JP3341688B2 - Control method for image processing apparatus and image processing apparatus controlled by the method - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention uses an interpolation operation for interpolating interpolated data stored corresponding to points discretely existing in a predetermined dimensional coordinate space in accordance with a combination of the coordinate values. The present invention relates to a control method for controlling a device. Also,
The present invention relates to an image processing device that executes color correction of input image data by applying the interpolation operation.

[0002]

2. Description of the Related Art A technique of interpolating a table storing predetermined data corresponding to discrete coordinate values to obtain data corresponding to an arbitrary coordinate value, that is, a so-called data interpolation has been conventionally used for controlling various devices. Have been applied. Data interpolation is very effective when dealing with data that is difficult to function. Also, applying data interpolation has the advantage that the amount of data stored in the table in advance can be reduced. As a specific example taking advantage of such an advantage, image processing for reading a color original or the like using an image input unit such as a scanner, and reproducing and displaying the read image data using a display such as a CRT or a color printer or the like. A control process of the device, that is, a color correction process is included.

[0003] Image output devices such as displays and color printers have unique color reproduction characteristics. As a technique for properly reproducing the color of a color image input using a scanner or the like according to an output device, there is a method of performing a color correction process on a color image according to the color reproduction characteristics of an image output device to be used. Proposed. As one of such color correction techniques, for example, there is a technique disclosed in Japanese Patent Application Laid-Open No. 63-2669. In this technology, red (hereinafter referred to as R
), Green (hereinafter, referred to as G), and blue (hereinafter, referred to as B) are prepared. This color correction table stores in advance color correction contents for all positions in a color space represented by three-dimensional coordinates. The image processing apparatus performs color correction by referring to the color correction table.

[0004] This color correction method is not practical because the storage capacity of the color correction table to be used becomes enormous. For example, when the input original color image data has a gradation number of 8 bits (256 gradations) for each of R, G, and B colors, the number of colors is 256 to the third power, which is about 16.78 million colors. Assuming that the data after color correction is also 8 bits, for the three colors R, G, and B, 4
It requires as much as 8 megabytes of storage capacity.

On the other hand, as a technique for solving the problem relating to the storage capacity by suppressing the capacity of the color correction table,
An image processing method has been proposed in which a color space is divided at predetermined intervals and color correction data prepared only for grid points obtained by the division is used. As one of such image processing methods, there is a processing by data interpolation.
And a processing method described in JP-A-144481.
There is a processing method described in 185075. In these methods, for image data other than the above-mentioned grid points, color correction data is obtained by performing an interpolation operation using the data of the grid points, and a color correction process is performed.

Specifically, a case is considered in which interpolation is performed using color correction data prepared for eight grid points existing near image data in a three-dimensional color space. The eight grid points correspond to vertices of a cube (hereinafter referred to as a small grid) formed by grid points around the image data. The color correction data at eight grid points is represented by T (a, b, c),.
.. T (a + 1, b + 1, c + 1), the color correction data T corresponding to the image data is obtained by the following equation. T = (1−γa) (1−γb) (1−γc) T (a, b, c) + γa · (1−γb) (1−γc) T (a + 1, b, c) + (1−γb) γa) · γb · (1-γc) T (a, b + 1, c) + (1-γa) (1-γb) · γc · T (a, b, c + 1) + γa · γb · (1-γc) T (a + 1, b + 1, c) + γa · (1-γb) · γc · T (a + 1, b, c + 1) + (1-γa) · γb · γc · T (a, b + 1, c + 1) + γa · γb .Gamma.c.T (a + 1, b + 1, c + 1) Here, .gamma.a, .gamma.b, and .gamma.c are weighting factors determined according to the distance in the color space between the image data and each grid point. In addition, there is a method of performing interpolation using four grid points near image data. According to this method, interpolation calculation is performed by selecting four vertices of the small lattice according to the positional relationship of the image data in the small lattice.

If such processing is performed, there is no need to prepare an enormous color correction table as described above. Further, since color correction data can be obtained with a relatively small error for each pixel by interpolation, there is an advantage that a sufficiently good image can be obtained without taking special measures. In view of the advantage that the storage amount of data can be reduced in this way, the technique of data interpolation is applied to control processing of various devices.

[0008]

However, in the control processing by data interpolation, it is necessary to execute complicated interpolation calculation processing in order to obtain data corresponding to an arbitrary combination of coordinate values. For example, in the above-described method of interpolating using eight grid points near the image data, interpolation of one color component of each pixel requires 24 multiplications and 7 additions. When performing an interpolation operation for all pixels and all color components, an enormous amount of operation is required. In general, data interpolation is frequently and repeatedly performed, and thus an enormous amount of calculation is often required as a whole.

Further, as the dimension of a table storing data increases, the processing content for obtaining one data tends to become more complicated. For example, when three-dimensional data is interpolated as in the above-described image processing apparatus, when an interpolation operation is performed using four grid points near the image data, the small grid is used in accordance with the positional relationship of the image data. Requires a conditional branch to select four from the vertices. For example, RGB 3
It is assumed that eight grid points of K, R, G, B, C, M, Y, and W exist near the image data in the dimensional color space as shown in FIG. Cubes formed by these lattice points are 6 of Tetra0 to Tetra5 shown in FIG.
Can be divided into four tetrahedra. When performing an interpolation operation using four grid points, first, it is determined to which of these six tetrahedrons the image data belongs based on the magnitude relation between the R component, the G component, and the B component of the image data. We need to do that. Note that this determination method will be described later in detail. If the dimension of the table to be interpolated further increases, the processing content becomes more complicated.

[0010] For the above reasons, data interpolation requires a very long processing time. As for the image processing apparatus, recently, the resolution of the output image has been increased and the number of pixels tends to increase, so that the time required for the image processing has been further increased. In recent years, although the processing speed of the CPU has been remarkably improved, the image processing speed cannot be sufficiently improved by this alone. There is a similar tendency in various other control processes to which data interpolation is applied.

It is a first object of the present invention to solve these problems and to provide a technique capable of shortening the data interpolation processing time in the control processing of the apparatus. It is a second object of the present invention to provide a technique for applying the technique to color correction in image processing and shortening the color correction processing time without increasing a color correction error.

[0012]

Means for Solving the Problems and Their Functions / Effects In order to solve the above problems, the present invention has the following configuration. The control method according to the present invention interpolates parameters related to control processing of the device stored in correspondence with grid points discretely existing in a coordinate space of a predetermined dimension in accordance with a combination of the coordinate values. A control method for performing control processing of the apparatus, wherein: (a) discretely setting each of the dimensions of the coordinate space;
Grid point defined by the combination of
An interpolated table storing the parameters correspondingly;
The interpolation calculation data necessary for calculating the value corresponding to the input data by interpolating the interpolated table is offset.
A step of preparing in advance an offset correction table of a type smaller than the number of small grids stored in correspondence with the set coordinate values ; and (b) setting a finite number of sets in advance for each of the dimensions in the coordinate space. And (c) inputting any one of a plurality of small grids formed in the dimension by the adjacent grid points.
Determining whether the input data belongs to a grid; (d) converting the input data into coordinate values of a reference grid point serving as a reference among the grid points of the small grid to which the input data belongs; (E) reading the interpolation calculation data corresponding to the offset coordinate value from the offset correction table, and converting the reference grid point and the reference value. reading the parameters corresponding to a predetermined grid point adjacent to the grid point from the object interpolation table, and a step of performing interpolation calculation該被interpolated data by using the interpolation operation data, the interpolation operation de
A predetermined grid point used for the interpolation
Grid point specifying data specified by the relative relationship with the reference grid point
Multiplying the interpolated data in the interpolation calculation.
It is the gist of the data including the weight coefficient data to be processed . The step (a) further includes the step of:
To a combination of coordinate values set discretely for each dimension
For a grid point defined by
Grid point information table that stores grid point information
Is also prepared in advance, and the step (c) is performed
Is performed based on the grid point information.
You may. Further, the offset prepared in the step (a) is used.
A plurality of correction tables may be prepared.
May be a single type of table common to children
No.

In this control method, in the interpolation operation, when interpolating the interpolated table corresponding to each grid point, the input data is converted into the coordinate value and the offset coordinate value of the reference point of the small grid to which the data belongs. Since the offset correction table for providing the interpolation calculation data corresponding to the offset coordinate values is prepared, the interpolation calculation data can be easily obtained by referring to the table. Conventionally, an interpolation operation that has been processed after complicatedly dividing conditions according to an offset coordinate value can be executed simply by referring to an offset correction table. Naturally, the amount of calculation required for the interpolation calculation also decreases. As a result, in the above-described interpolation calculation, the processing speed can be improved without increasing the error of the interpolation calculation, and the speed of the entire control process can be improved.

The control method of the present invention can be applied in the following various modes. As a first aspect, the device is an image processing device that performs color correction of color data of an image, and the input data is color data representing a color component by a combination of gradation values in a two-dimensional or more color space. In addition, the table to be interpolated may be a color correction table indicating a correspondence between the input data and a color component expressed in a color system different from that of the input data.

This makes it possible to execute the color correction processing for converting the color components of the image data into the color components represented by another color system at a high speed. For example, as the color correction processing, RG
There is a process of correcting the color representation expressed in three dimensions of B into a color system expressed in four colors of cyan, magenta, yellow, and black.

According to a second aspect, the input data is data comprising a combination of parameters related to an operation state of the device, and the interpolated table is a table storing parameters related to a control amount of the device. May be used.

Various parameters can be selected as parameters relating to the operating state and parameters relating to the control amount depending on the device to be controlled. For example, when the control target is a jet engine, the altitude and speed at which the jet engine is operated, the throttle rating, and the like can be selected as parameters related to the operating state. Further, thrust can be selected as a parameter related to the control amount. Of course, since these parameters have a correlation, a part of them may be replaced, altitude, speed, and thrust may be selected as parameters related to the driving state, and throttle rating may be selected as a parameter related to the control amount. .

As devices to be controlled, there are various devices such as engines, transportation equipment, and industrial machines. In addition, various devices such as an electric circuit and an optical system are to be controlled in addition to the devices that operate mechanically as described above. In addition,
The second aspect can be applied not only to the process of actually controlling the device, but also to a simulation of the operating state of the device.

As a third aspect, when the present invention is applied to fuzzy control of a device, the input data is a value of a parameter representing a state quantity related to the control, and the interpolated table includes a membership table. It may be a table storing values.

The fuzzy control refers to a control theory based on ambiguous parameters including fluidity. A description will be given of a vehicle speed control as an example. In the fuzzy control, control of the type "accelerates if the vehicle is slow" is performed. Instead of controlling the vehicle to match a certain speed, the vehicle is controlled using an ambiguous parameter of “slow”.
A function that mathematically represents such an ambiguous parameter is called a membership function, and the function value of the function is called a membership value. When controlling using the ambiguous parameter of “slow”, the membership function sets the probability of being recognized as “slow” with respect to the speed of the vehicle from 0 to 1.0.
Is a function given by the real value of.

In the fuzzy control, a control amount is obtained by interpolating a membership function stored as a table according to a state amount related to the control. Interpolation of membership functions is frequently performed in fuzzy control. Therefore, if the control method of the present invention is applied to the interpolation of the membership function in the above-described manner, the processing of fuzzy control can be executed at a very high speed.

According to a fourth aspect, in a control process of the apparatus, when a process of performing coordinate conversion from data represented by a first coordinate system to a second coordinate system is performed, the input data is converted to the second coordinate system. The data to be interpolated may be data comprising a combination of coordinate values in one coordinate system, and the interpolated table may be a table storing coordinate values in a second coordinate system when the respective grid points are subjected to the coordinate conversion. it can.

This makes it possible to perform the coordinate conversion at a high speed. Various coordinate systems can be selected as the first and second coordinate systems. For example, a three-dimensional orthogonal coordinate system (x, y, z) is selected as a first coordinate system, and a second coordinate system is selected.
Let us consider a case in which a three-dimensional polar coordinate system (r, θ, φ) is selected as the coordinate system of. The conversion from the rectangular coordinate system to the polar coordinate system is performed by the following equation (1) including a square root and a trigonometric function. Square root and trigonometric functions are time-consuming operations. r = √ (xx × + y × y + z × z); θ (rad) = cos−1 (x / √ (xx × y × y)); φ (rad) = cos−1 (z / √ (xx × + y × y + z × z)) (1);

According to such calculation, for example, point A (1, 0, 0) and point B (1, 1, 0) in the first coordinate system
Is transformed as follows: A: (1, 0, 0) → Ap: (r, θ, φ) = (1, 0, π / 2); B: (1, 1, 0) → Bp: (r, θ, φ) = (2, π / 4, π / 2);

In the coordinate conversion to which the interpolation calculation according to the fourth aspect is applied, coordinate values in the first coordinate system and coordinate values in the second coordinate system are stored as tables for some representative points. . Coordinate conversion is performed by interpolating this table. In the above example, point C (1,0.5,
0) in the second coordinate system are Ap, Bp
It can be obtained by interpolating the coordinate values of the points.
In the fourth aspect, since the interpolation operation can be performed at high speed, the conversion from the first coordinate system to the second coordinate system can be performed at high speed, and the control processing can be performed at high speed. it can.

When the first coordinate system and the second coordinate system are not in a relationship that can be converted by linear conversion, such as when the first coordinate system and the second coordinate system are a rectangular coordinate system and a polar coordinate system, the coordinates according to the fourth aspect are used. The conversion contains some errors. Such an error varies depending on the interval between grid points in the table to be interpolated. Therefore, it is desirable to set the table to be interpolated at intervals in which the error at the time of coordinate conversion falls within a sufficiently allowable range.

In the above embodiment, various coordinate systems can be applied to the first coordinate system and the second coordinate system.
Needless to say. Further, if there is a unique correspondence between the two coordinate systems, the dimensions of the two coordinate systems do not necessarily need to match. For example, the first coordinate system is a three-dimensional rectangular coordinate system, the second coordinate system is a two-dimensional rectangular coordinate system, and the first coordinate system is a three-dimensional rectangular coordinate system.
It is also possible to apply the interpolation calculation according to the above-described mode when performing the conversion of projecting the coordinates represented by the coordinate system of (1) onto the plane represented by the second coordinate system.

According to a fifth aspect, in the control process of the apparatus, when the data represented by the first coordinate system is converted into the data represented by the second coordinate system by linear transformation, the input data Is data comprising a combination of parameters for specifying the linear transformation, and the interpolated table is a table storing coefficients multiplied by the data represented by the first coordinate system in the linear transformation. It can also be.

In this way, the linear transformation of the coordinates can be executed at a very high speed. A typical linear transformation includes rotation of coordinates. In such a case, the rotation angle of the coordinates can be selected as a parameter for specifying the linear transformation. For example, coordinate values (xe, ye, ze) in a first orthogonal coordinate system including the Xe axis, the Ye axis, and the Ze axis
Is converted to coordinate values (xb, yb, zb) in the second orthogonal coordinate system including the Xb axis, the Yb axis, and the Zb axis. As shown in FIG. 33, the first orthogonal coordinate system is first rotated by ψ (rad) about the Ze axis, and then Y
θ (rad) rotation around the e-axis and finally φ around the Xe-axis
(Rad) It is assumed that the image is rotated and converted into the second coordinate system. ψ, θ, and φ are angles referred to as so-called Euler angles regarding coordinate conversion. Such a linear transformation can be performed by the following equation (2) in which a 3 × 3 transformation matrix is multiplied by a coordinate value in the first coordinate system. The nine components a11 to a33 of the transformation matrix are set in a known manner according to the above-described Euler angles.

