of the present invention, a step of converting a system value of a target image device into a system value of a reproduced image device using spectral color characteristic data indicating a spectral color characteristic of the image device. And a color management method.

According to the present invention, since color reproduction is performed using data indicating the spectral color characteristics of the image equipment, when the target medium and the medium to be reproduced are under the same light source, the problem of metamerism is reduced. In the case of printing using three or more primary colors (high-fidelity printing), a plurality of solutions are generated in the conventional method using tristimulus values, but the present invention can find an optimum solution from the aspect of accuracy.

(10) In the color management method according to the ninth aspect, in the color management method according to the ninth aspect, data obtained by multiplying the spectral color characteristic data by a spectral distribution of a light source is used as the spectral color characteristic data. It is characterized by the following.

According to the present invention, more optimized color reproduction is possible by multiplying the spectral distribution of the light source. (11) In the color management method according to any one of the ninth and tenth aspects, the invention according to the eleventh or tenth aspect provides a weighting coefficient for the wavelength of the spectral color characteristic data in advance as the degree of approximation of the spectral color characteristic data. Multiply and calculate.

In the present invention, by using the weight coefficient,
The spectral color characteristic data can be calculated in consideration of human vision. (12) In the color management method according to the ninth aspect, in the color management method according to the ninth aspect, the system value is changed to a system value of an image device associated in advance so as to fall within the color gamut of the image device to be reproduced. I do.

According to the present invention, the color management can be performed without any significant shift in hue by controlling in advance so as to fall within the color gamut of the image device to be reproduced.

[0036]

DESCRIPTION OF THE PREFERRED EMBODIMENTS A color data holding method and a color management method according to an embodiment of the present invention will be described.
The description will be made with reference to the drawings.

First, a color data holding method and a color management method according to an embodiment of the present invention will be described with reference to FIG. FIG. 1 is an example of a processing device used in an embodiment of the present invention, and shows a block diagram for restoring spectral data.

Here, reference numeral 100 denotes spectral data restoring means for restoring spectral color characteristic data, and an input tone converting unit 110 as first converting means for performing tone conversion of an input signal from a target device; Multidimensional LUT 120 as conversion means
And a spectral data multiplying / adding unit 130 as a third conversion unit for performing weighted addition, and an antilogarithmic gradation conversion unit 140 as a fourth conversion unit for performing grayscale conversion into an antilogarithm. .

Here, the number of primary colors of the image equipment (not shown) is N,
Assuming that the number of data (basic spectral distribution data) representing the basic spectral distribution is M and the number of wavelengths of the spectral data is L,
The input gradation conversion unit 110 is a one-dimensional LUT for gradation conversion
(Input N1, output about 1 byte), multi-dimensional LUT
Numeral 120 denotes an N-dimensional LUT (grid number NT, output about 2 bytes), and a multiplying / adding unit 130 multiplies M pieces of spectral data (output is 2 bytes) by data from the multi-dimensional LUT 120 and adds them. , The antilogarithmic gradation conversion unit 1
Numeral 40 denotes a one-dimensional LUT (input N2, output about 2 bytes) for gradation conversion. Each LUT is combined with an interpolation process, and the data amount can be reduced.

Reference numeral 150 denotes light source spectral distribution data generating means for generating data corresponding to the spectral distribution of the light source.
Reference numeral 60 denotes a multiplying unit that multiplies the spectral color characteristic data from the spectral data restoring unit 100 by the spectral distribution of the light source.
Reference numeral 170 denotes a convolution operation unit that performs a convolution operation on the spectral color characteristic data multiplied by the spectral distribution of the light source and a color matching function to output tristimulus values.

In FIG. 1, the spectral data restoring means 1
Although four conversion means are provided in 00, as described below, not all of them are used, but only necessary ones may be used.

<Method of Retaining Color Characteristics Data of Imaging Apparatus> Here, the invention of a method of retaining color data having spectral data will be described with reference to FIG.

