JPH1056568A - Image processor - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide the uniformity of black dots of a low density part and white dots of a high density part and also to provide smooth image quality feeling over an entire area of density. SOLUTION: This processor has a means which switches the means that makes half-tone image data 48 from an error diffusing method to a dither matrix method by comparing the density of gray scale image data 40 with a prescribed value and deciding whether the density is larger or smaller, a means which decides an area where the output that makes the data 48 of the error diffusing method and the dither matrix method is different, and a means which switches processes about the processing that makes the data 48 based on an area decided result. Furthermore, the processor uses the pattern which is created according to a prescribed rule based on the data 48 that is created in the error diffusing method in switching density or its adjacent density as threshold matrix of the dither matrix method.

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 relating to a device such as a printer or a plotter, and more specifically,
The present invention relates to an image processing device that converts grayscale image data into halftone image data.

[0002]

2. Description of the Related Art Normally, image data is so-called gray scale data having density information.

For example, 8-bit image data can represent 256 levels of density from 0 to 255. In the case of a color image, red (R), green (G),
The three colors of blue (B) are often represented by a total of 24 bits of 8 bits each. In this case, each color represents 256 levels of density, and it is possible to represent about 16.7 million colors of 256 to the third power in RGB.

A recent personal computer monitor can express 16.7 million colors by setting. Human sensitivity can determine how much resolution (number of colors and shades) and how much resolution is needed depends on the type of images handled (paintings and photographs that impress people, computer graphics, and sales trends). Bar graph to explain)
Since it is not directly related to the present invention, it will not be described any further, but the discriminating ability of a person regarding the discrimination of color and shade is surprisingly high.

[0005] When printing grayscale image data having a large amount of expressive power, it is necessary to convert the data into halftone image data.

At present, ink-jet printers and thermal transfer printers that are widely available have only two types of expression capability, whether dots are finally or not. The halftone image data mentioned here can also be expressed as pseudo gradation data.

That is, multi-valued (grayscale image with shading) is expressed by binary (struck / not struck) printing, and typical methods include a dither matrix method and an error diffusion method. Widely used. One of the requirements of a good printer is that the halftone image can be printed indistinguishable from a grayscale image.

A representative example of the prior art is the well-known Floyd and S.
teinberg published in 1976 [An Adaptive Algor
ithm for Spatial Grayscale].

In this method, a binary value (hit / not hit) is determined by comparing a predetermined "threshold value" with the size of grayscale image data, and the density error generated at that time is determined in the vicinity. This is a method of scattering image data.

It has the characteristic that halftone image data is converted while preserving the density of the original grayscale image data.

Another typical method is halftone image data conversion using a dither matrix.

The basic idea of this method has been described in many books, so that detailed explanation is unnecessary.
The dither matrix is a set of spatially arranged thresholds, and since image data is usually larger than the dither matrix, halftone image data is formed while repeatedly comparing with the dither matrix.

The error diffusion method has excellent characteristics as a method for converting a grayscale image to a halftone image. At the beginning, problems of software processing time and hardware circuit scale were pointed out due to the enormous amount of calculation, but with recent technological advances, those problems are no longer major obstacles.

However, the following two points are raised as major problems.

The first point is that a transient behavior at an edge or a boundary appears. This is sometimes described by the words "start delay" and "Hakiyose". For example, if the grayscale image data is very low,
Until the error accumulates, the threshold value is not easily reached and the output of halftone image data cannot output 1. Conversely, the same can be said for the 0 output (no hit) when the density is high.

The second point is that the processing is performed in a raster order, and that periodic data called a highly directional texture, which is generated by repeating error distribution by a fixed matrix, is generated. This is also called “earthworm image” and does not give a pleasant impression to human vision. This is not a problem since the dots are densely formed when the density becomes conspicuous at a certain level or more in a low density portion. Conversely, the same can be said for the generation of white dots when the density is high.

Next, the problem of halftone image data conversion by the dither matrix will be described.

First, the periodicity due to the repetition of the dither matrix does not give a pleasant impression to human vision. When printing with a typical Bayer dither matrix, most people are worried about the lattice periodicity.

