JP2004304814A - Image processing unit and method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an image processing unit which can solve the problem of dot generation delays at the beginning of a low-density area or a high-density area or a lasting effect problem, after the low-density area or the high-density area, as well as can make a high-speed binarization image processing, without using a complex processing circuit or the like which does not generate side effects that lead to deterioration in the image quality. <P>SOLUTION: The image processing unit performs the binarization conversion of a multiple-tones image data into a double-tones image data, consisting of a first tone value and a second tone value (the first tone value < the second tone value), using an error diffusion method or an average error minimization method. In a predetermined multiple-tones image data region, the relation where the threshold for the binarization to be set for the tone value of the predetermined multiple-tone image data is smaller or equal to the threshold to be set for a tone value greater than the predetermined tone value is true. The binarization method 36 performs the binarization processing, after adding random noise to the set threshold or the corrected pixel data. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

  The present invention relates to an image processing apparatus for converting and outputting multi-tone image data into two-tone image data capable of displaying half-tone images, and in particular, to multi-tone image data using an error diffusion method or an average error diffusion minimum method. The present invention relates to improvements in an image processing apparatus and method for converting and outputting displayable two-tone image data.

  Conventionally, multi-tone image data read using an image input such as a scanner, or multi-tone graphic image data calculated using a computer, for example, reproduced and displayed using a display or a printer, or 2. Description of the Related Art Reproduction and display are performed using a facsimile, a digital copying machine, or the like.

  At this time, there is no problem when using an image output device capable of reproducing and displaying multi-gradation image data, but for example, when a printer device or a display device that cannot perform gradation control in dot units is used. It is necessary to perform a binarization process for reducing the gradation of each pixel to two gradations.

  Further, when the data capacity is to be reduced in order to store or transfer the multi-tone image data, a binarization process is similarly performed to reduce the number of tones of each pixel to two. Is widely practiced.

  As described above, there are various methods for binarizing multi-tone image data. Among them, the error diffusion method and the equivalent average error minimization method which are the most excellent in image quality are widely used. The error diffusion method and the average error minimum method have an excellent feature that continuous gradation control is possible while having high resolution.

According to the error diffusion method, a quantization error generated at the time of binarization of a certain pixel is diffused and added to surrounding pixels that have not been binarized. On the other hand, the average error minimization method corrects the data value of the next pixel of interest with a weighted average value of quantization errors generated in neighboring binarized pixels. The error diffusion method and the average error minimization method are only logically different when the error diffusion operation is performed, and are logically equivalent. As an example using the error diffusion method, there is “Image Processing Method and Apparatus” of JP-A-1-284173.
JP-A-1-284173

  However, the conventional image processing apparatus using the error diffusion method or the average error minimum method has the following problems when converting multi-gradation image data into two-gradation image data.

[First problem]
The generation of black dots at the rising portion of a low-density region (region with sparse black dots) and the generation of white dots at the rising portion of a high-density region (region with sparse white dots) are greatly delayed. As a result, In the worst case, there is a problem that an image may be deformed.

[Second problem]
Further, even after the low-density area or the high-density area ends, abnormal error diffusion to peripheral pixels remains, and image data following the low-density area is on the high-density side, and image data following the high-density area is on the low-density side. There is a problem that a phenomenon called "tailing" that is distorted toward the density side occurs.

  Considering the first problem in the case of a two-tone printer device that prints black ink dots on white paper, the black dots become larger due to ink bleeding, while the white dots become larger than the surrounding black dots. The first problem is particularly conspicuous in low-density areas, because it is crushed by blurring and becomes less noticeable.

  The first problem and the second problem will be described in more detail with reference to the drawings.

  FIG. 13 is an original image 100 displayed using multiple gradations. In the original image 100, a square low-density area 120 having a density gradation value 3 (0 is the lowest density) exists in a square high-density area 110 having a density gradation value 252 (255 is the highest value). Further, a straight line 130 having a 45-degree gradient of the density gradation value 231 (slightly lower than the background density 252) is drawn at the lower right of the low-density area 120.

