JPH11261818A - Image processor and image processing method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an image processor for preventing dots from being concentrated and displayed and the dots from being omitted at a boundary with a dense area at the time of binarizing and displaying multi-valued data for indicating the area of the thin density of images. SOLUTION: When the density of the multi-valued data of a picture element PI of attention is low and binarized data are on further, the range of other picture elements PE to be the object of an error diffusion range is widened and the range of the other picture elements PE is narrowed at the other time. By dynamically changing the error diffusion range based on the result of binarization, the dots are not concentrated around the dot and the images for which the image quality in the boundary of gradation is excellent, are displayed.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

The present invention relates to an image processing apparatus such as a printer and an image processing method.

[0002]

2. Description of the Related Art In a printing apparatus such as a printer or a facsimile which displays an image using binary data indicating dot on / off, a halftone of image data which is displayed in multiple gradations by 256 or more multivalued data is obtained. For display, a binarization process using a dither method using a dither matrix or a binarization process using an error diffusion method is employed.

[0003]

In recent years, the processing capability of personal computers (personal computers) has been improved, and accordingly, the image processing capabilities of personal computers have been improved, so that high-quality images can be processed and displayed. Has become. In addition, the performance of image input devices such as scanners and digital cameras has also been improved, and these devices can easily process high-quality color images that could only be handled by photographic devices in the past, using digital devices such as personal computers. I can do it. For this reason, even in a color printer which is an output device, a high resolution and a halftone beautiful,
There is a demand for higher quality color printing.

[0004] Multi-value data of multi-gradation image data, for example, 256 multi-value data φM is binarized to form binary data φB.
Is converted to a threshold value Th, for example, 128. If the multivalued data φM is equal to or greater than the threshold value Th, the binary data φB is turned on “1”, and the multivalued data φM is turned on.
If M is less than or equal to threshold Th, binary data φB is turned off “0”
Is adopted. Then, an image processing method is adopted in which dots are displayed when the binary data φB is on, and no dots are displayed when the binary data φB is off. In an image processing method using an error diffusion method for displaying a halftone, multi-value data φM and binary data of a pixel of interest are determined according to the result of displaying or not displaying a dot of a certain pixel (pixel of interest). By calculating the difference from φB as an error value E and using it as one of the judgment materials for determining whether or not to display dots for other pixels constituting the periphery of the target pixel, an original image of multiple gradations is obtained. Almost the same brightness (density) is obtained.

In a conventional image processing apparatus, an error value E
Is distributed at a preset rate to affect other pixels in a preset range,
The setting of the range or the setting of the distribution ratio was hardly reviewed in relation to the image quality. Therefore,
In response to the demand for color prints of higher quality, close to photographic quality, or better, the inventors of the present application have determined that the influence of factors such as the range of error diffusion or the distribution ratio on image quality. Further, the present application provides an image processing apparatus and an image processing method capable of recording a high-quality image by a method such as display or printing by appropriately setting a range or a distribution ratio of error diffusion. It is intended to be.

[0006]

For this reason, the inventors of the present application have examined how factors such as the range of error diffusion or the distribution ratio affect a binarized image. 1 and 2 show an example of the experimental results. FIGS. 1A and 2A show the result of binarizing and displaying an area where the value of the multi-value data φM is “7” in the low image density portion 1. The high portion 2 is a result of binarizing and displaying an area where the value of the multi-value data φM is “125”. These results are represented by one color, that is, black K.

[0007] The result shown in FIG.
Is distributed to other pixels PE in a relatively narrow range as shown in FIG. 1B.
The result shown in FIG. 2A is obtained by distributing the error generated in the pixel of interest PI to a relatively wide range of other pixels PE as shown in FIG. 2B.

In FIG. 1B, the target pixel PI
The error value E generated at (m, n) is
Two pixels PE (m, n + 1) and PE (m, n + 2)
And the four pixels PE (m + 1, n−2) on the next line are distributed to PE (m + 1, n + 1). On the other hand, in FIG. 2B, the error value E generated at the pixel of interest PI (m, n) has the same four pixels PE (m, n +) on the same line.
1) to PE (m, n + 4), and eight pixels PE (m + 1, n-5) of the next line to PE (m + 1, n + 2)
Are distributed.

