JP2006203526A - Information processing method and processor - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To raise the precision of color reproduction processing by reconstructing the color gamut surface profile of a color space. <P>SOLUTION: This information processor has a color gamut surface information acquisition process for acquiring color gamut surface information concerning the geometric profile of a polyhedron representing the surface of a color gamut, a profile information acquisition process for acquiring profile information of a polygon constituting the polyhedron, a polygon detection process for comparing the acquired profile information with predetermined profile information and detecting a polygon to be an object of reconstruction, and a reconstruction process for reconstructing the detected polygon into a plurality of polygons. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to reconstructing a color gamut.

  In recent years, with the spread of personal computers, an image is input by an image input device such as a digital camera or a color scanner, and the image is displayed or confirmed using an image display device such as a CRT or LCD, or a color printer or the like. Systems have been developed that output images with image output devices. In these systems, color matching processing (color gamut mapping) is usually performed in order to correct the difference in appearance due to the difference in color gamut between the input device and the display / output device. By performing color gamut mapping, the difference in color appearance between devices is absorbed. Currently, various methods have been proposed for this color gamut mapping process. For example, Non-Patent Document 1 proposes a method of performing color gamut mapping toward one point ((L, a, b) = (50, 0, 0)) on the gray axis in the CIELAB space. In addition, a method has been proposed in which region division is performed for each hue on the CIELAB space and mapping is performed in an optimal compression direction for each region. In addition, SGCK technique is recommended in TC8-03 of the International Commission on Illumination (CIE).

  As described above, various color gamut mapping methods have been proposed according to the application, but most of these methods require input / output color gamut surface information (and sometimes image gamut information). It is said. Therefore, in order to perform accurate mapping, not only the accuracy of the mapping but also the approximation accuracy of the color gamut surface must be high.

  As an approximation method of the color gamut surface, for example, a patch created by slicing device signal values R, G, and B of an input / output device each by 9 slices is output by the input / output device, and the color is measured. There is a method for calculating the surface information in the colorimetric value space based on the surface information above. In this method, if the number of slices is increased, high approximation accuracy can be obtained. However, since it cannot be used unless the surface information on the device RGB space is known, for example, the surface information on the colorimetric value space cannot be calculated from an arbitrary set of points of the input / output device.

  On the other hand, as an example of a method for calculating a color gamut surface from an arbitrary set of points of input / output devices, there is a method using a convex hull. Convex hull is the smallest convex polyhedron that contains all of a given set of points inside, and many proposals have already been made as a method for calculating Convex hull. By using these methods, the gamut surface can be calculated from an arbitrary set of points.

E. G. Pariser, “An Investigation of Color Gamut Reduction Techniques”, IS & T Symp. Elec. Prepress Tech. -Color Printing, pp. 105-107 (1991)

  As described above, when calculating color gamut surface information from an arbitrary point set of input / output devices, a technique using Convex hull is often used. However, when the color gamut surface is calculated using Convex hull, there is a problem that approximation accuracy deteriorates in a region where the color gamut surface is concave.

  FIG. 1 shows an example of an original color gamut surface of an input / output device and an approximate color gamut surface using Convex hull. However, FIG. 1 shows an LC plane in a certain hue in the CIELAB space. As shown in FIG. 1, the actual color gamut is concave, but in the region 10 approximated to be convex, the approximate color gamut surface 11 is different from the original color gamut surface 12. This indicates that the method using the convex hull cannot be accurately approximated in a region where the color gamut surface is concave.

  If the approximation of the gamut surface is not accurate, the accuracy of gamut mapping will also deteriorate. FIG. 2 shows how the mapping accuracy deteriorates. The mapping method shown in FIG. 2 maps an input point 20 outside the output color gamut onto the output color gamut surface toward a convergence point 23 within the output color gamut. As shown in FIG. 2, since the approximation accuracy of the output color gamut is poor, the actual mapping destination 21 and the point 22 that should be mapped are shifted, and a good mapping result cannot be obtained.

  In the SGCK technique, a color gamut scaling process is performed. According to the Convex Hull technique, the polygons that are sometimes generated can be very large. In such a large polygon, a large error occurs in scaling, and as a result, color reproducibility may deteriorate. For example, when the color gamut obtained by the Convex Hull technique as shown in FIG. 30 is scaled, the result is as shown in FIG. FIG. 39 is a schematic diagram comparing the result of scaling the color gamut obtained by the Convex Hull technique with the ideal scaling result in the same hue section, and a large error occurs in the scaling. Here, the area indicated by the solid line is an equi-hue cross section calculated from the gamut scaling result obtained by the Convex Hull technique, and the area indicated by the dotted line is an area when the actual device gamut is scaled. is there. Therefore, in order to improve the accuracy of the color gamut mapping and thus improve the color reproducibility, means for reducing this scaling error is required.

  The present invention has been made to solve the above-described problems, and an object thereof is to improve the accuracy of color reproduction processing by reconstructing the color gamut surface shape of a color space.

  In order to achieve the above object, the present invention has the following structure.

  The invention described in claim 1 is a color gamut surface information acquisition step for acquiring color gamut surface information relating to a geometric shape of a polyhedron indicating the surface of the color gamut, and shape information acquisition for acquiring shape information of a polygon constituting the polyhedron. A step, a polygon detection step of comparing the acquired shape information with predetermined shape information to detect a polygon to be reconstructed, and reconstructing the detected polygon into a plurality of polygons And a reconstruction process.

  The invention according to claim 9 includes a color gamut surface information acquisition step for acquiring color gamut surface information relating to the geometric shape of the polyhedron indicating the surface of the color gamut, an analysis step for analyzing the color gamut surface information, and the analysis result. And a vertex adding step of adding a vertex constituting the surface of the color gamut.

  According to the present invention, the color gamut surface shape can be reconstructed, and the accuracy of color reproduction processing can be improved.

  According to the fourth aspect of the present invention, the color gamut surface shape can be generated with high accuracy based on the color information acquired in the color information acquisition step.

  According to the invention of claim 10 of the present application, the accuracy of the color gamut mapping process can be improved.

(First embodiment)
FIG. 3 is a block diagram illustrating a hardware configuration of the color processing apparatus 1 according to the present embodiment. Reference numeral 101 denotes an arbitrary point set acquisition unit that acquires an arbitrary point set 1001 in the color gamut of the output device.

  As shown in FIG. 7, the arbitrary point set 1001 requires a number of data that can sufficiently reproduce the shape of the color gamut, and is generally obtained by measuring the color of a patch.

  For example, it can be obtained by outputting a patch with an output device such as a printer and measuring the output patch with a colorimeter.