  FIG. 14A shows an image obtained by binarizing the multi-tone image data of the original image 100 shown in FIG. 13 by the conventional technique using the error diffusion method. Note that FIG. 14B is a schematic diagram showing the location of FIG. 14A. In this binarized image, a binarization operation is performed in which the upper left corner pixel of the original image 100 is used as a binarization start point, binarized by one line in the right direction, and then moved to the left end of the line one pixel below. It was obtained repeatedly. On the other hand, FIG. 12 shows an output example when the same original image 100 is ideally binarized, and is obtained by the image processing apparatus of the present invention.

  Comparing the two, in FIG. 14, the generation of white dots is delayed in the upper side and the left side area 140 (see FIG. 14B) of the square high-density area 110, and the upper side and the square of the square low-density area 120 are further reduced. In the region 142 on the left side, the generation of black dots is delayed. That is, the first problem described above occurs in the regions 140 and 142.

  Further, a part 132 of the straight line 130 has disappeared in the lower right region 150 due to the trailing effect of the low-density region 120 having the square shape of the gradation value 3. As described above, the second problem described above occurs in the region 150.

  In order to solve such first and second problems, an "image processing apparatus" according to Japanese Patent Application Laid-Open No. 1-130945 has been proposed.

  In the first embodiment described in this proposal, when handling input density data of 256 gradations from 0 to 255, when the input density data value is 1 or more and 29 or less, the threshold value is as follows. It changes randomly.

  When the input data is 1-4, the threshold is randomly changed in the range of 20-230, when the input data is 5-14, 50-200, and when the input data is 15-29, the threshold is changed in the range of 100-150. . The width of the random noise is plus or minus 105 when the data is 1 to 4, plus or minus 75 when the data is 5 to 14, and plus or minus 25 when the data is 15 to 29. The result is that the closer the low density area is, the more noise is added. However, the expected values of the thresholds are all constant at 125.

  In this case, the threshold value becomes very small due to a large threshold noise when the density is low. For this reason, even at the rising portion of the low-density area, a pixel which is binarized to the 255 side occurs, and the delay of dot generation is improved. However, if this method is used to improve the first problem, the low-density area becomes a very noisy and low-quality image. Further, in this conventional example, a special mechanism called a determination circuit is provided, and using this determination circuit, the binarization result of the binarized pixel around the target pixel is checked. A determination process is performed so that a pixel is not binarized with a dot. This seems to be to improve the above problem a little. However, for that purpose, a complicated judgment process is required in which the judgment circuit refers to the binarization results of many pixels, that is, 12 pixels in the vicinity, and it takes a long processing time and the image quality is not yet sufficient. There was a problem. In addition, the conventional technique has not sufficiently improved the second problem.

  Further, in “Other Embodiment 1” described in the related art, the equation defining the signal 410 is different from FIG. 1 showing the example, and it is difficult to accurately understand the equation. However, since this prior art states that "it is possible to provide a function of threshold setting similar to that of the above-described embodiment and to reduce the hardware scale", as in the first embodiment, It is considered that a large amount of noise is added to the threshold value in the low density region. Therefore, the “other embodiment 1” described in the conventional example has the same problem as the “first embodiment”.

  Further, as another method for improving the first and second problems, an "image processing apparatus" disclosed in JP-A-3-112269 and an "image forming apparatus" disclosed in JP-A-4-126644 are disclosed. And other proposals. In these conventional techniques, an average density value is estimated by referring to a binarization result of a plurality of pixels in the vicinity of a target pixel, and the target pixel is binarized using the average density value as a threshold value.

  However, these conventional techniques have the following problems (1) and (2).

  (1) It is necessary to refer to the binarization results of as many as 10 or more peripheral pixels, and there is a problem that processing time is required and a complicated processing circuit is required.

  (2) Further, in an edge portion where data changes rapidly, it is not appropriate to use an average density value of peripheral pixels, and as a result, inappropriate binarization is performed and noise occurs. There was a problem.

  The present invention has been made in view of such a conventional problem, and its object is to end the low-density area, the delay of dot generation at the rising portion of the high-density area, the low-density area, and the high-density area. In addition to solving the problem of trailing, there is no side effect that leads to image quality degradation, and multi-tone image data can be processed at high speed into two-tone image data without using complicated processing circuits. An image processing apparatus and method are provided.