Xb = sinθcosψ ・ xe + (-sinθsinψ) ・ ye + cosθ ・ ze = a11 ・ xe + a12 ・ ye + a13 ・ ze; yb = (-sinφcosθcosψ + cosφsinψ) ・ xe + (sinφcosθsinψ + cosφcosψ) ・ ye + sinφsinθ ・ ze = a21 ・ xe + a22 ・ ye + a23 ・ ze; zb = (cosφcosθcosψ + sinφsinψ) ・ xe + (-cosφcosθsinψ + sinφcosψ) ・ ye + (-cosφsinθ) ・ ze = a31 ・ xe + a32 ・ ye + a33・ Ze; ・ ・ ・ (2)

In the coordinate conversion processing to which the interpolation operation is applied according to the fifth mode, a table that gives the nine components a11 to a33 for the combination of the Euler angles ψ, θ, and φ is stored as an interpolated table. The nine components a11 to a33 can be obtained by interpolating the interpolated table according to the Euler angles given in the coordinate conversion processing. According to the interpolation calculation according to the fifth aspect,
Since such interpolation calculation can be performed at high speed, coordinate conversion can be performed at high speed, and control can be performed at high speed. It should be noted that the data interpolation according to the fifth aspect can be applied to coordinate transformation of various dimensions, and can be applied to a coordinate system other than the rectangular coordinate system.

As described above in the first embodiment, the control method of the present invention can be applied to a process of controlling an image processing device and performing color correction of image data. In this process, since the amount of data to be processed is enormous, the control method of the present invention can be particularly effectively used. The present invention can be configured as an image processing apparatus to which the above-described control method is applied, as described below.

The image processing apparatus of the present invention color-corrects and outputs a multicolor image represented by using coordinate values corresponding to a predetermined number of gradations for each dimension in a color space of two or more dimensions. An image processing apparatus, wherein for each pixel of the image,
Input means for inputting color image data consisting of the coordinates, than the number of gradations of the color space for each of the each dimension
Corresponding to the grid points obtained by dividing
Color correction data relating to the amount of color correction
The color image data is stored in any one of a plurality of small lattices formed corresponding to the dimension by the adjacent lattice points.
A small grid determination unit for determining whether the color image data belongs to the color image data; a coordinate value of a reference grid point serving as a reference of the small grid to which the color image data belongs; and the color image data and the reference grid for each dimension. Image data conversion means for converting the image data into offset coordinate values representing deviations from the coordinate values of points; and interpolation calculation data necessary for obtaining color correction data corresponding to the color image data by interpolating the color correction table. And an offset correction table memory storing the correspondence between the offset coordinate values and the number of types of offset correction tables smaller than the number of small grids, and reading out interpolation calculation data corresponding to the offset coordinate values from the offset correction table. And color correction data corresponding to the reference grid point and a predetermined grid point adjacent to the reference grid point. Read from the table, performs interpolation calculation of the color correction data with the interpolation operation data, and a color correction means for outputting the operation result as the corrected color image data, the interpolation operation data, the interpolation Starring
The predetermined grid point used in the calculation is relative to the reference grid point.
Point specifying data specified by a dynamic relationship and the interpolation calculation
Weighting factor data multiplied by the color correction data in
It is assumed that the data includes the following. In addition,
Reduce the color space for each dimension to a number smaller than the number of gradations
A case consisting of the coordinate values of the grid points obtained by dividing
Grid point information that stores child point information as a grid point information table
Information table memory, and the small grid determination means
Means for making a determination based on the grid point information.
May be. Also in this case, the offset
The correction table may be a plurality of types of tables.
And a single type of table common to all small grids
No.

With the above-described image processing apparatus, the color correction table can be interpolated at a high speed without increasing the error of the interpolation operation based on the operation described in the control method. Processing speed can be improved.

Here, as a color space of two or more dimensions, R
Not only GB and CMY color spaces, but also various color spaces represented by XYZ color system, L * a * b * color system, L * C * h color system, Munsell color system, etc. You can think of a color space. When these coordinate values are represented by n-bit digital information, the number of gradations is 2
(For example, 256 for 8 bits). However, the number of gradations other than 2 to the nth power, such as 100 gradations or 17 gradations, may be used.

The grid points in the above invention are obtained as intersections when the color space is divided into a number smaller than the number of gradations in each dimension, and the division is made by sections at regular intervals. You don't need to be. This means that it is not necessary to divide each dimension from a low-density area to a high-density area at a constant interval, and that it is not necessary to unify dimensions. That is, a certain dimension (for example, R
The image may be divided at regular intervals in the RGB color space (R direction), and may be divided at non-uniform intervals in the other dimensions (G and B directions). Further, the number of divisions does not need to be unified among the dimensions. Although the offset correction table is preferably a single type of table, a plurality of types of tables may be prepared according to the division of the color space.

In the above image processing apparatus, the color space is divided so that the data amount of the color correction data stored in the color correction table and the data amount of the interpolation calculation data stored in the offset correction table are substantially the same. It is desirable that the division be made.

By dividing the color space as described above,
The total amount of memory required for storing the color correction data and the amount of memory required for storing the interpolation calculation data can be suppressed to a minimum. This point will be described by taking as an example a case where an n-dimensional color space having L gradations in each dimension is divided at equal intervals. At this time, since all the small grids are the same, the offset correction table is a single table. If the color space is divided at intervals of m gradations,
The color space is divided into (L / m) sections for each dimension. Assuming that A pieces of color correction data are prepared for each grid point, the data amount is represented by the following equation (3). Data amount of color correction data = A · (L / m) ＾ n (3) Here, ＾ represents a power remainder operator.

On the other hand, the offset correction data corresponds to B coordinate values corresponding to m coordinate values for each dimension included in the small grid.
Assuming that the data is prepared individually, the data amount is given by the following equation (4).
It is represented by Offset correction data amount = B · m ＾ n (4) The sum of the two is obtained by the above equations (3) and (4), and the well-known mathematical theorem, the addition and geometric mean theorem, is obtained. Is applied, a relationship represented by the following equation (5) is established. Total data = A ・ (L / m) ＾ n + B ・ m ＾ n ≧ 2√ (A ・ (L / m) ＾ n ・ B ・ m ＾ n) = 2√ (A ・ B ・ L ＾ n) ・(5) According to the arithmetic / arithmetic mean theorem, the sum of the data in Equation (5) above becomes the minimum value 2√ (A ・ B ・ L ＾ n) because the arithmetic mean 2 Term, A · (L / m)
＾ n (the amount of color correction data) and B · m ＾ n (the amount of offset correction data) are equal. Accordingly, it can be seen that when the data amount of the color correction data is equal to the data amount of the offset correction data, the memory capacity for storing the data is minimized. From the above, the division space m of the color space that minimizes the memory capacity is given by solving the following equation: A ・ (L / m) ＾ n = B ＾ m ＾ n. If the solution is mx, then mx = (A · L ＾ n / B) ＾ (１ ／ n).

Here, since the number of gradations L and m and the dimension n of the color space in the above equation are integers and the gradation mx does not always have an integer solution, the data amount of the color correction data and the offset correction data Relationship where the amount of data is exactly equal,
That is, the relationship of the number of gradations m = mx is hardly established. Also,
Actually, since the number of gradations is often represented by bits, m = 2
It is desirable that the relationship of ＾ X (X is an integer) also be established.
Therefore, even if the data amount of the color correction data and the data amount of the offset correction data are not strictly equal, if the color space is divided so that the two are substantially the same, the memory capacity for storing both data is approximately the same. Can be kept to a minimum. Specifically, the number of gradations m that satisfies the condition of 2 ＾ X (X is an integer) in the range of mx / 2 ≦ m ≦ 2 · mx with respect to the gradation mx may be obtained. Further, when the number A of data per grid point of the color correction data is substantially equal to the number B of data per coordinate point of the offset correction data, the color space has a gradation number of N bits for each dimension. If the number of gradations m of the division is set to an interval consisting of (N / 2) bits, the total of the data amount becomes minimum.

When the color space is a three-dimensional color space,
Indicates that the grid point specifying data corresponds to the offset coordinate value.
Accordingly, a small number of four grid points used for the interpolation
At least part of the data is specified, and the weighting coefficient data is coefficient data multiplied by the color correction data corresponding to each of the grid points in the interpolation calculation according to the offset coordinate value. desirable.

By adopting such a configuration, the offset correction table can be applied to an interpolation operation (hereinafter, referred to as a tetrahedron method) using four points including a reference grid point and other three points. The tetrahedron method is known as an interpolation calculation method that requires the least amount of calculation when the color space is three-dimensional. In the tetrahedron method, it is necessary to select four grid points to be used for the interpolation operation according to the offset coordinates. However, the offset correction table in the above configuration stores at least a part of the grid points as grid point specifying data. I have. Therefore, a grid point can be selected by referring to the table. As a result, the process of selecting grid points is simplified, and image processing for a three-dimensional color space can be performed at a very high speed. The weight coefficient data may be data directly multiplied by the color correction data or data used for calculating a coefficient multiplied by the color correction data.

When the color space is not divided at uniform intervals, a normalizing means for performing coordinate conversion so that each interval between the lattice points is a constant virtual lattice point interval in each dimension is provided. The image data conversion means normalizes the coordinate value of a reference grid point serving as a reference for the small grid to which the color image data belongs and the deviation between the color image data and the coordinate value of the reference grid point for each dimension. It is desirable that the means be converted to an offset coordinate value represented by coordinates converted by the means.

By adopting such a configuration, if an offset correction table is prepared for a small grid consisting of virtual grid point intervals, the table can be applied to all the small grids. As a result, even when the color space is not divided at uniform intervals, the offset correction table can be unified into one type, and the memory capacity for storing the interpolation calculation data can be reduced. In addition, it is not necessary to use the offset correction table by performing conditional branching according to the grid to which the color image data belongs, so that image processing can be performed at high speed.

In order to minimize the color correction error in the interpolation operation, it is desirable that the above-mentioned virtual grid point interval coincides with the largest one among several types of grid point intervals according to the division of the color space. . Specifically, when the color space is divided by two types of intervals a and b, 90% of the entire grid point interval consisting of the interval a occupies 90% of the total, and the remaining 10% grid point interval is the interval. If b
It is desirable to set the virtual grid point interval to the interval a. However, the virtual grid point interval does not need to match any of the grid point intervals, and can be set freely. Therefore, for example, an interval slightly smaller than the lattice point interval may be set so that the data capacity of the offset correction table is relatively small.

When the above-mentioned normalizing means is provided, it is preferable that the interval after the normalization is an integral multiple of the interval between the lattice points in a predetermined low density area of the color space.

With this configuration, it is possible to suppress a decrease in image quality due to an error that occurs when performing interpolation calculation of color correction data through normalization processing. The color image data has coordinate values composed of integers corresponding to a predetermined number of gradations for each dimension of the color space. When the conversion is performed, the coordinate values of the color image data may not be integer values. Here, since the offset correction table is prepared corresponding to the coordinate value represented by an integer, it is necessary to round the color image data after the coordinate conversion to an integer value, which causes a so-called rounding error. (The coordinate conversion and integer conversion are collectively referred to as “normalization processing” below). As described above, this error causes the image quality to deteriorate in the low density area. However, according to the above configuration,
In the low-density region of the color space, the ratio between the grid point interval and the virtual grid point interval (virtual grid point interval / grid point interval) is always an integer, so that even if the normalization processing is performed, no rounding error occurs, Good image quality can be obtained.

Here, the low concentration region in the present invention is defined as
This does not mean an area where the color of the output image is light, but an area where the density of dots output to the image output device is low. For example, when the final image output is an ink jet printer that expresses a gradation by turning on and off dots, it refers to a region where the density of ink dots such as CMY is low. When the output device is a CRT or the like, an area where white dots are sparsely distributed (a dark color area) when attention is paid to white dots, and an area where black dots are sparsely distributed when attention is paid to black dots. (Light-colored area). In addition, if a printer is provided with high-density ink and low-density ink for the same color and separates dark dots and light dots, not only areas where light dots are sparsely distributed (areas with light colors), An area where dark dots are sparsely distributed (a dark area) also corresponds to a low-density area for the ink.

In the image processing apparatus having the normalization processing, the image data converting means may determine, based on the color image data, coordinates of a reference grid point serving as a reference for a small grid to which the color image data belongs. A reference grid point table for storing values, and an offset representing a deviation between the color image data and the coordinate value of the reference grid point in the coordinates converted by the normalization means for each dimension according to the color image data. And at least one of an offset coordinate table for storing coordinate values, and converting at least one of the conversion from the color image data to the coordinate values of the reference grid points and the offset coordinate values to data corresponding to the color image data. Means for reading from the reference grid point table or the offset coordinate table. It is also desirable.

When the above-described normalization process is involved, the calculation in the normalization process becomes complicated, which may hinder image processing at high speed. For example, in order to determine which small grid the color image data belongs to, it is necessary to compare the magnitude relationship between the coordinate values of the color image data and the grid points one by one for each dimension. . Also, when obtaining the normalized offset coordinate values,
It is also necessary to perform a rounding process for converting the coordinate values to integers. According to the above configuration, since these processes accompanying the normalization process can be executed by referring to the reference grid point table and the offset coordinate table, image processing can be performed at high speed.

From the viewpoint of speeding up the processing, it is desirable to have both the reference grid point table and the offset coordinate table, but depending on the memory capacity for storing these data, one of them is required. Only a table may be prepared. Also, these tables
Although it is desirable to prepare for all the dimensions of the color space, it may be prepared for only some of the dimensions, or a table shared by a plurality of dimensions may be used.

Further, as shown below, the present invention may take an embodiment as a computer-readable recording medium on which a computer program or the like is recorded. The first recording medium of the present invention is an image for performing color correction on a multicolor image represented by using coordinate values corresponding to a predetermined number of gradations for each dimension in a two-dimensional or more color space, and outputting the image. A computer-readable recording medium that records a program that causes a computer to perform the processing, and for each pixel of the image, a function of inputting color image data including the coordinate values, and the color space for each of the dimensions. A function of referencing a grid point information table stored for the color space with grid point information comprising coordinate values of grid points obtained by dividing the grid point into a number smaller than the number of gradations, and corresponding to each grid point. A function of referring to a color correction table storing color correction data relating to a color correction amount; and a function of referring to a plurality of small grids formed corresponding to the dimension by the adjacent grid points. It belongs to a small grid of any said color image data, a function of determining, based on the lattice point information,
The color image data, the coordinate value of a reference grid point as a reference for the small grid to which the color image data belongs,
A function of converting the color image data into an offset coordinate value representing a deviation between the coordinate value of the reference grid point and the color correction data corresponding to the color image data by interpolating the color correction table; The interpolation calculation data necessary for the calculation includes a predetermined value used for the interpolation calculation.
Grid point is specified by a relative relation with the reference grid point.
Grid point specifying data and the predetermined case in the interpolation calculation.
Weight multiplied by the color correction data stored corresponding to the child point
A function of referring to an offset correction table that stores data including only coefficient data in correspondence with the offset coordinate value, and reading out interpolation calculation data corresponding to the offset coordinate value from the offset correction table,
The color correction data corresponding to the reference grid point and a predetermined grid point adjacent to the reference grid point is read from the color correction table, and the color correction data is subjected to an interpolation calculation using the interpolation calculation data. And a function of outputting as corrected color image data by a computer.