The second problem lies in the basic characteristics of the dither matrix. A point hit at a density of 10 in the dither matrix is always hit at a higher density. Since a 10 * 10 dither matrix has 100 meshes, a maximum of 100 levels of density can be expressed.

At a density of 10, 10 in the dither matrix
Dots are scattered.

At a density of 15, the 15 in the dither matrix
Dots are scattered.

At a density of 60, 60 in the dither matrix
Dots are scattered.

Ten dots having a density of 10 correspond to a density of 15 or 6
It must be included in 0. This is necessary to ensure the continuity of the density of the dither matrix. However, no matter how well the dots are dispersed at each density, there is a limit in ideal dot dispersion because the past dots are dragged, and especially at a printing rate of about 20% or more, a "grainy feeling" is felt. In other words, it can be described as "lacking smoothness."

Various methods for solving the first problem of the dither matrix method have been proposed, but the second problem is related to the basic characteristics of the dither matrix. Inferior in texture such as smoothness as compared with the diffusion method.

The present invention has been made to solve the above-mentioned conventional problems, and realizes uniformity of black dots in a low-density part and uniformity of white dots in a high-density part. It is an object of the present invention to provide an image processing apparatus capable of realizing, at low cost, extremely high-quality halftone image data capable of realizing a smooth image quality feeling over the entire area.

[0026]

The gist of the present invention will be described with reference to the accompanying drawings.

An error diffusion method for converting the grayscale image data 40 into halftone image data 48 by comparing the magnitude relationship with a predetermined threshold value and distributing an error generated at the time of conversion to peripheral pixels; 40
Is converted to halftone image data 48 by comparing the grayscale image data 40 with halftone image data 48 using a dither matrix method.
An image processing device for converting the halftone image data 48 from the error diffusion method to the dither matrix method by comparing whether the density of the grayscale image data 40 is smaller or larger than a predetermined value. Means for determining an area where the halftone image data 48 output of the error diffusion method and the dither matrix method are different, and means for switching a process relating to the halftone image data 48 processing based on the area determination result; An image processing apparatus characterized in that a pattern generated according to a predetermined rule based on halftone image data 48 generated by an error diffusion method at or near a switching density is used as a threshold matrix of a dither matrix method. It is.

Further, in the image processing apparatus according to the first aspect of the present invention, the density of the gray scale image data is reduced by using a dither matrix method in which periodicity is not noticeable in a low density portion where the density of the gray scale image data is smaller than a predetermined value. Using a dither matrix method in which periodicity is inconspicuous in a high density part larger than a predetermined value and further larger than a separately determined value, and using an error diffusion method in a middle density part between the low density part and the high density part. The present invention relates to an image processing apparatus characterized by the following.

The image processing apparatus according to any one of claims 1 and 2, wherein the dither matrix pattern generating means counts a peripheral pixel density function represented by a dither matrix value at each density. Means, a second means for counting the density within a predetermined neighborhood of the pixel of interest, and a third means for counting the number of dots existing within a predetermined radius. is there.

The image processing apparatus according to any one of claims 1 to 3, wherein a delay amount of the halftone image data 48 conversion processing is switched based on a region determination result. It is related to.

The image processing apparatus according to any one of claims 1 to 4, wherein an error generation amount near the switching density is switched based on a region determination result. is there.

Further, in the image processing apparatus according to any one of claims 1 to 5, an error value generated in the halftone image data 48 conversion processing even during the halftone image data 48 conversion processing by the dither matrix method. The present invention relates to an image processing device characterized by storing and transmitting.

Further, in the image processing apparatus according to any one of the first to sixth aspects, it is preferable that the error value generated in the halftone image data 48 conversion process is clamped to a predetermined value for both the upper limit value and the lower limit value. The present invention relates to an image processing apparatus having features.

[0034]

BEST MODE FOR CARRYING OUT THE INVENTION Preferred embodiments of the present invention (how to implement the invention) will be briefly described with reference to the drawings, showing the operational effects thereof.