As shown in these figures, by using the error diffusion method, the difference between the halftones of the multi-value data φM is well represented by the binary data φB. However, by repeating some simulations while changing the distribution range of such error values, the inventors of the present application were able to find out the influence of the distribution range on the image quality. First, as shown in FIG. 1A, in the case where the distribution range of the error is narrow, that is, in the case where the distribution ratio to the other pixels in the vicinity of the target pixel is high, in the region 1 where the image density is low,
There is a phenomenon that dots are displayed in a concentrated manner. On the other hand, as shown in FIG.
In other words, in the case where the error is distributed to other pixels further away from the pixel of interest, the dots are uniformly and clearly displayed in the low image density area 1, but the low image density area 1 and the low image density It has been found by the inventors of the present application that a phenomenon that dots are not displayed (dots are missing), which is not seen in FIG.

The display behavior of dots in such a low image density area is not so noticeable. However, in order to obtain a higher-quality image, it is important to display the halftone area more uniformly and beautifully. For that purpose, the dots are displayed almost uniformly in the low image density area, and furthermore, the density is low. It is necessary to prevent the display state of the dots from changing even at the boundary between the low and high areas. On the other hand, it is also possible to set an intermediate distribution range or distribution ratio in which the concentration of dots is low in a low-density area, and further, the dropout of dots at the boundary between a high-density area and a low-density area is reduced. Although it is possible, in the present invention, by dynamically changing the distribution range or the distribution ratio depending on the display result of the dots, the merits when the distribution range is narrowed and the advantages when the distribution range is widened are positively increased. To significantly improve the display grade in the halftone area.

That is, according to the image processing apparatus of the present invention capable of binarizing multi-gradation image data, an error value generated when binarizing a target pixel is calculated by using an error value located around the target pixel. An error distributing unit that distributes the error value of the pixel of interest, and a binarizing unit that binarizes the multi-value data of the pixel of interest in consideration of the error value of the pixel of interest. It is characterized in that the range of other pixels or the distribution ratio of error values to other pixels can be changed based on the binarization result in the conversion unit and the multi-value data. Also, according to the present invention,
In an image processing method for binarizing multi-tone image data, an error value generated when binarizing a target pixel is distributed as an error value of another pixel located around the target pixel. A distribution step, and a binarization step of binarizing the multi-value data of the pixel of interest in consideration of an error value of the pixel of interest,
The error distribution step is characterized in that a range of another pixel or a distribution ratio of an error value to another pixel can be changed based on a result of binarization in the binarization step and multi-valued data.

Such an image processing apparatus or image processing method is employed in which the diffusion range or distribution ratio of error values is not fixed, but can be changed for each pixel of interest by the result of binarization and multi-valued data. By doing so, when the multi-value data of the target pixel is smaller than a predetermined value and the density is low, when the target pixel is turned on, the range of another pixel is expanded, or the distribution ratio of the error value to other neighboring pixels is increased. By lowering it, the distribution ratio of the error value to other surrounding pixels can be increased. Therefore, it is possible to prevent the pixels from being displayed in a concentrated manner in the low-density area of the image, and to express the low-density area with more evenly distributed dots. Also, when the target pixel is not on, the range of other pixels can be narrowed, or the distribution ratio of error values to other nearby pixels can be increased, so that the dots are evenly distributed even at the boundary with the high density area. It is possible to display a high-quality image which is displayed, has no missing dots (skipping), and has a beautiful halftone display.

The image processing apparatus and the image processing method of the present invention can be realized in a personal computer or the like using software. Further, the above-described image processing can be performed by hardware. For this purpose, the error distribution unit includes an error memory capable of recording the error value of each pixel constituting the line, a register group capable of storing the error values of other pixels, and a register group for each of the register groups. An error calculation unit that can add the error value of the target pixel at a predetermined distribution ratio and a recording unit that can record at least a part of the added register value in the error memory. It is desirable to provide a setting unit that can be changed based on the result of the conversion and the multi-value data.

