JP4553259B2 - Image processing apparatus, image processing method, program, and recording medium - Google Patents

Description

  The present invention relates to an image processing apparatus, an image processing method, a program, and a recording medium for correcting an arbitrary color image signal to a color of a color image output apparatus with a limited color reproduction range, and relates to a color facsimile, a color printer, and a color hard copy. The present invention relates to a technique suitable for a color conversion device such as a color printer and software for a color printer operating on a workstation.

  In general, when an image displayed on a monitor is printed out from a printer in a computer system or the like, the color reproduction range of the monitor and the printer is greatly different, so that the color appearance of the monitor display color and the print color is substantially equal. A so-called color matching process for adjustment is required. As color matching processing, for example, a color conversion method is known in which an interpolation operation is performed with reference to a multi-dimensional lookup table (hereinafter referred to as a color conversion table) in consideration of color characteristics of a monitor and a printer.

  However, in the process of creating the color conversion table, various noises may be generated and mixed in the values of the created color correction table. For example, in the color gamut mapping process that corrects the difference between the color gamuts of the monitor and the printer, the color gamut shape of the printer is complicated, so that the color conversion table cannot be properly controlled and may cause noise. . An error in a color prediction equation that models the correspondence between a printer color signal and a device-independent color signal may cause noise. If noise generated for the above reason is mixed in the color conversion table, the change in the value of the table is not smooth. As a result, gradation changes in the image after color conversion are not smooth, and problems such as pseudo contours are likely to occur when the image is printed by a printer.

  Thus, in order to remove noise from the color conversion table, a method of performing a smoothing process on the value of the color conversion table has been proposed. For example, Patent Document 1 proposes a method of creating an LUT using a three-dimensional smoothing filter based on data read by a scanner, and Patent Document 2 smoothes the calculation result of lattice point data and sets parameters. Patent Document 3 proposes a method for smoothing color conversion parameters according to smoothing conditions, and Patent Document 4 proposes a method for generating a color correction table using a smoothing degree evaluation function. is doing.

JP-A-8-275007 JP 2001-144981 A JP 2003-179664 A JP 2003-204443 A

  In the conventional smoothing method, a three-dimensional lookup table for interpolation calculation that converts the input color signal into a color signal that can be reproduced by the printer is created, and the output value of the three-dimensional lookup table is simply averaged or weighted average It is to become. However, the gamut compression process itself that converts the color signal into a reproducible color signal is a non-linear color conversion process, and includes lattice points that should not be smoothed. An example is shown in FIG. In FIG. 15, lattice point data P0, P1, and P2 in the input color space are converted into G0, G1, and G2 by gamut processing, and output signal values corresponding to the G0, G1, and G2 are stored in the three-dimensional lookup table. Assume that it is described as a grid point output. In this case, if the grid point output value for P1 is smoothed by the grid point output values for P0 and P1, the color is replaced with the color X in FIG. It can be seen that although the output value for P1 originally should output the maximum saturation color of the printer as in G1, saturation reduction has occurred due to the smoothing process. Also, smoothing over and over again to ensure continuity improves the continuity of the grid point output value, but it becomes the same situation as reducing the number of divisions in the three-dimensional lookup table, and color conversion Accuracy is also reduced.

The present invention has been made in view of the above problems,
An object of the present invention is to provide an image processing apparatus, method, program, and recording for creating a color conversion parameter that substantially matches the expected gradation characteristics even if the color gamut shape of the image output apparatus includes a complex uneven shape. To provide a medium.

The present invention relates to a color in which an output value (hereinafter referred to as a grid point output value) in the first color gamut of the output device is associated with each grid point obtained by dividing the color space represented by the input image data in a grid pattern. An image processing apparatus for correcting the grid point output value in a conversion table, wherein the color gamut description means describes a second color gamut of the output device with less color data than the first color gamut. Reference table creating means for creating an output value (grid point output value) in the second color gamut of the output device associated with each grid point, and a target grid point of the reference table Based on the change amount calculated by the change amount calculating means, and a weight coefficient of the output value near the target lattice point in the color conversion table based on the change amount calculated by the change amount calculating means Determine the weight The number determining means, and most important; and a smoothing means for smoothing the lattice point output value of the color conversion table using the weighting factor.