Means for comparing whether the density of the grayscale image data 40 is smaller or larger than a predetermined value, and switching the halftone image data 48 conversion means from the error diffusion method to the dither matrix method; Means for judging an area where the halftone image data 48 output of the matrix method is different, and means for switching a process related to the halftone image data 48 processing based on the area judgment result, and an error diffusion method at or near the switching density The grayscale image data 40 is obtained by simply combining a pattern generated according to a predetermined rule based on the halftone image data 48 generated by the dither matrix method and the error diffusion method into the halftone image data 40.
It is possible to prevent the occurrence of an extremely unsightly defective image area due to the discontinuity of processing near the switching point of the two methods that occurs when converting to 48, the uniformity of black dots in the low density part and the high density Can realize uniformity of white dots in each area, and can realize a smooth feeling of image quality over the entire area from the low density area to the high density area, and can produce extremely high quality halftone image data 48 at low cost. Can be realized.

[0036]

FIG. 1 is a block diagram showing an embodiment of the present invention.
FIG. 2 shows a schematic flowchart of the processing. First, FIG. 2 will be described. In FIG. 2, grayscale image data 40
Of the processing flow for sequentially converting the image data into halftone image data 48.

In the following description, the grayscale image data value takes an integer value from 0 to 255, and the halftone image data value takes two values, 1 and 0.

In FIG. 2, first, a gray scale image data value Di is obtained. Hereafter, the grayscale image data value of the image data is represented by D. The subscript i means i-th.

Next, the obtained grayscale image data value Di and the error value Δi accumulated so far are added. Similarly, the subscript i means i-th.
Since the error may be negative, it is added or subtracted.

Next, it is determined whether or not the acquired grayscale image data value Di is smaller than a predetermined value DENSITY1. If the value is smaller than the predetermined value, the process proceeds to the low-density binarization determination unit 4.

Next, it is determined whether or not the acquired grayscale image data value Di is larger than a predetermined value DENSITY2. If the value is larger than the predetermined value, the process proceeds to the high-density binarization determination unit 6.

The grayscale image data value Di is DEN
If so-called intermediate density exists between SITY1 and DENSITY2, the process proceeds to the binarization determination unit 7 for intermediate density.

Next, the error values generated by each binarization judging section are accumulated in a predetermined area. This error value is used when the subsequent grayscale image data 40 is converted into halftone image data 48.

The above processing is sequentially performed on all the grayscale image data 40, so that the halftone image data 48
Will be completed.

Next, the contents of the binarization processing of the intermediate density portion will be described with reference to FIG.

FIG. 7A illustrates an error distribution matrix.

Indicates the position of the grayscale image data 40 to be converted into halftone image data, and 1/4, 1/8 of the peripheral pixels represent 1/4 or 1/4 of the error generated at each peripheral position. , 1/8 are added to the pixel of interest. That is, the value of the target pixel ★ is as follows.

DATi = Di + A1 * 1/8 + A2 * 1/8 + A3 * 1/4 + A4 * 1/8 + A5 * 1/8 + A6 * 1/4 or less The value obtained by adding the error value is represented as DAT.

Similarly, the subscript i means the i-th.

In this example, the error distribution matrix is shown in FIG.
Although shown in (A), the value inside may be another value or the size of the matrix may be changed.

FIG. 3B shows a flow of the binarization processing of the intermediate density portion. First, the magnitude relationship between DAT and the threshold is determined. In this case, when the DAT is equal to or larger than the threshold value 128, the pixel is determined to be black and the error value of the pixel is DAT-255
Becomes

When DAT is smaller than the threshold value 128, it is determined to be white, and the error value of the pixel is DAT.

As described above, the pixel of interest is processed by being affected by the error that occurs when the surrounding pixels are converted into halftone image data, and the error that occurs at that time also affects the pixels that are subsequently converted into halftone image data. You can see the basic characteristics.

Although the threshold value has been described as 128 here, it may be another value or may include some random number component.

Next, the contents of the low-density portion binarization processing will be described with reference to FIG.