(1) When using a CRT display device: CR
In the case of a T display device or a CRT image, since additive color mixing in the real number region is well established, the number of primary colors is N = 3 and the number of basic spectral distribution data is M = 3.

In this case, the LUT of the input gradation conversion unit 110
The input signal (system value of the target imaging device)
Is converted to an exact number. The multi-dimensional LUT 120 is unnecessary, and the fine adjustment of the white point can be realized by adjusting the amplitude of the underlying spectral distribution in advance. Since the CRT image data has been gamma-converted, the input tone conversion unit 11 described above must be used to return the image data to an exact number.
Use 0. Therefore, the multidimensional LUT 120 and the anti-grayscale conversion unit 140 are unnecessary.

In this case, the number D of the spectral distribution contribution data is as follows: when N1 = 33 as the gradation data, D = N1 + L * M * 2 = 33 + 35 * 3 * 2 = 33 + 210 = 243 (bytes). The spectral distribution can be accurately predicted.

(2) In the case of a density modulation printer: Since the density area of a mixed color is linear, the input signal is temporarily logarithmically converted by the input gradation conversion unit 110 or the multidimensional LUT 120.

The logarithmically converted input signal is used as a weighting coefficient, and a multiplication / addition unit 130 adds a logarithmic region to a basic spectral distribution (basic spectral distribution data). To return to an exact number.

In this case, four basic spectral distributions M are appropriate, that is, a spectral distribution on a white background and a spectral distribution determined by the type of dye (for example, Y, M, C). In this case, the input gradation conversion unit 110 as the first conversion unit can be omitted.

In this case, the number of data D of the spectral distribution contribution data is as follows: N1 = 17 and N2 = 33 as gradation data: D = N1 ³ * M * 2 + L * M * 2 + N2 * 2 = 17 * 17 * 17 * 4 * 2 + 35 * 4 * 2 + 33 * 2 ≒ 40 Kbytes, which can accurately predict the spectral distribution.

(3) In the case of an area-modulated printer: For a secondary color (Y + M, M + C, C + Y, etc., in which two types of primary colors overlap), in the case of N-color printing, it has 2 ^N spectral data. Is desired. That is, the number of colors used is N
, The number of basic spectral distribution data is N to
It is assumed to be one of 2 N powers. In the present invention, since ^2N mixed colors become primary colors, it is possible to transmit these without deteriorating the accuracy of spectral distribution characteristics.
However, in the case of low accuracy, 3 to 4 spectral data may be used.

In this case, the multidimensional L
Processing can be performed by the UT 120 and the spectral data multiplication / addition unit 130 that is a third conversion unit that performs weighted addition, and the input gradation conversion unit 110 that is the first conversion unit and the fourth conversion unit
It is possible to omit the antilogarithm conversion unit 140 as a conversion unit.

In this case, when the number D of spectral distribution contribution data is NT = 17 as gradation data, D = NT ³ * M * 2 + L * M * 2 = 17 * 17 * 17 * 4 * 2 + 35 * 4 * 2 ≒ 40 Kbytes, which can accurately predict the spectral distribution.

In the above examples (1) to (3), the input gradation conversion unit 110 as the first conversion means and the second
The multi-dimensional LUT 120 as the conversion means can be replaced with a neural network. In addition, the input gradation conversion unit 110 and the multidimensional LUT 120 can use functions of matrix operation and gradation conversion.
The Neugebauer equation can also be used in 120 and the spectral data multiplication / addition unit 130.

Then, the multiplication section 160 calculates the spectral distribution (spectral color characteristic data) estimated as described above,
The data is multiplied by the spectral distribution data of the light source held by the light source spectral distribution data generating means 150.

Further, with respect to the spectral color characteristic data multiplied by the spectral distribution data of the light source, the convolution operation unit 170 outputs a value integrated by the convolution operation of the color matching function as a tristimulus value. For color matching functions, refer to CIE Publication1
Defined in 5.2. As a result, tristimulus values are calculated and become device-independent values.

