JP2007060422A - Color processing apparatus and method thereof - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To solve a problem that it has been difficult to realize sophisticated color reproduction in response to the visible sense of humans. <P>SOLUTION: A color processing method disclosed herein for calculating a color signal value in a second color gamut by applying a nonlinear arithmetic operation to a color signal value in a first color gamut in a prescribed color system includes a step S602 for establishing a sample color, on the basis of the cross-reference between the first and second color gamuts; steps S605, S606 of establishing a spline coefficient on the basis of the established coefficient; a step S607 for generating an intermediate color gamut by a spline arithmetic operation on the basis of the established coefficient; and a step S602 for determining the coefficient when the intermediate color gamut is sufficiently closer to an output color gamut or reestablishing the sample color when not closer. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to a color processing apparatus and method, and in particular, color processing for converting a color signal value expressed in a first color system to a color signal value in a second color system via a predetermined color system. The present invention relates to an apparatus and a method thereof.

  In recent years, with the spread of devices that handle color images, opportunities to handle the same color image with different devices are increasing. For example, a monitor or a printer can be considered as an output destination of an image taken with a digital camera. However, since the color reproducible range (hereinafter referred to as a color gamut) is different for each device, there is a problem that the color displayed on the monitor and the color output by the printer are different even for the same image. Therefore, CMS (Color Management System) for absorbing the difference in color gamut between different devices and realizing the same color reproduction as possible is important.

  A general CMS is configured as a conversion system using a PCS (Profile Connection Space) which is a device-independent color space (XYZ, CIELAB, etc.) as a common color space. For mutual conversion between a device-dependent color space (RGB, CMYK, etc.) and PCS, a profile in which a conversion formula or a conversion table (LUT: Look Up Table) is described is used.

  Here, the conversion process in the CMS will be described in more detail. First, an input device-dependent color space is converted into a PCS using a profile corresponding to the input device (hereinafter referred to as an Src profile). Next, the color gamut of the input device is converted to the color gamut of the output device on the PCS. Then, the PCS is converted into an output device-dependent color space with a profile corresponding to the output device (hereinafter referred to as a Dst profile). By executing the above processing, the difference between the color gamut of the input device and the output device is absorbed.

  As a profile used in such a CMS, for example, in the case of a device that expresses additive color mixture such as a monitor, a profile that describes coefficients of matrix calculation and gamma calculation is mainly used. On the other hand, in the case of devices expressed by subtractive color mixing such as printing equipment, the relationship between the device color space and the PCS is generally very complex, so a profile describing the LUT to express that relationship with higher accuracy is available. Used. Since a general LUT is described as having a grid-like value in a color space, colors that are not represented in the LUT are calculated by interpolation.

  Linear interpolation such as cubic interpolation or tetrahedral interpolation is widely used for the interpolation calculation for the LUT. As shown in FIG. 13, cubic interpolation is a method of calculating a color from the values of eight points (P1, P2, P3, P4, P5, P6, P7, P8) on the LUT surrounding the color to be calculated. Tetrahedral interpolation is a method of calculating a color from the values of four points using a tetrahedron obtained by dividing a cube composed of eight points used in cube interpolation into six. For example, in the case of tetrahedral interpolation, the value of the point P belonging to the tetrahedron constituted by (P1, P3, P4, P8) is obtained by the following equation. Here, DL, Da, and Db are normalized distances in the axial directions indicating the position of the point P in the cube surrounding the point P.

P = P1 + DL ・ (P8-P1) + Da ・ (P3-P1) + Db ・ (P4-P1)
However, if linear interpolation is used when the lattice spacing of the LUT is coarse, the color calculation accuracy decreases because the value is calculated from a local region surrounded by a cube or tetrahedron. In addition, due to the fact that the linear interpolation is switched with the surface of the cube or tetrahedron as a boundary, the value changes sharply, the smoothness is lost, and a pseudo contour may appear in the reproduced image.