According to the present invention, even if the color gamut shape of the image output device is uneven, it is possible to create a color conversion table that substantially matches the expected gradation characteristics, and the relative grid points of interest and adjacent grid points are relative to each other. A color conversion table that maintains the relationship can be created.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

Example 1:
Overall configuration of the image processing system:
FIG. 1 is a block diagram showing a configuration example of an image processing system using an image processing apparatus according to the present invention. In the figure, 10 is a computer, 11 is a display, 12 is an image output device, 13 is a colorimeter, 14 is a color conversion device, 15 is a color conversion parameter storage unit, and 16 is an image processing device. The image output apparatus 12 is an output apparatus for printing out image data. For example, an image forming apparatus such as a color printer or a color copying machine can be used.

Image processing system operation:
The computer 10 outputs the image data for printing out the image data inside the computer using the image output device 12. This image data is a color signal composed of R (red), G (green), and B (blue) color components for normal display. The image data transmitted from the computer 10 is transmitted to the color conversion device 14. The color conversion device 14 reads the color conversion parameters from the color conversion parameter storage unit 15 and converts them into color signals that can be transmitted to the image output device. Next, the converted color signal is transmitted to the image output device 12 and printed out. The color conversion parameters stored in the color conversion parameter storage unit 15 can be rewritten, and when the creation of the color conversion parameters is instructed by the operator, the image processing device 16 executes the color conversion parameter creation processing. .

  As the color conversion processing by the color conversion device 14, a memory map interpolation method using a three-dimensional lookup table (3D-LUT) is generally used. As shown in FIG. 2, the memory map interpolation method divides an input color space into three-dimensional figures of the same type (here, cubes), and outputs values (= This is a method for obtaining an output value using (grid point output value). For example, in order to obtain the output value at the input coordinates P, a cube including the coordinates of the input is selected, and eight grid point output values of the selected cube and positions of the input in the cube are selected. Linear interpolation is performed based on (distance from each vertex). In this embodiment, the input color signals correspond to the input R (red), G (green), and B (blue) signals, and the output P is Y (yellow), M (magenta), and C (cyan). ) Corresponds to a signal. Further, the grid point output value is obtained by calculation in advance by the image processing device 16 using a method described later, and is stored in the color conversion parameter storage unit 15 as a 3D-LUT. FIG. 2 illustrates a cube interpolation method. Various types of memory map interpolation methods using a 3D-LUT, such as tetrahedral interpolation, triangular prism interpolation, and cube interpolation, have been proposed, and any method is used in the present invention. It doesn't matter.

  In the image processing system described above, the image data is displayed as RGB signals for the sake of convenience. However, the present invention is not limited to this, and device signals such as image data read by a scanner or image data of a digital camera may be used. Of course, the present invention can also be applied to a device-independent color space such as CIELAB (L * a * b *). In addition, the color conversion device 14, the color conversion parameter storage unit 15, and the image processing device 16 according to the present invention may be realized as a part of the image output device 12 or may be realized by software in the computer 10. . For example, the function of the image processing apparatus 16 can be realized by a printer driver existing as a program in the computer.

Configuration of the image processing device 16:
FIG. 3 shows a flowchart of a color conversion parameter creation method by the image processing apparatus 16. The image processing device 16 has a function of creating a color conversion profile representing the correspondence between the input signal RGB data and the output signal CMY data, and a colorimeter 13 is connected as shown in FIG. .

