JP3423157B2 - Image processing device - Google Patents

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image processing apparatus, and more particularly to an image processing apparatus having a color reduction processing function.

[0002]

2. Description of the Related Art Color reduction processing means for color multi-valued data included in a conventional image processing apparatus is roughly classified into two types. The first color-reduction processing means creates a histogram from the input image data, extracts a color that is frequently used, allocates many interpolated colors in the vicinity thereof, and performs color-reduction processing, or an RGB three-dimensional method in which pixels are distributed. The second color reduction processing means has a method of performing color reduction processing up to a target color number by dividing a color space into subspaces, extracting representative colors from high-frequency portions, and assigning interpolation colors in the same manner. A color reduction method for creating a histogram for each color of red (R), green (G), and blue (B), obtaining the appearance frequency, extracting a high-frequency color, and associating the color with the original data, or a pixel with a short color distance And a method of performing color reduction processing. These color reduction processing means are applied to all devices having an image processing function.

[0003]

In the first color-reduction processing means, it is possible to accurately know the color that frequently appears in the input data, but the number of colors of the input data is stored regardless of the amount of data. There is a problem that a large capacity memory for doing so is required and the calculation accompanying this is also required for a long time. In addition, the second color-reducing means has a smaller memory capacity than the first color-reducing means, but has a problem that colors that are less frequently used may be extracted from the input image data.

The present invention has been made in view of the above problems, and it is an object of the present invention to save the memory allocated when performing color reduction processing, improve the color reduction processing speed, and obtain good colors after color reduction processing. An object of the present invention is to provide an image processing device capable of simultaneous reproduction.

In the histogram of the original image data before color reduction
When the pixel values are concentrated in a very narrow band
Representative color frequently used colors color classification number table by the value of the division number n of each primary color is not less than the target decrease the number of colors in such
Can be extracted as and save enough memory
It is an object of the present invention to provide a simple image processing device.

It is another object of the present invention to provide an image processing apparatus which can reduce data reference and calculation time.

Furthermore, by adding the maximum / minimum pixel values to the image data after the color reduction processing, an image processing apparatus capable of making effective the effect of the image processing such as dither after color reduction and error diffusion is provided. The purpose is to provide.

[0008]

According to a first aspect of the present invention, there is provided a reading means for reading a document as color multi-valued image data, a storage means for storing the image data, and image data for each color of red, green and blue. An image processing apparatus having a first histogram creating unit for creating a histogram for each of the red, green and blue histograms created by the first histogram creating unit. Means, first selecting means for selecting a pixel element having the highest frequency in the division range, and first table creating means for producing a color division table by combining the division ranges of the red, green and blue colors. , Distributed
Color classification with high frequency of occurrence of values in multi-valued color image data
The range of each color division range used in the table is divided
A second table that divides to create a second color classification table
And creating means, the color classification table created in the second table creation means divides the value of the color multi-value image data, requests the occurrence frequency of large color classification table of the distributed values of the color multi-value image data in the order And a pixel element of the division range selected by the first selection means is allocated to each red, green and blue color division range of the color division table extracted by the extraction means. An image processing apparatus characterized by having a representative color extracting means for extracting as a representative color of the divided range.
It

[0009]

According to a second aspect of the present invention, in the first aspect of the present invention, an arbitrary pixel on each main scanning line of the image data is extracted and a red, green and blue color histogram is created for the pixel. An image processing apparatus having a second histogram creating means.
It

According to a third aspect of the present invention, in the first aspect of the present invention, an arbitrary pixel on each sub-scanning line of the image data is extracted, and a histogram of each color of red, green and blue is created for the pixel. An image processing apparatus having a third histogram creating means.
It

[0012] The invention of claim 4, wherein, in the invention according to any one of claims 1 to 3, the maximum value of each color pixel range as the representative color, and a second selecting means for selecting a minimum value Image processing device characterized by having
It is a place .

[0013]

BEST MODE FOR CARRYING OUT THE INVENTION An embodiment of an image processing apparatus according to the present invention will be described below with reference to the drawings.