As shown in FIG. 2, when the grayscale image data value Di is equal to or less than a predetermined value (DENSITY1), the binarization processing is shifted to the binarization determination unit 4 in the low density part. The predetermined value here corresponds to the density at which start-up delay (Hakiyose) and texture (earthworm image) which occur in the normal error diffusion processing described with reference to FIG. Although it depends on the type of error diffusion and the characteristics of the printing press, the present inventors consider that the 15th to 35th places (at 256 levels) are the corresponding predetermined values. In other words, if the value is equal to or less than the predetermined value, the image quality of the error diffusion is remarkably deteriorated.

In the transition from the error diffusion method to the dither matrix method, and conversely, in the transition from the dither matrix method to the error diffusion method, if the switching is performed as it is, the halftone image is disturbed at the switching section due to the discontinuity of the processing. It cannot be on a practical level. This can be easily guessed by a person experienced in image processing. The answer to the proposition of how to clear this discontinuity will be apparent from the description of FIG.

At low densities of less than DENSITY 1, the dither matrix method is used. The characteristic of the dither matrix method is a first key. When transitioning from the error diffusion processing to the dither matrix method, what kind of dither matrix method is effective for smooth switching is a dither matrix method having the characteristics of the error diffusion method. It is desirable that the dither matrix method has the characteristic of the error diffusion method at or near a change point where the process shifts from the error diffusion method processing to the dither matrix method.

Prior to the description of FIG. 4, a method of generating a dither matrix will be described with reference to FIG.

First, an error diffusion pattern at a density at which the error diffusion is switched to the dither matrix is generated.

For example, if error diffusion is used up to the density 32 and elements of the dither matrix are taken in at the density 31 or lower, an error diffusion pattern is generated at the density 32. It should be noted that the uniformity of the dither matrix is important as described above, so the error diffusion pattern obtained here must not have a "delayed start". It is desirable that the cycle of the data be as unobtrusive as possible.

For this purpose, an error diffusion pattern is generated in a space sufficiently larger than a required dither matrix size, and an error diffusion pattern in an area sufficiently distant from an end is obtained. Specifically, when a dither matrix having a size of 128 * 128 is created, an error diffusion pattern is generated with a size of about 200 * 200, and a central 128 * 12
8 is obtained.

It is not necessary to stick to the number 200 * 200. It is related to the density of the pattern to be obtained. According to the study of the present inventor, when a dither matrix having a density of 32 and a size of 128 * 128 is formed, 136 * 136 is sufficient in performance. In this example, a dither matrix pattern having a density of 32 is formed in this manner.

A method for obtaining a dither matrix pattern when the density is reduced will be described below.

128 * with 256 levels of density resolution
When a dither matrix pattern having a size of 128 is generated, the number of dots corresponding to each density (128 * 128/256 = 64) is normally 64.

In the dither matrix having the density of 32, 6
There should be 4 * 32 = 2048 dots.
If 64 dots are deleted from this, a dither matrix having a density of 31 is generated. If 64 dots are deleted therefrom, a dither matrix having a density of 30 is generated.

As a method of deleting dots, it is important to thin out dots as uniformly as possible.

Here, the present inventor defined three values of dither matrix density functions (1), (2) and (3) and used them as criteria for judging dot uniformity (non-uniformity).

First, the concept of the density function (1) is as follows.

When a dot is formed at a certain point, it is considered that a dot is formed at that point on behalf of a surrounding blank point. If there are many representative blank points, there will be many blanks around, and if it is small, dots will be dense.

Density function (1) Dp = ΣLi (i = 1 to n) where Dp: density function of a certain dot P (1) n: number of surrounding blank points representing a certain dot P L: certain Distance from dot P to surrounding blank points Also, if a certain blank point is equidistant to a plurality of dots, the distance is averaged. This is illustrated in FIG.

As is clear from the above description, if the density function (1) is large, a lot of blanks exist around the area, and if the density function (1) is small, the dots are dense. The density function (2) expresses the density of adjacent peripheral pixels.