  In step 101, the image processing device 16 constructs a color prediction formula to obtain color reproduction characteristics. The color prediction formula is a model in which the output characteristics of the image output apparatus are modeled by mathematical formulas, and a colorimetric value can be obtained from the output color signal by calculation. For example, the output signal value (c, m, y) is calculated. Can be converted into device-independent color signals (for example, L, a, b). A general method for constructing a color prediction formula is to output a color patch with an image output device and measure the color of the output patch with a spectrocolorimeter. Next, a color prediction formula is constructed by approximating the relationship between the color measurement data of the color patch and the output color signal. As such a color prediction formula, a neural network, a multi-degree polynomial, a 3D-LUT, or the like can be used.

  Next, in step 102, a reference 3D-LUT is created. This reference 3D-LUT is a color conversion parameter created by describing the color gamut shape of the output device with a simple polyhedron, and easily constructs a color conversion parameter with less gradation after gamut compression processing. be able to.

  Next, in step 103, a color conversion 3D-LUT is created. This is a 3D-LUT created using color gamut information that precisely describes the color gamut shape of the output device. In general, the color reproduction characteristics of the output device are very complicated, and the color gamut shape is often a complicated uneven shape.

  The relationship between the gamut shape and the grid point output value will be described with reference to FIG. In FIG. 4, Si represents input RGB data before gamut compression on a color space with lightness and saturation as axes. The compressed color when Si is compressed into a simple printer color gamut is expressed as Gi, and the compressed color when compressed into a precise printer color gamut is expressed as Ri. If the direction when the gamut compression is fixed, Gi is arranged at equal intervals with the adjacent lattice points Gi-1, Gi + 1, but Ri is different in the interval between the adjacent lattice points Ri-1 and Ri + 1. . In spite of the equal intervals of the input data Si, the printer output result becomes uneven, and as a result, uneven gradation is conspicuous and the color reproducibility is lowered.

  Therefore, in step 104, a 3D-LUT having excellent gradation continuity is constructed by correcting the color conversion 3D-LUT while referring to the continuity of the grid point output values of the reference 3D-LUT. The color conversion 3D-LUT created by the above processing is stored in the color conversion parameter storage unit 15 and used when the color conversion device 14 performs color conversion processing.

Explanation of 3D-LUT creation method for reference:
A method for creating a reference 3D-LUT will be described in detail with reference to FIG. First, in step 201, the color gamut of the image output apparatus 12 is simply described. The simple description method is to describe the color gamut with only a small amount of color data. For example, the cyan, magenta, yellow, red, blue, and green solid colors of the image output device 12 and the paper white and black totals. A color gamut is described using only the eight output color data (FIG. 6A). Output color data may be obtained by actually measuring eight colors with a printer, or by using the color prediction formula described above. When a color gamut is described with eight colors, it is simply described with a dodecahedron as shown in FIG.

  The color space of the color gamut description described above is described using a perceptual uniform color space such as CIE Lab space or CIECAM space so as to be suitable for gamut processing. In the example of FIG. 6A, a CIECAM JCH value for eight colors of RGBCMYWBk is described.

  Next, in step 202 to step 206, a gamut process is performed on the color gamut obtained in step 201 to generate a reference 3D-LUT. The processing from step 202 to step 206 shows the flow of gamut processing using the direct mapping method. The direct mapping is a method of obtaining an output color that can be reproduced by the image output apparatus by performing gamut processing for each grid point. Every time gamut compression is performed on the grid point I, a description is made in the 3D-LUT. Hereinafter, an example of the gamut processing method will be described.

Description of specific example of gamut processing method:
(1) Outline of Gamut Processing The input color space (RGB space) includes color signals that cannot be reproduced by the image output apparatus. Therefore, gamut processing is performed to convert the grid points Pi (j, c, h) into color signals Po (j ′, c ′, h ′) that can be reproduced by the image output apparatus (FIG. 7).
Next, an overall flow of gamut processing will be described. FIG. 8 is a flowchart for explaining a specific example of the gamut processing method. First, in S301, the color signal value [r, g, b] at the lattice point Gi is converted into a color signal Pi (j, c, h) for gamut processing. The color space for performing gamut processing may be another color space as long as it has color components corresponding to lightness, saturation, and hue, as in the LCH signal standardized by CIE, but it has human visual characteristics. It is desirable to use a color appearance model such as CIECAM02 that more accurately reflects. Therefore, the color signal value [r, g, b] at the lattice point Gi is converted into a color signal Pi (j, c, h) according to the lightness J, saturation C, and hue H of CIECAM02. This conversion method is described in detail in the CIE Technical Report (CIE 159).