When the program recorded on such a recording medium is executed by a computer, the computer realizes each of the above functions, and as a result, can perform the above-described image processing. Examples of the recording medium include a flexible disk, a CD-ROM, a magneto-optical disk, an IC card, a ROM cartridge, a punched card, a printed matter on which a code such as a barcode is printed, and an internal storage device of a computer (a memory such as a RAM or a ROM). And various computer-readable media such as external storage devices). The present invention can also be configured as a program supply device that supplies, via a communication path, a computer program that causes a computer to realize the functions of each step or each unit of the above invention.

According to the second recording medium of the present invention, a multicolor image represented by using coordinate values corresponding to a predetermined number of gradations for each dimension in a color space of two or more dimensions is converted into the color space. Is a computer-readable recording medium on which data used in an image processing apparatus for performing color correction and outputting by using predetermined data prepared corresponding to grid points obtained by dividing Grid point information consisting of coordinate values of grid points obtained by dividing each dimension of the color space of dimensions or more into smaller numbers than the predetermined number of gradations for each dimension was stored for the color space. A grid point information table, a color correction table storing color correction data relating to a color correction amount corresponding to each grid point, and a color correction data corresponding to the color image data by interpolating the color correction table. Calculation The interpolation calculation data required when the color image data is coordinated with the coordinate values of a reference grid point serving as a reference of a small grid formed in correspondence with the dimensions of the color space by the grid points adjacent to the color image data. An offset correction table for storing a deviation of data in correspondence with an offset coordinate value represented for each dimension.

As described above, the image processing apparatus of the present invention performs color correction of color image data using the grid point information table, the color correction table, and the offset correction table. When the data is provided in the form of the above-described second recording medium of the present invention, the computer can realize the function as the image processing apparatus by reading the data.

Among the data recorded on this recording medium, the offset correction table is data having a special structure provided by the present applicant for the first time as data used for image processing. Although the grid point information table and the color correction table themselves have been used in image processing, they are very close to the offset correction data in the present invention. Therefore, the second recording medium of the present invention in which these data are recorded is not merely a recording of existing data, but a recording of the most characteristic part in the present invention as data having a special structure. .

In the recording medium, it is desirable that the data amount of the color correction data stored in the color correction table and the data amount of the interpolation calculation data stored in the offset correction table are substantially the same. By doing so, since the data amounts of the color correction table and the offset correction table are stored with substantially the same size, the recording capacity can be minimized as described above. According to the recording medium, a medium whose recording capacity is limited to a relatively small value, such as a so-called flexible disk, can be effectively used.

The interpolation operation in the present invention can be widely applied to other than the control processing of the apparatus. Therefore,
The present invention can also be configured as an invention of an interpolation method for performing a general-purpose interpolation operation using a computer. In other words, the interpolation method of the present invention is used in a series of predetermined processes, and stores parameters related to the control process of the device, which are stored in correspondence with grid points discretely existing in a predetermined dimensional coordinate space, on a computer. (A) a grid point defined by a combination of coordinate values discretely set for each of the dimensions of the coordinate space, A grid point information table storing grid point information including a combination of the coordinate values, an interpolated table storing the parameters corresponding to the respective grid points, and interpolating the interpolated table to correspond to the input data. The number of types of offsets smaller than the number of the small grids, in which interpolation calculation data required when calculating the value to be stored is stored corresponding to the offset coordinate value. (B) inputting input data consisting of a combination of a finite number of coordinate values set in advance for each of the dimensions in the coordinate space; (C) determining, based on the grid point information, to which of the plurality of small grids formed in the dimension by the adjacent grid points the input data belongs, based on the grid point information; An offset representing the deviation between the coordinate value of the reference grid point serving as a reference among the grid points of the small grid to which the input data belongs and the coordinate value of the input data and the coordinate value of the reference grid point for each dimension. (E) reading interpolation calculation data corresponding to the offset coordinate value from the offset correction table, Reading the parameters corresponding to a predetermined lattice points adjoining to the reference lattice point from said target interpolation table, and a step of performing interpolation calculation該被interpolated data by using the interpolation operation data, the interpolation operation de
A predetermined grid point used for the interpolation
Grid point specifying data specified by the relative relationship with the reference grid point
Multiplying the interpolated data in the interpolation calculation.
This is an interpolation method which is data including weight coefficient data .

According to such an interpolation method, the interpolation operation can be executed at high speed by effectively utilizing the hardware resources of the computer based on the operation described in the control method and the like. Naturally, the invention can be configured as a recording medium on which a program for realizing these interpolation methods is recorded.

[0060]

DESCRIPTION OF THE PREFERRED EMBODIMENTS Embodiments of the present invention will be described below based on examples. First, an example in which the data interpolation apparatus of the present invention is applied to a color image processing apparatus will be described. FIG. 1 is an explanatory diagram showing an outline of a color correction data interpolation method performed by a color image processing apparatus as an embodiment of the present invention, and FIG. 2 shows a configuration of a color image processing system using the color image processing apparatus. FIG. (1) Configuration of Image Output Device: For convenience of description, the overall configuration of a color image processing system will be described first with reference to FIG. The color image processing system includes a scanner 12 as an image input device, a personal computer 90,
A color printer 22 as an image output device. The personal computer 90 includes a color display 21 and an input unit 9 including a keyboard, a mouse, and the like.
2 is provided. Scanner 12 as image input device
Reads color image data from a color original,
Original color image data OR consisting of three color components of G and B
G is supplied to the computer 90.

In this embodiment, each color of R, G, B is represented by 8-bit digital data.
The number of gradations is 256. The color of each pixel is represented by a coordinate value indicating an existing position in a three-dimensional color space using the R, G, and B colors as coordinate axes. Note that the color space is in other formats,
For example, even if any color system such as L * a * b * is adopted,
The color of each pixel can be represented as a coordinate value indicating a position in the color space. The computer 90 as an image processing device inputs the coordinate values represented in each pixel as color image data. The input of the color image data may use, for example, a video camera, a host computer for creating computer graphics, or other means in addition to the scanner 12.

Next, the outline of the computer 90 constituting the image processing apparatus will be described. This computer 90
Are provided with a CPU, a RAM, a ROM, and the like (not shown), and an application program 95 operates under a predetermined operating system.
In the present embodiment, Pentium II (In
tel). The operating system includes a video driver 91 and a printer driver 9.
6 is incorporated and an application program 9
5, the final color image data FNL is output via these drivers. An application program 95 for retouching an image reads an image from the scanner 12 and displays the image on the CRT display 21 via the video driver 91 while performing predetermined processing on the image. When the application program 95 issues a print command, the computer 9
0 receives the image information from the application program 95 and sends it to the printer 22.
Is converted into a printable signal (here, a binarized signal for CMY).

In the example shown in FIG. 2, the printer driver 9
6, a rasterizer 97 that converts color image data handled by the application program 95 into dot-based image data, and an ink color used by an image output device (here, the printer 22) for the dot-based image data. A color correction module 98 that performs color correction according to CMY and color development characteristics, and a color correction table CT and an offset correction table O referenced by the color correction module 98
T, a halftone module 99 is provided that generates so-called halftone image information that expresses density in a certain area based on the presence or absence of ink in dot units from the image information after color correction.

The computer 90 performs color correction and gradation conversion as image processing for matching the input original color image data ORG with the color reproduction characteristics of an image output device such as the printer 22. The color correction is a process of correcting a color according to the output characteristics of the image output device, such as gamma correction. Further, the gradation number conversion means that if the number of gradations that can be output by an image output device such as the printer 22 is smaller than the number of gradations of the original color image data ORG, the color image data can be output by the image output device. This is a process of converting into the number of gradations. In this embodiment, the image data ORG read from the scanner 12 is read.
Has 256 gradations (8 bits) for each of the colors R, G, and B. On the other hand, since the printer 22 is an inkjet printer that can output only two gradations by turning on and off the ink, the computer 90 has 256 colors. The image data of the gradation is converted into two gradations and output as final color image data FNL to the image output device. As the image output device, for example, a color display 21 or the like may be used in addition to the printer 22. The data interpolation device in this specification corresponds to the color correction module 98, the color correction table CT, the offset correction table OT, and a data input / output unit to the color correction module (not shown) in FIG.

Next, the structure of the printer 22 as an image output device will be briefly described. FIG. 3 is a schematic configuration diagram of the printer 22. As shown in FIG.
Is a mechanism for transporting the paper P by the paper feed motor 23, a mechanism for reciprocating the carriage 31 in the axial direction of the platen 26 by the carriage motor 24, and a mechanism for driving the print head 28 mounted on the carriage 31 to eject ink. And a mechanism for controlling dot formation, and a control circuit 40 for controlling the exchange of signals with the paper feed motor 23, carriage motor 24, print head 28 and operation panel 32.

The carriage 31 of the printer 22 includes
Black ink cartridge 71 and cyan (hereinafter, referred to as C), magenta (hereinafter, referred to as M), yellow (hereinafter, referred to as Y)
) Can be mounted with a color ink cartridge 72 containing three color inks. The print head 28 below the carriage 31 has a total of four ink ejection heads 61.
In addition, an inlet pipe 65 (see FIG. 4) for guiding ink from the ink tank to each color head is provided upright at the bottom of the carriage 31. When a black ink cartridge 71 and a color ink cartridge 72 are mounted on the carriage 31 from above, an inlet tube is inserted into a connection hole provided in each cartridge, and ink from the ink cartridge to the ejection heads 61 to 64 is inserted. Supply becomes possible.

The mechanism for ejecting ink will be briefly described. As shown in FIG. 4, the ink cartridges 71, 7
When the cartridge 2 is mounted on the carriage 31, the ink in the ink cartridge is sucked out through the introduction pipe 65 by utilizing the capillary phenomenon, and is sent to the respective color heads 61 to 64 of the print head 28 provided below the carriage 31. Be guided. When the ink cartridge is installed for the first time,
Ink is supplied to each color head 61 to 64 by a dedicated pump.
In the present embodiment, illustration and description of the structure of a pump for suction, a cap for covering the print head 28 at the time of suction, and the like are omitted.

As shown in FIG. 4, each of the color heads 61 to 64 is provided with 32 nozzles Nz for each color. Each of the nozzles is one of the electrostrictive elements and has excellent responsiveness. The piezo element PE is disposed. Piezo element PE
FIG. 5 shows the structure of the nozzle Nz in detail. As shown in the drawing, the piezo element PE is installed at a position in contact with an ink passage 68 that guides ink to the nozzle Nz. As is well known, the piezo element PE is an element that distorts the crystal structure due to the application of a voltage and converts electro-mechanical energy very quickly. In the present embodiment, by applying a voltage having a predetermined time width between the electrodes provided at both ends of the piezo element PE, the piezo element PE expands by the voltage application time as shown in the lower part of FIG. One side wall 68 is deformed. As a result, the volume of the ink passage 68 contracts in accordance with the expansion of the piezo element PE, and the ink corresponding to the contraction is discharged as particles Ip at a high speed from the tip of the nozzle Nz. When the ink particles Ip soak into the paper P mounted on the platen 26, printing is performed.

The mechanism for transporting the paper P is a paper feed motor 2
A gear train (not shown) for transmitting the rotation of No. 3 not only to the platen 26 but also to a paper transport roller (not shown) is provided. The mechanism for reciprocating the carriage 31 includes an endless drive belt 36 extending between the carriage motor 24 and a slide shaft 34 erected in parallel with the axis of the platen 26 and slidably holding the carriage 31. Pulley 38,
Position detection sensor 3 for detecting the origin position of carriage 31
9 and so on.

In the printer 22 having the hardware configuration described above, the carriage 31 is reciprocated by the carriage motor 24 while the paper P is conveyed by rotating the platen 26 and other rollers by the paper feed motor 23, and at the same time, the print head By driving the piezo elements PE of the respective color heads 61 to 64 to discharge the respective color inks, the paper P
A multicolor image is formed on top.

The control circuit 40 includes a CP (not shown)
U, etc., but color correction according to the output characteristics of the printer 22 is processed inside the computer 90, and the printer 22 does not perform any processing related to conversion of the number of tones, color correction, etc. Not. The processing executed inside the printer 22 is performed by receiving data output from the computer 90 and performing the above-described paper feed and carriage 3 operation.
Only the piezo elements PE of each color of the print head 28 are driven in synchronization with one reciprocating operation. Accordingly, a detailed description of the control circuit 40 of the printer 22 and its processing will not be repeated. However, various processes executed in the computer 90 may be performed by a CPU provided in the control circuit 40 of the printer 22.

(2) Image processing: Next, the contents of image processing by the image processing apparatus will be described. Here, the image processing refers to performing a color correction process on the input color image data for each pixel using the color correction table CT. In the present embodiment, the color correction processing is performed by executing the interpolation calculation of the color correction table CT. Hereinafter, first, an outline of the interpolation calculation performed in the present embodiment will be described.

FIG. 6 shows the state of the lattice points in the present embodiment. In the present embodiment, a three-dimensional color space composed of 8 bits for each of R, G, and B, that is, 256 gradations, is divided into four for each dimension.
It is divided by bits, that is, at grid point intervals of 16 gradations. Therefore, as shown in FIG. 6, the small grids formed by the adjacent grid points form a cube having the grid point interval as one side. Although only a few grid points are shown in FIG. 6 for convenience of illustration, in this embodiment, there are 17 grid points for each dimension including the origin. Note that the number of gradations and the grid point interval of each color can be freely set according to the desired image quality or the like.

The color correction table CT stored inside the computer 90 is a table prepared for each grid point shown in FIG. In this color table, R, G, and R of each grid point are set so that a color document for reading such as the scanner 12 and an output color image printed on recording paper using the color printer 22 have the same color.
Tone value data of B (synonymous with coordinate value data)
The gradation correction data represented by the gradation value of the Y color is stored for each grid point. As shown in FIG. 6, for example, for a grid point Q, three data of (c, m, y) are prepared.

The color image data is represented as one point in the color space, and exists in any of the small grids shown in FIG. If the color image data is data that coincides with the grid points shown in FIG. 6 (for example, point Q in FIG. 6), the color correction table CT is referred to and replaced with color correction data corresponding to the grid points. Although the color correction processing is completed, if the color image data does not match the lattice points shown in FIG. 6 (for example, point P in FIG. 6), the color correction processing is performed by interpolating the color correction data of the adjacent lattice points. Need to be run.

In this embodiment, when color image data exists in a certain small grid, four grid points (tetrahedrons) are set from the small grid in accordance with the position of the data in the small grid.
Is selected, and an interpolation operation is performed (hereinafter, referred to as a tetrahedral method). The tetrahedron method is used because the interpolation calculation method has good interpolation accuracy, and since the number of grid point data to be referred to is small and the calculation amount is small, high-speed processing can be performed. As the interpolation calculation method, a method using all grid points of the small grid may be applied.