(1) In order to achieve the above goal, the present invention provides:
The multi-tone image data is converted into two-tone image data including a first tone value and a second tone value (first tone value <second tone value) by using an error diffusion method or an average error minimum method. In an image processing apparatus for performing binarization conversion to
Error correction means for correcting the multi-tone image data of the pixel of interest by adding an error diffused from neighboring binarized pixels, and outputting as corrected pixel data;
Threshold setting means for setting a different threshold value for binarization in accordance with the gradation value of the multi-gradation image data of the pixel of interest;
A binarizing unit that binarizes and converts the corrected pixel data into the binary image data based on the set threshold value;
Has,
In a predetermined multi-tone image data area, a threshold value set for a tone value of the predetermined multi-tone image data is a threshold set for a tone value larger than the predetermined tone value. The relationship below the value holds,
The binarizing means includes:
A binarization process is performed after adding random noise to the set threshold value or the corrected pixel data.

The area of the predetermined multi-tone image data is
The range can be substantially the entire range from the first gradation value to the second gradation value.

The area of the predetermined multi-tone image data is
At least one of the vicinity of the first gradation value and the vicinity of the second gradation value can be set.

Assuming that an intermediate value between the first gradation value and the second gradation value is m,
The area of the predetermined multi-tone image data is near the first tone value, and a tone value larger than the predetermined tone value is near m, or
The predetermined multi-tone image data area is near the m, and a tone value larger than the predetermined tone value is near the second tone value.
Can be substantially satisfied.

(2) Further, according to the present invention, the multi-gradation image data is converted into a first gradation value and a second gradation value (first gradation value <second gradation value) using an error diffusion method or an average error minimization method. Image processing apparatus for binarizing and converting into binary image data consisting of
Error correction means for correcting the multi-tone image data of the pixel of interest by adding an error diffused from neighboring binarized pixels, and outputting as corrected pixel data;
Threshold setting means for setting a different threshold value for binarization in accordance with the gradation value of the multi-gradation image data of the pixel of interest;
A binarizing unit that binarizes and converts the corrected pixel data into the binary image data based on the set threshold value;
Where data is a gradation value of multi-gradation image data of the pixel of interest, m is an intermediate value between the first and second gradation values, and slsh is the threshold value.
The threshold setting means,
If data is a value near the first gradation value, data ≦ slsh ≦ (m + data) / 2
When data is a value near the second gradation value, (m + data) / 2 ≦ slsh ≦ data
Set the threshold slsh to satisfy any of
The binarizing means includes:
A binarization process is performed after adding random noise to the set threshold value or the corrected pixel data.

The threshold setting means,
slsh = (data * (K-1) + m) / K (where K is a constant represented by an integer of 2 or more)
The threshold slsh can be set to satisfy

The threshold setting means,
The threshold value slsh can be set stepwise according to the value of the gradation value data.

The threshold setting means,
If data <m−L1, slsh = data + L1
If m−L1 <data <m + L2, slsh = m
When m + L2 <data, slsh = data−L2
(However, L1 and L2 are constants represented by integers between 0 and m)
The threshold slsh can be set to satisfy

(3) Further, according to the present invention, the multi-gradation image data is converted into a first gradation value and a second gradation value (first gradation value <second gradation) by using an error diffusion method or an average error minimization method. Value) in the image processing method for binarizing and converting into binary gradation image data
An error correction step of correcting the multi-tone image data of the pixel of interest by adding an error diffused from neighboring binarized pixels and outputting the corrected pixel data;
A threshold setting step of setting a threshold for binarization;
A binarizing step of binarizing and converting the corrected pixel data into the binary image data based on the set threshold value;
Wherein the threshold value set in the threshold value setting step for the gradation value of the predetermined multi-tone image data is a threshold value set for a gradation value larger than the predetermined gradation value. Lower relationship holds in a given multi-tone image data area,
In the binarization step,
A binarization process is performed after adding random noise to the set threshold value or the corrected pixel data.