2, FIG. 3 and FIG. 4 are histograms created by this image processing apparatus, FIG. 2 is a histogram of the R component of R, G and B which are the three primary colors of colors, and FIG. FIG. 4 shows a histogram of G component, and FIG. 4 shows a histogram of B component.

In each figure, the horizontal axis represents the gradation frequency, and in this figure, the horizontal axis as the pixel range is divided into eight equal parts. The number of equal parts is determined by the value n described later,
In this case, n = 8. Of course, a number other than 8 may be used. These equal intervals are called division ranges.

The vertical axis indicates the frequency of appearance of pixels.
In FIG. 2, the pixel range is divided into eight, and the divided ranges correspond to R1, R2, R3, ..., R8, respectively. For example, if one pixel is represented by a total of 24 bits by using 8 bits for each of the three colors, R is represented by an 8-bit width, that is, a pixel range of 256 gradations. Therefore, in this case, the division range R1 is 0 to 31, R2 is 32 to 63,
R3 is 64-95, R4 is 96-127, R5 is 128
~ 159, R6 is 160-191, R7 is 192-22
3 and R8 correspond to the range of 224 to 255.

Further, the gradation frequency of the pixel element having the highest frequency in each divided range is indicated by an arrow, and the highest frequency gradation degree in each of the divided ranges R1 to R8 is indicated by an arrow. It is a frequency. 3 and 4, the pixel range is divided by the division ranges G1 to G8 and B1 to B8 as in FIG. 2, and the arrow indicates the gradation frequency of the pixel element with the highest frequency in each division range. There is.

FIG. 5 is an example of an RGB histogram. This RGB histogram shows the frequency of appearance of pixels on the color division table of 8 × 8 × 8 = 512 combinations of the division ranges R1 to R8, G1 to G8 and B1 to B8 shown in FIGS. 2, 3 and 4. It is a graph. The value 512 is the cube of n. The target color reduction number is 25
In the case of 6, the color division table in the high-order upper 256 of the 512 color division tables is extracted,
Create a palette corresponding to that color category.

FIGS. 6, 7 and 8 are also examples of histograms. In FIG. 5, in order to simplify the explanation, it is assumed that R1G
It is assumed that the 5 tables 1B1, R1G2B1, R1G3B1, R1G4B1 and R8G8B8 are high-ranking high-rank tables among all color classification tables, and the other color classification tables are extremely low-frequency tables. . FIGS. 6, 7 and 8 are histograms for the division ranges obtained by further dividing the division range of each RGB component of the above-mentioned 5 tables with n = 2. The highest frequency gradation in each divided range is ~
Is the gradation frequency indicated by the arrow. r1 and r2 are ranges that divide the range R1 in the color classification table R1G1B1, and r3 and r4 are color classification tables R1G2B.
It is a range that divides the range R1 in 1 and the other is the same. A new color division table ++ is created from the color division table by the same method as the above method from the histograms FIG. 6, FIG. 7 and FIG. Here, each color component of the five color division tables is divided, but of course any number of color division tables can be divided in the same manner. The number of divisions of these can also be changed.

FIG. 9, FIG. 10 and FIG. 11 are examples of diagrams showing patterns for selecting extracted pixels, and pixels 1, 2, and 3 filled with black in each drawing are pixels to be extracted. Figure 9
Indicates that even-numbered pixels are extracted in each line in the main scanning line direction. In FIG. 10, in each line in the main scanning line direction, if the line is an odd numbered line in the sub scanning direction, even numbered pixels are extracted,
If the line is an even-numbered line in the sub-scanning direction, it indicates that odd-numbered pixels are extracted. FIG. 11 shows that odd-numbered pixels are extracted in each line in the sub-scanning direction. Of course, the pixels may be extracted by an extraction pattern other than the above.

FIG. 1 is a flow chart for explaining the operation of this image processing apparatus.