FIG. 1 shows the relationship between the location of the color image data and the tetrahedron used for the interpolation operation. The cube shown in the center of FIG. 1 indicates a small grid including the image data P in FIG. The vertex K of the cube indicates the point closest to the origin among the vertices of the small lattice, and the vertices R, G, and B indicate the lattice points moved by one in the R, G, and B directions, respectively. For each dimension, grid points are sequentially assigned from the origin to 0, 1, 2,
(Hereinafter, this number is referred to as “grid point number”), and the grid point is represented by coordinate values (R, G, B) in the R, G, and B directions. The coordinate value of each vertex of the cube in FIG. 1 can be expressed by the following equation (6) using a lattice point interval D (constant) and natural numbers ir, ig, and ib of 0 to N. Vertex K (ir, ig, ib) × D Vertex R (ir + 1, ig, ib) × D Vertex G (ir, ig + 1, ib) × D Vertex B (ir, ig, ib + 1) × D Vertex Y (ir + 1, ig + 1) , Ib) × D Vertex M (ir + 1, ig, ib + 1) × D Vertex C (ir, ig + 1, ib + 1) × D Vertex W (ir + 1, ig + 1, ib + 1) × D (6)

In the tetrahedron method, as shown in FIG.
Six tetrahedrons (Tetra0 to Tetra5) by dividing by two planes (KGWM, KBWY, KCWR)
Divided into In the interpolation operation, these tetrahedrons are properly used according to the position where the color image data exists. The position where color image data exists in the small grid is represented by a coordinate value (hereinafter, referred to as an offset coordinate value) with the vertex K (hereinafter, referred to as a base point) of the small grid as an origin. Let the coordinate values of the color image data be P (Rd, Gd, Bd)
Set the offset coordinate value to (Roff, Goff, Boff)
Then, the offset coordinate values are calculated from the coordinate values (ir × D, ig × D, ib) of the base point K from the color image data.
× D) can be subtracted, so that it can be obtained as in the following equation (7). Roff = Rd−ir × D Goff = Gd−ig × D Boff = Bd−ib × D (7) The relationship between the small grid and the offset coordinate values is shown in FIG.

As the tetrahedron used for the interpolation operation, a tetrahedron consisting of four points close to the color image data is selected in order to reduce the error. Specifically, the relationship between the offset coordinate values and the tetrahedron used for the interpolation calculation is as follows (see FIG. 1). Roff>Goff> Boff → Tetra0; Roff>Boff> Goff → Tetra1; Boff>Roff> Goff → Tetra2; Goff>Roff> Boff → Tetra3;

After the tetrahedron is selected in this way, a weight coefficient for multiplying the color correction data at each grid point is calculated based on the offset coordinate values. FIG. 8 about calculation of weight coefficient
This will be described with reference to FIG. FIG. 8 shows a tetrahedron (Tetra0 in FIG. 1) to which the color image data of FIG. 7 belongs. At this time, the weight coefficient WW by which the vertex W is multiplied is
It is given by the ratio (vp / V) between the volume V of the tetrahedron KRYW and the volume vp of the tetrahedron KRYP (hatched portion in the figure). When the color image data P coincides with the vertex W, the weight coefficient WW = 1, and when the data P is on the plane KRY, the weight coefficient WW = 0. Weight coefficients are similarly obtained for other vertices. That is, the weight coefficient WK of the vertex K is the ratio of the volume V to the volume of the tetrahedron RYWP,
The weight coefficient WR of the vertex R is the ratio of the volume V to the tetrahedron KWYP,
The weight coefficient WY of the vertex Y is obtained by the ratio between the volume V and the tetrahedron KWRP.

Note that each weight coefficient is represented by an offset coordinate value (Roff, Goff, Boff) and a grid point interval D
Can also be used. For example, considering the weight coefficient WW of the vertex W, the tetrahedron KRYW and the tetrahedron KRY
Since P has a common bottom surface KRY, the volume ratio of both is KR
This is nothing but the ratio of the height of the vertex W to the height of the point P when Y is the bottom surface. Since the height of the vertex W is the lattice point interval D and the height of the point P is Boff, WW = Boff / D is obtained. Other weighting factors are similarly obtained. The following equation (8) summarizes these. Tetra0: WK = (D-Roff) / D; WR = (Roff-Goff) / D; WY = (Goff-Boff) / D; WW = Boff / D (8)

Similarly, other tetrahedrons Tetra1 to Tetr
For a5, the following equations (9) to (1)
It is as 3). Tetra1: WG = (D-Roff) / D; WR = (Roff-Boff) / D; WM = (Boff-Goff) / D; WW = Goff / D (9)

Tetra2: WK = (D-Boff) / D; WB = (Boff-Roff) / D; WM = (Roff-Goff) / D; WW = Goff / D (10)

Tetra3: WG = (D-Goff) / D; WG = (Goff-Roff) / D; WY = (Roff-Boff) / D; WW = Boff / D (11)

Tetra4: WG = (D-Goff) / D; WG = (Goff-Boff) / D; WC = (Boff-Roff) / D; WW = Roff / D (12)

Tetra5: WK = (D-Boff) / D; WB = (Boff-Goff) / D; WC = (Goff-Roff) / D; WW = Roff / D (13)

The color correction data corresponding to the color image data P can be obtained by multiplying the color correction data of each grid point by the weight coefficient obtained as described above and taking the sum. That is, if the color correction data corresponding to the vertices K, W, R, and Y are CTK, CTW, CTR, and CTY, respectively, the color correction data CTP corresponding to the color image data P
Is obtained as in the following equation (14). CTP = WK * CTK + WW * CTW + WR * CTR + WY * CTY (14) In the present embodiment, since the color correction table CT has three-dimensional data of C, M, and Y, ,
This is executed for each of the data of M and Y.

In this interpolation operation, the weight coefficient is obtained by the volume ratio as described above, but the weight coefficient may be obtained by another method. For example, the weight coefficient may be determined based on the distance between each vertex of the tetrahedron and the color image data P.

Next, the specific processing contents in this embodiment will be described with reference to FIG. FIG. 9 is a flowchart showing the flow of the image processing routine. This routine is executed by the CPU of the computer 90.

When the image processing routine is executed, the CPU
Reads color image data (step S10).
0). Here, the read color image data is obtained by changing the original image data ORG captured using the scanner 12 into data in dot units by the rasterizer 97, and has color information including R, G, and B for each pixel. Data. Next, the CPU executes a base point calculation process (step S105) and an offset coordinate calculation process (step S110). The contents of each of these processes will be described with reference to FIGS.

FIG. 10 shows the flow of the base point calculation process. In the base point calculation processing, the same processing is executed for each of the R, G, and B color data. For convenience of explanation, a process performed on R data will be described as an example. In the base point calculation processing, first, the image data is divided by the grid point interval D (step S150). 7 and 8, the image data Rd / D is calculated. Next, the result of this operation is converted to an integer by truncating the decimal part (step S15).
5). The integer value thus obtained is output as base point data irk. The base point data irk is defined as follows for the small grid to which the color image data belongs.
This represents the grid point number of the grid point near the origin in the R direction. In the next step, the CPU determines whether or not the above processing has been completed for all the R, G, B colors (step S160). If the processing has not been completed for all the colors, the above steps S150, S155 Is also performed for each of the colors G and B. After calculating the base point data (irk, igk, ibk) for each color in this way, the base point calculation processing is temporarily terminated, and the process returns to the image processing routine. The base point data is
In addition to the grid point number, a coordinate value of a base point may be calculated.

FIG. 11 shows the flow of the offset coordinate calculation process. In the offset coordinate calculation process, the same process is executed for each of the R, G, and B color data. In the offset coordinate calculation process, the data of the base point previously obtained is read (step S165). Next, a difference between the image data and the coordinate value of the base point is obtained (step S170). This difference becomes the offset coordinate value.
Taking the data of R as an example, the coordinate value of the base point is represented by irk × D using the base point data irk and the lattice point interval D, so that the offset coordinate value Roff for the color image data Rd is Roff
= Rd-irk x D. In the next step, the CPU determines whether or not the above processing has been completed for all the R, G and B colors (step S175).
65 and S170 are also performed for each of the colors G and B. Thus, the offset coordinate values (Roff, G
After calculating (off, Boff), the offset coordinate calculation process is once ended, and the process returns to the image processing routine. Note that the offset coordinates may be obtained by an operation of Roff = Rd% D. Here,% represents a surplus operator.

Returning to FIG. 9, the image processing routine will be described. After the offset coordinate calculation processing is completed, the CPU executes interpolation calculation data reading (S115). This is the offset coordinates (Roff, Goff, Boff)
Is a process of reading data stored in the offset correction table OT based on the data. The offset correction table stores two types of data: grid point specifying data for specifying grid points used in the interpolation calculation, and weight coefficient data for giving a weight coefficient to be multiplied to each grid point in the interpolation calculation. Here, the contents of the offset correction table will be described.

The offset correction table is a table for storing the above-mentioned grid point specifying data and weight coefficient data for each of the offset coordinate values (0, 0, 0) to (D, D, D). . FIG. 12 shows an example of the offset correction table. FIG. 12 shows offset coordinate values (0, 0,
0) to (D, D, D), two grid point specifying data AD1 and AD2 and WK, WW, W1, W2
It is an example of an offset correction table provided with the following four weight coefficient data. The reason why only two grid point specifying data AD1 and AD2 are required in spite of performing the interpolation calculation by the tetrahedron method using the four grid points around the image data is as follows. That is, as shown in FIG. 1, since each tetrahedron used in the interpolation operation by the tetrahedron method always includes the vertices K and W, the grid point specifying data includes the other two points according to the offset coordinate value. This is because it is sufficient to store data for specifying

The grid point specifying data will be specifically described with reference to FIG. For example, if the offset coordinates are (0,0,
0), that is, the vertex K in FIG. 1 is included in all tetrahedrons Tetra0 to Tetra5 as shown in FIG. Therefore, in this embodiment, for convenience, Tetr
Data that specifies the vertex B in the grid point specifying data AD1 of the offset correction table,
Data for specifying the vertex C is stored in D2. The offset coordinates (0, 0, 1) and (0, 0, 2) are also points on the side KB in FIG. 1 and are included in both Tetra5 and Tetra2.
ra5. Although not shown in FIG. 12, for example, when the relationship of Roff>Goff> Boff holds and Tetra0 of FIG. 1 is used for the interpolation calculation (corresponding to FIG. 8), the grid point specifying data AD Is A
Data for specifying the vertex R as D1 and data for specifying the vertex Y as AD2 are stored. The data used to specify each vertex will be described later.

Next, the weight coefficient data WK, WW, W1, W2 stored in the offset correction table will be described. Weight coefficient data WK is a weight coefficient applied to vertex K, data WW is a weight coefficient applied to vertex W, data W
Reference numerals 1 and W2 mean weighting factors applied to vertices specified by the grid point specifying data AD1 and AD2, respectively.
As shown in FIG. 12, for example, the offset coordinates (0, 0,
0), that is, for the vertex K in FIG. 1, the weight coefficient multiplied by the vertex K in the interpolation calculation by the tetrahedron method becomes the value 1 and all the other weight coefficients become the value 0. Therefore, in the offset correction table, WK = 1 , WW = W1 = W2 =
0 data is stored. Similarly, the vertices specified by the grid point specifying data AD1 and AD2 and the weighting factors to be multiplied by the vertices K and W can be obtained corresponding to the respective offset coordinates. Coefficient data WK, WW, W1,
It is stored as W2. In FIG. 12, WK1, W
W1 and other data for which no specific value is indicated
It means that the real number obtained as described above is stored. Note that this real number is a value obtained in the range of 0 ≦ weight coefficient data ≦ 1.

Next, an example of what kind of data specifies each vertex in the grid point specifying data will be described. For that purpose, it is necessary to first explain the arrangement of the color correction tables CT in the memory in the present embodiment. In the present embodiment, the color correction table CT stores the origin (0, 0,
(0, 0) to (Nr-1, Ng-1, Nb-1). Here, Nr, Ng, Nb are R, G, B
It means the number of grid points in each dimension, and each grid point is represented as (ir, ig, ib) by arranging grid point numbers in the order of R, G, B. In this embodiment, as described above,
Nr = Ng = Nb = 17. FIG. 13 shows a data array of the color correction table CT in the present embodiment.

As shown in FIG. 13, the color correction table CT
Are arranged on the memory in the order of B, G, and R in which the grid point numbers are increased. Specifically, (0,0,
0), (0, 0, 1) ... (0, 0, Nb-1)
Then (0,1,0) (0,1,1) ... (0,1, N
It is lined with b-1). In this order (0, Ng-1, N
The next line up to b-1) is (1, 0, 0), (1, 0,
1) ... (1, 0, Nb-1), and the last is (Nr-
1, Ng-1, Nb-1). First, data is arranged in this order for C color, and then M,
Y data is arranged. In FIG. 13, for convenience, the number of color correction data is shown for each appropriate segment. By referring to the number of data, in a state where the color correction data is arranged one-dimensionally, an arbitrary grid point (ir, ig, ib)
It can be understood that the data position (corresponding to the address on the memory) AG corresponding to the following can be specified as in the following equation (15).

Using this equation, the position on the ROM of the base point K (irk, igk, ibk) obtained previously, that is, the address, can be obtained. The above equation (15)
Of the base point K obtained by the above operation, irk · Ng
Nb + igk Nb + ibk is hereinafter referred to as base point address AK.

Using the above equation (15), it is possible to specify the relative positional relationship with respect to the base point K for the color correction data of each vertex of the small grid. For example, the data of the vertex R in FIG.
Only exists behind. In other words, the relative address of the vertex R with respect to the base point address AK is Ng ·
Nb. Base point K (irk, i
gk, ibk), vertex R is (irk + 1, ig
(k, ibk). Focusing on this point, when using the vertex R for the interpolation calculation, it is understood that the relative address values Ng and Nb should be stored as the grid point specifying data AD1 of the offset correction table OT. When other vertices are used, the grid point specifying data AD can be prepared in the same manner. In this embodiment, the relative position data on the memory thus obtained is used as the grid point specifying data AD1 and AD2. Therefore, the grid point specifying data for each vertex is as follows. Vertex G ... Nb; Vertex B ... 1; Vertex C ... Nb + 1; Vertex M ... Ng · Nb + 1; Vertex Y ... Ng · Nb + Nb;

The grid point specifying data changes according to the change in the arrangement order of the data of the color correction table CT. As the grid point specifying data, various modes such as storing a code meaning each vertex can be considered. According to the above-described method of storing a relative address on a memory, a color correction described later is performed. Since data can be read very easily, it is preferable from the viewpoint of speeding up processing.

Returning to FIG. 9, the image processing routine will be described. After reading the interpolation calculation data by reading the offset correction table described above (step S115), the CPU executes reading of the color correction data (step S120). This processing will be described with reference to FIG. FIG. 14 is an explanatory diagram showing the relationship between the data in the color correction table CT and the data read in the offset correction table. The grid point numbers (0, 0, 0) to (Nr-1, Ng-1, Nb) in FIG.
-1) is a color correction table CT on the memory as in FIG.
Are shown. The grid points (irk,
igk, ibk) represents the base point (vertex K), and the data corresponding to the base point is stored on the memory behind the data of the grid point (0, 0, 0) by the base point address AK. It is shown that. Therefore, based on the base point address AK,
The color correction data corresponding to the base point can be read.

