JPH11205600A - Image processor and image processing method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an image processor which improves the reproducibility of a high density part and a low density part and an image processing method. SOLUTION: Density data GOUT of an attentional pixel is binarized into the attentional pixel and the black pixel (S9) and a binarization error HG is zero (S10) when the data GOUT is larger than high density reference value GOSAB (S8). That is, the distribution of the binarization error to surroundings is inhibited. Also, an attentional pixel is binarized into a white pixel (S11) and the error HG is zero (S10) when the data GOUT of the attentional pixel is smaller than low density reference value GOSAW (S1 and S8). Consequently, the reproducibility of a solid black area and a white area becomes satisfactory because the pixels of high density can surely be black pixels and the pixels of low density can surely be white pixels.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention is applied to a facsimile apparatus, an image scanner apparatus, and the like, and provides an image processing apparatus for reproducing halftones applied to multi-value density data of an optically read image. The present invention relates to an image processing method.

[0002]

2. Description of the Related Art In a device such as a facsimile device which expresses an optically read image by a binary image, various halftone processes are conventionally applied for expressing a halftone image such as a photograph. ing. One of the halftone processes is an error diffusion process. In the error diffusion process, when a certain pixel is binarized, a binarization error which is an error between the multi-value density data before binarization and the density data after binarization of this pixel is calculated. That is, in the binarization processing, the density data of the target pixel is compared with a certain threshold value, and the pixel is determined as a white pixel or a black pixel. For this reason, for a pixel having intermediate density data, an error necessarily occurs between a pixel before binarization and a pixel after binarization.

[0003] The binarization error is distributed to peripheral pixels with appropriate weighting. Then, in the binarization processing for a certain pixel of interest, the density data of the pixel of interest and the binarization error distributed from the peripheral pixels are added, and the addition result is compared with a predetermined binarization threshold. Is done. For example,
As shown in FIG. 2, the above-described binarization error occurs when binarization is performed on a pixel P0 of a certain line along the main scanning direction at the time of reading by a scanner provided in a facsimile apparatus or the like. I do. This binarization error is caused by the pixel P0
Error diffusion coefficient 1/4 or 1
/ 8.

Conversely, when attention is paid to the pixel on the side to which the binarization error is distributed, as shown in FIG. 3, the pixel A of interest has neighboring pixels B, C, D, E, F , G, the binarization error is distributed. Therefore, attention pixel A
Is performed based on the value obtained by adding the error distributed from the surrounding pixels B, C, D, E, F, and G to the density data of the pixel A.

[0005] In this way, by distributing the binarization error generated in each pixel to the peripheral pixels, a halftone expression is achieved. The binarization process is performed based on a certain threshold value. For example, when binarizing multi-valued data of 256 gradations (white: “0”, black: “255”), the binarization threshold is, for example, an intermediate value “12”.
8 ". Then, the peripheral pixels B, C, D, E,
Based on the cumulative error value, which is a value obtained by accumulating the binarization errors distributed from F and G, and the value of the target pixel, the target pixel becomes a white pixel according to the following equations (1) and (2). Or, it is determined as a black pixel.

Black judgment: (cumulative error value) + (pixel value of interest) ≧ 128 (1) White judgment: (cumulative error value) + (pixel value of interest) <128 (2) After this binarization, the binarization error of the pixel of interest is
It is calculated according to equation (3) or equation (4). That is,
At the time of black determination, the binarization error has a negative value, and at the time of white determination, the binarization error has a positive value.

For black determination: (binary error) = (cumulative error value) + (pixel value of interest) −255 (3) For white determination: (binary error) = ( (Cumulative error value) + (pixel value of interest) −0 (4)

[0008]

In the halftone processing by the error diffusion method as described above, since the error at the time of binarization is reflected at the time of the binarization processing of the peripheral pixels, the gradation performance is said to be good. There are advantages. However, on the other hand, as the error diffusion process for a plurality of pixels progresses, the binarization errors are accumulated, and the pixels to be black pixels are binarized to white pixels, or the pixels to be black pixels are white pixels. May be binarized. That is, white pixels appear in a solid black area,
There is a possibility that black pixels may appear in the white area.