  Next, in S302, the hue h of Pi (j, c, h) is corrected to h ′ so that the colors before and after the gamut processing match as much as possible. Next, in step S303, with reference to the gamut data of the output device, saturation compression is performed with a constant hue to obtain an output color signal Po (j ', c', h ').

(2) Detailed description of each part Next, the detail of a gamut process is demonstrated.
Hue correction (FIG. 8: S302)
The gamut of the display device and the gamut of the printer device are greatly different in shape, and there are many colors that cannot be reproduced by the output device in the input color signal. When such colors are mapped to the gamut of the output device with a constant hue, the color change before and after the mapping (T), especially at the point where the saturation is the maximum among the colors included in the gamut of the input device (= maximum saturation color). → To) tends to stand out.

  This decrease will be described with reference to FIG. FIG. 9A is a relationship diagram between saturation and hue, and FIG. 9B is a relationship diagram between brightness and saturation. In FIG. 9B, the gamut in the hue hi and the gamut in the hue ho of the output device are shown superimposed on the same two dimensions. For comparison, a mapping color To when the color signal T is mapped with a constant hue is also illustrated. Referring to FIG. 9, between To mapped on the hue h of T and the color M on the hue ho, M is clearly closer to T, and it is desirable to correct the hue. Therefore, a hue correction table for converting the maximum saturation color (corresponding to T in the figure) that is prominent in color change into a color that is as inconspicuous as possible is created, and the hue is corrected for the input color signal Pi. Such a hue correction table can be easily constructed as a one-dimensional lookup table for converting the hue h of the input signal into the hue h ′.

Saturation compression (Fig. 8: S303)
In the saturation compression processing, the input color signal Pi (j, c, h ′) after hue correction is converted into a color signal Po (j ′, c ′, h ′) within the color reproduction range of the printer on the surface of constant hue. To do. Since Pi and Po are on the same hue, it can be considered that the saturation compression process performs a two-dimensional conversion from Pi (j, c) to Po (j ′, c ′). Here, since the color reproduction range of the output device is described in a dodecahedron, the color reproduction area is obtained by obtaining the intersection of a straight line connecting Pi and the point X in the printer color reproduction area and one plane of the dodecahedron. Compression to a point on the boundary can be performed. Such a gamut compression method can be realized by using a calculation method proposed in Japanese Patent No. 3171081.

  In addition to the above-described method of mapping only the color outside the gamut to the gamut boundary surface as described above, perceptual matching that uniformly compresses and maps the entire color space so as to preserve the gradation of the input color outside the gamut of the output device. You may use the method of.

  When the output color signal (j ′, c ′, h ′) is obtained by the gamut conversion process described above, the color signal CMY for the output device is converted by using the above-described color prediction formula, so that the reference 3D- An LUT can be created.

