JPH0614183A - Color image processing device - Google Patents

Abstract

PURPOSE:To transform a color image expressed on a CRT monitor with wide color reproducible range, etc., to a color image signal adaptive for hard copy output without losing original information quantity. CONSTITUTION:In a color image processing device which generates a new color separation signal from the color image signal separated to three colors of R(red). G(green), and B(blue) by matrix transformation, a coefficient handled at a matrix transformation circuit 103 is decided by the distribution of uniform color space of a chrominance signal included in an inputted color image signal by a CPU 107, buffer memory 106, a sampling circuit 105, and a selector 101 at the matrix transformation circuit 103.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a color image processing apparatus, for example, a color image processing apparatus for converting a color separation signal to obtain a new color separation signal.

[0002]

2. Description of the Related Art Currently, a color CRT monitor, a color hard copy device, and the like are commonly used as a device for visualizing and outputting a color image. The former intensity-modulates the emission levels of R, G, and B phosphors on the tube surface to form a visible image by additive color mixing. On the other hand, the latter forms a color image on paper by subtractive color mixture using Y (yellow), M (magenta), C (cyan), and K (black) color materials.

[0003]

In principle, the two image display devices described above have different color reproduction abilities, and as shown in FIG. 3, a CRT generally has a wider color reproduction range than a hard copy. Therefore, if there are two colors of point A and point B in a certain image, they will be reproduced as different colors when this image is displayed on a CRT, but if hard copy is output, both points A and B will be reproduced as point C. As a result, the two colors cannot be distinguished from each other, and as a result, the information originally contained in the image is lost.

Therefore, when outputting an input color image in hard copy, a method of converting the color signal in the image so that it falls within the color reproduction range of the hard copy and then outputting it is conceivable. That is, if the conversion is performed so that the point A becomes the point D and the point B becomes the point E in FIG. 3, the colors can be distinguished even on the hard copy. However, since such a conversion method is not uniform, the output image may be unnatural as a whole depending on the method.

In order to solve such a problem, the following proposal has been made as a conversion method of an input color image. That is, it is possible to map the color signal information included in the input image to the color reproduction range of the hard copy while keeping the basic primary colors (red, green, blue, cyan, magenta, yellow) of the color image. This proposal refers to the distribution of color signals contained in the input image, detects the color with the highest saturation, and maps that color to the color reproduction range of the hard copy device. The input color image is evaluated on the R, G, and B signal spaces in the detection. However, since the R, G, and B spaces do not include the nonlinearity that humans have in their visual characteristics, even if the color with the highest saturation is detected, it will be the most saturated even if seen by the human eye. There was a problem that there was no guarantee that the color would be high.

The present invention has been made in view of the above-mentioned drawbacks of the conventional example, and an object of the present invention is to determine the coefficient of matrix conversion by the distribution in a uniform space of color signals included in an input color image signal. The point is to provide a color image processing apparatus that can determine the color.

[0007]

[Means for Solving the Problems]
To achieve the object, a color image processing apparatus according to the present invention converts input color separation data into color space data in a color image processing apparatus that matrix-converts color separation data to obtain new color separation data. The conversion unit includes a conversion unit, a detection unit that detects the distribution of the color space data converted by the conversion unit, and a matrix conversion unit that performs matrix conversion according to the distribution detected by the detection unit.

[0008]

According to this structure, the converting means converts the input color separation data into color space data, the detecting means detects the distribution of the color space data converted by the converting means, and the matrix converting means detects the detecting means. Matrix conversion is performed according to the distribution detected by.

[0009]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings. <First Embodiment> FIG. 1 is a block diagram showing the arrangement of a color image processing apparatus according to the first embodiment of the present invention. In the figure, 101 is a selector, 102 and 104 are buses,
103 is a matrix conversion circuit, 105 is a sampling circuit, 106 is a buffer memory, 107 is a CPU, 10
Reference numeral 8 denotes a ROM that stores a program for operating the CPU 107, and 109 denotes a RAM that stores various parameters and various programs.