For example, even if the image is a solid black image visually determined to be black, the density of each pixel takes a value smaller than the absolute black level (for example, "255") when read by a scanner. Therefore, even in a solid black area, the binarization error does not become zero, and when the binarization error is accumulated, at a certain pixel of interest, the difference between the density value of the pixel and the binarization error value is obtained. The addition result falls below the binarization threshold (for example, “128”), and is binarized into white pixels. In a region that is visually determined to be white, black pixels appear according to the same principle.

An object of the present invention is to provide an image processing apparatus and an image processing method capable of solving the above-mentioned technical problems and improving the reproducibility of a high density portion.
A further object of the present invention is to provide an image processing apparatus and an image processing method capable of improving the reproducibility of a high density portion and a low density portion.

[0011]

According to the first aspect of the present invention, there is provided an image processing apparatus for binarizing a multi-value density of each pixel constituting an image by an error diffusion process. An apparatus, wherein each pixel constituting an image is sequentially regarded as a target pixel, and an added value of a density value of the target pixel and a binarization error which is an error generated at the time of binarization in peripheral pixels having a predetermined positional relationship. Error diffusion processing means (steps S2, S3, S4, S6 in FIG. 4) for binarizing the pixel of interest into a high-density pixel or a low-density pixel by comparing
Error distributing means (steps S5 and S7 in FIG. 4) for distributing a binarization error generated when the target pixel is binarized to peripheral pixels having a predetermined positional relationship; Means for inhibiting distribution of the binarization error by the error distribution means when the value is higher than the predetermined high density reference value corresponding to higher density (YES in step S8 in FIG. 4). And step S10).

The means for inhibiting the distribution of the binarization error may be a means for setting the binarization error of the pixel of interest to zero. The high-density reference value is preferably a value corresponding to a density that can be identified with a high-density pixel after binarization when a pixel having that density is observed with the naked eye. According to the above configuration, when the density of the target pixel is a value corresponding to a higher density than the high density reference value, distribution of the binarization error is prohibited. Therefore, when an area (for example, a solid black area) which should be filled with high-density pixels (for example, black pixels) when observed by human eyes, low-density pixels (for example, white pixels) appear. Can be suppressed. Thereby, the reproducibility of the high density portion can be improved.

The invention according to claim 2 is means for binarizing the target pixel into a high-density pixel when the target pixel has a density value higher than the high-density reference value (FIG. 4). 2. The method according to claim 1, further comprising:
An image processing apparatus as described in the above. According to this configuration, when the density of the target pixel is a value corresponding to a higher density than the high density reference value, simple binarization processing is performed on the target pixel. This makes it possible to reliably binarize high-density pixels into high-density pixels.

Therefore, when a character image is included in the halftone image, both the halftone image portion and the character image portion can be reproduced well. According to a third aspect of the present invention, when the density value of the pixel of interest is a value corresponding to a density lower than the predetermined low density reference value corresponding to a density lower than the binarization reference value, the error distribution is performed. 3. The image processing apparatus according to claim 1, further comprising means for prohibiting binarization error distribution by means (NO in step S8 and step S10 in FIG. 4).

The low-density reference value is preferably a value corresponding to a density that can be identified with a low-density pixel after binarization when the pixel of that density is observed with the naked eye. According to this configuration, when the density of the target pixel is a value corresponding to a density lower than the low density reference value, distribution of the binarization error is prohibited. Therefore, the appearance of a high-density pixel can be suppressed in a region (for example, a white region) that should be filled with low-density pixels (for example, white pixels) when observed with human eyes. Thereby, the reproducibility of the low density portion can be improved, and the background fog can be prevented.