  Reference numeral 102 denotes a gamut surface approximation unit that approximates the gamut surface shape of the output gamut from the arbitrary point set 1001 of the output gamut acquired by the arbitrary point set acquisition unit 101 and calculates the gamut surface information 1002. is there.

  As shown in FIG. 7, the color gamut surface information 1002 is composed of the ID numbers of triangles that approximately constitute the surface of the color gamut and the coordinates of each vertex. The triangle is generated based on an arbitrary point set 1001 by a method using Convex hull. When a method using Convex hull is applied to the arbitrary point set 1001, a convex polyhedron covering all of the arbitrary point set 1001 is formed. The surface of the polyhedron approximately represents a color gamut and is formed by a set of triangles. Although it can be approximated by a set of polygons other than triangles, triangles are often used for approximation of the color gamut because the processing becomes simple. Various methods for approximating the surface shape using Convex hull have been proposed so far, and thus description thereof will be omitted in this embodiment.

  The gamut information holding unit 103 holds an arbitrary point set 1001 in the output gamut and the gamut surface information 1002 calculated by the gamut surface approximating unit 102. Reference numeral 104 denotes a buffer memory for temporarily storing each piece of data during the calculation. Reference numeral 105 denotes an accuracy determination unit that determines the approximation accuracy of the color gamut surface information 1002 calculated by the color gamut surface approximation unit 103. Reference numeral 106 denotes a color gamut surface reconstruction unit that reconstructs a low-accuracy region in the color gamut surface data according to the result determined by the accuracy determination unit 105. Reference numeral 107 denotes an output unit that outputs the color gamut information reconstructed by the color gamut surface reconstruction unit 106.

  The color processing apparatus 1 determines a triangle with poor accuracy among a plurality of triangles indicating the surface of the color gamut, and reconstructs the triangle determined to have poor accuracy.

  FIG. 6 is a flowchart showing a processing procedure executed by the color processing apparatus 1.

  In step S <b> 1, the arbitrary point set acquisition unit 102 acquires an arbitrary point set in the output color gamut held in the color gamut information holding unit 103 and stores it in the buffer memory 104. In step S2, the color gamut surface approximating unit 102 calculates the surface of the output color gamut by a method using the above-described convex Hall based on the arbitrary point set of the output color gamut acquired by the arbitrary point set acquiring unit 101 in step S1. Approximate the shape with a set of triangles. In step S3, the accuracy determination unit 105 determines the surface approximation accuracy of the output color gamut created in step S2. The accuracy determination method will be described later. In step S4, the color gamut surface reconstruction unit 106 reconstructs a triangle for the triangle determined to have poor approximation accuracy in step S3. The processing content of the color gamut surface reconstruction unit 106 will be described later. In step S5, the output unit 107 outputs the gamut surface information of the finally obtained output gamut.

  In this embodiment, the output color gamut is represented by a polyhedron. The accuracy determination in step S3 is performed for each triangle constituting the polyhedron. For example, as shown in the LC plan view of FIG. 1, there is a region 10 in which the actual color gamut is concave but approximated to be convex. As can be seen from the figure, the region 10 is approximated by a single triangle. Since a concave region that is originally approximated by a plurality of triangles is approximated as a convex shape by a single triangle, the triangle that approximates the region 10 becomes larger as indicated by 601 in FIG. Therefore, in the present embodiment, a triangle having an area larger than the threshold value K is determined to have poor accuracy, assuming that the area where the original color gamut surface is concave is approximated to be convex. Since only the area of the triangle is compared with the threshold value, the accuracy can be determined by simple processing.

  FIG. 9 shows an example of a specific processing flow of step S3.

  In step S31, the accuracy determination unit 105 sets a threshold value K. The threshold value K corresponds to the area of a triangle on the surface of the color gamut and is used when accuracy is determined. The value of K is determined empirically. The threshold value K may be held in advance in the accuracy determination unit 105 or may be acquired from the outside.

  In step S32, the accuracy determination unit 105 substitutes 1 for the count i. The count i is a counter for performing accuracy determination by the number of triangles.

In step S33, the accuracy determination unit 105 substitutes 0 for the accuracy flag F _i . The accuracy flag F _i is a flag for determining whether or not the approximate accuracy of the output color gamut surface is acceptable. The accuracy flag F _i corresponds to the triangular patch with ID number i. In this embodiment, it is assumed that the accuracy of the triangular patch with ID number i is acceptable when _Fi is 0, and not acceptable when Fi is 1.

  In step S <b> 34, the accuracy determination unit 105 acquires triangle information with ID number i from the color gamut surface information 1002 stored in the buffer memory 107 by the color gamut surface approximation unit 102 in step S <b> 24.

  In step S35, the accuracy determination unit 105 calculates the area of the triangle with the ID number i acquired in step S34. The area of the triangle is calculated from the coordinates of each vertex of the triangle.

In step S36, the accuracy determination unit 105 compares the threshold value K set in step S31 with the area A _i of the triangle of ID number i calculated in step S35. If A _i <K, the process proceeds to step S38. If A _i ≧ K, the process proceeds to step S37.

In step S37, since the area A _{i of} the triangle is larger than the predetermined threshold value K, it is determined that the approximation accuracy is poor, 1 is substituted into the accuracy flag F _i , and the process proceeds to step S38.

  In step S38, the accuracy determination unit 105 determines whether or not the ID number i matches the number N of all triangles. If i <N, the process proceeds to step S39. If i = N, the process ends.

  In step S39, the accuracy determination unit 105 increments the count i and returns to step S33.

  Next, a specific method for reconstructing a triangle determined to be inaccurate will be described.

  FIG. 10 shows an example of a specific processing flow of the color gamut surface reconstruction unit 106 in step S4.

  In step S41, the color gamut surface reconstruction unit 106 substitutes 1 for the count i. The count i is a counter for determining whether or not reconstruction is performed by the number of triangles.

In step S42, the gamut surface reconstruction unit 106 refers to the values of the set accuracy flag _{F i} in step S3. In the case of F _i = 0, since the triangle is determined to have good approximation accuracy, the reconstruction process ends. If F _i = 1, the process proceeds to step S43.

  In step S <b> 43, the color gamut surface reconstruction unit 106 acquires, from the buffer memory 104, the triangle ID information determined to have poor accuracy in step S <b> 3. In step S44, the color gamut surface reconstruction unit 106 defines a reconstruction range of the color gamut surface. The reconstruction range is a range for selecting data in the color gamut used for reconstruction of a triangle.