From these tristimulus values, L * a * b *
It can be converted to a uniform color space such as L * u * v *, a color space of a color appearance model such as Hunt, Nayatani, or a color space of a color appearance model defined by CIE (CIECAM97s). As a result, the values become device / observation environment / independent values.

FIG. 2 shows a modification of the spectral data restoring means 100. As in the case of FIG. 1, the number of primary colors of an unillustrated image device is N, and the number of wavelengths of spectral data is L. And
The spectral data restoring unit 100 in FIG. 2 includes an input gradation conversion unit 110 of a one-dimensional LUT (input N1 and output of about 1 byte) for gradation conversion, and an N which simply has many spectral data.
It can be configured with a multidimensional LUT 120 of a dimension L output LUT.

The number D of the spectral distribution contribution data in this case is as follows: D = NT ³ * 35 * 2 = 17 * 17 * 17 * 35 * 2 ＊ 344 Kbytes, assuming that the lattice point NT is the same as the above case. , Therefore, when spectral data is simply stored, the number D of data can be reduced by reducing the number of grid points, and the spectral distribution can be accurately predicted with a small number of data.

For example, assuming that NT = 9, D = NT ³ * 35 * 2 = 9 * 9 * 9 * 35 * 2 ≒ 51 Kbytes, and it is desirable to obtain an intermediate value with high accuracy by nonlinear interpolation. The interpolation method in such a case is described in “Colo
rimetric calibration in electronic imaging devices
using a look-up-table model and interpolations ''
Po-Chie Hung, Journal of Electronic Imaging 2 (1),
53-61 (1993).

<Color Management Method> The invention of the color management method will now be described. When spectral data is stored as described in the above color data holding method invention, the relationship between the spectral distribution and the input signal can be obtained. Here, a combination of input signals corresponding to a given spectral distribution can be obtained by the following procedure, for example.

A spectral distribution based on a combination of specific signal values of a target device is obtained. That is, the target spectral distribution is calculated. By comparing and evaluating the spectral distribution with the reproduction device, a signal value of the reproduction device with the lowest error evaluation function is obtained. As an evaluation method in this case, for example, the following equation is used. Here, Ltar (λ) is the target spectral distribution, Ldest
(λ) is the spectral distribution on the reproduction side. W (λ) is a weighting coefficient, for example, using the sum of three color matching functions.

[0062]

(Equation 1)

A plurality of these are obtained. Data is obtained by the least squares method. In addition, there are several methods for obtaining, but the specific method is as follows. Here, it is assumed that N = 3, but a value larger than that may be used, which is one of the advantages of the present invention. Theoretically, the number of pieces of spectral data is possible. This can be multiplied by the above-mentioned weight coefficient W (λ).

Here, the target spectral sensitivity Ltar is:

[0065]

(Equation 2)

Further, the spectral distribution Ldest based on the signal value of the reproducing device indicating the vicinity of the target spectral sensitivity Ltar is given by:

[0067]

(Equation 3)

When represented by the following A

[0069]

(Equation 4)

By calculating the above, a weight coefficient for the value of the reproduction device close to the target spectral distribution is obtained. That is,

[0071]

(Equation 5)

As a result, it is possible to determine the optimum combination of the signals of the reproduction devices. The closest point is searched for using the above weighting coefficients. The above input signal values are changed and repeated. Here, the value of the input signal, 5 ³ ~
33 is a combination of ^third signal value.

As described above, based on the information of the spectral data, the signal value of the target device is converted into the signal value of the device for reproducing. In addition, if this processing is roughly shown, it can be shown by the flowchart of FIG. That is, the target spectral distribution is calculated from the signal value of the target device (FIG. 3S
1), the spectral distribution is compared with the spectral distribution on the reproduction device side and approximated to obtain an approximate spectral distribution (S2 in FIG. 3);
The signal value of the reproduction device is obtained from the approximate spectral distribution (S3 in FIG. 3).