According to a fourth aspect of the present invention, when the density of the target pixel is a value corresponding to a density lower than the low density reference value, the target pixel is binarized into a low density pixel (see FIG. 4). 4. The method according to claim 3, further comprising a step S11).
An image processing apparatus as described in the above. According to this configuration, when the density of the pixel of interest is a value corresponding to a density lower than the low density reference value, simple binarization processing is performed on the pixel of interest. Thus, the low-density pixels can be surely binarized into low-density pixels.

According to a fifth aspect of the present invention, there is provided an image processing method for binarizing a multi-value density of each pixel constituting an image by an error diffusion process, wherein each pixel constituting the image is sequentially set as a target pixel. The addition value of the density value of the target pixel and the binarization error, which is an error generated at the time of binarization in surrounding pixels having a predetermined positional relationship, is illuminated with the binarization reference value, and the target pixel is set to a high density pixel or a low density pixel. Binarizing to a density pixel; distributing a binarization error generated when the target pixel is binarized to surrounding pixels having a predetermined positional relationship; Prohibiting the distribution of the binarization error to the pixels having a predetermined positional relationship in the vicinity when the value is also a value corresponding to a higher density than the predetermined high density reference value corresponding to the high density. An image processing method characterized in that:

According to this method, the same effect as that of the first aspect can be obtained. The invention according to claim 6 further includes a step of binarizing the pixel of interest into a high density pixel when the density value of the pixel of interest is a value corresponding to a higher density than the high density reference value. The image processing method according to claim 5, wherein According to this method, the same effect as the second aspect of the invention can be achieved.

According to a seventh aspect of the present invention, when the density value of the pixel of interest is a value corresponding to a density lower than a predetermined low density reference value corresponding to a density lower than the binarization reference value, 7. The image processing method according to claim 5, further comprising a step of prohibiting distribution of the binarization error to the pixels having the predetermined positional relationship. According to this method, the same effect as the third aspect of the invention is achieved.

The invention according to claim 8 further includes the step of binarizing the pixel of interest into a low density pixel when the density of the pixel of interest is a value corresponding to a density lower than the low density reference value. The image processing method according to claim 7, wherein According to this method, the same effect as the invention of claim 4 can be obtained.

[0021]

Embodiments of the present invention will be described below in detail with reference to the accompanying drawings. FIG. 1 is a block diagram showing an electrical configuration of a portion related to image processing of a facsimile apparatus to which an embodiment of the present invention is applied. The document set on the facsimile machine is read by the scanner 1. The scanner 1 includes an image sensor for reading an image, such as a CCD image sensor or a CIS image sensor. The image sensor may be an area sensor that reads two-dimensional data, or a niria image sensor that reads line data. Usually, a line image sensor is used to make the device inexpensive.

Image data of a document read by the scanner 1 is supplied to an input interface unit 2, which performs signal sample-and-hold processing and the like. In this embodiment, the input interface unit 2 is configured by an analog circuit, and the above processing is performed in an analog manner. The image data processed by the input interface unit 2 is applied to a digital AGC circuit 3, where a gain control for keeping the level of a signal (image data) within a desired range is performed, and an analog signal is converted to a digital signal. Is performed.

Next, the gain-controlled image data is applied to a shading correction circuit 4 to reduce or eliminate shading distortion. The shading distortion refers to unevenness in illumination of a reading light source when reading an original by the scanner 1 or unevenness in density between pixels due to other causes. The image data from which the shading distortion has been reduced or removed is next provided to the area separating circuit 5. In the region separating circuit 5, the input image data is image data (character data) obtained by reading characters, image data (photo data) obtained by reading a photograph, or a printed photograph, such as newspaper or magazine. It is determined whether the image data is image data (halftone data) of halftone dots obtained by reading the photographic image.