As a method for solving such a problem, a color calculation method by nonlinear calculation has been proposed (see, for example, Patent Document 1). According to such a nonlinear calculation method, when calculating a color from a plurality of representative colors, the color is calculated with a smooth change that naturally reflects the tendency of the color change. The color can be calculated.
JP2003-416614

  In general, when calculating colors, taking into account the realization of color reproduction that is good and good for humans, color reproduction based on human memory tends to be preferred over color reproduction that is faithful to actual colors. There is. Examples of colors based on human memory (hereinafter referred to as memory colors) include skin color and sky color. Such color reproduction of a memory color requires much higher color reproduction than faithful color reproduction. For example, since human vision is very sensitive to changes in hue and saturation of colors near gray such as skin color, highly accurate color reproduction is required. On the other hand, human vision is somewhat dull for high-saturation colors such as the sky color, and the color gamut varies greatly depending on the device, so it is possible to maintain the saturation and gradation of the original color rather than the color calculation accuracy. Desired. That is, it is required to control, according to human vision, which one should prioritize high-precision color reproduction or color reproduction that maintains the saturation and gradation of the original color.

  However, according to the color calculation method based on the non-linear operation shown in the conventional example, the color is calculated with a single non-linear operation for a certain color space. There was a problem that it was difficult to reproduce.

  The present invention has been made to solve the above-described problems, and an object thereof is to provide a color processing apparatus and method for realizing color reproduction that is visually favorable for human beings.

  As a means for achieving the above object, the present invention comprises the following arrangement.

  That is, when the color signal value expressed in the first color system is converted into the color signal value of the second color system via the predetermined color system, the first color in the predetermined color system A color processing method for calculating a color signal value in a second color gamut by performing a non-linear operation on the color signal value in the color gamut, the correspondence between the first color gamut and the second color gamut A sample color setting step for setting a sample color based on the sample color, a coefficient setting step for setting a coefficient in the nonlinear calculation based on the set sample color, and an operation for performing the nonlinear calculation based on the set coefficient And an evaluation step for evaluating the calculation result, and when the non-linear calculation result is determined to be inappropriate in the evaluation step, the sample color setting step is executed again, and the evaluation step Non-linear calculation result is appropriate If it is determined, characterized by determining the coefficient.

  For example, the non-linear operation is a multiple sum operation using a piecewise polynomial as a basis function.

  For example, the basis function is a spline function.

  With the above configuration, according to the present invention, it is possible to bring the color conversion result obtained by the non-linear calculation closer to a desired color reproduction, and it is possible to realize a color reproduction that is visually favorable to humans.

  Hereinafter, the present invention will be described in detail based on preferred embodiments with reference to the accompanying drawings.

<First Embodiment>
Apparatus Configuration FIG. 1 is a block diagram showing the configuration of a color processing apparatus according to this embodiment. In the figure, 101 is a CPU, 102 is a main memory, 103 is a SCSI interface, 104 is a network interface, and 105 is an HDD. Also, 106 is a graphic accelerator, 107 is a color monitor, 108 is a USB controller, 109 is a color printer, 110 is a keyboard / mouse controller, 111 is a keyboard, and 112 is a mouse. Reference numeral 113 denotes a local area network, and 114 denotes a PCI bus.

  Hereinafter, in the above configuration, processing for reading a profile, calculating a color from the LUT, and converting the color of the image will be described in detail. In this embodiment, the color of an image is converted using a color conversion application.

  When the user instructs the color processing apparatus of this embodiment to start the color conversion application, the OS program that receives the user's instruction starts the color conversion application stored in the HDD 105 and develops it in the main memory 102. When the color conversion application is activated, the window shown in FIG. 2 is displayed on the color monitor 107. In FIG. 2, reference numeral 201 denotes a button for instructing input of an image file to be converted. 202 is a button for instructing input of an Src profile, and 203 is a button for instructing input of a Dst profile. Reference numeral 204 denotes a button for instructing output of an image after color conversion. Reference numeral 205 denotes a display area for displaying the state of image color conversion, 206 denotes a button for instructing image color conversion, and 207 denotes a button for instructing termination of the application.

  Hereinafter, the operation of the color conversion application will be described with reference to the flowchart of FIG. First, in step S401, an image file to be subjected to color conversion is read. In the color conversion application, when the user instructs to read an image using the image file input button 201, the designated image is read via the HDD 105 or the network I / F 104 according to the instruction of the color conversion application. The read image is displayed in the display area 205a.

  In step S402, an Src profile having an LUT for converting a device-dependent color (RGB) representing an image into PCS (here, L * C * h) is read. In the color conversion application, when the user instructs to read the Src profile from the Src profile input button 202, the specified profile is read from the HDD 105 or from the outside via the network I / F 104. Here, if the LUT of the Src profile has R, G, B 5 slices each, a total of 125 RGB values and L * C * h values, the LUT has a data structure as shown in FIG. 4A. Composed.