  For example, as shown in FIG. 11, a straight line perpendicular to the surface of the triangle 1101 determined to be inaccurate is extended from each vertex of the triangle in the direction of the color gamut, and the obtained triangular prism region is used as the reconstruction range 1102. .

  In step S <b> 45, the gamut surface reconstruction unit 106 acquires an arbitrary point set 1001 in the output gamut stored in the gamut information holding unit 103 and the gamut surface information 1002.

  In step S46, the color gamut surface reconstruction unit 106 extracts a point set 1204 obtained by removing the point 1203 on the color gamut surface 1201 from the arbitrary point set 1001 in the output color gamut acquired in step S45. The extracted point set 1204 is a point in the second color gamut surface 1202 shown in the LC plane of FIG. The second color gamut surface 1202 shown in FIG. 12 is calculated from the point set 1204 extracted by using the Convex Hull by the color gamut surface reconstruction unit 106 after extracting the point set.

  In step S47, the color gamut surface reconstruction unit 106 reconstructs points included in the reconstruction range 1102 defined in step S44 among the points constituting the second color gamut surface calculated in step S46. Get as. The reconstruction point 1302 is used when the triangle 1101 determined to have poor approximation accuracy is reconstructed into a plurality of triangles. The relationship among the color gamut surface 1201, the second color gamut surface 1202, the reconstruction range 1102, and the reconstruction point 1302 is shown in FIG. As shown in FIG. 13, a point on the second color gamut surface 1202 included in the reconstruction range 1101 becomes a reconstruction point 1302.

  In step S48, the color gamut surface reconstruction unit 106 reconstructs the color gamut surface using the color gamut surface data and the reconstruction point 1302 acquired in step S47. Only the target triangle 1101 is reconstructed, and each vertex 1301 of the triangle 1101 is connected to the reconstruction point acquired in step S47 to construct a plurality of new triangles. The gamut surface after the triangle is reconstructed is as shown by 1401 in FIG. The reconstructed new triangle is composed of a vertex 1301 and a reconstructed point 1302 constituting the original triangle, and becomes a triangle as shown in FIG. FIG. 15 is a view of the reconstructed new triangle as viewed from the direction of the arrow 1402 in FIG. 14 and is constructed so as to be concave in the 1402 direction. Since the convex plane determined to have a large error is converted into a concave plane, it becomes close to the original color gamut surface and the approximation accuracy is good.

  If a polyhedron showing a color gamut is reconstructed using this embodiment, a polyhedron with good approximation accuracy can be provided when gamut mapping or displaying a color gamut in a pseudo three-dimensional manner.

(Second embodiment)
FIG. 4 is a block diagram illustrating a hardware configuration of the color processing apparatus 2 in the present embodiment. The configuration is almost the same as the configuration of the color processing apparatus 1 in the first embodiment. Therefore, description of 101 to 107 having the same configuration as that of the color processing apparatus 1 is omitted.

  Reference numeral 208 denotes a gamut mapping unit that maps input image data used in this embodiment into an output color gamut. Reference numeral 211 denotes an input unit for inputting image data and color gamut surface information 1002 used in this embodiment.

  In this embodiment, when performing gamut mapping, it is determined whether or not to reconstruct a triangle used for gamut mapping. If it is determined to reconstruct, a triangle used for gamut mapping is reconstructed.

  The specific processing flow is shown below using FIG.

  In step S <b> 61, the input unit 211 reads color image data to be gamut mapped and stores it in the buffer memory 104. In step S <b> 62, the input unit 211 acquires the color gamut surface information 1002 and holds it in the color gamut information holding unit 103. In step S63, the gamut mapping unit 208 acquires the pixels in the color image data from the buffer memory 104 and the color gamut surface information from the color gamut information holding unit 103, and extracts a triangle 1903 used for gamut mapping. In this embodiment, a triangle indicating an output color gamut that intersects a half line drawn from a point 1803 in the output color gamut to the input point 1801 is a triangle used for gamut mapping.

  In S64, the accuracy determination unit 105 determines the accuracy of the triangle used for the gamut mapping, and determines the accuracy of the gamut mapping. In this embodiment, the approximation accuracy of a triangle used for gamut mapping is determined, and if the approximation accuracy of a triangle is poor, it is determined that the accuracy of gamut mapping also deteriorates. The method for determining the approximation accuracy of the triangle used for gamut mapping is the same method as in the first embodiment. In S64, if it is determined that the mapping accuracy is deteriorated, the process proceeds to S65 in order to reconstruct the triangle without performing gamut mapping. In S65, the color gamut surface reconstruction unit 106 reconstructs the triangle determined to have poor mapping accuracy. The triangle reconstruction method is the same method as in the first embodiment.

  If it is determined in S64 that the mapping accuracy is not bad, in S66, the gamut mapping unit 208 performs gamut mapping on the point indicating the input pixel. Specific processing of gamut mapping will be described later.

  After gamut mapping in S66, the process proceeds to S67 to determine whether or not gamut mapping has been performed for all pixels included in the input image. After the reconstruction in S65 or when it is determined in S67 that gamut mapping has not been performed for all pixels of the input image, the process returns to S63 again to extract triangles used for gamut mapping. When gamut mapping has been performed for all pixels, the image data that has been gamut mapped in S68 is output to the output unit 107. The above is the processing flow in the present embodiment.

  Next, a method for extracting triangles used by the gamut mapping unit 208 for gamut mapping will be described.

  As described above, the output color gamut of this embodiment is composed of a collection of triangles obtained by Convex hull. Therefore, triangles are extracted using the processing shown in FIG.

  In step S81, a point that is easily recognized as being within the output color gamut, for example, a point of (L, a, b) = (50, 0, 0) is defined. In step S82, one triangle is extracted from the color gamut surface information 1002, and a plane equation defined by three vertices constituting the triangle is calculated. In step S83, an equation of a straight line connecting the output color gamut in-point 1803 (50, 0, 0) defined in step S81 and the point 1801 indicating the input pixel is calculated. In step S84, the intersection of the plane and the straight line is calculated from the plane equation calculated in step S82 and the straight line equation calculated in step S83. In step S85, it is determined whether or not the intersection calculated in step S84 is in a triangle. FIG. 18 shows an example of triangle inside / outside determination. As shown in FIG. 18, assuming that the vertices of the triangle are A, B, and C, respectively, and the intersection of the triangle and the straight line calculated in step S84 is P, each point is expressed by the following equation (3). Can be represented.

  At this time, if the point P is in the triangle ABC, the following equations (4) and (5) hold.

  If Expressions (4) and (5) hold, it is determined that the intersection is in the triangle. The triangle with the intersection point in it is a triangle used for determining the inside / outside of the input point, and is a target for determining whether or not to reconstruct as a triangle used for gamut mapping in the flow of FIG. If Expression (4) and Expression (5) do not hold, it is determined that the intersection is outside the triangle, and the process proceeds to Step S86.