Assuming that the maximum range of the other pixels is the m + 1-th to n + x-th and the m + 1-line ny-th to the n + z-th for the nth target pixel of the m-th line, one line is formed in the error memory. Pixel error value ERR
(I) is recordable, and the register group includes m lines n + 1
X registers R0 (j) corresponding to each pixel from the (n + x) -th pixel to the (n + x) -th pixel,
Y + z + 1 registers R corresponding to each pixel of the
1 (k), and the error calculation unit uses these registers R
With respect to 0 (j) and R1 (k), the error value E of the target pixel is calculated by the following equation (1) using the distribution ratios K0 (j) and K1 (k).
From the register R1.
The error value of (−y) is converted to the error value ERR (n−
y), and further set by the setting unit to the distribution ratio K0
(J) and K1 (k) can be changed based on the binarization result of the pixel of interest and the multi-value data, so that the distribution ratio is changed according to the binarization result of the pixel of interest to dynamically change the diffusion range of the error value. Can be changed to

R0 (x) = ERR (n + x) + K0 (x) × E (1) For R0 (1) to R (x−1), R0 (j) = R0 (j + 1) + K0 (J) × E (2) R1 (z) = K1 (z) × E (3) From R1 (−y) to R1 (z−1), R1 (k) = R1 (k + 1) + K1 (k) × E (4) where i, j, k, m, n, x, y, and z are positive integers.

By using this error memory and performing image processing using a processing circuit or software capable of executing the operations of equations (1) to (4), input image data is processed for each scanning line. To a printer or the like. Accordingly, it is not necessary to binarize the entire screen at a time, and printout or the like can be executed by sequentially processing each scanning line. Therefore, the time from input or acquisition of image data to output such as printout can be reduced. Therefore, in the image processing apparatus or the image processing method of the present invention, it is possible to output a high-quality image in a short processing time.

[0017]

Embodiments of the present invention will be described below with reference to the drawings. FIG. 3 shows a schematic configuration of the image processing apparatus 1 according to the present invention. For example,
The red, green, and blue (hereinafter, RGB) multi-value data φM for one line read from the scanner is input to the CPU.
To the memory control circuit 12 through the interface 11. In the memory control circuit 11, the input RGB image data for one line is written into the RAM 16,
The multi-value data written in the RAM 16 at the time when the writing for one line is completed is performed by the first color conversion circuit 1 for each pixel.
Sent to 3. In the first color conversion circuit 13,
Referring to a three-dimensional look-up table previously written in the RAM 17 by the CPU,
Is performed to remove the difference in color tone due to the model of the scanner. As a result, differences in characteristics between models of peripheral devices such as scanners are absorbed, and RGB multi-value data having substantially the same color tone can be obtained.

The RGB multi-value data of one pixel processed by the first color conversion circuit 13 is then converted to an edge emphasis circuit 14.
To perform edge enhancement processing. The RAM 18 attached to the edge enhancement circuit 14 holds data for two lines. R of one pixel subjected to edge enhancement processing
The multi-valued data of GB is further sent to the second color conversion circuit 15 and refers to a three-dimensional look-up table previously written in the RAM 19 by the CPU to obtain dark-colored cyan and magenta, yellow, black, The color is converted to light cyan and magenta (hereinafter CMYKcm). Then, the multi-value data φM of each color of CMYKcm of one pixel is sent to the binarization circuit 20 and converted into binary data φB. The binary data φB is sent to the memory control circuit 12 and recorded in the RAM 16.

When the binarization processing for one line is performed in this way, the CPU sends the binary data for one line to a printer or the like via the interface 11. Also,
The image processing apparatus 10 of the present example is also provided with an input / output interface 9 with a memory module, so that the set values of the circuits 12 to 20 can be stored.