Next, reading of the color correction data corresponding to the vertices specified by the grid point specifying data will be described. As described above, in step S115, the grid point specifying data used for the interpolation calculation is read. The grid point specifying data AD1 and AD2 store an address value relative to the base point. Therefore, the grid point specifying data AD1 and AD2 are stored in the base point address AK.
Each color correction data can be obtained by reading the color correction data stored in the location to which is added. In FIG. 14, the relative addresses Ng and Nb for specifying the vertex R (irk + 1, igk, ibk) are stored in AD1 as the grid point specifying data, and the vertex Y is stored in AD2.
An example of reading color correction data in the case where a relative address Ng · Nb + Nb specifying (irk + 1, igk + 1, ibk) is stored is shown. Similarly, since the vertex W has a positional relationship of (irk + 1, igk + 1, ibk + 1) with respect to the base point (irk, igk, ibk), its color correction data has Ng · Nb + Nb + 1 as the base point address AK in the memory. It is stored in the place where it was added.

Next, an interpolation operation is performed using the color correction data thus obtained and the weight coefficient data for the interpolation operation (step S125). The interpolation calculation is the same as the calculation content of Expression (14) described in “(2) Outline of Image Processing”. That is, the color correction data corresponding to the vertices K and W and the vertices specified by the grid point specifying data AD1 and AD2 are CTK, CTW, CT1 and CT2, respectively.
The weight coefficient data applied to each vertex is represented by WK, WW,
Assuming that W1 and W2, the color correction data CP corresponding to the color image data P is obtained by the following equation (16). CP = WK * CTK + WW * CTW + W1 * CT1 + W2 * CT2 (16) In the present embodiment, since the color correction table CT has three-dimensional data of C, M, and Y, ,
This is executed for each of the data of M and Y. In this manner, color correction data for one pixel can be obtained.
The PU temporarily ends the image processing routine.

According to the image processing apparatus described above, since the image processing is performed by interpolating the color correction table, it is possible to realize color correction with a very small error for each pixel. Moreover, by utilizing the offset correction table OT, it is possible to execute the interpolation calculation without branching by the condition judgment, so that the image processing can be performed at high speed. As described above, the tetrahedron interpolation calculation requires selection of grid points to be used in the interpolation calculation according to the offset coordinate values, so that a large number of branches are required depending on the condition judgment. Various calculations such as calculation of a weight coefficient are required. On the other hand, in the present embodiment, by utilizing the offset correction table OT, the interpolation calculation by the tetrahedron method can be realized without any branching due to such a condition judgment. As described above, image processing that can be executed without branching based on condition determination is performed by a pipelined processor, for example, Pentium II (Inte
This processor is suitable for processing by a company (registered trademark), and when such a processor is used, high-speed processing can be performed four to five times faster than conventional image processing including branching based on conditional judgment.

In this embodiment, a three-dimensional color space of 256 tones consisting of 8 bits for each of the R, G, and B dimensions is divided into 4 bits for each dimension, that is, at a grid point interval of 16 tones. ing. That is, N bits (in this embodiment, N =
8), the grid point interval is half of N
/ 2 bits. In this embodiment, the color correction table CT has three data (c, m, y) for each grid point, and the offset correction table OT has (AD1, AD2, WK, WW) for each coordinate value. , W1, W
The six data of 2) are prepared. When considering the data amounts of the color correction table CT and the offset correction table OT in the three-dimensional color space, the number of data for each grid point or each coordinate value is in a range that can be considered to be substantially equal. By dividing the color space by 4 bits corresponding to / 2 bits, the total of the data amounts of the color correction table CT and the offset correction table OT is minimized, and the memory capacity can be reduced. However, it is sufficient that the grid point interval is approximately half the number of bits of the number of gradations. In other words, it is sufficient that the data amount of the color correction table CT and the data amount of the offset correction table OT are approximately the same. When there is no such relationship, for example, when the grid point interval is reduced, the offset correction table becomes small, but the color correction table becomes very large, so that the total required memory capacity of both becomes large. Also,
When the lattice point interval is increased, the offset correction table becomes extremely large. Note that the lattice point interval may take a desired value according to the margin of the memory capacity.

In this embodiment, the offset correction table stores the grid point specifying data and the weight coefficient data. However, instead of the latter, data for calculating the weight coefficient may be stored. As described above, the weighting factor when performing the interpolation operation using each tetrahedron from Tetra0 to Tetra5 in FIG.
It is determined as (13). These equations are given by the following equation (17).
Can be summarized in the form of WK = (D-OFF1) / D; W1 = (OFF1-OFF2) / D; W2 = (OFF2-OFF3) / D; WW = OFF3 / D (17) where OFF1, OFF2, and OFF3 are Roff, Goff, B according to which tetrahedron in FIG.
It is a variable that takes any value of off.

For example, when an interpolation operation is performed using Tetra0, as is clear from equation (8), W1 = WR,
W2 = WY, OFF1 = Roff, OFF2 = G
off, OFF3 = Boff. In the above equation (17), considering that the lattice point interval D is constant without depending on the offset coordinate value, the weighting factor is OFF1, OF1
If there are three data of F2 and OFF3, it will be obtained. Therefore, in the offset correction table OT, the weight coefficient data WK, WW, W1, W2 shown above are displayed.
Instead of the four types of data, OFF1, OFF2,
If three types of data of OFF3 are stored, a weight coefficient can be calculated for each. By doing so, although a process of calculating a weighting factor from the data of the offset correction table is required, three types of data need only be stored. Thus, the size of the offset correction table OT can be suppressed.

Next, a processing method when the color image data is near the maximum coordinate value will be described. As described above, the number of gradations in the color space and the grid point interval can be freely set. Therefore, depending on the setting of the number of gradations and the grid point interval, the maximum coordinate value of each dimension may not be a grid point. For example, if the coordinate value of each dimension is represented by 256 gradations from 0 to 255 and the grid point interval is 17, the coordinate values of the grid points are 0, 17, 34,.
Therefore, the maximum coordinate value 255 is a grid point. On the other hand, if the grid point interval is 16, the coordinate values of the grid points are
240, (256), and the maximum coordinate value 255 does not become a grid point. As described below, the present embodiment can be applied even when the number of gradations and the grid point interval are set in such a state.

First, it will be explained that no special processing is required when the point of the maximum coordinate value coincides with the lattice point. For color image data having a maximum coordinate value in any dimension, an interpolation operation is performed using a small grid having a coordinate value larger than the maximum coordinate value, that is, a small grid in which no data actually exists, and FIG. An image processing routine will be executed. Therefore, in reading the color correction data (step S120 in FIG. 9), there is a possibility that data stored in a memory area that is irrelevant to the area where the color correction table is stored is read. However, in the interpolation of the color correction table, the weight coefficient applied to such data always has a value of 0, so that the result of the color correction processing is not affected. For this reason, no special processing is required for color correction. In order to more surely execute the image processing, a color correction table for a virtual grid point having a coordinate value larger than the maximum coordinate value may be prepared.

Next, a case where the maximum coordinate value does not coincide with the lattice point will be described. For example, when the number of gradations is 0 to 255, 2
Consider a case where there are 56 gradations and the grid point interval is 16 gradations. This embodiment corresponds to this case. At this time, for color image data having a coordinate value of 240 or more, the coordinate value 2
The interpolation calculation is performed using the virtual grid points having 56. Moreover, unlike the above-described example, the weight coefficient applied to the virtual grid point does not become zero. In this case, virtual color correction data that allows appropriate color correction data to be obtained at the maximum coordinate value may be stored as color correction data for a virtual grid point having a coordinate value of 256. By doing so, appropriate color correction data can be obtained even in a region where the coordinate value is 240 or more.

(3) Image processing apparatus in the second embodiment:
Next, an image processing apparatus according to a second embodiment of the present invention will be described. The hardware configuration of the image processing apparatus according to the second embodiment is the same as the apparatus according to the first embodiment described above.
The second embodiment differs from the first embodiment in that the color space is divided at non-uniform grid point intervals, and color correction data uses data prepared corresponding to the grid points. FIG. 15 shows an example of the division of the color space in the second embodiment. In FIG. 15, the grid point intervals are unified between the R, G, and B dimensions. However, the present embodiment can be applied even when the grid point intervals between the dimensions are not uniform. As a result of the non-uniform grid point spacing, the flow of the image processing routine executed by the CPU of the computer 90 is basically the same as the processing of the first embodiment (FIG. 9), but in some processing contents, This is different from the first embodiment.

When the image processing routine is started, as in the first embodiment, the color image data is read (step S100 in FIG. 9), and the base point calculation processing is executed (step S105). This embodiment differs from the first embodiment in the base point calculation process. FIG.
The base point calculation process will be described with reference to FIG.

In the base point calculation processing, the same processing is executed for each of the R, G, and B color data. For convenience of explanation, a process performed on R-color data will be described as an example. When the base point calculation process starts, the CPU initializes a variable n by substituting a value 0 (step S20).
0), the R component of the color image data is R (0)
It is determined whether a condition of ≦ Rd <R (1) is satisfied (step S200). Here, R (n) means the coordinate value of the n-th grid point from the origin in the R direction. Therefore, in the above determination, it is determined whether or not the color image data belongs to the small grid formed from the origin and the grid points adjacent thereto in the R direction.

If the result of this determination is no, the variable n
Is incremented by 1 and the same determination is made. That is, R
(1) ≦ Rd <R (2), R (2) ≦ Rd <R (3) ·
·································· etc. When this condition is satisfied, the color image data Rd is R (n) and R (n +
Since it belongs to the small grid consisting of the grid points of 1), the CPU substitutes the value of the variable n at this time for the base point irk in the R direction (step S205). In the next step, the CPU determines whether or not the above processing has been completed for all the R, G, and B colors (step S21).
0) If the processing has not been completed for all the colors, steps S200 and S205 are also performed for each of the colors G and B. Thus, for each color, the base point (irk, igk, i
bk). In this embodiment, the grid point number is obtained as the base point, but the coordinate value R (n) of the grid point may be obtained.

The base point calculation process is performed as shown in FIG.
May be performed by the method shown in FIG. in this case,
A table storing grid point number data of base points corresponding to the coordinate values in the R direction is prepared as a reference grid point table. FIG. 18 shows an example of the reference grid point table. The reference grid point table is a three-dimensional one-dimensional table prepared for each coordinate value in the R, G, and B directions. FIG. 18 shows an example of a table corresponding to the coordinate values in the R direction. For example, assuming that the coordinates of the grid point after the origin in the R direction are values 10, 20,..., The reference grid point table shows that the origin is the base point for the coordinate values 0 to 9 as shown in FIG. 0 (grid point number at the origin) is stored, and a value 1 (grid of grid point at coordinate 10) indicating that the point at coordinate 10 is a base point from coordinates 10 to 19 Point number)
Is stored, and data is similarly stored according to the coordinate values of the lattice points. In the base point calculation process, as shown in FIG.
Then, the value of the reference grid point table corresponding to is read (step S215). In the next step the CPU
Determines whether or not the above processing has been completed for all the R, G, and B colors (step S220). If the processing has not been completed for all the colors, the above step S215 is performed for each of the G and B colors. Also perform

This eliminates the need for branching based on the condition determination as shown in FIG. 16, so that the base point can be calculated at a high speed. Although a memory for preparing the above table is required, a one-dimensional table only needs to be prepared, and the required memory capacity can be relatively small.
When the coordinate values of the grid points match in each of the R, G, and B directions, the reference grid point tables in three directions can be shared, and one type of reference grid point table is prepared. Therefore, the required memory capacity can be further reduced. It should be noted that the reference grid point table may store the coordinate values instead of the base point numbers.

After the base point is calculated in this way, the CPU executes an offset coordinate calculation process (FIG. 9).
Step S110). This embodiment differs from the first embodiment in the offset coordinate calculation processing. The offset coordinate calculation processing will be described with reference to FIG.

In the offset coordinate calculation processing, the same processing is executed for each of the R, G, and B color data. For convenience of explanation, a process performed on R-color data will be described as an example. When the offset coordinate calculation process is started, the CPU calculates a difference between the color image data Rd and the coordinate value R (irk) of the grid point corresponding to the base point Rb previously obtained as a variable DUM1 (step S230). . The variable DUM1 is a value representing an offset from a grid point in the coordinate system shown in FIG. Next, DUM2 = DU
The variable DUM2 is calculated by calculating M1 / Rdist × Dr (step S235). Here, Rdist is a grid point interval in the R direction of the small grid to which the color image data Rd belongs, and Dr is a predetermined virtual grid point interval. That is, the variable DUM2 represents the offset coordinate value in the coordinate system obtained by converting the grid point interval of the small grid into the virtual grid point interval Dr by the above-described calculation. Next, the CPU converts the variable DUM2 into an integer, and uses it as an offset coordinate value (step S240). In order to reduce the so-called rounding error, it is preferable to round off the decimal part. However, the integer part may be rounded down or rounded up. Obtaining the offset coordinate value when the grid point interval is converted into the virtual grid point interval Dr by the above processing is referred to as normalization processing. In the next step, the CPU determines whether or not the above processing has been completed for all the R, G, B colors (step S245). For the same normalization processing (steps S230 to S2).
40) is executed to calculate offset coordinates.

In this embodiment, the virtual grid point interval Dr is made to coincide with the lattice point interval in the low density area, and is also made coincident in each of the R, G, and B directions. The low-density area refers to an area having a large coordinate value in the R, G, and B directions in FIG. As described above, a slight error in color correction may lead to deterioration of image quality in a low-density area, but by making the virtual grid point interval Dr equal to the grid point interval in the low-density area, This is because rounding errors due to the normalization processing do not occur, and good image quality can be obtained.

Even if the virtual grid point interval Dr and the grid point interval in the low density area do not match, the ratio between them (virtual grid point interval / grid point interval) must always be an integer. For example, even if the normalization processing is performed, a rounding error does not occur, and good image quality can be obtained. That is, both may be set so that the grid point interval is 1 / integer of the virtual grid point interval Dr, and the virtual grid point interval Dr is wider than the grid point interval. Under such conditions, for example, if the virtual grid point interval Dr is set so as to be the least common multiple of a plurality of grid point intervals having different intervals, a virtual grid point interval that does not cause a rounding error in any grid can be set. Is also possible. Note that the virtual grid point interval Dr can be set freely, and does not necessarily need to match any of the grid point intervals. Further, the intervals Dr, Dg, and Db may be different from each other in the R, G, and B directions.

This will be described more specifically. In this embodiment,
The number of gradations of R, G, and B is 256 gradations from 0 to 255, and the lattice point interval is 16 gradations. In the first embodiment, the color space is divided by the grid point interval of 16 from the origin, and the coordinate values of the grid points are set to 0, 16, 32... 240, (256). It was not. On the other hand, in the second embodiment, the maximum coordinate value 255
, The color space is divided by a grid point interval of 16, and the grid point interval near the origin is set to 15. At this time, the grid point coordinates are
0, 15, 31,... 239, 255. In this division, if the virtual grid point interval Dr is set to 16, no rounding error occurs except in the region with the highest density (small lattice near the origin). In the high-density region, the rounding error generally has little effect on the image quality, so that good image quality can be obtained. Also, the virtual grid point interval Dr is set to the grid point interval 1
If the least common multiple of 5 and 16 is 240, rounding errors can be prevented from occurring in any area.