  In step S86, if the straight line and the triangle do not have an intersection, the triangle ID number of the output color gamut surface data is updated, and the process returns to step S82.

  In this embodiment, a method for determining whether or not an input point is in the color gamut will be described. FIG. 19 shows the relationship between the input point 1801, the intersection point 1802, and the in-gamut point 1803. As shown in FIG. 19, there are two possible positional relationships of the input point 1801, the intersection point 1802, and the color gamut point 1803 (a) and (b). Assuming that the input point 1801 is Q, the intersection point 1802 is P, and the in-gamut point 1803 is R, the determination method of both is, for example, whether v in Expression (6) below is 0 ≦ v ≦ 1. Is possible.

  When 0 ≦ v ≦ 1, the distance from the input point 1801 to the color gamut internal point 1803 is equal to or less than the distance from the intersection 1802 to the color gamut internal point 1803. Therefore, the input point 1801 is determined to be within the output color gamut (the state shown in FIG. 19B).

  On the other hand, when 0 ≦ v ≦ 1, the distance from the input point 1801 to the color gamut internal point 1803 is larger than the distance from the intersection 1802 to the color gamut internal point 1803. Accordingly, it is determined that the input point 1801 is out of the output color gamut (the state shown in FIG. 19A).

  The above is the color gamut inside / outside determination method in this embodiment.

  Next, the gamut mapping process in the present embodiment will be described.

  The gamut mapping in step S66 is performed as shown in FIG. For example, when the input point 1801 indicating the input pixel is outside the output color gamut, the input pixel is set as post-mapping data.

  When the input point 1801 indicating the input pixel is outside the output color gamut, a straight line connecting the input point 1801 and the point 1803 in the output color gamut is defined, and the intersection with the triangle on the surface of the output color gamut that intersects the straight line Map. That is, the coordinates of the intersection are set as post-mapping data.

  The specific process flow for realizing the gamut mapping shown in FIG. 20 will be described below using the flow shown in FIG.

  In step S <b> 71, the gamut mapping unit 208 acquires pixel values (R, G, B values) of the input image held in the buffer memory 104. In step S <b> 72, the gamut mapping unit 208 acquires the color gamut surface information 1002 from the color gamut information holding unit 103.

In step S73, the gamut mapping unit 208 converts the input pixels (R, G, B values) acquired in step S71 into device-independent CIELAB values. In the present embodiment, the subsequent processing will be described assuming that the color space expressing the input image is sRGB (IEC 61966-2-1). If the RGB values of the input pixels are R _i , G _i , and B _i , first, the CIE tristimulus values XYZ are converted using the following equation (1).

Then, to convert the white point in the CIELAB as _{D 65.} The conversion formula is shown in Formula (2).

Hereinafter, the input pixel converted into CIELAB is assumed to be (L _i , a _i , b _i ). In the present embodiment, the case where the input image is expressed in the sRGB color space defined by IEC 61966-2-1 has been described as an example, so the conversion expressions shown in Expression (1) and Expression (2) are used. . However, the color space expressing the image may be other color spaces such as the AdobeRGB color space instead of the sRGB color space. In the case of another color space, Equation (1) corresponds to the color space. In addition, a white point corresponding to the color space is used as a white point when converting to CIELAB.

  In step S74, the gamut mapping unit 208 determines whether or not the input pixel is in the output color gamut using the triangle used for gamut mapping. If the triangle used for gamut mapping has been reconstructed in step S65, the triangle used for gamut mapping is newly extracted using the above-described method for extracting the triangle, and whether or not the input pixel is within the output color gamut. Determine.

  If it is determined that the input pixel is in the color gamut, the value of the input pixel is substituted into the post-mapping data, and the process proceeds to step S76.

  If it is determined that the input pixel is out of the color gamut, the process proceeds to step S75. In step S75, the post-mapping data is an intersection of a line connecting the input point 1801 and the in-gamut point 1803 and a triangle used for gamut mapping. Since the method for calculating the intersection point was described in the description of the color gamut inside / outside determination method, it is omitted here.

  In step S76, the gamut mapping unit 208 calculates an output device value corresponding to the post-mapping data 1901, for example, an RGB value. The device value is calculated using, for example, an LUT that represents the relationship between the device value and the CIELAB value, a conversion matrix, or the like. In the case of LUT, conversion is performed using a known technique such as tetrahedral interpolation.

  The above is the specific processing flow of gamut mapping in the present embodiment.

  In this embodiment, the object used to determine whether or not to reconstruct the triangle used for gamut mapping. Triangles used for gamut mapping are used for color gamut inside / outside determination. Further, when gamut mapping is performed on an input point outside the color gamut, the point after mapping becomes a point on a triangular plane used for gamut mapping. As shown in FIG. 2, when the actual color gamut is concave but the area 10 approximated to be convex is a triangle used for mapping, the mapping destination is 21. However, since the color indicated by the actual mapping destination 21 is outside the output color gamut, it cannot be output by the output device.

  In addition, when the point 24 used for the inside / outside determination is located between the original color gamut surface and the approximate color gamut surface, it may be determined that it is outside the color gamut but is within the color gamut. is there.

  Therefore, by using the method of reconstructing the triangle used for the gamut mapping of this embodiment, the above problem can be prevented and better gamut mapping can be performed.

(Third embodiment)
FIG. 3 is a block diagram illustrating a hardware configuration of the color processing apparatus 3 in the present embodiment. The configuration is almost the same as the configuration of the color processing apparatus 1 in the first embodiment. Therefore, description of 101 to 107 having the same configuration as that of the color processing apparatus 1 is omitted. Reference numeral 309 denotes a polyhedron display unit that displays a polyhedron indicating a color gamut on a display device such as a monitor in a pseudo three-dimensional manner. An instruction acquisition unit 310 acquires an instruction from the user.

  In the first embodiment and the second embodiment, a triangle having a large area is determined to have poor approximation accuracy. However, in the present embodiment, a process of reconstructing a triangle determined to be inaccurate according to a user instruction. The processing flow is shown below using FIG.

  In S <b> 51, the color gamut surface information 1002 is acquired from the color gamut holding unit 103.

  In S52, based on the acquired color gamut surface information 1002, the polyhedron display unit 309 displays the polyhedron indicating the color gamut in a pseudo three-dimensional display. The polyhedron is displayed on a display device such as a monitor as shown in FIG.