Explanation of 3D-LUT creation method for color conversion:
The creation method of the color conversion 3D-LUT can be the same except that the creation method of the reference 3D-LUT and the creation method of the color gamut data are different. Therefore, here, a method of creating color gamut data will be described.
(1) Description of precise color gamut The precise color gamut data describes the color gamut using more polyhedrons than the dodecahedron used in the reference 3D-LUT. An example is shown in FIG. As shown in FIG. 10, the identification ID of a polyhedron (polygon) covering the surface of the color gamut at a predetermined hue and brightness, the vertex list constituting the polygon, the coordinate value in the output color space of each vertex, the coordinate value in the PCS space, etc. It is represented by In the example of FIG. 10, the brightness is divided into 10 parts and the hue is divided into 12 parts every 30 ° in the device-independent color space, and IDs of polygons belonging to the divided lightness and hue are described. The ID numbers of vertices constituting each polygon are registered in another list. Furthermore, C, M, Y, L, a, and b data are also associated with the vertex ID number. It should be noted that in order to accurately describe the color gamut of an actual printer, the number of lightness and hue divisions becomes finer. By creating such color gamut information in advance, it is possible to access the polyhedron to be searched at high speed when performing gamut processing on the input signal.

(Create vertex list)
Colorimetric data (JCH values, etc.) for grid points on the cube surface in the CMY space is obtained using the color prediction formula, and a list of vertex IDs, CMY coordinate values, and JCH coordinate values is created. For the lattice points on the cube surface, a combination of output values such that two of the three components C, M, and Y are 0 or 255 may be used.

  For example, assuming a CMY space in which C, M, and Y are each divided into five, the total number of lattice points is 6 × 6 × 6 = 216. Assuming that the CMY color signal range is [0, 255] for C, M, and Y, the points (0, 0, 0), (255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0), (255, 0, 255), (0, 255, 255), a cube composed of 8 points (255, 255, 255) Therefore, if only the grid points on the surface are extracted, the number of vertices registered in the vertex list is 216−4 × 4 × 4 = 152 points. A CMY → JCH conversion is performed on the 152 grid points using a color prediction formula to create a vertex list.

(Create polygon list)
After the vertex list is created, a plurality of polygons (here, triangles) that cover the cubic color gamut in the device color space without excess or deficiency are determined. This triangle is defined using three vertices registered in the vertex list. Here, the triangle is determined by dividing all the quadrilaterals, which are the smallest units on the surface of the color gamut generated by dividing the cubic color gamut in the CMY color space into a grid, by diagonal lines. Then, a polygon list is created by registering a triple of vertex list indices corresponding to the vertices of each triangle. Accordingly, when C, M, and Y are divided into five as described above, the number of polygons is 5 × 5 × 6 × 2 = 300 (pieces).

  With the above configuration, it is possible to obtain a polygon list that covers the color gamut of the target CMY device represented by a triple of the vertex list on the gamut surface of the CMY device in the JCH color space and the vertex list index. However, since only a vertex list and a polygon list require a large amount of calculation when specifying a polygon in an arbitrary brightness and hue, the polygon ID is further associated with the brightness and hue. For example, it is assumed that the JCH coordinate values of the three vertices of the triangle constituting the polygon with ID = 1 are (90, 70, 39), (85, 75, 39), (80, 80, 32).

  Since this polygon is located at lightness 80-90 and hue 32 ° -39 °, polygon ID = 1 is registered in the memory corresponding to hue 30 ° and lightness 80 in the table of FIG. The above table is executed for all the polygons to complete the table of FIG. By performing such association, gamut processing can be performed at higher speed.

  In the above description, the vertex list and the polygon list are used as the data structure that represents the polyhedron. However, the present invention is not limited to this, and any data structure that can represent the target color gamut is used. Various data structures may be used. For example, each polygon may be directly represented by the coordinates of three vertices without dividing the vertex list and the polygon list.

  When precise color gamut data can be constructed by the above method, the color conversion 3D-LUT is created by performing gamut processing in the same manner as the creation of the reference 3D-LUT. The control parameters for performing the gamut processing are the same as those at the time of creating the reference 3D-LUT, and the same table is used for the hue correction table.

Explanation of correction method of color conversion 3D-LUT:
Next, a method for correcting the color conversion 3D-LUT, which is a feature of the present invention, will be described. As described above, when local irregularities are present in the gamut shape, there is a problem that the grid point output value includes shakiness and does not change smoothly. Therefore, the color conversion 3D-LUT is corrected so as to reflect the characteristics of the reference 3D-LUT in which no rattling occurs.