Although the virtual grid point interval can be set freely, it is desirable to make it wider than any grid point interval. This is because the following inconvenience may occur when there is an area having a grid point interval wider than the virtual grid point interval Dr. For example, let us consider a case where the value of the virtual grid point interval Dr is 2 and the section of the grid point interval 6 is normalized. The points having offset coordinate values 0 to 5 in the original coordinate system are sequentially 0, 0, 1, 1 in the coordinate system after the normalization processing (the operation of rounding off the coordinate value / 6 × 2 to the decimal point). , 1 and 2. Before the processing, data having six points from the coordinate values 0 to 5 are assigned to any of the three points 0, 1, and 2 at the stage of integer conversion in the normalization processing. As a result, the color correction data for the three points of the coordinate values 2 to 4 in the original coordinate system have the same value, so that an error occurs in the image processing. Such an inconvenience occurs in a portion where the virtual grid point interval Dr is smaller than the grid point interval in the original coordinate system. on the other hand,
It is obvious that such an error does not occur when the above-mentioned integer is set to the value 6 and the virtual grid point interval Dr matches the grid point interval. Therefore, as described above, it is desirable to set the virtual grid point interval Dr to be wider than any of the grid point intervals.

On the other hand, the offset coordinate calculation process is performed as shown in FIG.
May be performed by the method shown in FIG. in this case,
As the offset coordinate table, a table storing offset coordinates corresponding to the coordinate values in the R direction is prepared. FIG. 21 shows an example of the offset coordinate table. The offset coordinate table is a three-dimensional one-dimensional table prepared for each coordinate value in the R, G, and B directions. FIG. 21 shows a table example corresponding to the coordinate values in the R direction. For example, assuming that the coordinates of the grid points after the origin are value 10, value 20... And the virtual grid point interval is value 10, as shown in FIG. Since the origin is the base point, the coordinate values of the color image data Rd are stored as offset coordinate values as they are, and the coordinates 10 to 19 are stored.
Up to this point, the grid point 1 (coordinate value 10) becomes the base point, so a value obtained by subtracting the coordinate value (value 10) of the base point from the color image data is stored as the offset coordinate value. Is stored in a similar manner. In this example, for the sake of simplicity, a case where the lattice point interval and the virtual lattice point interval coincide with each other is shown. Will be done. In the offset coordinate calculation process, as shown in FIG. 20, the value of the offset coordinate table corresponding to the coordinate value Rd of the color image data is read (step S245). In the next step, the CPU determines whether or not the above processing has been completed for all the R, G, and B colors (step S25).
0) If the processing has not been completed for all colors,
Similar processing is performed for each color of G and B (step S24).
Execute 5).

This eliminates the need for the calculation processing as shown in FIG. 19, so that the offset coordinates can be calculated at a high speed. Although a memory for preparing the above table is required, a one-dimensional table only needs to be prepared.
The memory capacity is relatively small. When the coordinate values of the grid points match in each of the R, G, and B directions, the reference grid point tables in three directions can be shared, and if one type of offset coordinate table is prepared As a result, the required memory capacity can be further reduced. The offset coordinate tables in three directions may be shared.

The above-described base point calculation processing (FIGS. 16 and 17) and offset coordinate calculation processing (FIG. 1)
9, FIG. 20) can be freely combined and executed. That is, the base point calculation processing of FIG. 16 and the offset coordinate calculation processing of FIG. 19 may be executed when there is not enough memory capacity. It may have one of the coordinate tables (a combination of the processing contents of FIGS. 17 and 19 or a combination of the processing contents of FIGS. 16 and 20). When the memory capacity is sufficient, both the reference grid point table and the offset coordinate table may be provided (combination of the processing contents of FIGS. 17 and 20).

According to the image processing apparatus described above, since the image processing is performed by interpolating the color correction table using the offset correction table OT, the error of each pixel is very small as in the first embodiment. Color correction can be performed at high speed. Moreover, in the second embodiment, since the color space can be divided at non-uniform intervals, for example, a low-density area can be divided at smaller intervals than other areas, and the image quality can be improved. it can. Further, since it is not necessary to match the grid point intervals between the dimensions, the grid point intervals can be freely set for each color component (dimension) according to the visual sensitivity. As a result, it is possible to reduce the capacity of the color correction table while maintaining the image quality.

The image processing in the image processing apparatus described above is realized by the computer 90 executing various processes shown in FIG. 9 according to programs. Therefore, the present invention can also be embodied as a storage medium storing a program for realizing each of the above functions. That is, the program for realizing the above functions is provided in a form stored in a computer-readable storage medium such as a floppy disk or a CD-ROM. The computer 90 reads the program from the storage medium and transfers the program to an internal storage device or an external storage device. Alternatively, a program may be supplied to a computer via a communication path. When implementing the functions of the program, the program stored in the internal storage device is executed by the microprocessor of the computer. Further, the computer may read the program stored in the storage medium and directly execute the program.

In addition to the programs for executing the various processes shown in FIG. 9, an embodiment may be adopted as a recording medium on which a grid point information table, a color correction table, and an offset correction table are recorded. . In this case, the computer 90 executes various processes shown in FIG. 9 while using the tables recorded on the recording medium.
It should be noted that such a recording medium may be used to record a reference grid point table and an offset coordinate table together.

In the above-described embodiment, the image processing routine is set to the CP
Although U is described as being executed by software, it may be configured by hardware. Further, an image processing device constituted by hardware may be incorporated in the printer 22.

FIG. 22 shows an example of the configuration of a color correction table when the device is configured by hardware. This is because, for example, a color correction table for cyan is set to R as shown in FIG.
By storing the data in the OM and assigning a predetermined address line to the grid point numbers (ir, ig, ib) in the R, G, and B directions of the grid points, cyan correction data corresponding to the coordinate values is output from the data bus. It is like that. Correction tables for other colors can be similarly configured. Note that C
The signal line S means a chip select signal for controlling the validity / invalidity of the signal input to the ROM. Next, FIG. 23 shows a configuration example of the offset correction table. This is R
The offset correction data is stored in the OM, and a predetermined address line is assigned to the offset coordinate data, so that the offset correction data is output from the data bus. Here, the control code assigned to the address lines (A8 to A10) in FIG. 23 means that there are several types of offset correction data (grid point specifying data and weight coefficient data) for one piece of offset coordinate data. This is used for type determination.

(4) Third Embodiment The present invention can be applied to various apparatuses other than the color correction processing in the image processing apparatus. Hereinafter, as a third embodiment, an example in which the control method of the present invention is applied to control processing in a flight control device of an aircraft will be described. FIG. 24 is a block diagram illustrating a software configuration of the aircraft flight control device. The flight control device according to the present embodiment includes a control command input unit 302, a flight state detection unit 30
4, a weight estimating unit 306, a resistance estimating unit 308, a target thrust value setting unit 310, and an engine control unit 312. Actually, the flight computer and the sensors mounted on the aircraft 300 function as the flight control device by configuring the respective functional blocks described above. This flight control device controls the aircraft 3 in accordance with the control command value.
00 controls the thrust of the turbofan engine mounted on the engine. Of course, it also has a function of controlling the flight attitude of the aircraft, but for convenience of illustration, only a functional block for controlling the thrust of the engine is shown.

The flight control device includes a control command input unit 30
2, a command value relating to the flight state of the aircraft 300 is input. The command value may be given by a pilot inputting a flight altitude and a flight speed through an input panel installed in a cockpit, or may be given by operating a control wheel and a throttle lever. Weight estimating section 306 calculates the weight of aircraft 300. The weight can be estimated, for example, by subtracting the consumed fuel weight from the initial flight weight.

The flight altitude, flight speed, and estimated weight input as the steering command value are passed to the resistance estimating unit 308. The resistance estimating unit 308 calculates the aerodynamic resistance of the aircraft 300 based on these data. The aerodynamic resistance is calculated by the drag coefficient CD, the wing area (m2),
m2). The dynamic pressure is uniquely determined according to the flight altitude and the Mach number detected by the flight state detection unit 304 of the aircraft 300. The resistance coefficient is a dimensionless quantity obtained by interpolating a table according to the flight speed of the aircraft and the lift coefficient CL. Lift coefficient CL
Is a dimensionless quantity obtained by dividing the weight of the aircraft by the wing area and the dynamic pressure. The lift coefficient CL is determined by the angle of attack α during flight,
That is, it changes according to the angle between the body axis Xb and the speed vector Ve.

FIGS. 25 and 26 show a method of calculating the resistance coefficient.
It will be described based on. FIG. 25 is a graph showing the relationship between the Mach number, the lift coefficient CL, and the resistance coefficient CD for the aircraft 300. At the Mach number M1, the relationship between the lift coefficient CL and the resistance coefficient CD is given by a curve shown. The curve changes according to the Mach number. That is, the lift coefficient CL
If the two parameters of the Mach number are specified, the resistance coefficient CD is uniquely determined. The flight control device of the present embodiment stores the graph as a table. FIG. 26 shows an image of the table in this embodiment. The table of this embodiment defines discrete grid points as shown in the two-dimensional coordinate space of the lift coefficient CL and the Mach number, and stores a resistance coefficient CD for each grid point. The resistance coefficient CD is obtained by interpolating this table according to the lift coefficient CL and the Mach number.

Actually, the lift coefficient CL and the Mach number are values that change continuously. However, in the present embodiment, the lift coefficient CL takes a value in increments of 0.01, and the Mach number takes a value in increments of 0.01M. This is because a value smaller than this has no significant meaning in consideration of the influence on the detection resolution of the sensor, the estimation accuracy of the weight, and the resistance value. The grid points shown in FIG. 26 are set at equal intervals larger than these minimum steps. The lift coefficient is 0.
Lattice points are set in increments of 1 and the Mach number is in increments of 0.1M.

In this embodiment, the resistance coefficient CD of 2
An offset correction table used when interpolating the dimension table is prepared. The content of the offset correction table is the same as the table described in the first embodiment. In the first embodiment, the offset correction table is a table that stores weight values to be multiplied in interpolation and grid point specifying data for specifying grid points to be used for interpolation in correspondence with the R, G, and B offset coordinate values. Met. The offset correction table in the present embodiment is a table that stores weight values and grid point specifying data in association with the lift coefficient CL and the Mach number. As described above, in the present embodiment, the lift coefficient CL and the Mach number can take values obtained by equally dividing the intervals between the lattice points by ten. Therefore, the offset correction table indicates that the lift coefficient CL and the Mach number are 10 × 1
The table has the above data for each of the 0 combinations.

In this embodiment, the interpolation calculation of the resistance data is executed by using the offset correction table. First, the lift coefficient CL and the Mach number are converted into the coordinates of the lattice points in FIG. 26, that is, the base points and the offset coordinates by the same processing as in FIGS. In the first embodiment, the coordinate conversion is performed in the three-dimensional space of R, G, and B. However, this embodiment is different only in that the coordinate conversion is performed in the two-dimensional space of the lift coefficient CL and the Mach number. is there. next,
Based on the offset coordinate value, the grid point specifying data and the weight value are obtained by referring to the offset correction table. By multiplying the obtained weight value by the resistance coefficient CD stored at each grid point specified by the grid point specifying data, the resistance coefficient CD corresponding to the input flight altitude and Mach number is obtained. . Resistance estimation unit 3
08 multiplies the resistance coefficient CD thus calculated by the wing area (m2) and the dynamic pressure (kgf / m2) to obtain the resistance (kgf) of the aircraft 300 when flying at the input flight altitude and Mach number. presume.

In the above description, the table storing the resistance coefficient CD has been described as a two-dimensional table.
As shown in FIG. 26, for example, a three-dimensional table in which flap angles are added to elements may be handled.

On the other hand, the flight state detection unit 304
The parameters relating to the flight state of 00 are detected. For example, the current flight altitude, the flight Mach number, the current throttle rating, and the like are given. The throttle rating (%) refers to a parameter for adjusting the thrust of the engine, and is associated with the angle of the throttle lever. These data and the resistance estimated by the resistance estimating unit 308 are transferred to the target thrust value setting unit 310. The necessary thrust setting unit 310 calculates a thrust target value for shifting the flight state of the aircraft 300 to the flight altitude and the Mach number input by the control command input unit 302 in the following procedure.

The target thrust value setting section 310 first obtains the current engine thrust based on the data passed from the flight state detection section 304. The engine thrust is obtained by interpolating the table shown in FIG. FIG. 27 shows the correspondence between the Mach number, the throttle rating, and the thrust at the altitude H1 (ft). R1, R2, R
The throttle rating decreases in the order of 3, R4, and the thrust decreases. As described above, the thrust of the turbofan engine can be uniquely obtained by specifying the flight altitude, Mach number, and throttle rating of the aircraft.

FIG. 28 shows an image of the table in this embodiment. The table of the present embodiment defines discrete grid points as shown in the three-dimensional coordinate space of altitude, Mach number, and throttle rating, and stores thrust for each grid point. Thrust is obtained by interpolating this three-dimensional table according to a combination of altitude, Mach number, and throttle rating.

In practice, these quantities are continuously changing values. However, in this embodiment, the altitude is 1000f
In increments of t, the Mach number is in increments of 0.01M, and the throttle rating is in increments of 1%. For the same reason as in the case of interpolation of the resistance coefficient CD. The grid points shown in FIG. 28 are set at equal intervals larger than these minimum steps. An offset correction table used when interpolating a three-dimensional table storing thrust is also prepared. The content of the offset correction table is the same as the table described in the first embodiment. In other words, the offset correction table is a table that stores weight values to be multiplied in the interpolation and grid point specifying data for specifying grid points to be used for the interpolation, corresponding to the altitude, the Mach number, and the throttle rating.

In this embodiment, the thrust is obtained by using the offset correction table. The method is the same as the method described in the first embodiment. That is, the values of the flight altitude, the Mach number, and the throttle rating are converted into base points and offset coordinates, and the offset correction table is referred to based on the offset coordinate values to determine grid point specifying data and weight values. The weight value obtained in this way is
The thrust corresponding to the flight altitude, the Mach number, and the throttle rating is obtained by multiplying the thrust corresponding to the grid point specifying data. This value is the thrust in the current flight state.

The target thrust value setting section 310 sets a target value of the thrust based on the deviation between the thrust in the current flight state and the thrust in the flight state input as the command value. When the aircraft 300 is constantly flying, the thrust and the resistance are balanced, and the latter thrust is equal to the resistance value calculated by the resistance estimating unit 308. The target value of the thrust is set by so-called proportional integral control. The target value is set by multiplying the thrust deviation by a predetermined gain. Since the technique of proportional integral control is well known, further detailed description is omitted. The target value of the thrust set in this way is transferred to the engine control unit 312.

The engine control unit 312 sets the throttle rating of the engine based on the target value of the thrust. The setting of the throttle rating is performed by interpolating the tables shown in FIGS. As the altitude and the Mach number, the current flight state detected by the flight state detection unit 304 is used. In this embodiment, as shown in FIG. 27, the throttle rating and the thrust at a specific altitude and Mach number have a monotonically increasing relationship. Therefore, if the thrust is obtained by interpolating the table of FIG. 28 while gradually changing the throttle rating, the target value of the throttle rating can be set relatively easily. The interpolation method described in the thrust target value setting unit 310 can be applied to this interpolation calculation. Of course, it may be obtained by interpolating a table storing the throttle rating for each grid point specified by altitude, Mach number, and thrust.

According to the control device described above, the interpolation of the resistance coefficient CD and the engine data can be performed at a very high speed by applying the interpolation calculation of the present invention. Therefore, control processing of the aircraft 300 can be performed quickly, and appropriate control with less time delay can be realized.

In the above description, the control device for controlling aircraft 300 has been described. The control method of the present invention is not limited to this, and can be applied to various control processes using a table storing parameters related to control. The control target may be not only an aircraft but also any machine or device such as various engines, transportation equipment, and industrial machines. Not only those that operate mechanically but also those that operate electrically such as audio equipment may be used. Furthermore, the control method of the present invention may be applied not only to the case where various devices are actually controlled, but also to a simulator that simulates the operation of various devices.