  In S53, a user instruction is acquired from the instruction acquisition unit 310, and a triangle to be reconstructed is selected. At this time, since the polyhedron is displayed in a pseudo three-dimensional manner, the user can easily select a triangle to be reconstructed using a pointing device such as a mouse. Further, the area of each triangle may be displayed together with the polyhedron so that the user can easily select it.

  In S54, the color gamut surface reconstruction unit 106 reconstructs the selected triangle into a plurality of triangles. Since the method for reconstructing the triangle is the same as the method described in the first embodiment, the description thereof is omitted. In S55, it is determined whether there is another triangle to be reconstructed based on a user instruction. If there are other triangles to be reconstructed, the process returns to S52 to display the polyhedron again. If there is no other triangle to be reconstructed, the process proceeds to S56, where the color gamut surface information is output, and the process ends. In the present embodiment, a triangle to be reconstructed is designated based on a user instruction. Therefore, it is possible to perform processing that reflects the user's demands, such as not reconstructing triangles that constitute areas where good approximation accuracy is not required.

(Fourth embodiment)
In the second embodiment, the processing has been described on the assumption that the input image is expressed in sRGB (IEC 61966-2-1). However, the input to the color processing apparatus in this embodiment is particularly sRGB. The color space is not limited to this, and may be another color space such as AdobeRGB.

  In the first to third embodiments, the color gamut information is held in advance in the color gamut information holding unit. However, the present invention is not limited to this. For example, the color gamut information may be read from the outside via the input unit. Alternatively, a plurality of pieces of color gamut information may be held in advance in the color gamut information holding unit, and color gamut information corresponding to input from the outside may be selected.

  In the gamut mapping unit 208 in the second embodiment, the colors in the output color gamut are completely stored, and the colors outside the output color gamut include a line connecting the point indicating the color and a certain point in the color gamut, and the output color. Although the method of converting to the color of the intersection with the gamut surface has been described, for example, it may be converted to the color on the output gamut surface with the same lightness as the color outside the output gamut. Further, it may be converted into a color that minimizes the Euclidean distance (color difference ΔE) in the CIELAB space without changing the hue. Needless to say, it is not necessary to completely store colors in the output color gamut, and it may be mapped to preferable colors both inside and outside the output color gamut.

  In the second embodiment, the CIELAB space is used as a mapping space. However, this may be a CIEUV space or an XYZ space, or a color appearance space such as CIECAM97, CIECAM97s, or CIECAM02. It doesn't matter.

  Moreover, in the said Example, the accuracy of the color gamut surface was determined using the area of a triangle as triangle shape information. However, for example, the determination may be made based on the sum of the lengths of the sides of the triangle, or more specifically, any method can be used as long as it can determine the approximate accuracy of the color gamut surface.

  In the color gamut surface reconstruction unit in the above embodiment, a triangular prism region including a triangle is defined as a reconstruction range. However, the reconstruction range is not limited to this, and for example, a triangle and an origin are connected. It may be a triangular pyramid region or a cube surrounding a triangle.

(Fifth embodiment)
In the above embodiment, a color gamut surface shape generated from an arbitrary point set is analyzed, a low accuracy region is detected and reconstructed to generate a high accuracy color gamut surface shape.

  On the other hand, the present embodiment divides an area satisfying a predetermined condition of the color gamut information in order to make the scaling process performed in the color gamut mapping highly accurate.

  FIG. 23 is a block diagram showing a system configuration of a color reproduction editing apparatus as a fifth embodiment. 23, 401 is the CPU, 402 is the main memory, 403 is the SCSI interface, 404 is the network interface, 405 is the HDD, 406 is the graphic accelerator, 407 is the color monitor, 408 is the USB controller, 409 is the color printer, 410 Is a keyboard / mouse controller, 411 is a keyboard, 412 is a mouse, 413 is a local area network, 414 is a PCI bus, and 415 is a scanner.

  A digital image printer output operation in the above configuration will be described. First, an image application stored in the HDD 405 is activated by the CPU 401 based on an OS program that has received a user instruction. Subsequently, the digital image data stored in the HDD 405 is transferred to the main memory 402 via the PCI bus 414 via the SCSII / F 403 based on a command from the CPU 401 in accordance with processing in the image application according to a user instruction. Alternatively, image data stored in a server connected to the LAN or digital image data on the Internet is transferred to the main memory 402 via the PCI bus 414 via the network I / F 404 in response to a command from the CPU 401. Hereinafter, the embodiment will be described on the assumption that the digital image data held in the main memory 402 is image data in which each RGB color signal is expressed by 8 bits without a sign. The digital image data held in the main memory 402 is transferred to the graphic accelerator 406 via the PCI bus 414 in response to a command from the CPU 401. The graphic accelerator 406 performs D / A conversion on the digital image data, and then a color monitor through a display cable. 407. As a result, an image is displayed on the color monitor 407. Here, when the user instructs the image application to output the digital image held in the main memory 402 from the printer 409, the image application transfers the digital image data to the printer driver. The CPU 401 converts digital image data into CMYK digital image data based on a process flowchart described later of the printer driver, and transmits the CMYK image data to the printer 409 via the USB controller 408. As a result of the above series of operations, a CMYK image is printed by the printer 409.

  Hereinafter, the printer driver operation in the above configuration will be described with reference to the flowchart of FIG.

  First, in step S <b> 101, RGB 24-bit image data (each color signal is 8-bit unsigned image data) transferred from the image application is stored in the main memory 402.

  In step S102, a color correction LUT is created based on the processing described later. The color correction LUT has a data structure as shown in FIG. 25 which describes the correspondence between the color coordinate data of the grid points in the RGB color space and the coordinate values of the L * a * b * color space reproduced by the grid points. Is done. The R / G / B value step is described at the top of the data structure, and thereafter, the color coordinates corresponding to each grid point are L * value, a * value, and b * value are R, G, and B, respectively. Nested in order. In the following, the color correction LUT is also referred to as color distribution data because the data structure indicates a color distribution.

  In step S103, the RGB 24-bit image data is converted into L * a * b * fixed-point data based on the color correction LUT and stored in the main memory 402 again. Tetrahedral interpolation is used for the conversion.

  In step S104, the previous L * a * b * fixed-point data is converted into CMYK 32-bit image data (CMYK color signals are unsigned 8-bit image data) according to a predetermined color conversion LUT, and again stored in the main memory 402. Store.

  In step S <b> 105, the CMYK 32-bit image data stored in the main memory is transmitted to the printer 409 via the USB controller 408.

  Hereinafter, the color correction LUT creation processing in step S102 will be described with reference to the flowchart of FIG.