FIG. 4 shows a schematic process in the binarization circuit 20 of the present embodiment using a flowchart. In the binarization processing, the same processing is performed for each color of CMYKcm, so that one color, for example, dark cyan C
Will be described. First, at step 21, the target pixel PI
Is added to the multi-value data φM representing the cyan C of the pixel PI. Then, in step 22, the added value is compared with a threshold value Th to be binarized. Further, in step 23, an error value E for another pixel PE is calculated. In order to adjust the shading so that the halftone is appropriately displayed by error diffusion, step 2 is performed.
In 3, the error value E is calculated as follows.

When the binarization result is ON “1”, E = φM + ERR-255 (5) When the binarization result is OFF “0” E = φM + ERR (6) However, in this example, The multi-value data φM is assumed to be 8-bit data having a value of 0 to 255, but is not limited to this.

As described above, by calculating the error value E generated at the target pixel PI, when a dot is displayed at the target pixel PI, a negative error is distributed to other surrounding pixels PE, and the density adjustment is performed. Done. On the other hand, when no dot is displayed at the target pixel PI, a plus error is distributed to other surrounding pixels PE, and density adjustment is performed.

Further, in the binarization circuit 20 of this embodiment, in step 24, the setting of the distribution ratio at the time of error diffusion according to the binarization result and the value of the multi-value data φM is changed. As shown in FIG. 5, the binarization circuit 20 of the present example includes two modes 0 and 1 having different distribution ratios, the multi-value data φM indicating the input image density is 8 or less, and Result of binarization (binary data φ
When B) is "1", the mode 0 has a wide error diffusion range, that is, a large distribution ratio to other surrounding pixels.
Is selected, and under other conditions, mode 1 having a narrow error diffusion range, that is, a large distribution ratio to other nearby pixels, is selected.

In this example, the maximum range of error diffusion is
The following first to fourth other pixels PE (PE (m, n + 1) to P (m, n + 1)) on the same line of the pixel of interest PI (m, n)
E (m, n + 4)) and the fifth pixel preceding the pixel of interest PI on the next line to the second pixel P following
E (PE (m + 1, n-5) to PE (m + 1, n +
2)) and each other pixel P
The range of error diffusion is adjusted by changing the distribution ratio K with respect to E.

For example, in mode 0, four other pixels PE (PE (m, n +
1), PE (m + 1, n-1) to PE (m + 1, n +)
The distribution coefficient K for 1)) is 2/16, and the distribution coefficient K for the other eight other pixels PE is 1/16. On the other hand, in mode 1, two other pixels PE (PE (m, n + 1) and PE
The distribution coefficient K for (m + 1, n)) is 4/16, and four other pixels PE (PE (m, n + 2), P
The distribution coefficient K for E (m + 1, n-2), PE (m + 1, n-1) and PE (m + 1, n + 1) is 2/1.
It is 6. Then, the other six pixels P around it
The distribution coefficient K for E is set to 0, and the range in which the error is dispersed is limited to six around the pixel of interest PI.

In step 24, after the distribution coefficient is set according to the binarization result of the target pixel PI, an error calculation is performed in step 25, and the error value ERR of other surrounding pixels is obtained. The processing of steps 21 to 25 is performed on the multi-value φM of each pixel, and when the binarization processing is completed for each color of each pixel constituting one line, printing is performed based on the data.

FIG. 6 shows the result of printing using the binary data φB processed by the image processing apparatus 10 of this embodiment, enlarged to such an extent that the dot display becomes clear. The input multi-value data φM is the same as that in FIGS. 1 and 2 described above. As can be seen from this figure, the area 1 where the image density is low
Therefore, the phenomenon that dots are displayed concentratedly has not appeared,
Further, an area 1 having a low image density and an area 2 having a high image density
Dots do not fall out at the boundary between, and the dots are uniformly displayed over the entire area 1 having a low image density. As described above, in the image processing method employed in the image processing apparatus 10 of the present embodiment, the distribution coefficient of the error variance is changed depending on the result of binarization in a region where the value of the multi-value data φM is low and the image density is low. Usually, the diffusion range is narrowed so that the boundary between the high density region and the low density region is displayed well, and when a dot is hit, the diffusion range is widened and a negative error value is set in a wide range. Is dispersed and dots are suppressed from being concentrated. As a result, as shown in FIG. 6, it is possible to obtain a high-quality image in which dots are displayed almost uniformly up to the vicinity of the boundary in the low density region 1 of the image.