<Modification of Color Management Method> A modification of the color management method will now be described. To compress the color gamut in advance, compression is performed according to the concept of the conventional color management, and a spectral distribution corresponding to the device signal value at that time is used. This enables color management while maintaining hue. Conversely, if this is not done, it will be unpredictable what color the color will be compressed to.

When the observed white point is different: In the case of a reflective object, the calculation is performed after the color temperature of the light source is virtually changed to match the white point.

In the case of a self-luminous body such as a CRT and a reflector, the calculation is performed after virtually changing the color temperature of the light source on the reflector side to match the white point. If there is no basic spectral distribution (basic spectral distribution data), the data amount can be further reduced by using the spectral distribution data registered in advance.

For example, the type (media) of the subject is registered in advance, such as A for photographic paper, B for ink jet,. And according to the type of subject,
By determining the symbol as a representative symbol for the spectral distribution, by sending the symbol without sending all the data,
Interpretation on the receiving side is possible, and the amount of data to be transmitted can be reduced.

Note that the spectral data in this embodiment includes not only the spectral data itself but also equivalent data obtained by linear conversion or the like. <Effects of Embodiment> According to the embodiment of the color data holding method and the color management method described in detail above, the following effects can be obtained.

The basic characteristic distribution data and the spectral distribution contribution data are included in the color characteristic data indicating the color characteristics of the image device, so that the data amount can be significantly reduced. For example, when N = 17, the number of wavelengths of spectral data L = 35, and the spectral distribution data M is three colors, if there is simply spectral data, 17 ³ * 2 * 35 = 34391.
According to the present embodiment, the location of 0 bytes is 17 bytes.
³ * 3 * 2 + 35 * 3 * 2 + 33 * 2 = 29,754 bytes, which makes it possible to calculate spectral color characteristics with high accuracy while maintaining a data amount that is not much different from maintaining color characteristics with conventional tristimulus values. Will be possible.

At least for some colors, unlike the case of the neural network, it is possible to guarantee that the spectral distribution of a specific color (such as a primary color) is completely reproduced, and to maintain high accuracy.

Since the signal value on the reproduction device side is obtained by performing evaluation using the spectral distribution, metamerism is less likely to occur. Since the signal value on the reproduction device side is obtained by performing evaluation using the spectral distribution, the optimum value can be determined even in the case of Hi-Fi printing using four or more colors.

[0082]

According to the present invention described in detail above, the following effects can be obtained. (1) According to the first aspect of the present invention, by including the basic spectral distribution data and the spectral distribution contribution data in the color characteristic data, the spectral characteristics are not lost and the combination of the device signal values can be simply performed. Compared to the case where spectral data is provided, less data can be used. And since it has a spectral feature, an image can be reproduced with high accuracy even if the light source is changed at the reproduction destination.

(2) According to the second aspect of the present invention, by linearizing the color mixing characteristics, it is possible to reduce the size of the multidimensional matrix and reduce the number of basic spectral distribution data while maintaining accuracy. As a result, the data capacity and the processing amount are reduced.

(3) In the third aspect of the present invention, since ^2N mixed colors are primary colors, it is possible to transmit these without deteriorating the accuracy of spectral distribution characteristics. (4) In the invention according to claim 4, when the imaging device is an input device, the basic spectral distribution data can be changed according to the type of the subject, so that optimal data can be transmitted according to the type of the subject. become.

(5) According to the fifth aspect of the present invention, the symbol is transmitted without sending all data by determining the symbol representative of the spectral distribution according to the type of the subject. , Can be interpreted on the receiving side, and the amount of data to be transmitted can be reduced.

(6) According to the sixth aspect of the present invention, in the commonly used spectral distribution, the basic spectral distribution data is omitted, and the predetermined data is used.
The amount of data can be significantly reduced.

(7) According to the seventh aspect of the present invention, since the spectral characteristics are incorporated in the image data, even if the light source changes, the apparent color can be reproduced on the reproduction side.