This will be described with reference to FIG. The most influential when observing the dot density is the nearest neighboring pixels. Here, we extract the surrounding 12 pixels of the target pixel and, when there is a dot in it, calculate the sum of the surrounding 12 pixels by multiplying by a predetermined weighting coefficient shown in FIG. 9 to obtain the density function (2). And

As is clear from the above description, if the density function (2) is large, many dots exist very near the target pixel, and if it is small, the number of dots is small. This is illustrated in FIG.

The density function (3) represents the number of dots existing in a predetermined area around the target pixel. this is,
This function determines the density of the corresponding area when viewed macroscopically to some extent without considering the distance from the target pixel.

For example, a large printed matter is viewed by a person at a distance of 1 meter or more. In that case, the unit pixel is considered to be somewhat large. This is illustrated in FIG.
It is.

As shown in FIG. 10, the range to be counted by the image density is switched. If the center density is 128 or less, the density is counted for the black dot, and the center density 128
In the above, the density is counted for white dots.

If the density is extremely low, the dot does not exist unless the range for counting is expanded, and if it is too wide, the difference in density cannot be measured. According to the examination results of the present inventors, the values shown in FIG. 10 were judged to be good. However, the specific numbers themselves do not limit the content of the present invention.

In FIG. 7, the aforementioned density function (1),
By using (2) and (3), a dot to be deleted for determining the next density dither matrix is selected.

First, the dot with the highest density is found using the density function (1).

If there is only one candidate, it is determined as a dot to be deleted. If there are multiple candidates,
The dot with the highest density is found using the density function (2). When the number of candidates is reduced to one, it is determined as a dot to be deleted.

When there are a plurality of candidates, a dot having the highest density is found using the density function (3).

If there are still a plurality of candidates, they may be selected randomly or in the order in which they are found, based on the study results of the present inventors.

In this procedure, the first 64 deleted data correspond to the next density dither matrix.

Here, the determination is made by sequentially using the density functions (1) to (3), but the determination may be made by integrating two or three function values. However, such a method was adopted in order to save processing time.

In the same manner, the above operation may be repeated until there are no more dots.

With this method, a dither pattern having a density of 0 to 31 can be formed. In this case, the same value as the error diffusion threshold is set in a portion corresponding to the density 32 to the density 255. This is superior in maintaining continuity of processing.

The method of generating the dither matrix in the high density area can be realized by the same concept. In the low concentration part,
Although the processing has been focused on the uniformity of the dots (black points), the uniformity of the white points may be focused on the high density part.

For example, if the density 224 is processed by error diffusion and the elements of the dither matrix are added from the density 225 to the density 255, the values existing in the dither matrix are the value from the density 225 to the density 255 and the rest is the density 1
It can be understood that the same effect as in the low-density portion can be obtained by setting 28 (the same value as the threshold value of the error diffusion).

In FIG. 4, first, it is determined whether or not the density of the target pixel is included in the area where the error diffusion is switched to the dither matrix. In the above description, it has been described that the elements of the dither matrix are incorporated at the density of 31 or less by using error diffusion up to the density of 32.

For example, density 31 to density 25 correspond to this boundary area, and processing is divided from density 0 to density 24. In the boundary area, an area where the image processing is different at the density 23 between error diffusion and the dither matrix is extracted. This is Figure 6
Will be described. The gray scale image data density takes a value from 0 to 255 and a peripheral error value is added thereto. When converting to halftone image data, four patterns shown in FIG. 6 are generated by error diffusion and dither matrix. .

Pattern 1 is a case where dots are not formed in both the error diffusion and the dither matrix.

Pattern 2 is a case where dots are not formed by error diffusion but are formed by dither matrix.

Pattern 3 is a case where dots are not formed in the dither matrix but are formed in the error diffusion.

Pattern 4 is a case where dots are formed for both the error diffusion and the dither matrix.

The patterns 1 and 4 have no problem at all.
In the low density region, there is no pattern 3 and only the pattern 2 becomes a problem. This is clear from the above-described method of creating the dither matrix.