  Next, in step S403, a Dst profile having an LUT for conversion to a device-dependent color (RGB) representing an image from the PCS is read. In the color conversion application, when the user instructs reading of the Dst profile from the Dst profile input button 203, the specified profile is read from the HDD 105 or from the outside via the network I / F 104. Here, the DUT profile LUT has a set of L * C * h values and RGB values, and has a data structure as shown in FIG. 4B.

  Following the above processing, in the color conversion application, the user presses the conversion execution button 206 to execute color conversion of the read image. In step S404, a spline coefficient used for color conversion is calculated. Details of the spline coefficient calculation will be described later. In step S405, the color of the input image is converted by a spin function using the spline coefficient obtained in step S404. Details of this conversion method will be described later. In step S406, the converted image is previewed in the display area 205b.

  Thereafter, in the color conversion application, the user outputs the converted image from the image file output button 204. Then, when the application is instructed by the end button 207, all data and applications are deleted from the main memory 102, and the application is ended.

Spline Coefficient Calculation Processing Hereinafter, the spline coefficient calculation processing in step S404 will be described in detail with reference to the flowchart of FIG.

Prior to describing specific processing, first, a spline function used in color calculation will be described. Here, if the output value for the input value (l _o , c _o , h _o ) is (L _o , C _o , H _o ), the relationship between the input value and the output value expressed using the spline function is become that way.

In the above equation, B _i and B _j are aperiodic basis functions related to lightness and saturation, and B _k ^c is a periodic basis function related to hue. Accordingly, the input values (l _o , c _o , h _o ) are represented by cylindrical coordinates as shown in FIG. Further, α, β, and γ are spline coefficients in the respective dimensions of lightness, saturation, and hue, and i, j, and k are basis function numbers in the respective dimensions of lightness, saturation, and hue. The present embodiment is characterized in that optimum color conversion by a spline function is realized by appropriately calculating the spline coefficients α, β, and γ.

Hereinafter, the flowchart shown in FIG. 5 will be described. First, in step S601, the correspondence between the L * C * h value of the Src profile and the L * C * h value of the Dst profile is acquired for all the pairs from the LUT represented by the data structure of FIGS. 4A and 4B. To do. Here, in the following description, L * C * h values are assumed to be 5 data for each dimension, a total of 125 data, and for the i-th data, the L * C * h values of the input color gamut are expressed as l _i , c Let _i , h _i , and the L * C * h value of the output color gamut be expressed as L _i , C _i , H _i . Here, the input color gamut corresponds to a color gamut reproducible in the input device, and similarly, the output color gamut corresponds to a color gamut reproducible in the output device.

  In step S602, the input value of the sample point is set so as to surround the input color gamut, and the output value of the sample point is obtained from the LUT. Here, as shown in FIGS. 7A and 7B, an arbitrary number of sample points can be set for each dimension of the input color gamut (in this embodiment, lightness, saturation, and hue component). In the following description, the number of sample points for each component of lightness, saturation, and hue is assumed to be 5 points. The details of the sample point setting method will be described later.

Next, in step S603, nodes are determined as appropriate, basis functions are calculated based on the nodes, and stored in the main memory 102. Here, as a method for determining a node, the number of nodes and coordinates may be determined in advance, or the node may be determined based on a change in input value. A general De Boor-Cox algorithm is used to calculate the basis function after the node is determined. For example, if the basis function is the B-Spline basis at the third position, the aperiodic B-Spline basis becomes as shown in Fig. 8 for four nodes (q ₀ , q ₁ , q ₂ , q ₃ ) . The periodic B-Spline base is as shown in FIG. 9 for five nodes (q ₀ , q ₁ , q ₂ , q ₃ , q ₄ , where q ₀ = q ₄ ). In the following description, the number of nodes related to the aperiodic lightness and saturation components is 4 points, the number of nodes related to the periodic hue components is 6 points, and the basis function is the 3rd rank B-Spline basis.

  In step S604, the number of loops is determined. If it is the first time, the process proceeds to step S605, and if it is the second time or later, the process proceeds to step S606.