FIG. 11 is a flowchart for explaining the color conversion 3D-LUT correction processing. In the figure, Gn is the total number of grid points, and Cn is the number of output colors. In this embodiment, since the grid point output values are 3 signals of C, M, and Y, the number of output colors Cn is 3, but of course, in the case of 4 colors of CMYK, Cn = 4. S 404 and S 405 perform weighted smoothing on the output value of the color conversion 3D-LUT for the output color cs and the grid point i, and iteratively smoothes all output colors and grid points. It is a process to perform.

In S404, a difference value Δ (i, j) between the output value of the target lattice point i of the reference 3D-LUT and the output value of the adjacent lattice point j is calculated. For example, at the target lattice point i shown in FIG. 12A, there are 26 adjacent lattice points. Therefore, a difference value Δ (i, j) between the 26 points and the target lattice point is calculated. If the cyan output value of the target lattice point i is 100 and the cyan output value of the adjacent lattice point j is 105, Δ (i, j) = 5. After obtaining the difference values from the 26 points in the above procedure, the output value of the target lattice point i of the color conversion 3D-LUT is calculated in S405. The output value is calculated using a Gaussian filter, and the weighting coefficient of the Gaussian filter is calculated using the difference value Δ (i, j) obtained in S404. That is, when the standard deviation σ of Δ (i, j) is set, the weight coefficient Wj of the adjacent grid point j is

として求める。ここで、ガウシンアンフィルタの計算には、Δ（ｉ、ｉ）も使用するため、重み係数は計２７個になる。
次に、重み係数の総和が１にするために、

Asking. Here, since Δ (i, i) is also used in the calculation of the Gaussian filter, there are a total of 27 weighting factors.
Next, in order to set the sum of the weighting factors to 1,

により、Ｗｊの総和を計算し、次式により平滑化後の格子点出力値ＬＵＴｔを算出する。但し、ＬＵＴｃ（ｊ、ｃｓ）は色変換用３Ｄ−ＬＵＴの格子点ｊの出力値、ＬＵＴｔ（ｊ、ｃｓ）は平滑化後の３Ｄ−ＬＵＴの格子点ｊの出力値である。

Thus, the sum of Wj is calculated, and the grid point output value LUTt after smoothing is calculated by the following equation. However, LUTc (j, cs) is the output value of the grid point j of the 3D-LUT for color conversion, and LUTt (j, cs) is the output value of the grid point j of the 3D-LUT after smoothing.

上記の平滑化処理の場合、隣接格子点は２６個であったが、入力色空間の外郭面上の格子点の場合、隣接格子点として２６点より少なくなる。例えば、図１２（ｂ）の例では隣接格子点として８点しかない。また、入力色空間の外郭辺上の格子点で２個、入力色空間の頂点では隣接格子点は０である。そのため、格子点の座標位置に応じて適切な隣接格子点を選択して平滑化を行う。８頂点の場合には平滑化は行わず、色変換用３Ｄ−ＬＵＴの出力値をそのまま使用するようにする。

In the case of the above smoothing process, there are 26 adjacent grid points, but in the case of grid points on the outer surface of the input color space, there are fewer than 26 adjacent grid points. For example, in the example of FIG. 12B, there are only 8 adjacent grid points. In addition, two grid points on the outer edge of the input color space, and adjacent grid points are 0 at the vertices of the input color space. Therefore, smoothing is performed by selecting an appropriate adjacent grid point according to the coordinate position of the grid point. In the case of 8 vertices, smoothing is not performed and the output value of the color conversion 3D-LUT is used as it is.

  Through the above processing, a color conversion 3D-LUT that reflects the continuity of the grid point output values of the reference 3D-LUT can be created, and color reproduction with less shaky gradation can be performed. . In the above embodiment, the smoothing process is performed by referring to the difference value between the output value of the target lattice point and the adjacent lattice point. However, the weight coefficient may be obtained using the color difference instead of the difference value. I do not care.