  In step S201, mapping source color reproduction information and mapping destination color reproduction information determined by a method (not shown) are acquired. These color reproduction information may be, for example, sRGB conversion formulas or device color reproduction characteristics measured in advance. As an example, a schematic diagram of color reproduction of a printer is shown in FIG.

  In step S202, the same-color section information of the color gamut of the mapping source and the mapping destination is obtained from the acquired color reproduction information of the mapping source and the color reproduction information of the mapping destination based on the processing flow described later. Generate in space. Here, 180 pieces of information are generated every two degrees as the equi-hue cross section information.

  In step S203, based on the SGCK technique recommended in TC8-03 of the International Commission on Illumination (CIE) for the mapping operation in step 404, the equi-hue cross-section information of the color gamut of the mapping source is obtained as described later. Scale in L * a * b * color space.

  In step S204, color gamut mapping is performed in the L * a * b * color space, as will be described later, based on the SGCK technique.

  In step S205, the mapping result in step S204 is converted from L * a * b * values to RGB values using the color reproduction information of the mapping destination to generate a color correction LUT.

  An outline of mapping processing (color gamut mapping) by the SGCK technique will be described with reference to the flowchart of FIG. An example of the processing is shown in FIGS. Details of the process are described in the TC8-03 document of the International Commission on Illumination.

  In step S501, the color to be mapped is acquired. In FIG. 37a, the solid line indicates the color reproduction of the mapping source, and the halftone dot indicates the color reproduction of the mapping destination.

  In step S502, the brightness value is scaled so as to normalize the brightness range. An example of the change in color reproduction due to this processing is shown in FIG. Here, the dashed line represents the color reproduction of the mapping source, the solid line represents the color reproduction after processing, and the dotted line represents the color gamut of the mapping destination.

  In step S503, the brightness value is scaled using a sigmoid function. An example of the change in color reproduction due to this processing is shown in FIG. Here, the dashed line represents the color reproduction that is the processing result of step S502, the solid line represents the color reproduction after processing, and the dotted line represents the color gamut of the mapping destination.

  In step S504, the lightness value is corrected based on the saturation value. An example of the change in color reproduction due to this processing is shown in FIG. Here, the one-dot broken line represents the color reproduction that is the processing result of step S503, the solid line represents the color reproduction after the processing, and the dotted line represents the color gamut of the mapping destination.

  In step S505, brightness and saturation are mapped while the hue value is preserved. This mapping will be described with reference to FIG. In the figure, the solid line represents the color gamut of the mapping source scaled in step S203, and the broken line represents the color gamut of the mapping destination. When mapping the color M, first, the equal hue section information closest to the hue of the color M is acquired from the scaled equal hue section information and the mapped destination hue section information. Subsequently, the lightness value that gives the maximum saturation with the same hue as M is calculated from the equi-hue cross-section information of the mapping destination, and the color on the L * axis having the lightness is defined as F. Thereafter, an intersection point Bsrc between the line connecting F and M and the scaled uniform hue section information of the mapping source is calculated, and an intersection point Bdst between the line connecting F and M and the uniform hue section information of the mapping destination is calculated. calculate. Subsequently, a point C that internally divides between Bdst and F at 1: 9 is calculated. Here, if the distance between M and F is less than the distance between Bdst and C, no mapping is performed for the color M. On the other hand, if it exceeds, M is the value that internally divides Bsrc and C, and the color that internally divides between Bdst and C is the mapped value of M.

  Next, the creation processing of the mapping source color gamut information and the mapping destination color gamut information in step S202 will be described with reference to the flowchart of FIG.

  In step S301, color distribution information in the color gamut is generated from the color gamut information of the mapping source, and initial color gamut information of the mapping source is generated from the generated color distribution information using the Convex Hull technique. As an example of these, FIG. 29 shows color distribution information, and FIG. 30 shows initial color gamut information generated by the Convex Hull technique. Note that the color gamut information is expressed by a combination of a plurality of polygons.

  In step S302, intersections between the hue plane and the polygons in the color gamut are obtained, and these points are connected to generate an equivalent hue cross section. In FIG. 31, the equi-hue surface is indicated by a one-dot chain line, and the equi-hue section is indicated by a dotted line.

  In step S303, the line segment selection information of the line segment that forms the equi-hue section is cleared. In step S304, one line segment is selected according to the line segment selection information, and information is acquired. In step S305, it is determined whether the length of the selected line segment is longer than a predetermined value. If true, the process proceeds to step S306, and if false, the process proceeds to step S307.

  In step S306, one or more internal dividing points are inserted into the selected line segment and divided so that the selected line segment is composed of a line segment shorter than a predetermined value. This operation is illustrated in FIG. In the figure, line segment L indicates the selected line segment, and points M1 and M2 indicate internal dividing points. Thereby, it divides | segments into a some line segment like T1-T3.

  In step S307, the selected line segment information is updated so that the next different line segment is selected. In step S308, it is determined whether all line segments have been selected. If the determination is true, the process is terminated. At this time, the length of all line segments is shorter than a predetermined value. If false, the process proceeds to step S304.

  An example of the processing result by the above-described series of operations is shown in FIG. FIG. 33b shows a result of performing the above-described processing on the equi-hue cross section shown in FIG. 33a. In FIG. 33b, the number of points is increased compared to FIG. 33a.

  The gamut scaling processing based on the SGCK technique for the gamut equal hue section information generated in step 402 in step S203 will be described with reference to the flowchart of FIG.

  In step S401, equi-hue cross-section information of the color gamut generated in step 402 is acquired. In step S402, the lightness value is scaled so as to normalize the lightness range for each vertex of the line segment constituting the equi-hue cross section information of the color gamut, as in step S502. In step S403, the lightness value is scaled using the sigmoid function for each vertex of the line segment constituting the equi-hue cross section information of the color gamut, as in step S503. In step S404, the lightness value is corrected based on the saturation value for each vertex of the line segment constituting the equi-hue cross section information of the color gamut, as in step S504. In step S405, the color gamut information that has undergone the processing in steps S402 to S404 is stored in the main memory 402.

  As described above, in this embodiment, the processes in steps S402 to S404 are performed on the vertex. And between each vertex, it calculates by linearly interpolating the scaled vertex data.

  In the present embodiment, in the color gamut information generation process shown in FIG. 28, a line segment longer than a predetermined value is divided from the line segments constituting the equal hue section of the color gamut. That is, the vertex data to be processed in steps S402 to S404 is increased. Thereby, it is possible to control so that an error caused by linear interpolation is equal to or less than a predetermined value, and it is possible to realize highly accurate scaling processing.