In step S605, the output value of the sample point set in step S602 or step S609 described later is used as a spline coefficient. That is, the i-th spline coefficients α _i , β _i , and γ _i corresponding to the i-th sample point are expressed by the following equations.

α _i = L _i
β _i = C _i
γ _i = H _i
On the other hand, in step S606, the spline coefficient is obtained using the basis function calculated in step S603 and the sample point set in step S602 or step S609 described later. That is, the following matrix equations are solved for the L * component, the C * component, and the h component, respectively.

Y = MC
Here, M is a matrix generated from the input values of the basis function and the sample point, C is a vector representing the spline coefficient, and Y is a vector representing the output value of the sample point.

  Hereinafter, the calculation method of the spline coefficients α, β, γ by the above equation will be described in detail.

First, the matrix M is a 125 × 125 matrix, and the s row t column component m _st is set as follows.

m _st = B _i (l _u ) B _j (c _v ) B _k (h _w )
s = 25u + 5v + w
t = 25i + 5j + k
0≤i≤4, 0≤j≤4, 0≤k≤4, 0≤u≤4, 0≤v≤4, 0≤w≤4
Therefore, the s-th output L _s is expressed using the above spline function as follows.

  When this is converted into a determinant, it becomes as follows.

L = Mα _vector
Then, by solving this determinant by LU decomposition, a vector α _{vector of} spline coefficients is calculated as follows.

α _vector = [α ₀ , α ₁ , ..., α ₁₂₄ ]
Similarly, the s-th output C _s is expressed using the above spline function as follows.

  When this is converted into a determinant, it becomes as follows.

C = Mβ _vector
Then, by solving this determinant by LU decomposition, a vector β _{vector of} spline coefficients is calculated as follows.

β _vector = [β ₀ , β ₁ , ..., β ₁₂₄ ]
Further, the s-th output h _s is expressed using the above spline function as follows.

  When this is converted into a determinant, it becomes as follows.

h = Mγ _vector
Then, by solving this determinant by LU decomposition, a vector γ _{vector of} spline coefficients is calculated as follows.

γ _vector = [γ ₀ , γ ₁ , ..., γ ₁₂₄ ]
In the present embodiment, the selection condition for the spline coefficient calculation method in step S604 has been described as the number of loops. However, other selection conditions and the user may arbitrarily select the coefficient calculation method. In addition, although the example in which the spline coefficient is determined by the method of step S605 and step S606 has been described, other coefficient determination methods may be used as long as an appropriate spline function can be obtained in color calculation.

  As described above, when the spline coefficient is calculated in step S605 or step S606, the process proceeds to step S607. In step S607, an intermediate color gamut is generated using the determined spline coefficient to determine whether the spline coefficient is sufficient for final color conversion. Here, the intermediate color gamut is calculated by substituting 125 input values corresponding to the Src profile shown in FIG. 4A into the spline function. That is, if the L * C * h value in the input gamut is (l, c, h) and the corresponding L * C * h value in the intermediate gamut is (L ', C', H '), the input gamut And the intermediate color gamut are as shown in the following equation.

Next, in step S608, condition determination is performed by comparing the intermediate color gamut with the output color gamut. Here, each color gamut value for a plurality of representative input values is obtained, and the average color difference ΔE _ab is used as a determination condition. That is, L * C * h of the output color gamut corresponding to the same input value (l, c, h) as the intermediate color gamut calculated from the correspondence relationship between the input color gamut acquired in step S601 and the output color gamut. The value (L, C, H) is compared with the L * C * h value (L ′, C ′, H ′) in the intermediate color gamut. Accordingly, the average color difference ΔE _ab is obtained as follows.

However, a = C ・ cos (πH / 180), b = C ・ sin (πH / 180)
If ΔE _ab is equal to or larger than the predetermined threshold E _{th, the process} proceeds to step S609, and if ΔE _ab is smaller than the threshold E _th , the process proceeds to step S610. Note that other conditions may be used as the determination conditions in step S608. For example, in addition to the color difference, any one of brightness difference, saturation difference, hue difference, gradation, or a combination of two or more thereof may be applied.

  In step S609, the intermediate color gamut generated in step S607 is set as an input color gamut for the second and subsequent spline coefficient calculations, and then the process returns to step S602 and the above process is repeated. As a result, a sample point for a new input color gamut is set, and a new spline coefficient is calculated.