Example 2:
In the above embodiment, all the lattice points are sequentially smoothed. However, the smoothing procedure is not limited to this, and the smoothing process may be performed from the grid point where the discontinuity of the grid point output value is conspicuous.

  FIG. 13 shows a flowchart of this embodiment. First, the smoothing process of the lattice point i is performed by the repetition process of S501 to S506. The difference value calculation in S503 and the smoothing processing method in S504 are the same as those in the first embodiment. Next, in S505, the difference between the grid point output value LUTt after smoothing and the grid point output value LUTc before smoothing is obtained and described in the difference value list. In the difference value list, it is assumed that grid point numbers, difference values, output values of the color conversion 3D-LUT, and the like are associated with each other. When the difference value is larger, the continuity between the reference 3D-LUT and the color conversion 3D-LUT is not consistent, and the influence of smoothing is greater. Therefore, in S507 to S510, the smoothing process is performed in order from the grid point data having the largest difference value.

  In S505, since the difference values are described in the order of the grid point IDs, the sorting is performed in descending order in S507. The smoothing process is always performed on the top of the difference value list, that is, the lattice point with the largest difference value. In order to determine whether or not smoothing has been sufficiently performed, in S508, it is determined whether or not the maximum difference value is equal to or less than a threshold value MAXD. If the maximum difference value is equal to or less than MAXD, the process ends. Otherwise, the smoothing process using the Gaussian filter described above is performed on the lattice point k having the maximum difference value, and the color conversion 3D-LUT is rewritten. When the smoothing is completed, the difference value for the lattice point k and its adjacent lattice points changes, so the difference value list is updated. For example, when there are 26 adjacent lattice points of the lattice point k, the difference value list for 27 lattice points including the lattice point k is updated. The above is repeated until the maximum difference value is equal to or less than the threshold value MAXD, and a color conversion 3D-LUT is constructed.

  In the above example, correction is performed automatically in order from the grid point data of the color conversion 3D-LUT having a large difference value. However, the operator presents information on the target grid point to the operator, The grid point output value may be corrected manually. That is, using the difference value list rearranged in S507, the values of the target lattice point and the adjacent lattice point to be smoothed are presented to the operator, and the operator specifies whether or not to perform smoothing. It may be.

Example 3:
In the above-described embodiment, the level of smoothing is controlled by the standard deviation of the output values of adjacent grid points. However, with this method, there may be a noticeable reduction in saturation in grid point data that is gamut compressed near the maximum saturation color of the printer.

  Therefore, in order to prevent saturation reduction near the highest saturation color, it is also possible to control smoothing according to the color difference between the output values of the grid points of the reference 3D-LUT and the color conversion 3D-LUT. That is, the output value of the reference 3D-LUT for the lattice point i is (Cri, Mri, Yri), and the output value of the color conversion 3D-LUT is (Csi, Msi, Ysi). In the case of smoothing, the color difference of each output value is obtained using a color prediction formula created in advance, and the standard deviation σ of the Gaussian filter is determined according to the color difference. When the color difference is small, σ is also set to a small value, and when the color difference is large, σ is increased. When the color difference is small, smoothing becomes weak and excessive smoothing can be suppressed.

  FIG. 14 shows a configuration example when the image processing system of FIG. 1 is realized by software. The image processing system shown in FIG. 14 is implemented by, for example, a workstation or a personal computer, and controls the entire CPU 21, a ROM 22 storing a control program of the CPU 21, and a RAM 23 used as a work area of the CPU 21. A hard disk 24, a display 103 for displaying image data, and an image forming apparatus 100 such as a color printer.

  Here, the CPU 21, ROM 22, RAM 23, and hard disk 24 have functions as the computer 10 of FIG. In this case, the function of the image processing device 16 shown in FIG. That is, the CPU 21 can be provided with the function as the image processing apparatus of the present invention.