Therefore, what kind of processing is specifically performed when the target image is in the boundary region is that DAT (the sum of the image data and the error) is equal to or larger than the threshold value of the dither matrix and the error diffusion threshold is set. Value (in our example, 12
8) It is only necessary to find a pixel that is less than. In the error diffusion, the data in this area has a threshold of 12
No dot is printed because it is 8, but the density 31 in this example is used.
In the following areas, the threshold value is replaced with a dither matrix, and dots are formed. This is because of the discontinuity. If it is determined that the area is a discontinuous area, D
Subtract the correction value from AT.

If the pixel of interest is in the boundary area and not in the discontinuous area, the above dither matrix is compared with the size, and if it is large, it is determined to be black; if it is small, it is determined to be white.

When the calculation of the error is in the above-described discontinuous region,
Add the correction value subtracted from DAT and restore the original value. The part surrounded by a dotted line in the series of processing is the delay processing unit 37.

As described above, the error diffusion has a halftone image data processing delay characteristic that the conversion of the halftone image data is delayed until an error accumulates. On the contrary, the dither matrix has a straightforward halftone image data processing without any delay. It has the characteristic that the conversion process is performed.

This causes “image disturbance” in the switching section between the error diffusion method and the dither matrix method.

To solve this problem, a delay processing unit 37
Is effective. That is, in the discontinuous area portion (dots are not formed in error diffusion but dots are formed in the dither matrix), a correction value is subtracted from DAT, thereby making it difficult to form discontinuous dots. However, when the calculation of the error is in the discontinuous area, the correction value subtracted from the DAT is added to maintain the error preservation, and a delay effect is provided so that the error is hit when accumulated.

In FIG. 4, when the pixel of interest is not within the boundary area, that is, in the processing for the density 0 to the density 24,
The threshold value is the above-described dither matrix, and does not add an error from peripheral pixels. That is, the dither matrix processing itself. However, errors are accumulated.

In other words, even in the dither matrix area, the error of the white point is DAT, and the error of the black point is DAT.
-255.

In the dither matrix area, there may be a question as to why an unused error is calculated when the error amount is not added to be compared with the threshold value.

This may be considered in the case where the transition from the dither matrix area (low density area or high density area) to the error diffusion area (medium density area) is made. Information indicating where a black dot or a white dot is hit in the dither matrix can be transmitted to the error diffusion region (medium density portion) by error propagation, and thus a smooth transition can be achieved in terms of image.

Next, the contents of the binarization process for the high density portion in FIG. 2 will be described with reference to FIG.

Compared to FIG. 4, the difference is that when the target pixel is in the boundary area and in the discontinuous area, the DAT
And the point where the correction value added to DAT is subtracted and returned to the original value when the pixel is in the discontinuous area after the black determination.

In a high-density portion, white spots are difficult to strike by error diffusion. In order to cope with this characteristic, a correction value is added to DAT to increase the value. Conversely, the correction value is subtracted from the error value. The basic way of thinking is the same as that described with reference to FIG.

By performing such processing in the high density portion, the start delay (white spot) of white dots is eliminated, and the texture is also eliminated.

In FIG. 4 or FIG. 5, the calculated error is clamped to a predetermined value.

That is, in this example, the upper limit is limited to 128 and the lower limit is limited to -128.

This avoids that errors are accumulated too much. This leads to an argument about how many bits an error has when hardware is used. According to the study results of the present inventor, when the grayscale image data is 8 bits (256 steps), a good image can be obtained by clamping from +128 to −128.

FIG. 1 is a block diagram in the case where the above processing is implemented by hardware.

The gray scale image data 40 is input. The error values of the peripheral pixels are stored in the error accumulation RAM 41. The memory also writes the error when converting the halftone image data while reading the error.

The error accumulation RAM control unit 42 controls the error accumulation RAM 41. The addition / subtraction unit 43 adds the error value and the grayscale image data 40.

The threshold value RAM 44 stores the threshold value and the dither matrix of the error diffusion process. This only needs to be set once, and only reading is performed during the halftone image data conversion process.

The threshold value RAM control unit 45 controls the threshold value RAM 44.