If character data, photograph data and halftone data are mixed in the input image data, each data is separated into regions. On the output side of the region separation circuit 5, a filter unit 6 that performs different processing for each type of region-separated data is connected. The filter unit 6 includes a differentiation filter 6A, an integration filter 6B, an integration differentiation filter 6C, and a data through filter 6D. Then, the filter unit 6 receives, from the region separation circuit 5, a region separation signal indicating which region each pixel belongs to, and performs an appropriate filter process on each pixel. That is, the character data is stored in the differential filter 6.
A, and a process for sharpening the outline is performed. The halftone data is supplied to the integration filter 6B, and the data is smoothed. Also, the boundary pixels of each region are given to the integral / differential filter 6C, and are subjected to a process for alleviating a sudden change in density of different pixel information (in this case,
Data through filter 6D may be selected. ). Further, data other than the character data and the halftone dot data, that is, the photographic data, is given to the data through filter 6D and sent to the next circuit as it is. The determination of the filter corresponding to the region separation signal can be selected by a register.

The data from the filter section 6 is applied to a zoom / smoothing circuit 9. In the case of enlarging or reducing an image, the zoom / smoothing circuit 9 performs an enlarging or reducing process and a process of correcting image distortion accompanying the enlarging or reducing process. If the image is not enlarged or reduced, the zoom / smoothing circuit 9 does not perform any processing on the image data.

The image data having undergone the processing up to the zoom / smoothing circuit 9 is then subjected to processing in one of the following circuits depending on the type, and multi-valued data (for example,
Conversion from 8 bits (0 to 255)) to binary data (0 or 1) is performed. That is, when the image data is photograph data or halftone data and is to be subjected to halftone processing, the image data is supplied to the γ correction circuit 10 and the sensitivity characteristics of the data are adjusted to match the characteristics of human eyes. Will be corrected. Further, it is given to the error diffusion circuit 11 and subjected to a process for good halftone expression, and then binarized. This embodiment is characterized by the configuration and processing of the error diffusion circuit 11.

On the other hand, if the image data is character data and data to be subjected to simple binarization processing, it is supplied to the binarization circuit 12. The binarization circuit 12 adjusts a slice level for binarization to distinguish a background from characters, line drawings, and the like. At this time, an automatic density adjustment process is also performed so that the density becomes appropriate. The output of the binarizing circuit 12 is supplied to an isolated point removing circuit 13 to remove isolated black points and white points that appear due to noise or the like.

The binarized data generated by the above processing is supplied to the output circuit 14, and is output to a transmitting circuit (not shown) or to a printing circuit. FIG. 2 is a diagram for explaining processing in the error diffusion circuit 11. In the error diffusion processing, when a certain pixel P0 is binarized, a binarization error is obtained by a method described later based on the multi-value density data of the pixel P0 and the binarization result (white or black). This is a process of distributing the binarization error to pixels around the pixel P0 at a predetermined distribution ratio.

In this embodiment, the binarization error generated at the pixel P0 is caused by the pixel P1 adjacent to the pixel P0 on the downstream side in the main scanning direction RM and the sub-scanning direction RS when the scanner 1 reads an image. , P5 by the error diffusion coefficient １ ／. A pixel P2 adjacent to the pixel P1 on the downstream side in the main scanning direction RM and a pixel P2
5, two pixels P3 on the upstream side in the main scanning direction RM.
P4 and a pixel P6 that is adjacent to the pixel P5 on the downstream side in the main scanning direction RM are distributed by multiplying the error diffusion coefficient by ８.

The error diffusion circuit 11 sequentially processes each pixel constituting the image as a target pixel. In the error diffusion process for the pixel of interest, an error distributed from surrounding pixels is added to the density data of the pixel of interest, and the result of this addition is subjected to a binarization process. This processing will be described with reference to FIG. When the pixel of interest A is considered as the center, the binarization errors HG (G), HG (E), and HG (D) at the pixels G, E, and D of the previous line L1 are distributed to the pixel of interest A. The distribution is multiplied by a factor of 1/8. Alternatively, the binarization error HG (F) at the pixel F is distributed by multiplying the error diffusion coefficient by 1/4. With respect to the current line L2 including the pixel of interest A, the binarization error HG (B) at the pixel B adjacent on the upstream side in the main scanning direction RM is distributed by multiplying the error diffusion coefficient by 1/4. , The binarization error HG (C) at the pixel C located on the upstream side in the main scanning direction RM is distributed by multiplying the error diffusion coefficient by 1/8.