FIG. 7 shows an example of the binarization circuit 20 which implements the above processing in hardware. This binarization circuit 20
Is an error value E generated when the target pixel PI is binarized.
And an error distribution unit 21 that distributes the error value ERR of another pixel PE located around the pixel of interest PI, and binarizes the multi-value data φM of the pixel of interest PE by adding the error value ERR of the pixel of interest. And a binarizing unit 50 that performs the conversion. Further, the error distribution unit 21 includes an error memory 22 capable of recording an error value ERR (i) of each pixel constituting the line, and a register group 23 capable of storing an error value ERR of another pixel PE.
And an error calculation unit 24 that can add the error value E of the pixel of interest PI to each register of the register group 23 at the distribution ratio K, and can record at least a part of the added register value in the error memory 22. And a setting unit 26 that can change the distribution ratio K based on the binarization result φB and the multi-value data φM of the target pixel PI.

As described above, the image processing apparatus 10 of this embodiment
, The maximum range of the other pixels PE for the n-th pixel of interest PI on the m-th line is from the (m + 1) -th line to the n-th line.
The fourth and m + 1 lines are from the (n-5) th to the (n + 2) th lines. Accordingly, the register group corresponding to these other pixels PE includes four registers R0 (1) to R0 (4) corresponding to the nth to (n + 4) th pixel PEs on the same line (m line). , And eight registers R1 (-5) to R1 (2) corresponding to each of the (n-5) th to (n + 2) th pixels PE of the next line (m + 1 line). And a second register group 31.

Corresponding to these register groups 30 and 31, error calculator 24 also has a first error calculator 32 for performing an operation on first register group 30 and a second register group 3.
And a second error calculating section 33 for performing an operation on the first and second registers. Each of the calculating sections 32 and 33 includes an adder 36 for setting the result of addition to each of the registers 35.
And an error distribution circuit 3 which multiplies the generated error value E supplied from the binarization section 50 by a predetermined distribution ratio K and adds the multiplied error value E to the adder 36.
7 is provided. Then, in the error calculator 24,
The error value of the target pixel PI when the distribution ratios K0 (j) and K1 (k) are set for each register R0 (j) of the first register group 30 and each register R1 (k) of the second register group 31. It is configured to add E as follows.

R0 (4) = ERR (n + 4) + K0 (4) × E (11) For R0 (1) to R (3), R0 (j) = R0 (j + 1) + K0 (j ) × E (12) where j = 1 to 3 R1 (2) = K1 (2) × E (13) For R1 (−5) to R1 (1), R1 ( k) = R1 (k + 1) + K1 (k) × E (14) where k = −5 to 1 Further, each error distribution circuit 37 corresponding to each of the registers R0 and R1 has an error value E The control signal φ1 from the setting unit 26 is input, and the respective distribution ratios K0
As shown in FIG. 5, (j) and K1 (k) can be changed according to the result of the input multi-value data φM and the output binary data φB. Therefore, the error distributing unit 21 of the present example performs multi-value data φM and binary data φ
B is a circuit that can dynamically change the range of error diffusion according to the result of B. By performing an error operation in the error distribution unit 21, a high-quality binary image of the above-described halftone is obtained. Data can be obtained.

The error memory 22 stores, in the present example, the calculation result of the error generated when binarizing the pixel of the line immediately before the line (m) to be processed, in this example, the line (m-1). Is recorded, and another pixel PE (m, n + 4) at the end of the error diffusion range is read by the reading unit 38 of the recording unit 25.
Is read out, and the result calculated according to the above equation (1) is stored in the register R0.
(4) is set. The error value is sequentially stored in the register R0 as the pixel of interest PI moves.
(3) The calculation is performed while moving to R0 (2) and R0 (1), and finally, the error value ERR of the target pixel PI is supplied to the binarization unit 50.