Next, the operation of the above configuration will be described with reference to FIG. The input R, G, B signals are input to the selector 10
When the image signal compression is executed by 1, the signal is output to the bus 102, and the matrix conversion circuit 103 outputs the output signals R ′, G ′, and B ′. When the color signal distribution is detected in the input image, it is output to the bus 104, sampled by the sampling circuit 105 at a predetermined sampling pitch, and written in the buffer memory 106. CPU
Reference numeral 107 sequentially reads the image signals in the buffer memory 106, and detects the color signal with the highest saturation in the image by the method described later. From the detection result, the CPU 107 calculates the matrix conversion coefficient by the method described later, and sets the calculated coefficient in the matrix conversion circuit 103.

FIG. 2 is a circuit block diagram for realizing the matrix conversion circuit 103 according to the first embodiment. In the figure, an input color image signal is inputted from the upper part of the drawing. The image signal is sequentially transferred downward in the drawing together with a drive clock, a reset signal, etc., which are not shown. 201
Is a minimum extraction circuit that extracts the minimum value of R, G, and B, and outputs a minimum value signal X (= min (R, G, B)). Reference numerals 202, 203, and 204 denote subtraction circuits that take the difference between the input signal and the minimum value signal. The subtraction circuit 202 outputs R-X, the subtraction circuit 203 outputs G-X, and the subtraction circuit 204 outputs B-X. 205, 206, 207 are 202, 203, 20
4 is a latch circuit for temporarily latching the output of No. 4. 20
Reference numerals 8, 209 and 210 denote multiplication circuits for calculating the quadratic terms of R-X, G-X and B-X. The multiplication circuit 208 is (R-
X) × (G−X), and the multiplication circuit 209 is (G−X) × (B
-X), the multiplication circuit 210 outputs (B-X) * (R-X). 211 is 6 obtained by the multiplication circuits 205 to 210.
This is a matrix conversion circuit that performs matrix conversion of more than one signal value. Specifically, the calculation of the following equation (1) is performed,
Outputs R, dG, and db. That is,

[0012]

[Equation 1]

[0013] The matrix conversion coefficient a _ij used here is obtained by the method described later. 212, 21
3,214 is dR, dG, dB obtained by the above equation (1).
And the original R, G, B signals are added, and the converted color separation signals R ′, G ′, B ′ are output. That is, the following equation (2) is executed.

[0014]

[Equation 2]

Next, the matrix conversion coefficient a of the equation (1)
How to obtain _ij will be described. The purpose of the matrix conversion is to map the colors of a wide color range included in the input image to the color reproduction range of the hard copy, but here, the reproduction of red is considered first. Assuming that the image signal is represented by 8 bits for each color of R, G, and B, the red color with the highest saturation contained in the input image is R = 200, G = 1.
It is assumed that the color signal is 5, B = 0. This color is shown in Figure 3.
It is at point R _i on the chromaticity diagram. However, the red color with the highest saturation that can be reproduced by hard copy is the _RH point, so that with the conventional method, the color between the _RH point and the _RC point is hard-printed as the _RH point. become.

On the contrary, when the color signal value at the point R _H is represented by R, G, B signals, it is usually about R = 160, G = 20, B = 10. Therefore, the input color signals R = 200, G =
If the conversion is such that 15 and B = 10, then R _i
The points are converted into R _H points, the colors in between are mapped to the inside of the R _H points, and the color reproduction area included in the input image is mapped to the hard copy color reproduction area while the primary colors of the input signal are preserved. You can

If such a correspondence relation is set for all six primary colors, then 18 simultaneous linear equations can be formed from the above equations (1) and (2). A _ij is still 18 as an unknown
Since there are individual numbers, they can be uniquely solved and matrix variable coefficients can be determined. An example of the correspondence is shown below.

[0018]

[Equation 3] (3) The desired procedure is obtained as the color signal conversion of FIG. 1 by the above procedure. Here, the 6 most saturated primary color signal values contained in the input image signal (R, G, B on the left side of equation (3))
Value) is required to be detected. A method therefor will be described in detail below with reference to the drawings.

FIG. 4 is a diagram schematically plotting the color reproduction range of the CRT and the color reproduction range of the hard copy on the L ^* , u ^* , v ^* , uniform color space. The L ^* , u ^* , and v ^* spaces are uniform color spaces defined by the CIE (International Commission on Illumination) and are said to correspond to human visual characteristics in a substantially linear manner. In order to convert the R, G, B signals into L ^* , u ^* , v ^* signals, the following equations (4) to (6) may be used.