In the binarization process for the target pixel A, first, a cumulative error value RG (A) obtained by adding the errors distributed to the target pixel A is calculated according to the following equation (5).

[0032]

(Equation 1)

The gamma correction circuit 1 for the pixel of interest A
A binarization processing target value T (A) shown in the following equation (6) obtained by adding the multi-value density data GOUT (A) given from 0 and the accumulated error value RG (A), and a predetermined binarization Binarization of the target pixel A is performed by comparing the threshold value TH. T (A) = GOUT (A) + RG (A) (6) However, in this embodiment, the data GOUT (A) of the target pixel A is a high density reference value GOSAB (for example, “230”). ), The target pixel A is binarized to a black pixel without performing the error diffusion process, and the data GOUT (A) of the target pixel A is a low density reference value (for example, “25”). If it is smaller, the target pixel A is binarized to a white pixel without performing the error diffusion process. In this case, “do not perform error diffusion processing” means that the binarization error in the target pixel A is not distributed to peripheral pixels, that is, the binarization error in the target pixel A is set to zero. .

The density corresponding to the data larger than the high density reference value GOSAB is a density that is clearly observed as black by the human eye. Is unnecessary. Similarly, a density corresponding to data smaller than the low-density reference value GOSAW is a density that is clearly observed as white by the human eye, and a halftone expression is also applied to an image area of such data. Not required.

FIG. 4 is a flow chart for explaining the processing contents of the error diffusion circuit 11. First, it is determined whether or not the density data GOUT (data after γ correction) of the target pixel is a value within a range from a low density reference value GOSAW to a high density reference value GOSAB (step S1). If the density data GOUT has a value within the above range, the cumulative error value RG is added to the density data GOUT to obtain a binarization processing target value T (step S).
2). If the binarization target value T is equal to or greater than the binarization threshold value (“128” in this embodiment) (step S3), the target pixel is binarized to a black pixel (step S4). In addition, the binarization error HG is obtained according to the following equation (7) (step S5). If the binarization target value T is less than the binarization threshold, the target pixel is binarized to a white pixel (step S6), and the binarization error HG is calculated according to the following equation (8). Required (step S
7).

HG = GOUT + RG-255 (7) HG = GOUT + RG-0 (8) On the other hand, the density data GOUT is not less than the low density reference value GOSAW and not more than the high density reference value GOSAB. If the value is out of the range, the density data GOUT is set to the high density reference value GOSAB.
Is determined (step S8).
Then, the density data GOUT is changed to the high density reference value GOSAB.
Is exceeded, the target pixel is binarized to a black pixel (step S9). At this time, the binarization error HG is set to zero (step S10), and the binarization error is not distributed to the target pixel.

In step S6, the density data GOUT
Does not exceed the high density reference value GOSAB, it can be determined that the density data GOUT is less than the low density reference value GOSAW, and the target pixel is binarized to a white pixel (step S11). Also in this case, the binarization error HG is set to zero (step S10), and the binarization error is not distributed to the target pixel.

Table 1 below summarizes the above processing in the error diffusion circuit 11.

[0039]

[Table 1]

As described above, according to this embodiment, the error diffusion processing is not performed on the pixels of the density data larger than the high density reference value GOSAB and the pixels of the density data smaller than the low density reference value GOSAW. , Simple binarization processing is performed, and distribution of a binarization error from such a pixel to peripheral pixels is not performed. Therefore, a good image can be reproduced without white pixels appearing in a solid black area or black pixels appearing in a white area. As a result, the solid black area and the white area can be reproduced while being filled with the black pixels and the white pixels, respectively, so that the reproducibility of the solid black portion is improved and the background fogging can be prevented.