(5) Fourth Embodiment: As a fourth embodiment to which the control method of the present invention is applied, a control device for performing fuzzy control will be described. FIG. 29 is a block diagram showing a software configuration of the control device. Actually, the CPU and the memory mounted on the control device are configured by realizing respective functional blocks. Note that the control device of the present embodiment can control any device similarly to the control device of the third embodiment. Hereinafter, for convenience of explanation, a case where a vehicle is set as a control target will be described as an example.

The fuzzy control device of the present embodiment includes a state quantity detection section 400, a fuzzy inference section 402, a membership function 404, and a control quantity output section 406.
The state quantity detector 400 detects the current state quantity of the control target. The state quantity means a parameter related to control. When the control target is a vehicle, for example, the vehicle speed, the inter-vehicle distance to the preceding vehicle, the acceleration of the vehicle, and the like correspond to the state quantity. The detected state quantity is transferred to the fuzzy inference unit 402.

The fuzzy inference unit 402 specifies the control amount based on the fuzzy inference while referring to the membership function 404 based on the detected state amount. Fuzzy inference refers to a process of specifying a control amount based on ambiguous parameters including liquidity. For example, when the control target is a vehicle, the fuzzy inference unit 402 executes a type of control of “accelerate if the vehicle is slow”. Instead of controlling the vehicle to match a certain speed, the vehicle is controlled using an ambiguous parameter of “slow”.

A function that mathematically represents such an ambiguous parameter is a membership function. FIG. 30 shows an example of the membership function. As illustrated, the membership function is a function that gives a membership value of 0 to 1.0 to the state quantity. For example, a membership function can be formed by plotting the vehicle speed on the horizontal axis and plotting the probability of being recognized as “fast” on the vertical axis. The horizontal axis is prepared variously according to the device to be controlled and the parameter to be evaluated in fuzzy inference. Not only vehicle speed,
When the control is performed in consideration of the inter-vehicle distance, a membership function is further prepared in which the inter-vehicle distance is plotted on the horizontal axis and the probability of feeling that the inter-vehicle distance is small is plotted on the vertical axis. The membership function may be prepared as a two-dimensional function that gives a membership value to two or more state quantities. In this embodiment, corresponding to the grid points shown in FIG.
It was prepared as a one-dimensional table storing membership values. In practice, the state quantity is a value that changes continuously. However, in the present embodiment, for a reason similar to that of the third embodiment, a limited value at a fixed interval is taken. The grid points in FIG. 30 are provided at intervals larger than this interval.

The fuzzy inference unit 402 obtains a membership value by interpolating the membership function according to the state quantity. The control device of the present embodiment also has an offset correction table used when interpolating the membership function. Similar to the table described in the first embodiment, the contents of the offset correction table store weight values to be multiplied in the interpolation and grid point specifying data for specifying grid points to be used for the interpolation corresponding to the state quantities. It is a table to do.

In this embodiment, the membership value is obtained by using the offset correction table. The method is the same as the method described in the first embodiment. That is, the state quantity is converted into the base point and the offset coordinates, and the grid point specifying data and the weight value are obtained by referring to the offset correction table based on the offset coordinate values. The membership value corresponding to the state quantity is determined by multiplying the weight value thus determined by the membership value corresponding to the grid point specifying data.

The fuzzy inference unit 402 sets a control amount based on the membership value calculated as described above. For example, when controlling the vehicle based on the rule of “if the vehicle speed is slow and the distance between the vehicles is open, accelerate the vehicle”, by calculating the membership value based on the vehicle speed detected as the state quantity and the distance between the vehicles, It is determined whether or not the above condition is satisfied, and whether or not the vehicle should be accelerated is determined. The degree of acceleration may be further set by a membership function. Since the content of the fuzzy inference is not an essential part of the present invention, further detailed description is omitted. The control amount thus set is transferred to the control amount output unit 406. The control amount output unit 406 outputs a control signal corresponding to the set control amount and controls a device to be controlled. When the vehicle is a control target, the control amount includes an accelerator opening. The control amount output unit 406 adjusts the accelerator opening to control the vehicle speed.

By applying the control method of the present invention,
The interpolation speed of the membership function can be improved.
Interpolation of membership functions is frequently performed in fuzzy control. Therefore, if the control method of the present invention is applied to the interpolation of the membership function in the above-described manner, the processing of fuzzy control can be executed at a very high speed. This effect is particularly effective when the membership function has two or more dimensions.

(6) Fifth Embodiment: As a fifth embodiment, an example will be described in which the control method of the present invention is applied to control processing of a device requiring coordinate conversion. Hereinafter, coordinate conversion from a polar coordinate system to a rectangular coordinate system will be described as an example of coordinate conversion. FIG. 31 shows an example of performing the coordinate conversion. FIG.
In the figure, eq indicates the equator, and me indicates the meridian at 0 degrees. Pe
Indicates a point on the earth, and LAT and LON indicate the latitude (rad) and longitude (rad) of the point Pe, respectively. R is the radius of the earth. As shown, the latitude and longitude on the earth are a kind of polar coordinates. On the other hand, the east, west, north and south directions at a point on the earth can be approximately regarded as rectangular coordinates.

FIG. 31 shows how coordinates are converted from latitude and longitude to rectangular coordinates specified by a direction centered on a certain point Pe0 on the earth. Such coordinate transformation is used, for example, in a navigation system using a so-called global positioning system (hereinafter, referred to as GPS). As is well known, the GPS is a system that can know a position on the earth based on a signal from an artificial satellite. According to GPS, the position on the earth can be known by latitude, longitude, and altitude. On the other hand, when controlling the display of a map in a navigation system, it is desirable to specify its own position in a coordinate system on a map expressed based on north, south, east and west. In the following, it is assumed that the navigation system is mounted on an automobile, and the altitude is constant.

A case will be considered where the latitude and longitude are converted to an orthogonal coordinate system having the origin at a certain point Pe0 on the earth and the axes of east and north. Latitude (rad) and longitude (ra) of point Pe0
d) is (LAT0, LON0). For example, a point Pe0 and a point Pe1 (LAT
0, LON1) in the east-west direction is the latitude LAT
0 (see the broken line eq1 in FIG. 31) as
It is given as 8). Le = R · (LON1−LON0) · cos (LAT0) (18) Even between two points existing at different latitudes, if the difference between the two latitudes is small, the east and west can be approximated by the above equation (18). The distance in the direction can be determined.

On the other hand, the points Pe0 and P existing at the same longitude
Distance Ln in the north-south direction from e2 (LAT1, LON0)
Is given by the following equation (19) as the arc of the meridian. Ln = R · (LAT1−LAT0) (19) This formula is also valid for a case between two points existing at different longitudes.

Accordingly, a point Pe (LAT1, LON1) on the earth represented by an arbitrary latitude and longitude is expressed by the above equation (18) in an orthogonal coordinate system having the point Pe0 as the origin and the axes east and north. Using the distance given by (19) and (L
e, Ln).

In this embodiment, the coordinate conversion is realized as follows by using an interpolation operation. FIG. 32 is an explanatory diagram illustrating a table used in the present embodiment. In the coordinate conversion method of this embodiment, first, as shown in FIG.
Set some grid points according to the combination of longitude,
For each grid point, the values Le, L of the rectangular coordinate system
A table storing n is prepared. By interpolating this table, the values Le and Ln in the rectangular coordinate system can be obtained for arbitrary latitudes and longitudes.

The interpolation method for the table shown in FIG. 32 is the same as the interpolation method described in the first embodiment with reference to FIGS. In this interpolation method, an offset interpolation table is prepared in which weight values to be multiplied in interpolation and grid point specifying data for specifying grid points to be used for interpolation are stored in correspondence with combinations of latitude and longitude. In order to be able to prepare such a table, the latitude and longitude are not real numbers that can change continuously, but take values consisting of predetermined increments. The predetermined increment can be set according to the accuracy required for coordinate conversion. When the table of FIG. 32 is interpolated, coordinate values formed by a combination of latitude and longitude are converted into base points and offset coordinates, and grid point specifying data and weights are referenced by referring to an offset correction table based on the offset coordinate values. Find the value. Coordinate conversion is performed by multiplying the thus obtained weight values by the coordinate values Le and Ln stored corresponding to the grid point specifying data.

The equations (18) and (19) for the coordinate transformation described above are not linear. Therefore, if coordinate conversion is performed by interpolating the table as in the present embodiment, strictly, errors are included. This error changes according to the grid point interval of the table in FIG. Therefore, if the grid point interval of the table is set sufficiently small in accordance with the accuracy required in the coordinate conversion, the above error can be suppressed.

According to the interpolation calculation of this embodiment, the coordinate conversion can be performed at a very high speed. The above equation (18) is a calculation equation including a trigonometric function. Generally, a trigonometric function is an operation that requires a long time. The method of the present embodiment has the following advantages by enabling the coordinate conversion processing to be executed at a high speed. For example, coordinate conversion is frequently and repeatedly executed in control processing in a navigation system or the like. Therefore, if the coordinate conversion can be performed at a high speed, the entire process can be performed at a high speed. Further, when the above-described coordinate conversion is performed in the navigation system, it is necessary to execute the coordinate conversion in real time. The coordinate conversion method of the present embodiment can sufficiently meet such a demand.

In this embodiment, only the coordinate transformation on the ground surface is considered by treating the altitude as a constant. On the other hand, it is also possible to convert three-dimensional polar coordinates represented by latitude, longitude, and altitude into a three-dimensional rectangular coordinate system including an east axis, a north axis, and an altitude. In this embodiment,
Although the case where the origin of the polar coordinates and the origin of the orthogonal coordinates are different has been described as an example, it is needless to say that the present invention can be applied when the origins of both coincide. Further, the coordinate transformation to which the method of the present embodiment can be applied is not limited to between polar coordinates and rectangular coordinates, but can be applied between any coordinate systems.

(7) Sixth Embodiment Finally, as a sixth embodiment, an example will be described in which the control method of the present invention is applied to control processing of a device requiring linear conversion. Linear transformation is a type of coordinate transformation, and refers to transformation in which the relationship between coordinate values before transformation and coordinate values after transformation is represented using a matrix. A typical transformation is translation or rotation of the coordinate system. In the present embodiment, a rotation in a rectangular coordinate system will be described as an example.

As a device that requires linear conversion by rotation in a rectangular coordinate system in the control processing, a device of a so-called virtual reality (hereinafter, referred to as VR) device can be mentioned. FIG. 33 is an explanatory diagram showing a state of coordinate conversion used in VR. As shown in the figure, a VR subject 500 has a head-mounted display (hereinafter, referred to as HMD) 502.
To wear. In the VR, the image in the virtual space is coordinate-transformed according to the angle of the head of the subject 500 and projected on the HMD. The subject 500 can experience the movement in the virtual space by seeing the image projected on the HMD. Of course, in the VR technology, the coordinate transformation may be performed in combination with the parallel movement of the subject 500.

In general, rotation of a three-dimensional rectangular coordinate system includes angles ψ (rad), θ (rad),
φ (rad) is used. As shown in FIG. 33, the rectangular coordinate system fixed to the earth is Xe, Ye, Ze. Let Xb, Yb, Zb be the rectangular coordinate system fixed to the subject 500. The relationship between the two orthogonal coordinate systems can be expressed using Euler angles ψ, θ, and φ defined below.

First, a coordinate system Xe, Y fixed to the earth
e, Ze are rotated ψ (rad) around the Ze axis. As a result, the coordinate system Xe, Ye, Ze is changed to the coordinate system X in FIG.
e ′, Ye ′, and Ze are converted. ψ (rad) represents the coordinate axis Xb fixed to the subject as Xe-Ye in the earth coordinate system.
The angle projected on the plane is the angle that overlaps with Xe '.

Next, the coordinate systems Xe ′, Ye ′, Ze
Rotate θ (rad) around e ′. As a result, the coordinate system is converted into Xb, Ye ', Ze' in FIG. θ
(Rad) is the angle at which the Xb axis and the Xe 'axis coincide.

Finally, the coordinate systems Xb, Ye ', Ze'
Rotate φ (rad) about the b-axis. As a result, the coordinate system is converted into the coordinate systems Xb, Yb, Zb fixed to the subject 500. φ (rad) is an angle at which the Yb axis and the Yb ′ axis coincide.

The transformation from the coordinate values (xe, ye, ze) represented in the coordinate system fixed to the earth to the coordinate values (xb, yb, zb) represented in the coordinate system fixed to the subject is as follows. It is represented by equation (20). That is, if nine components a11 to a33 determined according to the Euler angles ψ, θ, and φ are specified, the coordinate conversion can be performed.

Xb = sinθcosψ ・ xe + (-sinθsinψ) ・ ye + cosθ ・ ze = a11 ・ xe + a12 ・ ye + a13 ・ ze; yb = (-sinφcosθcosψ + cosφsinψ) ・ xe + (sinφcosθsinψ + cosφcosψ) ・ yes sinφsinθ ・ ze = a21 ・ xe + a22 ・ ye + a23 ・ ze; zb = (cosφcosθcosψ + sinφsinψ) ・ xe + (-cosφcosθsinψ + sinφcosψ) ・ ye + (-cosφsinθ) ・ ze = a31 ・ xe + a32 ・ ye + a33・ Ze; ・ ・ ・ (20)

As is apparent from the above formula (20), the component a1
1 to a33 can be obtained by an operation including a trigonometric function according to the Euler angle. On the other hand, in the present embodiment, these components are obtained by interpolating the table. FIG. 34 is an explanatory diagram showing a table used in this embodiment. As shown in the figure, in this embodiment, several grid points are set according to the combination of the Euler angles ψ, θ, and φ, and a table storing the nine components a11 to a33 is prepared for each grid point. Also,
An offset correction table used in interpolation of this table is also prepared. As in the other embodiments, the offset correction table stores weight values to be multiplied in interpolation and grid point specifying data for specifying grid points used for interpolation. In order to make it possible to prepare an offset correction table, the Euler angle is not a real number that can change continuously, but takes a value in predetermined increments.

The interpolation method for the table shown in FIG. 34 is the same as in the other embodiments. First, a coordinate value composed of a combination of Euler angles ψ, θ, and φ is converted into a base point and offset coordinates. The grid point specifying data and the weight value are obtained by referring to the offset correction table based on the offset coordinate values. The components corresponding to the Euler angles ψ, θ, φ are calculated by multiplying the weight values thus obtained by the components a11 to a33 stored corresponding to the grid point specifying data. By calculating the above equation (20) using the components thus calculated, the coordinate system Xe,
The coordinate system Xb fixed to the subject 500 from Ye, Ze,
The coordinate conversion into Yb and Zb can be performed.

According to the interpolation calculation described in this embodiment, linear transformation of the coordinate system can be performed at high speed. VR technology requires frequent and real-time linear transformations. Therefore, the effectiveness of the linear conversion by the method described in this embodiment is very high. Of course, in cases other than the VR technology, when a part of the control processing includes a linear transformation, the overall control processing can be sped up by applying the linear transformation of the present embodiment.

As is apparent from the above formula (20), each component a
11 to a33 are not linear functions of the Euler angles ψ, θ, φ. Therefore, the value of each component obtained by interpolating the table of FIG. 34 includes an error strictly. This error is shown in FIG.
4 changes according to the interval between the respective grid points. The error can be suppressed if the grid point interval of the table is set sufficiently small according to the accuracy required in the coordinate conversion.