  The function of the CPU 21 as such an image processing apparatus can be provided in the form of, for example, a software package, specifically, an information recording medium such as a CD-ROM. For this reason, in the example of FIG. When the information recording medium is set, a program reading device 31 for driving the information recording medium is provided. In other words, the image processing apparatus and the image processing method of the present invention allow a general-purpose computer system equipped with a display or the like to read a program recorded on an information recording medium such as a CD-ROM, and to The present invention can also be implemented in an apparatus configuration that causes a processor to execute color conversion parameter creation processing. In this case, the program for executing the color conversion parameter creation processing of the present invention, that is, the program used in the hardware system is provided in a state recorded on the medium. The information recording medium on which the program is recorded is not limited to the CD-ROM, and a ROM, RAM, flexible disk, memory card, or the like may be used. The program recorded on the medium is installed in a storage device incorporated in the hardware system, for example, the hard disk 24, so that this program can be executed to realize a color conversion parameter creation function. The program for realizing the color conversion parameter creation processing of the present invention may be provided not only in the form of a medium but also by a server, for example, through communication.

1 shows a configuration of an image processing system of the present invention. It is a figure explaining a memory map interpolation method. The processing flowchart of color conversion parameter creation is shown. It is a figure explaining the continuity of a gamut shape and a grid point output value. A processing flowchart for creating a reference 3D-LUT is shown. An example of a simple color reproduction range model is shown. The concept of gamut processing is shown. The flowchart of a gamut process is shown. It is a figure explaining hue correction. An example of precise color gamut data is shown. 3 is a flowchart illustrating a process for correcting a color conversion 3D-LUT according to the first exemplary embodiment. It is a figure explaining the adjacent lattice point in smoothing processing. 9 is a flowchart illustrating a process for correcting a 3D-LUT for color conversion according to a second exemplary embodiment. An example of the configuration when the present invention is implemented by software is shown. It is a figure explaining the conventional smoothing process.

Explanation of symbols

DESCRIPTION OF SYMBOLS 10 Computer 11 Display 12 Image output device 13 Colorimeter 14 Color conversion device 15 Color conversion parameter memory | storage part 16 Image processing apparatus

Claims (4)

In a color conversion table in which each grid point obtained by dividing the color space represented by the input image data in a grid pattern is associated with an output value (hereinafter, grid point output value) in the first color gamut of the output device , An image processing apparatus for correcting the grid point output value , wherein color reproduction area description means for describing a second color reproduction area of the output device with less color data than the first color reproduction area; Reference table creating means for creating a reference table in which an output value (grid point output value) in the second color gamut of the output device is associated with a grid point; an output value of a target grid point of the reference table; A change amount calculating means for obtaining a change amount of an output value of an adjacent grid point, and a weight for determining a weight coefficient of an output value near the target lattice point in the color conversion table based on the change amount calculated by the change amount calculating means Factor determining factor When the image processing apparatus characterized by having a smoothing means for smoothing the lattice point output value of the color conversion table using the weighting factor. In a color conversion table in which each grid point obtained by dividing the color space represented by the input image data in a grid pattern is associated with an output value (hereinafter, grid point output value) in the first color gamut of the output device , An image processing method for correcting the grid point output value , wherein a color gamut description step for describing a second color gamut of the output device with less color data than the first color gamut, A reference table creating step of creating a reference table in which an output value (grid point output value) in the second color gamut of the output device is associated with a grid point; an output value of a target grid point of the reference table; A change amount calculation step for obtaining a change amount of an output value of an adjacent grid point, and a weight for determining a weight coefficient of an output value near the target lattice point in the color conversion table based on the change amount calculated in the change amount calculation step Coefficient determination process When the image processing method characterized by having a smoothing step of smoothing the lattice point output value of the color conversion table using the weighting factor. A program causing a computer to execute the image processing method according to claim 2 . A computer-readable recording medium recording a program for causing a computer to implement the image processing method according to claim 2 .

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