In the binarizing section 50, the error value ERR is added to the multi-value data φM by the adder 51, and becomes the error data E for binarizing the target pixel PI. The added error data E is determined by a predetermined threshold value Th (for example, 12
8) to generate binary data φB. The error data E is input to the selection circuit 54 together with the data converted to a minus value as in the above-described equation (5) by the subtractor 53, and the above-described equation (5) or the above-described equation is obtained based on the result of the binarized data φB. The error value E of (6) is generated. Then, this error value E is supplied to each error distribution circuit 37, and is used for calculating the error value of the next pixel of interest PI and calculating the error value of each pixel of the next line m + 1.

The calculation of the error value of each pixel PE on the next line m + 1 is performed by the second error calculation unit 33 and the second register 3.
1 is performed. First, an error value due to error diffusion is set in the rearmost register R1 (2) where the error is diffused, and the error value is calculated while sequentially moving through the register R1. When the value of the pixel PE (m + 1, n−5) at the forefront of the error diffusion range is finally obtained by the register R1 (−5), the error value ERR is written into the ERR (n− 5) and the error value of the next line m + 1 is stored.

As described above, in the binarization circuit 20 of this embodiment, the binarization processing of the pixels of each line is performed.
The resulting error value for the next line is stored in error memory 22. Therefore, multi-gradation image data can be binarized and output for each line, and can be sent to a printer line by line to start printing. For example, it is not possible to start printing without processing the image data for one page, and color printing can be started in a short time.
It can be completed in a short time. For this reason, by installing the image processing apparatus or the image processing method of this example in a printer or by installing the image processing apparatus or the image processing method in a personal computer as a printer driver, it is possible to print a high-quality beautiful image of a halftone in a short time.

FIG. 8 shows an example of the configuration of the error distribution circuit 37. This error distribution circuit 37 is provided with a first selector 4 for distributing the error value E according to mode 0 shown in FIG.
1 and a second selector 42 for distributing according to the mode 1, each of which is
The mode signal 0 is supplied when bit signals φm0 and φm1 are supplied.
And the distribution ratio in the mode 1 can be freely set. In this example, the signal φm0 or φm
1 is “000” and the distribution ratio K is 0, “001” is the distribution ratio K of 1/16, “010” is the distribution ratio K of 2/16,
With “100”, the distribution ratio K can be set to 4/16. The outputs of the first and second selectors 41 and 42 are input to the third selector 43, and the error diffusion range as shown in FIG. Can be switched based on the multi-value data φM and the binary data φB.

In the binarization circuit 20 of this embodiment, the values of the signals φm0 and φm1 of the error distribution circuit corresponding to the registers R0 and R1 are set as follows in accordance with FIG.

Register signal φm0 (mode 0) signal φm1 (mode 1) R0 (1) 010 100 R0 (2) 001 010 R0 (3) 001000 R0 (4) 001000 R1 (−5) 001000 R1 (−) 4) 001 000 R1 (-3) 001 000 R1 (-2) 001 010 R1 (-1) 010 010 R1 (0) 010 100 R1 (1) 010 010 R1 (2) 001 000 Error distribution circuit 37 of this example. Is used in the error distribution unit 21 using the signal φ supplied to each error distribution circuit.
By changing the values of m0 and φm1, the error diffusion range in mode 0 and mode 1 or the error distribution ratio K can be freely changed to other ratios in addition to the distribution ratio shown in FIG. The error distribution unit 21 has high flexibility.

The image processing apparatus 10 having the functions as described above can be mounted on the printer itself or mounted on a personal computer as a printer driver to perform beautiful halftone image processing. . Further, as shown in FIG. 9, the scanner 5 and the printer 6 can be connected to each other and the image read by the scanner 5 can be transferred to the printer 6 for output to the peripheral device management device 60 for use. It is. This peripheral device management device 60
Has a scanner control unit 61 connected to the scanner 5 and a printer control unit 62 connected to the printer 6.
66, the RAM 67 and the image processing apparatus 10. Therefore, the image processing apparatus 10 binarizes the multi-gradation image data read by the scanner 5 and sends the binary data to the printer 6 to perform color printing. Further, a network control unit 63 connected to the LAN 7 is also connected to the internal bus 64,
The data can also be sent and printed to a network computer (not shown) connected to N7.