L ^* = 116 (Y / Y ₀ ) ¹ / 3−16 u ^* = 13L ^* (u−u ₀ ) v ^* = 13L ^* (v−v ₀ ) ... (4) where u = 4X / (X + 15Y + 3Z) v = 9Y / (X + 15Y + 3Z) (5) Also, X = 0.6067R + 0.1736G + 0.2001B Y = 0.2988R + 0.5868G + 0.1144B Z = 0.0000R + 0.0661G + 1.1150B (6) The hexahedron 401 is the CRT. The color reproduction range, the hexahedron 402, represents the color reproduction range of the hard copy. Here, the color signal contained in the input image is sampled first,
The color with the highest saturation is detected on the L ^* , u ^* , v ^* color space, and the matrix coefficient of the equation (1) is determined from this value. Now, suppose that the signal value of a certain pixel in the input image is R _S , G _S , and B _S. Let this value be L ^* , u
^{When the} values are changed to ^* and v ^* values, the point S indicated by 403 in FIG. 4 is obtained. Of the signal values contained in the input image, the RGB signal values of the most saturated colors of the R, G, B, Y, M, and C6 primary colors need to be obtained. Then L ^* , u
Consider a vector in the ^* , v ^* space as shown in FIG. 5 (enlarged view of the range indicated by 400 in FIG. 4). The figure shows the case of the M (magenta) direction. M _H is the coordinates in the hardcopy magenta L ^* , u ^* , v ^* space, and M _C is the CRT.
Coordinates in the L ^* , u ^* , v ^* space of the same, and are the same as those in FIG. Vector M _H S from M _H to the input color S, and M _H
M _H M _C of taking the vector M _H M _C to M _C from M _H S
Find the direction cosine r _M of the direction. The larger r _M is, the input color S
It can be seen that the saturation in the magenta direction is high. r _M
Can be obtained by the inner product calculation of the following equation (3). That is,

[0021]

[Equation 4]

Similarly, for other primary colors, the direction cosines r _R , r _G , r _B , r _Y , and r _C of that direction are obtained, and if the maximum one is selected from these, the direction of the input color (hue )
You can see if it is the color of. In this way, if the maximum value of the direction cosine is obtained for each color for all pixels in the input image or for pixels sampled at regular intervals, the L ^* , u ^* , v ^* values when this maximum value is given are calculated. Inverted R
The GB signal becomes the color signal value to be obtained.

The above procedure is shown as a flow chart in FIG. The overall control of this process follows the CPU 107. In the figure, R _max , G _max , B _max , Y _max , M _max ,
C _max is the maximum value of the direction cosine in each primary color direction. These are set to 0 (step S601). Next, the R, G, B values of the specific pixel are read from the input image (step 602). The RGB signal values thus obtained are treated as data on the left side of the correspondence relationship between the primary colors in equation (3). The R, G, B values are converted into L ^* , u ^* , v ^* (step S603). Then, the cosines r _R and r of the six primary color directions
_G , r _B , r _Y , r _M , r _C are calculated (step S60
4), the maximum value is obtained from that (step S60)
5).

The maximum value is r_X (X is R, G, B, Y, M,
If any of C) reaches the maximum value (step S60)
6), r_X > X_max Then X_max R_X Replace with
(Step S607). At this time, L^* , U^* , V^* Belief
Signal is converted back to R, G, B signals, and maximum saturation signal in X direction
(Step S608). Sampling pair
When all the elephant pixels have finished, go to step S610 and
Upon completion, the flow proceeds to step S602 to repeat the above processing. Su
In step S610, the maximum saturation signal is changed to the matrix change.
Find the conversion factor. In this way, the optimal transformation for the input image
The conversion factor is obtained. Second Embodiment Now, in the above embodiment, the input image signal
To the color that maximizes the direction cosine in each primary color direction (input image signal
No. 403) is the maximum saturation color signal, but (input image signal
403) connects the hard copy primary color and the monitor primary color.
On the vertical line (the vector M in FIG. 5)_H M _C ) Far away from
The maximum saturation color is converted to the hard copy primary color.
It is clear that it causes a change in hue
It