(4) Further, according to the present invention, the multi-gradation image data is converted into a first gradation value and a second gradation value (first gradation value <second gradation) by using an error diffusion method or an average error minimization method. Value) in the image processing method for binarizing and converting into binary gradation image data
An error correction step of correcting the multi-tone image data of the pixel of interest by adding an error diffused from neighboring binarized pixels and outputting the corrected pixel data;
A threshold setting step of setting a threshold for binarization;
A binarizing step of binarizing and converting the corrected pixel data into the binary image data based on the set threshold value;
Where the grayscale value of the multi-grayscale image data of the target pixel is data, the intermediate value between the first grayscale value and the second grayscale value is m, and the threshold is slsh,
In the threshold setting step,
determining whether data is a value near the first tone value or a value near the second tone value,
If data is a value near the first gradation value, data ≦ slsh ≦ (m + data) / 2
When data is a value near the second gradation value, (m + data) / 2 ≦ slsh ≦ data
Set the threshold slsh to satisfy
In the binarization step,
A binarization process is performed after adding random noise to the set threshold value or the corrected pixel data.

(1) In the present embodiment,
The multi-tone image data is converted into two-tone image data including a first tone value and a second tone value (first tone value <second tone value) by using an error diffusion method or an average error minimum method. In an image processing device that converts and outputs
Error correction means for correcting the multi-tone image data of the pixel of interest by adding an error diffused from neighboring pixels that have already been binarized, and outputting the corrected pixel data;
Threshold value setting means for setting a binarization threshold value based on a gradation value of the multi-gradation image data of the pixel of interest;
A binarizing unit configured to convert and output the corrected pixel data to the two-tone image data based on the set threshold value;
Including
If the gradation value of the multi-gradation image data of the pixel of interest is data, the intermediate value between the first gradation value and the second gradation value is m, and the threshold value is slsh,
The threshold setting means,
The binarization threshold value slsh is set so as to satisfy at least one of an allowable range shown in the following equation according to the gradation value data of the multi-gradation image data of the target pixel.

If data is a value near the first gradation value, data ≦ slsh ≦ (m + data) / 2
When data is a value near the second gradation value, (m + data) / 2 ≦ slsh ≦ data
Here, the threshold value setting means includes:
When the gradation value data of the multi-gradation image data of the target pixel is a value near the first gradation value and the second gradation value, the binarization threshold slsh is set to the allowable range. It can be formed as follows.

Further, if necessary, the threshold value setting means includes:
When the gradation value data of the multi-gradation image data of the target pixel is a value near the first gradation value or the second gradation value, the binarization threshold value slsh is set to the allowable range. It can also be formed as follows.

Further, the threshold value setting means includes:
Based on the value of the gradation value data of the multi-gradation image data of the target pixel, the binarization threshold value slsh can be set according to the following equation.

slsh = (data * (K-1) + m) / K
Here, K is a constant represented by an integer of 2 or more.

Further, the threshold value setting means includes:
The binarization threshold value slsh can be set stepwise according to the value of the gradation value data of the multi-gradation image data of the target pixel.

Further, the threshold value setting means includes:
Based on the value of the gradation value data of the multi-gradation image data of the target pixel, the binarization threshold slsh can be set according to the following equation.

When data <m−L1, slsh = data + L1
When m−L1 <data <m + L2, slsh = m
When m + L2 <data, slsh = data−L2
Here, L2 and L2 are constants represented by integers between 0 and m.

Further, the binarization means includes:
It is also possible to add random noise to the set threshold value or the corrected pixel data, and perform the binarization process on the corrected pixel data.

Further, the error correction means includes:
A diffusion error storage unit that stores a diffusion error integrated value for each pixel;
Calculating a binarization error between the corrected pixel data and the binarization result, and performing an operation of distributing and distributing the binarization error to non-binarized pixels in the vicinity of the pixel of interest using an error diffusion method; To the diffusion error integrated value for each pixel stored in the error storage unit, a diffusion error from the pixel of interest is added to obtain a new diffusion error integrated value, and for each pixel stored in the diffusion error storage unit. An error diffusion unit that updates a diffusion error integrated value;
A data correction unit that adds the multi-tone image data of the pixel of interest and the diffusion error integrated value of the pixel of interest stored in the diffusion error storage unit, and calculates the corrected pixel data;
And it is also possible to correct the multi-tone image data of the target pixel by using the error diffusion method and output the corrected pixel data as corrected pixel data.