In the above example, the case where the error diffusion range is shown in FIG. 5 is described. However, it is of course possible to further widen or narrow the error diffusion range. Even in such a case, image processing similar to that of the present example can be performed by using the above-described general formulas (1) to (4), or by employing a circuit configuration based on these formulas. .

[0041]

As described above, according to the image processing apparatus and the image processing method of the present invention, when binarizing multi-valued data, the error diffusion range is not kept constant but is multiplied. The error diffusion range can be varied by the value data and the binarized data. Therefore, usually, the diffusion range of the error is narrowed to prevent the dropout of dots near the boundary between shades, and when a dot is hit, the diffusion range of the error (minus error) is enlarged to concentrate the dots. I try not to be hit. For this reason, the binarization can be performed so that the dot display corresponding to the halftone in the low density area has a substantially uniform density even in a microscopic range. Therefore, by employing the image processing apparatus and the image processing method of the present invention, it is possible to create and use image data that has a high resolution and clearly expresses a halftone.

Further, in the image processing apparatus and the image processing method disclosed in the present application, since the binarization processing can be performed for each line using the error memory, the binarization processing is performed at a very high speed. It is possible to provide an image processing apparatus capable of outputting a high-quality color printer close to photographic image quality in a short time.

[Brief description of the drawings]

FIG. 1A is an example in which an image output by binarizing multi-gradation image data is enlarged and shown in FIG. 1B, and a table when an error diffusion range is set to be narrow as shown in FIG. This is an example.

FIG. 2A is an example in which an image output by binarizing multi-gradation image data is enlarged and shown in FIG. 2B, a table when an error diffusion range is set wide as shown in FIG. This is an example.

FIG. 3 is a block diagram illustrating a schematic configuration of an image processing apparatus according to an embodiment of the present invention.

FIG. 4 is a flowchart illustrating an outline of a binarization process according to the embodiment of the present invention;

FIGS. 5A and 5B are diagrams schematically showing an error diffusion range employed in the binarization processing of the present example. FIG. 5A shows a wide diffusion range (mode 0), and FIG. 5B shows a narrow diffusion range. (Mode 1) is shown.

6 is an enlarged view showing an output image binarized in the error diffusion range shown in FIG. 5;

FIG. 7 is a circuit diagram illustrating a configuration example of a binarization circuit;

FIG. 8 is a circuit diagram illustrating a configuration example of an error distribution circuit.

FIG. 9 is a block diagram illustrating a configuration example of a peripheral device management device including the image processing device according to the present embodiment.

[Explanation of symbols]

 1. Area with low image density 2. Area with high image density 10. Image processing device 12. Memory control circuit 13. First color conversion circuit 14. Edge enhancement circuit 15. Second color Conversion circuit 20 binarization circuit 21 error distribution unit 22 error memory 23 register group 24 error calculation unit 25 recording unit register 36 adder 37 error distribution Circuit 50 ··· Binarization section

Claims (7)