  An example of the processing result of the above-described series of operations is shown in FIG. Here, the area indicated by the solid line is the calculated equal hue section, and the area indicated by the dotted line is the area of the scaling result. As is apparent from the figure, the scaling error is smaller than that in FIG. 39, which is an example in the case of not using this embodiment.

(Sixth embodiment)
In the fifth embodiment, as shown in FIG. 28, when the length of the line segment constituting the hue section of the color gamut generated by the Convex Hull technique is long, the process of adding the vertex for dividing the line segment is performed. Is going. On the other hand, in this embodiment, vertex addition processing is performed for each side constituting the polygon.

  Hereinafter, differences from the fifth embodiment will be described.

  In the present embodiment, the process of creating the mapping source color gamut information and the mapping destination color gamut information in step S202 will be described with reference to FIG.

  In step S601, color distribution information in the color gamut is generated from the color gamut information of the mapping source, and initial color gamut information of the mapping source is generated from the generated color distribution information using the Convex Hull technique. As an example of these, FIG. 29 shows color distribution information, and FIG. 30 shows initial color gamut information. Note that the color gamut information is expressed by a combination of a plurality of polygons.

  In step S602, the edge selection information of the edges constituting the polygon is cleared. In step S603, one side is selected according to the side selection information, and information is acquired. In step S604, it is determined whether the length of the selected side is longer than a predetermined value. If true, the process proceeds to step S605, and if false, the process proceeds to step S606. In step S605, each of the two polygons including the selected side is divided as follows. First, a point is inserted at the midpoint of the selected side. Then, a new side is generated by connecting a line segment to points other than the end points of the selected side. Finally, the polygon is divided into a plurality of triangular patches using the newly generated side. This operation is illustrated in FIG. In the figure, the solid line indicates the selected polygon and the midpoint where the point M is inserted. The dotted line represents a newly generated side, and is thereby divided into a plurality of triangular patches as T1 to TN. Note that there is a relationship of N = M−2 between the number M of polygon vertices and the triangular patch N generated by division. FIG. 41a shows a case where the polygon is a triangle, and FIG. 41b shows a case where the polygon is a polygon different from the triangle. As described above, the present embodiment is not limited to the shape of a polygon.

  In step S606, the selected side information is updated so as to select a different side next.

  In step S607, it is determined whether all sides have been selected. If the determination is true, the process ends. At this time, the length of all sides is shorter than a predetermined value. If false, the process proceeds to step 603.

  FIG. 42 shows an example of processing results obtained by the series of operations described above. This is a result of processing performed on the color gamut information shown in FIG.

  As described above, in this embodiment, the process of adding the vertex is performed for each side constituting the polygon. Therefore, as shown in FIG. 42, the vertices constituting the polygon are appropriately arranged.

  In this embodiment, the gamut scaling processing described in the fifth embodiment with reference to FIG. 34 is performed on each vertex of the polygon constituting the gamut information. FIG. 43 shows an equi-hue sectional view calculated from the result of scaling processing performed on the color gamut information shown in FIG. Here, the area indicated by the halftone dots is the equi-hue cross section calculated by the present embodiment, and the area indicated by the solid line is an area when the actual device color gamut is scaled. Compared to FIG. 39, which is an example in the case where the present embodiment is not used, the scaling error is smaller.

  According to the present embodiment, by preparing means for re-dividing the generated color gamut information, it is possible to improve the scaling accuracy in the color gamut scaling required for the subsequent color gamut mapping. It becomes. As a result, it is possible to improve the accuracy of the color gamut mapping and to improve the color reproducibility of the output image.

  In this embodiment, the description has been made assuming that the color gamut information is composed of polygons or triangular patches. However, the present embodiment can also be applied when the color gamut information is expressed as a set of nonlinear curved surfaces.

(Seventh embodiment)
In the sixth embodiment, whether or not to add a vertex is determined for each side constituting the polygon. In the present embodiment, vertex addition processing is performed for each polygon based on the area of the polygon.

  The creation processing of the mapping source color gamut information and the mapping destination color gamut information in step S202 in this embodiment will be described with reference to the flowchart of FIG.

  In step S701, color distribution information in the color gamut is generated from the color gamut information of the mapping source, and initial color gamut information of the mapping source is generated from the generated color distribution information using the Convex Hull technique. In step S702, the polygon selection information in the polygon constituting the color gamut information is cleared. In step S703, information is acquired by selecting one polygon in accordance with the polygon selection information. In step S704, it is determined whether the area of the selected polygon is larger than a predetermined value. If true, the process proceeds to step S705, and if false, the process proceeds to step S706. In step S705, the selected polygon is divided as follows. First, a point is inserted at the center of gravity of the selected polygon. Thereafter, the polygon is divided into triangular patches by connecting line segments to the vertices of the polygon. Further, the generated triangular patch is divided into two by drawing a line from the inserted point so that the areas are equal.

  In step S706, the selected polygon information is updated so as to select a different polygon next. In step S707, it is determined whether all the polygons have been selected. If the determination is true, the process ends. At this time, the size of all the polygons is smaller than a predetermined area. If it is false, the process proceeds to step S703. This operation is illustrated in FIGS. 45a and b. In the figure, the solid line indicates the selected polygon, and the barycentric point where the point M is inserted is shown. The dotted line represents a newly generated side, and is thereby divided into a plurality of triangular patches as T1 to TN. Note that there is a relationship of N = 2 × M between the number M of vertexes of the polygon and the triangular patch N generated by division.

  In the fifth to seventh embodiments, the SGCK technique is used as the color gamut mapping. However, the present invention can also be applied to other color gamut mapping methods that perform lightness or saturation scaling processing.

(Modification)
In the above embodiment, the color gamut surface is approximated by a set of triangles. However, you may approximate with other polygons, such as a rectangle, instead of a triangle.

  Note that the above embodiment can be applied to a system composed of a plurality of devices (for example, a host computer, an interface device, a reader, a printer, etc.), but a device composed of a single device (for example, a copier, a facsimile machine, etc.) ) May be applied.

  In addition, a recording medium in which a program code of software that realizes the functions of the above-described embodiments is supplied to a system or apparatus, and the computer (or CPU or MPU) of the system or apparatus stores the program code in the recording medium. The method described in this embodiment may be performed by reading out and executing. In this case, the program code itself read from the recording medium realizes the functions of the above-described embodiment, and the recording medium storing the program code constitutes the present embodiment.

  As a recording medium for supplying the program code, for example, a floppy (registered trademark) disk, a hard disk, an optical disk, a magneto-optical disk, a CD-ROM, a CD-R, a magnetic tape, a nonvolatile memory card, a ROM, or the like is used. I can do it.