In the present embodiment, the case where the present invention is applied to the VR technique has been described as an example. However, it can be said that the above-described control method can be applied to various control processes requiring linear conversion other than the VR technique. Not even. It is possible to target not only a rectangular coordinate system but also various coordinate systems. Of course, the coordinate space is not limited to three dimensions. Further, in the present embodiment, the rotation of the coordinate system has been described as an example, but the processing including the parallel movement can be processed by the same method.

Although various embodiments of the present invention have been described above, the present invention is not limited to these embodiments, and various embodiments can be made without departing from the spirit of the present invention. For example, the processing by hardware shown in FIGS. 22 and 23 is applicable not only to the first and second embodiments, but also to all other embodiments.

[Brief description of the drawings]

FIG. 1 is an explanatory diagram showing the relationship between the existing position of color image data and a tetrahedron used for interpolation calculation.

FIG. 2 is a schematic configuration diagram of an image processing system using the image processing device of the present invention.

FIG. 3 is a schematic configuration diagram of a printer 22 used in the image processing system.

FIG. 4 is an explanatory diagram illustrating a schematic configuration of a dot recording head of the printer 22.

FIG. 5 is an explanatory diagram showing a dot formation principle in the printer 22.

FIG. 6 is an explanatory diagram showing how a color space is divided in the first embodiment.

FIG. 7 is an explanatory diagram showing offset coordinates in a small grid.

FIG. 8 is an explanatory diagram showing an interpolation calculation method using the tetrahedron method.

FIG. 9 is a flowchart illustrating a flow of an image processing routine.

FIG. 10 is a flowchart illustrating a flow of a base point calculation process in the first embodiment.

FIG. 11 is a flowchart illustrating a flow of an offset coordinate calculation process in the first embodiment.

FIG. 12 is an explanatory diagram showing an example of an offset correction table OT.

FIG. 13 is an explanatory diagram showing an example of an arrangement of a color correction table CT on a memory.

FIG. 14 is an explanatory diagram showing a relationship between data of an offset correction table OT and data of a color correction table CT on a memory.

FIG. 15 is an explanatory diagram showing how a color space is divided in the second embodiment.

FIG. 16 is a flowchart illustrating a first flow of a base point calculation process in the second embodiment.

FIG. 17 is a flowchart illustrating a second flow of the base point calculation process in the second embodiment.

FIG. 18 is an explanatory diagram illustrating an example of a reference grid point table.

FIG. 19 is a flowchart illustrating a first flow of offset coordinate calculation processing in the second embodiment.

FIG. 20 is a flowchart illustrating a second flow of the offset coordinate calculation process in the second embodiment.

FIG. 21 is an explanatory diagram illustrating an example of an offset coordinate table.

FIG. 22 is an explanatory diagram showing a configuration example of a color correction table by hardware.

FIG. 23 is an explanatory diagram showing a configuration example of an offset correction table by hardware.

FIG. 24 is an explanatory diagram showing a software configuration of a flight control device as a third embodiment.

FIG. 25 is a graph showing a relationship among a resistance coefficient CD, a lift coefficient CL, and a Mach number.

FIG. 26 is an explanatory diagram showing a table for giving a resistance coefficient.

FIG. 27 is a graph showing the relationship between thrust, altitude, Mach number, and throttle rating.

FIG. 28 is an explanatory diagram showing a table for giving a thrust.

FIG. 29 is an explanatory diagram illustrating a software configuration of a control device according to a fourth embodiment.

FIG. 30 is an explanatory diagram showing a table for giving membership values.

FIG. 31 is an explanatory diagram showing a state of coordinate conversion as a fifth embodiment.

FIG. 32 is an explanatory diagram showing a table for giving values in a rectangular coordinate system from latitude and longitude.

FIG. 33 is an explanatory diagram showing a state of linear conversion as a sixth embodiment.

FIG. 34 is an explanatory diagram showing a table for providing a component of linear conversion from an Euler angle.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 12 ... Scanner 21 ... Color display 22 ... Color printer 23 ... Paper feed motor 24 ... Carriage motor 26 ... Platen 28 ... Print head 31 ... Carriage 32 ... Operation panel 34 ... Sliding shaft 36 ... Drive belt 38 ... Pulley 39 ... Position detection Sensor 40 Control circuit 42 Programmable ROM (PROM) 61, 62, 63, 64 Ink ejection head 65 Inlet tube 68 Ink passage 71 Black ink cartridge 72 Color ink cartridge 90 Personal computer 91 ... video driver 92 ... input unit 95 ... application program 96 ... printer driver 97 ... rasterizer 98 ... color correction module 99 ... halftone module 300 ... aircraft 302 ... steering command input unit 304 ... flight state detection unit 3 06 ... weight estimation section 308 ... resistance estimation section 310 ... thrust target value setting section 312 ... engine control section 400 ... state quantity detection section 402 ... fuzzy inference section 404 ... membership function 406 ... control quantity output section 500 ... subject 502 ... head Mount display (HMD)

Continuation of the front page (58) Field surveyed (Int.Cl. ⁷ , DB name) H04N 1/40-1/409 H04N 1/46 H04N 1/60

Claims (16)

(57) [Claims] An interpolating operation is performed on a parameter related to a control process of a device, which is stored corresponding to a grid point discretely existing in a coordinate space of a predetermined dimension, according to a combination of the coordinate values. A control method for performing control processing of an apparatus, comprising : (a) discretely setting each of the dimensions of the coordinate space;
Grid point defined by the combination of
The interpolated table in which the parameters are stored correspondingly, and the interpolation calculation data required when the interpolated table is interpolated to calculate the value corresponding to the input data are offset.
A step of preparing in advance an offset correction table of a type smaller than the number of small grids stored in correspondence with the set coordinate values ; and (b) setting a finite number of sets in advance for each of the dimensions in the coordinate space. And (c) inputting any one of a plurality of small grids formed in the dimension by the adjacent grid points.
Determining whether the input data belongs to a grid; (d) converting the input data into coordinate values of a reference grid point serving as a reference among the grid points of the small grid to which the input data belongs; (E) reading the interpolation calculation data corresponding to the offset coordinate value from the offset correction table, and converting the reference grid point and the reference value. Reading the parameter corresponding to a predetermined grid point adjacent to a grid point from the table to be interpolated, and performing an interpolation calculation of the interpolated data using the interpolation calculation data , wherein the interpolation calculation data is Predetermined used for interpolation calculation
Grid point is specified by a relative relation with the reference grid point.
Grid point specifying data and the interpolated
Data including weight coefficient data multiplied by the data.
Control how. 2. The control method according to claim 1, wherein: The step (a) may further include the respective dimensions of the coordinate space.
By a combination of coordinate values set discretely for each
For the defined grid points, the combination of the coordinate values From
A grid point information table that stores grid point information
It is a process of preparing, In the step (c), the determination is made based on the grid point information.
Control method, which is a step performed by 3. The control method according to claim 1, wherein the apparatus is an image processing apparatus that performs color correction of color data of an image, and the input data is a gradation value in a two-dimensional or more color space. A control method, wherein the interpolated table is a color correction table indicating a correspondence between a color component represented by a color system different from that of the input data and the input data. 4. The control method according to claim 1, wherein: Offset correction table prepared in step (a)
Is a single type of table common to all small grids.
Law. 5. An image processing apparatus for color-correcting and outputting a multicolor image represented by using coordinate values corresponding to a predetermined number of gradations for each dimension in a two-dimensional or more color space. Input means for inputting color image data consisting of the coordinate values for each pixel of the image, wherein the color space is smaller than the number of gradations for each of the dimensions
Colors corresponding to grid points obtained by dividing into numbers
Color correction data related to the amount of color correction as a color correction table
A color correction table memory storing, among the plurality of small grating formed in correspondence to the dimension by the lattice points adjacent said color image data Izu
A small grid determining unit for determining whether the color image belongs to the small grid, the color image data, the coordinate value of a reference grid point serving as a reference of the small grid to which the color image data belongs, and the color image for each dimension Image data conversion means for converting data and offset coordinate values representing a deviation from the coordinate value of the reference grid point; necessary for obtaining color correction data corresponding to the color image data by interpolating the color correction table An offset correction table memory for storing the correspondence between the interpolation calculation data and the offset coordinate values as an offset correction table of a type smaller than the number of the small grids; and setting the interpolation calculation data corresponding to the offset coordinate values to the offset. The color correction data corresponding to the reference grid point and a predetermined grid point adjacent to the reference grid point is read out from the correction table. Reading out data from said color correction table, performs interpolation calculation of the color correction data with the interpolation operation data, and a color correction means for outputting the operation result as the corrected color image data, the interpolation operation data Is a predetermined value used for the interpolation calculation.
Grid point is specified by a relative relation with the reference grid point.
Grid point specifying data and the color correction in the interpolation operation
Data including weight coefficient data multiplied by the data.
That image processing apparatus. 6. The image processing apparatus according to claim 5, further comprising: dividing the color space by the number of gradations for each of the dimensions.
Of the grid points obtained by dividing into smaller numbers
Grid point information consisting of values is stored as a grid point information table
An image processing apparatus comprising a grid point information table memory for performing the determination , wherein the small grid determination unit performs the determination based on the grid point information. 7. The image processing apparatus according to claim 5 , wherein the division of the color space is performed based on a data amount of color correction data stored as the color correction table and interpolation calculation data stored as the offset correction table. The image processing apparatus is a division in which the data amount is substantially the same. 8. The image processing apparatus according to claim 5 , wherein said color space is a three-dimensional color space, and said grid point specifying data is based on said offset coordinate value.
Therefore, at least four grid points used in the interpolation calculation
Image processing apparatus , wherein the weighting coefficient data is coefficient data multiplied by the color correction data corresponding to each of the grid points in the interpolation calculation according to the offset coordinate values. . 9. The image processing apparatus according to claim 5 , wherein
Further, a normalization unit for normalizing the coordinate values so that each interval between the grid points is a constant interval for each of the dimensions, the image data conversion unit includes a small grid to which the color image data belongs. And the coordinate value of a reference grid point serving as a reference, and the offset coordinate value representing the deviation between the color image data and the coordinate value of the reference grid point for each dimension by the coordinate value normalized by the normalization means. An image processing device which is means for converting into 10. The image processing apparatus according to claim 5 , wherein the interval after the normalization is an integral multiple of the interval between the grid points in a predetermined low density area of the color space. . 11. The image processing apparatus according to claim 5 , wherein the image data conversion unit is configured to determine, based on the color image data, coordinates of a reference grid point serving as a reference for a small grid to which the color image data belongs. A reference grid point table for storing values, and an offset representing a deviation between the color image data and the coordinate value of the reference grid point in the coordinates converted by the normalizing means for each dimension according to the color image data. And at least one of an offset coordinate table for storing coordinate values, and converting at least one of the conversion from the color image data to the coordinate values of the reference grid points and the offset coordinate values, the data corresponding to the color image data to An image processing device which is means for reading from a reference grid point table or the offset coordinate table. . 12. The image processing apparatus according to claim 5, wherein
hand, The offset correction table has a single common
An image processing device that is a type table. 13. A computer realizes image processing for color-correcting and outputting a multicolor image represented using coordinate values corresponding to a predetermined number of gradations for each dimension in a color space of two or more dimensions. A computer-readable recording medium having recorded thereon a program for causing a computer to input color image data consisting of the coordinate values for each pixel of the image; and A function of referencing the grid point information table stored for the color space with grid point information comprising coordinate values of grid points obtained by dividing the grid points into smaller numbers, A function of referring to a color correction table storing color correction data relating to a correction amount; and a function of a plurality of small grids formed by the adjacent grid points corresponding to the dimensions. A function of determining which small grid the image data belongs to based on the grid point information; and a coordinate value of a reference grid point serving as a reference for the small grid to which the color image data belongs. When,
A function of converting the color image data into an offset coordinate value representing a deviation between the coordinate value of the reference grid point and the color correction data corresponding to the color image data by interpolating the color correction table; A predetermined grid point used for the interpolation calculation is used as interpolation calculation data required for the calculation.
Grid point feature that specifies the relative relationship with the reference grid point.
Constant data and the predetermined grid point in the interpolation calculation.
Corresponding to the weighting coefficient data to be multiplied by the stored color correction data.
And a function of referring to an offset correction table that stores data including the data corresponding to the offset coordinate values, and reading out interpolation calculation data corresponding to the offset coordinate values from the offset correction table, Color correction data corresponding to a predetermined grid point adjacent to the reference grid point is read from the color correction table, an interpolation calculation of the color correction data is performed by using the interpolation calculation data, and the calculation result is corrected color A recording medium on which a program for realizing a function of outputting as image data by a computer is recorded. 14. A grid point obtained by dividing a multicolor image represented by using coordinate values corresponding to a predetermined number of gradations for each dimension in a color space of two or more dimensions by dividing the color space. A computer-readable recording medium on which data used for an image processing apparatus for performing color correction and outputting by using predetermined data prepared in accordance with a color space is provided. A grid point information table that stores, for the color space, grid point information including coordinate values of grid points obtained by dividing the number of grid points into a number smaller than a predetermined number of gradations for each dimension; A color correction table storing color correction data relating to the amount of color correction corresponding to the lattice point; and an interpolation required when calculating the color correction data corresponding to the color image data by interpolating the color correction table. The calculation data is calculated by calculating the deviation of the color image data from the coordinate value of a reference grid point serving as a reference of a small grid formed corresponding to the dimension of the color space by the grid points adjacent to the color image data. And an offset correction table stored in correspondence with the offset coordinate value represented by (1). 15. The recording medium according to claim 14 , wherein the data amount of the color correction data stored in the color correction table is substantially equal to the data amount of the interpolation calculation data stored in the offset correction table. A recording medium. 16. Using a computer, a parameter used in a series of predetermined processes and associated with a control process of the device and stored in correspondence with grid points discretely existing in a coordinate space of a predetermined dimension. A data interpolation method for interpolating according to a combination of coordinate values, wherein: (a) a grid point defined by a combination of coordinate values discretely set for each of the dimensions of the coordinate space; A grid point information table storing grid point information composed of combinations; an interpolated table storing the parameters corresponding to the respective grid points; interpolating the interpolated table to calculate a value corresponding to the input data And storing the interpolation calculation data required when corresponding to the offset coordinate value, and an offset correction table of a type smaller than the number of the small grids. In a memory space of the computer, and (b) inputting input data consisting of a finite number of combinations of coordinate values for each dimension in the coordinate space; (c) Determining, based on the grid point information, to which of the plurality of small grids formed in the dimension by the adjacent grid points the input data belongs; and (d) the input. Data, the coordinate value of the reference grid point as a reference among the grid points of the small grid to which the input data belongs, and offset coordinate values representing the deviation between the input data and the coordinate value of the reference grid point for each dimension. And (e) reading interpolation calculation data corresponding to the offset coordinate value from the offset correction table, and obtaining the reference grid point and the reference grid point. Reading the parameters corresponding to a predetermined grid points adjacent from the object interpolation table, and a step of performing interpolation calculation該被interpolated data by using the interpolation operation data, the interpolation operation data, the interpolation operation Prescribed used
Grid point is specified by a relative relation with the reference grid point.
Grid point specifying data and the interpolated
Data including weight coefficient data multiplied by the data.
Interpolation method that.