Therefore, in the second embodiment, the colors detected as the maximum saturation color signal are limited to the hard copy primary color and the monitor primary color which are linear colors in the uniform color space.
This is shown in FIG. Similar to FIG. 5, FIG. 7 shows a case where the maximum saturation color in the magenta direction is detected, and the meanings of the symbols are the same as in FIG. Here as in the first embodiment, the color signal S that maximizes the direction cosine in the vector M _H M _C direction is obtained, but after S is obtained, the maximum saturation color signal is actually obtained. The point Q is indicated by 601 in FIG. Q
The point is a point on the vector M _H M _C which is separated from the M _H by a distance r _M , that is, a point where the maximum chroma color signal obtained in the first embodiment is projected on the vector M _H M _C. This point 6
The L ^* , u ^* , v ^* values of 01 can be obtained by a simple vector operation. It is possible to obtain the required signal value by inversely converting this value into the R, G, B signals. <Third Embodiment> In the above two embodiments, the CIE L ^* , u ^* , and v ^* are used as the uniform color space, but the same effect can be obtained by using other uniform color spaces.

For example, the CIE L ^* , a ^* , b ^{* are also used.}
It is also possible to use. Further, in order to simplify the conversion operation to the uniform color space, the R, G, B signals are 1 /
The third power is converted into R ′, G ′, B ′, which is expressed by the following equation (4).
Signal values close to a substantially uniform color space (here, L, c1,
It is also possible to handle the data after converting it into c). R '= (R / 255) ^1/3 G' = (G / 255) ^1/3 B '= (B / 255) ^1/3 L = 100 (R' + G '+ B') / 3 c1 = 100 ( R′−G ′) c2 = 100 (R ′ / 2 + G ′ / 2−B ′) (4) L, c1, and c2 obtained here are slightly inferior in color space uniformity, but the calculation is simple. Therefore, there is an advantage that the load on the CPU is reduced.

The present invention may be applied to a system composed of a plurality of devices or an apparatus composed of a single device. Further, it goes without saying that the present invention can be applied to the case where it is achieved by supplying a program to a system or an apparatus.

[0028]

As described above, according to the present invention, a color image represented by a CRT monitor or the like having a wide color reproduction range is suitable for hard copy output without losing the original information amount. It can be converted into a signal.

[Brief description of drawings]

FIG. 1 is a block diagram showing the configuration of a color image processing apparatus according to a first embodiment of the present invention.

FIG. 2 is a matrix conversion circuit 10 according to the first embodiment.
3 is a circuit block diagram that realizes No. 3;

FIG. 3 is a CIE xy chromaticity diagram.

FIG. 4 is a diagram schematically plotting a color reproduction range of a CRT and a color reproduction range of a hard copy on L ^* , u ^* , v ^* , and a uniform color space.

5 is an enlarged view of a main part of FIG.

FIG. 6 is a flowchart for explaining the operation according to the present embodiment.

FIG. 7 is a diagram illustrating a vector when detecting the maximum saturation color in the magenta direction according to the second embodiment.

[Explanation of symbols]

 101 selector 102, 104 bus 103 matrix conversion circuit 105 sampling circuit 106 buffer memory 107 CPU 201 minimum extraction circuit 202, 203, 204 subtraction circuit 205, 206, 207 latch circuit 208, 209, 210 multiplication circuit 211 matrix conversion circuit

Continuation of the front page (51) Int.Cl. ⁵ Identification number Office reference number FI technical display location G06F 15/72 310 9192-5L H04N 1/46 9068-5C

Claims (3)

[Claims] 1. A color image processing apparatus for converting color separation data into a matrix to obtain new color separation data, and a conversion means for converting the input color separation data into color space data, and a conversion means for converting the color separation data. A color image processing apparatus comprising: a detection unit that detects a distribution of color space data; and a matrix conversion unit that performs a matrix conversion according to the distribution detected by the detection unit. 2. The color image processing apparatus according to claim 1, wherein the converted color space data is data on a uniform color space. 3. The color image processing apparatus according to claim 1, wherein the matrix conversion means has matrix conversion coefficient determination means for determining matrix conversion coefficients according to the distribution detected by the detection means.