[Claims] 1. An image processing apparatus capable of binarizing multi-gradation image data, wherein an error value generated when binarizing a target pixel is determined by calculating an error value of another pixel located around the target pixel. An error distributing unit for distributing as a value, and a binarizing unit for binarizing multi-value data of the pixel of interest in consideration of an error value of the pixel of interest, wherein the error distributing unit includes the binarizing unit. An image processing apparatus, wherein a range of the other pixel or a distribution ratio of an error value to the other pixel can be changed based on a binarization result and the multi-valued data in. 2. The method according to claim 1, wherein the error distribution unit includes:
When the multi-value data of the pixel of interest is smaller than a predetermined value, and the binarization result of the pixel of interest is turned on, the range of the other pixel is expanded, or the other pixel in the vicinity is expanded. An image processing apparatus, wherein a distribution ratio of an error value to a pixel is lowered to increase a distribution ratio of an error value to the other neighboring pixels. 3. The error distribution unit according to claim 1, wherein:
An error memory capable of recording an error value of each pixel constituting the line; a register group capable of storing the error values of the other pixels; and a predetermined error value of the target pixel for each register of the register group. An error calculating unit capable of adding at least a part of the value of the added register in the error memory; a binarizing result of the pixel of interest and the multiplying unit; An image processing apparatus comprising: a setting unit that can be changed based on value data. 4. The method according to claim 3, wherein the maximum range of the other pixel is n + 1 to n + x and m + 1 line ny with respect to the n-th pixel of interest in the m-line.
From the nth to the (n + z) th, and the error memory stores an error value E of a pixel constituting one line.
RR (i) can be recorded, and the register group includes x registers R0 (j) corresponding to each of the (m + 1) th to (n + x) th pixels of the m line,
Y + z + 1 registers R1 (k) corresponding to each of the (n + 1) -th to (n + z) -th pixels of the (m + 1) -th line, and the error calculation unit stores these registers R0 (j) and R1 (k) Distribution ratios K0 (j) and K1 (k)
Then, the error value E of the target pixel is added as follows, and the recording unit records the error value of the register R1 (−y) as the error value ERR (ny) of the error memory. The setting unit determines the distribution ratios K0 (j) and K1
(K) is changed based on the binarization result of the pixel of interest and the multi-value data. R0 (x) = ERR (n + x) + K0 (x) × E For R0 (1) to R (x−1), R0 (j) = R0 (j + 1) + K0 (j) × E R1 (z) = K1 (z) × E From R1 (−y) to R1 (z−1), R1 (k) = R1 (k + 1) + K1 (k) × E where i, j, k, m, n , X, y, z are positive integers. 5. An image processing method for binarizing multi-tone image data, comprising: converting an error value generated when binarizing a target pixel to an error value of another pixel located around the target pixel. An error distribution step of distributing the target pixel, and a binarization step of binarizing the multi-valued data of the pixel of interest in consideration of an error value of the pixel of interest. An image processing method, wherein a range of the other pixel or a distribution ratio of an error value to the other pixel can be changed based on a binarization result and the multi-valued data. 6. The error distribution step according to claim 5, wherein the multi-value data of the target pixel is smaller than a predetermined value,
And, when the binarization result of the pixel of interest is turned on, the range of the other pixel is expanded, or the distribution ratio of the error value to the other pixel in the vicinity is reduced to reduce the other pixel in the vicinity. An image processing method comprising: increasing the distribution ratio of an error value to a pixel of (c). 7. The method according to claim 5, wherein, for the n-th pixel of interest in the m-th line, when the maximum range of the other pixel is n-th to n + x-th and m + 1-th to ny to n + z-th, The error distribution step uses an error memory capable of recording an error value ERR (i) of a pixel forming one line, and uses a number of registers R0 (j) corresponding to each of the nth to n + xth pixels of the m line. ) And the distribution ratios K0 (j) and K0 for the (y + z + 1) registers R1 (k) corresponding to the (n−y) th to (n + z) th pixels of the (m + 1) th line.
An arithmetic step of adding the error value E of the target pixel at 1 (k) as follows, and recording the error value of the register R1 (-y) as the error value ERR (ny) of the error memory; Prior to this calculation step, there is provided a setting step of setting the distribution ratios K0 (j) and K1 (k) based on the binarization result of the target pixel and the multi-value data. Image processing method. R0 (x) = ERR (n + x) + K0 (x) × E For R0 (1) to R (x−1), R0 (j) = R0 (j + 1) + K0 (j) × E R1 (z) = K1 (z) × E From R1 (−y) to R1 (z−1), R1 (k) = R1 (k + 1) + K1 (k) × E where i, j, k, m, n , X, y, z are positive integers.