For each pixel of the color-reduction target image, it is determined whether all pixels are extracted or a specific pixel is extracted as shown in FIG. 9, FIG. 10 or FIG. 11 (step S1101), and all pixels are extracted. The number of extracted pixels (data) D is calculated (step S1102), and the data number counter d is initialized (step S1103).

In step S1104, the color classification table ++
Is created, and if the color classification table ++ has not been created (step S1117) (No), n is determined (step S1105).
n is a numerical value that determines the division range width of the histogram for each of the R, G and B components, and the number of color division tables created by combining these division ranges,
An arbitrary value is determined in consideration of the number of colors of the color reduction target image, the data amount, the target number of color reductions, and the like (step S1105).
In addition, n cubed color division tables have been created (step S1106) and prepared so as to form a histogram, and the process proceeds to step S1107.

If it is determined in step S1104 that the color classification table ++ has already been created (Y
es), and proceeds to step S1107. Step S1
In 107, it is judged whether or not the data number counter d is equal to the total number D of extracted pixels.
In (o), one pixel that has not been extracted before is extracted and referred to (step S1108). Next step S1
In 109, it is determined whether or not the color classification table ++ has been created, and when a new color classification table ++ has been created (Yes), the process proceeds to step S1113. If the color classification table ++ has not been created yet (No)
In order to create a histogram as shown in FIG. 2, the R component is converted into an R histogram (step S111).
0), the G component is converted into a G histogram to create a histogram as shown in FIG. 3 (step S111).
1), the B component is converted into a B histogram to create a histogram as shown in FIG. 4 (step S1112).
to add. Further, the RGB histogram is created by adding the combination of the RGB values to the corresponding table in the already created color classification table (step S1).
113). After the above operation, d is incremented (step S1114). Step S1108 to Step S
The operations up to 1114 are repeated until d = D.

Result determined in step S1107 d = D
If (Yes), one unextracted color division table with the highest frequency is extracted from each color division table of the created RGB histogram (step S1115). It is determined whether or not the total of the color division tables extracted in step S1116 has reached the target color reduction number. If the total of the extracted color division tables has not reached the target color reduction number (No), the process proceeds to step S1117. To do. When the total of the extracted color classification tables has reached the target number of color reductions (Yes), the process proceeds to step S1119. In step S1117, it is determined whether or not the number of unextracted color division tables is 0, and if the number of unextracted color division tables is not 0 (No), the process proceeds to step S1115 to extract a frequently used table. The operations from step S1115 to step S1117 are repeated until the total number of extracted color classification tables reaches the target number of color reductions. If the number of unextracted color classification tables is 0 as a result of the determination in step S1117 (Yes), the process proceeds to step S1118.

The operation of step S1118 is an operation when there is no unextracted color division table before the total number of the extracted color division tables reaches the target number of color reductions, and the operation is performed before that time. Each RGB component of the color classification table is further subdivided and combined as shown in FIGS. 6, 7 and 8 to obtain a new color classification table +
+ Is created (step S1118). After repeating the operations of steps S1101 to S1103 for this table, the operations of steps S1108, S1113 and S1114 are repeated until d = D (step S1107). At this time, the operations of steps S1110 to S1112 are omitted by the color classification table check routine (step S1109).

After obtaining the color division tables for the target number of color reductions (step S1116), it is determined in step S1119 whether or not the color division table combined with the maximum / minimum value of each component is used. No)
In the extracted color segmentation table, the R, G, and B components that have the highest frequency of appearance in each color segmentation table are extracted from the histogram and are assigned as the representative colors of the color segmentation table to create a color reduction palette ( Step S1
120) and the operation ends. The palette thus created is used during the color reduction process.

When the color division table combined with the maximum and minimum values of each component is used in the determination of step S1119 (Yes), this table is created (step S1121). For example, when RGB components are represented by 8 bits for each color, since there are 256 gradations of 1 component to 255, the (R, G, B) table is (0, 0,
0), (0,0,255), (0,255,0),
(0,255,255), (255,0,0), (25
5,0,255), (255,255,0) and (25
5, 255, 255) are created.