  On the other hand, in step S610, the spline coefficient calculated in step S605 or step S606 is stored in the main memory 102.

As described above, in this embodiment, as shown in FIG. 5, the spline coefficient is determined when the average color difference ΔE _ab between the intermediate color gamut and the output color gamut becomes smaller than the predetermined threshold value _Eth . That is, a spline coefficient is set such that the intermediate color gamut approaches the desired level (shown as a predetermined threshold value _Eth ) with respect to the output color gamut.

  Although details will be described later, in the color conversion using the spline function of the present embodiment, the tone of the color is prioritized by using the spline coefficient calculated in step S605. On the other hand, high-precision color reproduction is prioritized by using the spline coefficient calculated in step S606.

Sample Point Setting Process Hereinafter, the sample point setting method in step S602 will be described in detail with reference to the flowchart of FIG.

  First, in step S1001, the maximum saturation value of the input color gamut is obtained. Next, in step S1002, a sample point is set in consideration of the maximum saturation obtained in step S1001. That is, as shown in FIGS. 7A and 7B, an arbitrary number of sample points are set so as to surround the input color gamut.

  Here, regarding the number of sample points, increasing the number increases the number of points that serve as a reference for color calculation, thereby improving the accuracy of color calculation. On the contrary, if the number is reduced, the color calculated by the non-linear function relatively increases and changes smoothly so that the gradation is improved. 7A and 7B show examples in which the number of sample points is different for the same input color gamut, and FIG. 7A is suitable for color conversion that prioritizes gradation because the number of sample points is smaller. This method is more suitable for color conversion that prioritizes color accuracy because it has more sample points. Note that the number of sample points to be set is set in advance.

  In step S1003, if the currently focused sample point is outside the input color gamut, the process proceeds to step S1004, and if it is within the input color gamut, the process proceeds to step S1005. In step S1004, the sample point is clipped to the surface of the input color gamut that minimizes the color difference, and then the process proceeds to step S1005.

  In step S1005, based on the correspondence between the Src profile and Dst profile acquired in step S601 in the LUT, the output value of the sample point is calculated using an appropriate interpolation method such as tetrahedral interpolation.

  In step S1006, it is determined whether output values for all sample points have been calculated. If all output values have been calculated, the process ends. If not, the process returns to step S1003.

Color Conversion Using Spline Function Hereinafter, color conversion using the spline function in step S405, that is, color conversion processing using the spline coefficient value determined according to the flowchart of FIG. 5, will be described in detail using the flowchart of FIG. To do.

  First, in step S1101, the RGB value of a certain pixel is read from the input image. In step S1102, the read RGB value is converted into PCS (L * C * h value) using the Src profile. In step S1103, it is determined whether or not the input L * C * h value exists in an area surrounded by the sample points set in step S602 (hereinafter referred to as input space). If it exists, the process proceeds to step S1105. If it does not exist, the process proceeds to step S1104, and the input L * C * h value is clipped to the surface of the input space that minimizes the color difference, and then the process proceeds to step S1105. In step S1105, the input L * C * h is substituted into the spline function to convert the color. Here, assuming that the input value is (l, c, h) and the output value is (L, C, H), the spline function is as follows.

  In step S1106, the L * C * h value calculated in step S1105 is converted into an RGB value using the Dst profile. In step S1107, it is confirmed whether or not color conversion has been performed for all pixels of the input image. If the color conversion has been completed for all pixels, the process is terminated. If the color conversion has not been completed, the process proceeds to step S1101.

  Here, FIGS. 12A and 13B show an example of a color conversion result using the spline function of the present embodiment. FIG. 12A shows a case in which the spline coefficient calculated in step S606 in FIG. 5 is used. Since the spline function passes through the set sample points, it is possible to calculate a color with high accuracy. On the other hand, FIG. 12B shows the case where the spline coefficient calculated in step S605 of FIG. 5 is used, and the spline function is a function that approximates the tendency of changes in sample points, and therefore is smoother than the spline function that interpolates sample points. And the tone of the color is improved.