(8) In the invention according to the eighth aspect, a color management system based on colorimetry by converting a signal value of an image device into a tristimulus value or a color value derived from the tristimulus value. Will be consistent with

(9) According to the ninth aspect of the invention, since color reproduction is performed using data indicating the spectral color characteristics of the image equipment, when the target medium and the medium to be reproduced are under the same light source, metamerism Problems are reduced. In the case of printing using three or more primary colors (high-fidelity printing), a plurality of solutions are generated in the conventional method using tristimulus values, but the present invention can find an optimum solution from the aspect of accuracy.

(10) According to the tenth aspect of the present invention, the spectral color characteristic data obtained by multiplying the spectral color characteristic data by the spectral distribution of a light source is used, so that more optimized color reproduction is achieved. Is possible.

(11) According to the eleventh aspect of the present invention, the spectral color characteristic data is calculated by previously multiplying the wavelength of the spectral color characteristic data by a weighting factor as the degree of approximation of the spectral color characteristic data. Color characteristic data can be calculated.

(12) According to the twelfth aspect of the invention, by changing the system value of the image device associated in advance so as to fall within the color gamut of the image device to be reproduced,
It is possible to perform color management in which the hue is not largely shifted.

[Brief description of the drawings]

FIG. 1 is a block diagram showing, for each functional block, an electrical configuration of an apparatus used in a color data holding method and a color management method according to an embodiment of the present invention.

FIG. 2 is a block diagram illustrating a main part of an electrical configuration of a modification of the apparatus used in the color data holding method and the color management method according to the embodiment of the present invention.

FIG. 3 is a flowchart showing an operation state of a color data holding method and a color management method used in the embodiment of the present invention.

FIG. 4 is a flowchart showing a conventional processing procedure.

[Explanation of symbols]

 REFERENCE SIGNS LIST 100 spectral data restoring means 110 input tone conversion unit 120 multidimensional LUT 130 multiplication / addition unit 140 antilogarithmic tone conversion unit 150 light source spectral distribution generation unit 160 multiplication unit 170 convolution operation unit

Claims (12)

[Claims] 1. Color characteristic data indicating color characteristics of an image device includes basic spectral distribution data indicating a basic spectral distribution, and spectral distribution contribution data relating an input signal and contribution of the spectral distribution. And a method for retaining color data. 2. The color data holding method according to claim 1, wherein a gradation change for linearizing a color mixing characteristic of an image device is performed on the spectral distribution contribution data. 3. The data number of the basic spectral distribution data is:
2. The color data holding method according to claim 1, wherein when the number of colors to be used is N, the number is any one of N to 2 to the N-th power. 4. The color data holding method according to claim 1, wherein when the image device is an input device, the basic spectral distribution data can be changed according to a type of a subject. 5. The color data holding method according to claim 1, wherein when the image device is an input device, the basic spectral distribution data is selected from any one of a plurality of media. 6. The method according to claim 1, wherein when the basic spectral distribution data does not exist, predetermined spectral data is used as basic spectral distribution data.
The described color data retention method. 7. The color data holding method according to claim 1, wherein said color characteristic data is incorporated in a part of image data. 8. The method according to claim 1, wherein the color characteristic data is used to convert a signal value of an image device into a tristimulus value under a specific condition or a color value derived from the tristimulus value. 1. The color data holding method according to 1. 9. The method according to claim 1, further comprising the step of using the spectral color characteristic data indicating the spectral color characteristic of the image device to convert the system value of the target image device into the system value of the image device to be reproduced. Management method. 10. The color management method according to claim 9, wherein data obtained by multiplying the spectral color characteristic data by a spectral distribution of a light source is used as the spectral color characteristic data. 11. The color according to claim 9, wherein the degree of approximation of the spectral color characteristic data is calculated by previously multiplying the wavelength of the spectral color characteristic data by a weighting coefficient. Management method. 12. The color management method according to claim 9, wherein the system value is changed to a system value of an image device associated in advance so as to fall within the color gamut of the image device to be reproduced.