  Further, by executing the program code read by the computer, not only the functions of the above-described embodiments are realized, but also an OS (operating system) running on the computer based on the instruction of the program code, etc. However, it is needless to say that a case where the part of the actual processing is performed and the function of the above-described embodiment is realized by the processing is included.

  Further, the program code read from the recording medium is written into a memory provided in a function expansion board inserted into the computer or a function expansion unit connected to the computer, and then the function expansion is performed based on the instruction of the program code. It goes without saying that the CPU or the like provided in the board or function expansion unit performs part or all of the actual processing, and the functions of the above-described embodiments are realized by the processing.

Example where the approximation accuracy of the color gamut surface deteriorates Example of poor gamut mapping accuracy due to poor approximation accuracy of the gamut surface The figure explaining the structure of the color processing apparatus 1 The figure explaining the structure of the color processing apparatus 2 The figure explaining the structure of the color processing apparatus 3 Flow for reconstructing gamut surface data The figure which shows the data structure of the arbitrary point sets 1001 and the color gamut surface information 1002 Diagram showing polyhedron generated using ConvexHull Flow to detect triangles above threshold Flow to reconstruct triangle Diagram showing the relationship between triangle and reconstruction range Diagram showing the positional relationship between the gamut surface and the second gamut surface Diagram showing the relationship between the gamut surface, the second gamut surface, and the reconstruction range Diagram showing the reconstructed color gamut surface Diagram showing the new triangles built Flow to detect inaccurate triangles when performing gamut mapping Flow for performing gamut mapping The figure which shows the method of determining whether P point exists on the triangle ABC. The figure which shows the method of determining whether the input point Q exists in a color gamut. Diagram showing mapping method Flow to reconstruct triangle Flow for determining inside / outside of input point Block diagram showing the system configuration of the color reproduction editing device Flowchart showing processing of printer driver in color reproduction editing apparatus The figure explaining the data structure of a color correction LUT Flowchart showing color correction LUT creation processing Diagram showing an example of color reproduction information Flowchart showing creation processing of equi-hue cross-section information of color gamut The figure which shows an example of the color distribution obtained from color reproduction information The figure which shows an example of the color gamut information produced | generated by Convex Hull technique The figure which shows an example of slicing color gamut information on a uniform hue plane The figure which shows an example of the divided line segment information The figure which shows an example of the produced | generated equi-hue cross-section information Flow chart showing scaling of gamut information using SGCK technique The figure which shows an example of the scaled equi-hue cross-section information Flow chart showing SGCK technique as gamut mapping The figure which shows the change of the color reproduction with SGCK technique Diagram explaining color gamut mapping with SGCK technique Result of scaling process by conventional technology Flow chart showing color gamut information creation processing The figure which shows an example of the division | segmentation of the polygon which comprises color gamut information The figure which shows an example of the produced | generated color gamut information The figure which shows an example of the equi-hue cross section of color gamut information Flow chart showing color gamut information creation processing The figure which shows an example of the division | segmentation of the polygon which comprises color gamut information

Claims (16)

A color gamut surface information acquisition step for acquiring color gamut surface information related to the geometry of the polyhedron indicating the surface of the color gamut;
A shape information acquisition step of acquiring shape information of a polygon constituting the polyhedron;
A polygon detection step of comparing the acquired shape information with predetermined shape information and detecting a polygon to be reconstructed;
A reconstruction step of reconstructing the detected polygon into a plurality of polygons. The information processing method according to claim 1, wherein the polygon detection step detects a polygon used when performing gamut mapping. A display step of displaying a polyhedron indicating the surface of the color gamut;
A selection step of selecting a polygon to be reconstructed based on a user instruction,
The information processing method according to claim 1, wherein the reconstruction step reconstructs the polygon selected in the selection step into a plurality of polygons. Furthermore, it has a color information acquisition step for acquiring color information,
The information processing method according to claim 1, wherein the reconstructing step reconstructs the detected polygon into a plurality of polygons using the color information. The color information acquisition step acquires a point indicating color information located inside a polyhedron indicating the surface of the color gamut,
The reconstruction step reconstructs the detected polygon into a plurality of polygons by using the detected polygon vertices and points indicating color information located inside the polyhedron indicating the surface of the color gamut. The information processing method according to claim 1, wherein the information processing method is constructed. The shape information is an area,
The information processing method according to claim 1, wherein the polygon detecting step detects a polygon having an area of the polygon equal to or greater than a predetermined value. The shape information is a side length;
The information processing method according to claim 1, wherein the polygon detecting step detects a polygon in which a side length of the polygon is a predetermined value or more.   The information processing method according to claim 1, wherein the polygon is a triangle. A color gamut surface information acquisition step for acquiring color gamut surface information related to the geometry of the polyhedron indicating the surface of the color gamut;
An analysis step of analyzing the color gamut surface information;
A vertex adding step of adding a vertex constituting the surface of the color gamut according to the analysis result. A color gamut mapping process for performing a color gamut mapping process using the color gamut surface information;
The information processing method according to claim 9, wherein the color gamut mapping processing step performs a scaling process using vertices constituting a surface of the color gamut. The analysis step includes
Generating step for generating equi-hue cross-section information from the color gamut surface information;
A line segment detecting step for detecting a line segment satisfying a predetermined condition from the line segments constituting the equi-hue cross section information;
The information processing method according to claim 9, further comprising a dividing step of dividing the detected line segment. The analysis step includes
A line segment detecting step for detecting a line segment longer than a predetermined length from the sides constituting the color gamut surface;
The information processing method according to claim 9, further comprising a line segment dividing step of dividing the detected line segment. The analysis step includes
A polygon detecting step for detecting a polygon having an area larger than a predetermined area from the polygon constituting the color gamut surface;
The information processing method according to claim 9, further comprising a polygon dividing step of dividing the detected polygon.   A program for causing a computer to execute the information processing method according to any one of claims 1 to 13. Color gamut surface information acquisition means for acquiring color gamut surface information relating to the geometrical shape of the polyhedron indicating the surface of the color gamut;
Shape information acquisition means for acquiring shape information of polygons constituting the polyhedron;
A polygon detection means for comparing the acquired shape information with predetermined shape information and detecting a polygon to be reconstructed;
A color processing apparatus comprising: reconstructing means for reconstructing the detected polygon into a plurality of polygons. Color gamut surface information acquisition means for acquiring color gamut surface information relating to the geometrical shape of the polyhedron indicating the surface of the color gamut;
Analyzing means for analyzing the color gamut surface information;
An information processing apparatus comprising: vertex adding means for adding a vertex constituting a surface of a color gamut according to the analysis result.

Images