Steps S1115 to S111
Table A obtained by subtracting the infrequent color segment table among the color segment tables extracted by the operations up to 7 by the number of color segment tables created in step S1121.
And the color classification table B created in step S1121
With respect to the color segmentation table in which each of the color segmentation tables is combined, the most frequent R, G, and B components in each color segmentation table are extracted from the histogram, and this is assigned as the representative color of each color segmentation table (step S1122), and the color reduction palette is set. Create and finish the operation.

[0030]

As is apparent from the above description, RGB
By dividing each histogram for each color and combining them to create a color division table, it is possible to allocate a very small memory to the combined table without dividing. Also, the RGB intensity ratio of the original image data can be known from the histogram of this table, and since color reduction is performed with reference to this, it is possible to create reduced color image data with relatively little deterioration.

Further, in the histogram of the original image data before color reduction, when pixel values are concentrated in a very narrow band, for example, even in color reduction such as monochromatic gradation, a color that is frequently used is used as a representative color. Can be extracted.

Further, in the case of reducing the color of an image of a large amount of data, the data reference time and calculation time can be shortened. Further, it is effective because the original image data is referenced again.

Further, when image processing such as dithering or error diffusion is performed after color reduction, more gradation is added when the original image is an image in which pixel values are concentrated in a very narrow band. Therefore, the effect of image processing can be improved.

[Brief description of drawings]

FIG. 1 is a flowchart showing an operation of an image processing apparatus according to the present invention.

FIG. 2 is a histogram of the R component created by the image processing apparatus according to the present invention.

FIG. 3 is a histogram of a G component created by the image processing apparatus according to the present invention.

FIG. 4 is a B component histogram created by the image processing apparatus according to the present invention.

FIG. 5 is a diagram showing R, G, and
It is a histogram with respect to the range which combined each pixel range of B.

FIG. 6 is a histogram of the R component created by the image processing apparatus according to the present invention.

FIG. 7 is a G component histogram created by the image processing apparatus according to the present invention.

FIG. 8 is a B component histogram created by the image processing apparatus according to the present invention.

FIG. 9 is a diagram showing pixels to be extracted on an image.

FIG. 10 is a diagram showing pixels to be extracted on an image.

FIG. 11 is a diagram showing pixels to be extracted on an image.

[Explanation of symbols]

Pixels to extract

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Claims (4)

(57) [Claims] 1. A reading means for reading an original as color multi-valued image data, a storage means for storing the image data, and a first histogram creation for creating a histogram for each image data for each color of red, green and blue. In the image processing device having means, dividing means for dividing the pixel range of each of the red, green and blue histograms created by the first histogram creating means into divided ranges; First selection means for selecting a high pixel element, first table creation means for creating a color classification table by combining the division ranges of the red, green and blue colors, and the value of the distributed color multi-valued image data Frequently appears
The range of each color division range used in the color division table
The second table for creating the second color classification table by dividing the
And Buru creating means, the color classification table created in the second table creation means divides the value of the color multi-value image data, sequentially a frequency of greater color classification table of the distributed values of the color multi-value image data Extraction means for extracting the required number, each red of the color classification table extracted by the extraction means,
Image processing, characterized in that it has a representative color extracting means for allocating pixel elements of the divided range selected by the first selecting means to the divided ranges of green and blue and extracting them as representative colors of the divided range. apparatus. 2. Any of the main scanning lines of the image data
Pixel extraction and red, green and
The second histogram that creates a histogram for each blue color
The system according to claim 1, further comprising a program creating unit.
Image processing device. 3. An arbitrary line on each sub-scanning line of the image data
Pixel extraction and red, green and
The third histogram that creates a histogram for each blue color
The system according to claim 1, further comprising a program creating unit.
Image processing device. 4. The pixel range of each color as the representative color
Has a second selecting means for selecting the maximum value and the minimum value.
The method according to any one of claims 1 to 3, characterized in that
The image processing device described.