Advantages of the present embodiment As described above, according to the present embodiment, non-linear operations in which color conversion parameters such as sample points and spline coefficients are changed are repeated until a condition for obtaining a desired color reproduction is satisfied. By applying, it is possible to determine the final color conversion parameter and calculate the color. The iterative calculation determines whether the color calculated from the nonlinear calculation for the first time satisfies a desired condition such as a color difference ΔE ≦ 10. If the condition is not satisfied, two colors are calculated for the calculated color. This is realized by repeating the operation such as applying the second non-linear operation. As described above, by appropriately setting the color conversion parameter for each repetition of the non-linear operation, it is possible to realize complicated color reproduction such as compatibility of color reproduction accuracy near gray and gradation of high saturation color. This makes it possible to obtain preferable color reproduction that is visually good for humans.

  In the present embodiment, an example in which B-Spline is used as a basis function has been described. However, any function using piecewise polynomials such as Rational B-Spline, Non Uniform Rational B-Spline, Bezier, and Rational Bezier can be used as the basis function. Since it is more desirable that the locality of the basis function exists, this embodiment has shown an example using the most representative B-Spline.

  In the present embodiment, an example in which the L * C * h color space is used as the color space for executing the nonlinear calculation for calculating the color has been described. However, there is no limitation on the color space to be used, and a CMYK color space, an L * a * b * color space, a JCh color space, an XYZ color space, an xy color space, or the like can be used. Furthermore, the present invention can be applied to ink color separation, and a 6-dimensional color space obtained by adding light cyan and light magenta to CMYK can also be used. Further, the color space to be subjected to color conversion and the color space after the color space may be the same as or different from the RGB color space in the present embodiment. For example, after an RGB color system image is input and color conversion is performed, a CMY color system image may be output.

  It should be noted that the present invention implements a basic conversion color calculation method and shows an application example of the calculation method. Therefore, the present invention is not limited to image color conversion as in the above-described embodiment, but can be applied to various uses related to image processing such as color correction, color gamut mapping, and calibration described in Patent Document 1.

<Other embodiments>
Although the embodiment has been described in detail above, the present invention can take an embodiment as a system, apparatus, method, program, storage medium (recording medium), or the like. Specifically, the present invention may be applied to a system composed of a plurality of devices, or may be applied to an apparatus composed of a single device.

  In the present invention, a software program for realizing the functions of the above-described embodiments is supplied directly or remotely to a system or apparatus, and the computer of the system or apparatus reads and executes the supplied program code. Is also achieved. The program in this case is a program corresponding to the flowchart shown in the drawing in the embodiment.

  Accordingly, since the functions of the present invention are implemented by computer, the program code installed in the computer also implements the present invention. In other words, the present invention includes a computer program itself for realizing the functional processing of the present invention.

  In that case, as long as it has the function of a program, it may be in the form of object code, a program executed by an interpreter, script data supplied to the OS, or the like.

  As a recording medium for supplying the program, for example, floppy (registered trademark) disk, hard disk, optical disk, magneto-optical disk, MO, CD-ROM, CD-R, CD-RW, magnetic tape, nonvolatile memory card , ROM, DVD (DVD-ROM, DVD-R).

  As a program supply method, the following method is also possible. That is, the browser of the client computer is connected to a homepage on the Internet, and the computer program itself (or a compressed file including an automatic installation function) of the present invention is downloaded to a recording medium such as a hard disk. It can also be realized by dividing the program code constituting the program of the present invention into a plurality of files and downloading each file from a different homepage. That is, a WWW server that allows a plurality of users to download a program file for realizing the functional processing of the present invention on a computer is also included in the present invention.

  In addition, the program of the present invention is encrypted, stored in a storage medium such as a CD-ROM, distributed to users, and key information for decryption is downloaded from a homepage via the Internet to users who have cleared predetermined conditions. It is also possible to make it. That is, the user can execute the encrypted program by using the key information and install it on the computer.

  Further, the functions of the above-described embodiments are realized by the computer executing the read program. Furthermore, based on the instructions of the program, an OS or the like running on the computer performs part or all of the actual processing, and the functions of the above-described embodiments can also be realized by the processing.

  Further, the program read from the recording medium is written in a memory provided in a function expansion board inserted into the computer or a function expansion unit connected to the computer, and then executed, so that the program of the above-described embodiment can be realized. Function is realized. That is, based on the instructions of the program, the CPU provided in the function expansion board or function expansion unit can perform part or all of the actual processing.