Threshold and Grayscale Image Data 40
Alternatively, the comparing section 46 outputs the halftone image data 48 by comparing the values obtained by adding the errors (the above-described DAT), and the error calculating section 47 calculates the error value generated there. Usually, the hardware circuit 49 is configured by an integrated circuit such as a gate array.

In the above description, the grayscale image data value is an integer value from 0 to 255, and the halftone image data value is one of two values, 1 or 0. Even if the grayscale image data value has different density levels, for example, it is 10-bit data and has 1024 density gradations, or the halftone image data value has, for example, four different levels, It is clear that the invention works effectively.

[0121]

As described above, according to the present invention, the problems of the conventional dither matrix method and error diffusion method are solved, and both the dither matrix method and the error diffusion method are simply combined. It is possible to prevent the occurrence of an extremely unsightly defective image area due to discontinuity of processing near the switching point between the two methods, which occurs when converting grayscale image data to halftone image data, and to prevent the occurrence of low density portions. Extremely high-quality halftone image data that achieves black dot uniformity and white dot uniformity in high-density areas, and achieves a smooth image quality over the entire area of density at low cost. Was completed.

[Brief description of the drawings]

FIG. 1 is a block diagram of an embodiment of the present invention.

FIG. 2 is a flowchart of an embodiment of the present invention.

FIG. 3 is a processing flow of a medium density part binarization processing determination part;

FIG. 4 is a processing flow of a low-density part binarization processing determination part;

FIG. 5 is a processing flow of a high-density part binarization processing determination unit;

FIG. 6 is an explanatory diagram of a discontinuous image.

FIG. 7 is a flowchart of dither matrix generation.

FIG. 8 is an explanatory diagram of a density function (1).

FIG. 9 is an explanatory diagram of a density function (2).

FIG. 10 is an explanatory diagram of a density function (3).

[Explanation of symbols]

 40 Grayscale image data 48 Halftone image data

Claims (7)

[Claims] 1. An error diffusion method for converting grayscale image data into halftone image data by comparing a magnitude relationship with a predetermined threshold value, and distributing an error generated at the time of conversion to peripheral pixels, and grayscale image data. An image processing apparatus that converts grayscale image data into halftone image data using a dither matrix method of converting the grayscale image data into halftone image data by comparing the grayscale image data with a predetermined threshold matrix. Means for switching the halftone image data conversion means from the error diffusion method to the dither matrix method by comparing whether the density is smaller or larger than a predetermined value, and outputting the halftone image data of the error diffusion method and the dither matrix method Means for judging an area having a different halftone image data based on the area judgment result. Means for switching a process related to data conversion processing, and using a pattern generated according to a predetermined rule based on halftone image data generated by an error diffusion method at or near the switching density as a threshold matrix of a dither matrix method An image processing apparatus characterized by the above-mentioned. 2. The image processing apparatus according to claim 1, wherein
Using a dither matrix method in which periodicity is not noticeable in a low density portion where the density of the grayscale image data is smaller than a predetermined value, and in a high density portion where the density of the grayscale image data is larger than a predetermined value and larger than a separately determined value An image processing apparatus using a dither matrix method in which periodicity is inconspicuous, and using an error diffusion method in a middle density portion between the low density portion and the high density portion. 3. The image processing apparatus according to claim 1, wherein the dither matrix pattern generation means counts a peripheral pixel density function represented by a dither matrix value at each density. An image processing apparatus, comprising: means, second means for counting the density of a pixel of interest in a predetermined vicinity thereof, and third means for counting the number of dots existing within a predetermined radius. 4. The image processing apparatus according to claim 1, wherein a delay amount of the halftone image data conversion processing is switched based on an area determination result. 5. The image processing apparatus according to claim 1, wherein an error generation amount near a switching density is switched based on a region determination result. 6. The image processing apparatus according to claim 1, wherein an error value generated in the halftone image data processing is stored and stored even during the halftone image data processing by the dither matrix method. An image processing device characterized by propagating. 7. The image processing apparatus according to claim 1, wherein an error value generated in the halftone image data conversion process is clamped to a predetermined value for both an upper limit value and a lower limit value. Image processing apparatus.