Since a character image in a photographic image and a character image formed so as to be superimposed on a background image are reliably reproduced with black pixels, such character images can be reproduced well. Although one embodiment of the present invention has been described, the present invention can be implemented in other embodiments. For example, in a situation where background fogging is not so problematic, error diffusion processing may be performed on density data equal to or lower than the high density reference value GOSAW, even if the density data is lower than the low density reference value GOSAW. Also in this case, the reproducibility of the solid black portion and the character image in the photograph or the background image can be favorably reproduced.

In the above embodiment, a facsimile apparatus is taken as an example. However, the present invention relates to an apparatus for processing image data obtained by optically reading an image, such as an image scanner or a digital copying machine. Can be widely applied to In addition, it is possible to make various design changes within the technical scope described in the claims.

[Brief description of the drawings]

FIG. 1 is a block diagram showing an electrical configuration related to image processing of a facsimile apparatus to which an embodiment of the present invention is applied.

FIG. 2 is a diagram for explaining an error diffusion process.

FIG. 3 is a diagram for explaining an error diffusion process.

FIG. 4 is a flowchart for explaining the operation of the error diffusion circuit.

[Explanation of symbols]

 1 scanner 11 error diffusion circuit

Claims (8)

[Claims] An image processing apparatus for binarizing a multi-value density of each pixel constituting an image by an error diffusion process, wherein each pixel constituting an image is sequentially set as a target pixel, and a density value of the target pixel and The pixel of interest is binarized to a high-density pixel or a low-density pixel by illuminating an added value with a binarization error, which is an error generated at the time of binarization, in a pixel having a predetermined positional relationship in the vicinity with a binarization reference value. Error diffusion processing means, and error distribution means for distributing a binarization error generated at the time of binarization of the pixel of interest to surrounding pixels having a predetermined positional relationship. Means for inhibiting distribution of the binarization error by the error distribution means when the value is higher than a predetermined high density reference value corresponding to high density. apparatus. 2. The image processing apparatus according to claim 1, further comprising means for binarizing the pixel of interest into a high density pixel when the density value of the pixel of interest is a value corresponding to a higher density than the high density reference value. Item 2. The image processing apparatus according to Item 1. 3. When the density value of a pixel of interest is a value corresponding to a density lower than a predetermined low density reference value corresponding to a density lower than the binarization reference value, the error distribution means performs 3. The image processing apparatus according to claim 1, further comprising a unit that prohibits distribution of the binarization error. 4. The apparatus according to claim 1, further comprising means for binarizing the pixel of interest into a low density pixel when the density of the pixel of interest is a value corresponding to a density lower than the low density reference value. 3. The image processing device according to 3. 5. An image processing method for binarizing a multi-valued density of each pixel constituting an image by an error diffusion process, wherein each pixel constituting an image is sequentially set as a target pixel, and a density value of the target pixel is determined. The pixel of interest is binarized to a high-density pixel or a low-density pixel by illuminating an added value with a binarization error, which is an error generated at the time of binarization, in a pixel having a predetermined positional relationship in the vicinity with a binarization reference value. And distributing a binarization error generated when the target pixel is binarized to surrounding pixels having a predetermined positional relationship. The density of the target pixel corresponds to a higher density than the binarization reference value. Prohibiting distribution of a binarization error to pixels having a predetermined positional relationship in the vicinity when the value corresponds to a higher density than a predetermined high density reference value. Processing method. 6. The method according to claim 1, further comprising the step of binarizing the pixel of interest into a high density pixel when the density value of the pixel of interest is a value corresponding to a higher density than the high density reference value. Item 6. The image processing method according to Item 5. 7. When the density value of the pixel of interest is a value corresponding to a density lower than a predetermined low density reference value corresponding to a density lower than the binarization reference value, the predetermined positional relationship is determined. 7. The image processing method according to claim 5, further comprising a step of prohibiting distribution of the binarization error to the pixels. 8. The method according to claim 1, further comprising the step of binarizing the pixel of interest into a low density pixel when the density of the pixel of interest is a value corresponding to a density lower than the low density reference value. 7. The image processing method according to 7.