It is a block diagram which shows the structure of the color processing apparatus in one Embodiment which concerns on this invention. It is a figure which shows the user interface example of a color processing application. It is a flowchart which shows color calculation application operation | movement. It is a figure which shows the example of a format of Src profile LUT. It is a figure which shows the example of a format of Dst profile LUT. It is a flowchart which shows the calculation method of a spline coefficient. It is a figure showing the cylindrical coordinate system of an input value. It is a figure which shows the example of a setting of a sample point. It is a figure which shows the example of a setting of a sample point. It is a figure showing an aperiodic spline basis function. It is a figure showing a periodic spline basis function. It is a flowchart which shows the setting process of a sample point. It is a flowchart which shows the color calculation calculation using a spline function. It is a figure which shows the example of a spline function which emphasizes color reproducibility. It is a figure which shows the example of a spline function with emphasis on gradation. It is a figure which shows the production | generation method of the tetrahedron in general tetrahedral interpolation.

Claims (16)

When converting the color signal value expressed in the first color system into the color signal value of the second color system via the predetermined color system, the first color gamut in the predetermined color system A color processing method for calculating a color signal value in a second color gamut by performing a non-linear operation on the color signal value of
A sample color setting step for setting a sample color based on the correspondence between the first color gamut and the second color gamut;
A coefficient setting step for setting a coefficient in the nonlinear calculation based on the set sample color;
A calculation step for performing the nonlinear calculation based on the set coefficient;
An evaluation step for evaluating the calculation result,
When it is determined in the evaluation step that the nonlinear calculation result is not appropriate, the sample color setting step is executed again,
A color processing method comprising: determining the coefficient when it is determined in the evaluation step that the nonlinear calculation result is appropriate.   The color processing method according to claim 1, wherein the nonlinear operation is a multiple sum operation using a piecewise polynomial as a basis function.   The color processing method according to claim 2, wherein the basis function is a spline function.   The color processing method according to claim 2, wherein the basis function is one of B-Spline, Rational B-Spline, Non Uniform Rational B-Spline, Bezier, and Rational Bezier.   In the sample color setting step, as the sample color, an input value is set so as to include the first color gamut, and an output value in the second color gamut is set for the input value. The color processing method according to claim 2. In the coefficient setting step,
When the execution of the sample color setting step is the first time, a first coefficient setting step for setting the output value of the sample color as the coefficient;
A second coefficient setting step for calculating the coefficient based on the sample color and the basis function when the sample color setting step is executed after the second time;
The color processing method according to claim 5, further comprising:   In the calculating step, a third color gamut is generated by performing the non-linear calculation based on the coefficient set in the coefficient setting step for a predetermined color in the first color gamut. The color processing method according to claim 1.   8. The color processing method according to claim 7, wherein in the evaluation step, the calculation result in the calculation step is evaluated by comparing the third color gamut with the second color gamut.   In the evaluation step, any one of brightness difference, saturation difference, hue difference, color difference, gradation in the third color gamut and the second color gamut, or a combination of two or more thereof, the calculation result The color processing method according to claim 8, wherein the color processing method is calculated as an evaluation value.   The color processing method according to claim 1, wherein the coefficient is a number corresponding to a dimension of the second color system.   The color processing method according to claim 1, wherein the predetermined color system is an lch color system. When converting the color signal value expressed in the first color system into the color signal value of the second color system via the predetermined color system, the first color gamut in the predetermined color system A color processing device that calculates a color signal value in the second color gamut by performing a non-linear operation on the color signal value of
A sample color setting means for setting a sample color based on the correspondence between the first color gamut and the second color gamut;
Coefficient setting means for setting a coefficient in the nonlinear calculation based on the set sample color;
A calculation means for performing the nonlinear calculation based on the set coefficient;
Evaluation means for evaluating the calculation result,
When it is determined by the evaluation means that the nonlinear calculation result is not appropriate, the sample color is again set by the sample color setting means,
The color processing apparatus, wherein the coefficient is determined when the evaluation unit determines that the nonlinear calculation result is appropriate.   The color processing apparatus according to claim 12, wherein the non-linear operation is a multiple sum operation using a piecewise polynomial as a basis function.   The color processing apparatus according to claim 13, wherein the basis function is a spline function.   A program that, when executed on an information processing apparatus, realizes the color processing method according to any one of claims 1 to 11 on the information processing apparatus.   A computer-readable recording medium on which the program according to claim 15 is recorded.

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