Further, the error correction means includes:
An error storage unit that stores an error for each pixel;
An error diffusion unit that calculates a binarization error between the corrected pixel data and the binarization result, and stores the binarization error in the error storage unit.
An average error is obtained by reading a binarization error of a pixel around the pixel of interest stored in the error storage unit and performing a predetermined weighting, and adding this average error to multi-tone image data of the pixel of interest. A data correction unit for calculating correction pixel data,
And it is also possible to correct the multi-tone image data of the target pixel by using the average error minimum method and output the corrected pixel data.

  In the present embodiment, multi-tone image data is input to an error correction unit and a threshold value determination unit.

  The error correction unit corrects the multi-gradation data of the target pixel by adding an error diffused from the neighboring binarized pixels, and outputs the corrected pixel data.

  The threshold value setting means sets a binarization threshold value based on the gradation of the multi-gradation image data of the target pixel. At this time, the gradation value data of the multi-gradation image data of the target pixel is changed to the first gradation value and the second gradation value (first gradation value <second gradation value) of the binarized image. ), The binarization threshold value slsh is set within an allowable range represented by the following equation.

When data is a value near the first gradation value, data ≦ slsh ≦ (m + data) / 2
When data is a value near the second gradation value, (m + data) / 2 ≦ slsh ≦ data
The binarization setting means converts the corrected pixel data into two-gradation image data composed of a first gradation value and a second gradation value capable of halftone display based on the set threshold value. I do.

  As described above, in the present embodiment, when the multi-tone image data value is small, the threshold value is small, and when the image data value is large, the threshold value of the multi-tone image data of the pixel of interest is increased. By optimizing the binarization threshold value according to the tonal value, accumulation of errors generated due to binarization can be eliminated, and dot generation of the target pixel can be performed satisfactorily.

  Further, when the binarization processing is performed using the error diffusion method or the average error minimum method as in the present embodiment, the output density as a whole hardly changes even if the binarization threshold is changed. It was confirmed that.

  As described above, according to the present embodiment, it is possible to eliminate the phenomenon that a large amount of error is accumulated in the low-density region or the high-density region. Alternatively, it is possible to fundamentally solve problems such as delay of dot generation at a rising portion of a high-density area and tailing after the end of a low-density area or a high-density area without a side effect of deteriorating image quality.

  That is, according to the present embodiment, the multi-tone image data is corrected using the error diffusion method or the average error minimization method, and furthermore, based on the tone value of the tone value image data of the target pixel, By providing the threshold setting means for optimizing the threshold, the phenomenon that a large amount of error is accumulated in the low-density region or the high-density region can be eliminated, and the phenomenon caused by the accumulation can be solved. In addition, problems such as delay in dot generation at the rising edge of low-density and high-density areas and “tailing” after the end of low-density and high-density areas are fundamentally eliminated without the side effects that lead to image quality degradation. Thus, there is an effect that an image processing apparatus that can be eliminated can be obtained.

  Further, according to the present embodiment, the threshold value setting means can be realized with a simple configuration such as performing a simple calculation or referring to a conversion table, and thus does not require a complicated processing circuit and has a high speed. And good image processing can be performed.

  Further, in the present embodiment, the dot generation speed can be adjusted by increasing or decreasing the threshold value set by the threshold value setting means. A secondary effect is obtained in that a significant effect can be expected.

  Next, preferred embodiments of the present invention will be described in detail with reference to the drawings.

[Description of the entire system]
FIG. 1 schematically shows a system using an image processing apparatus according to the present invention.

  The multi-tone image data 200 of the original image output from the tone image data output device 10 is input to the image processing device 30.

  The image processing device 30 converts the input multi-gradation image data 200 of the original image into two gradations that can be output by the binary image output device 20 and outputs it. That is, the multi-tone image data 200 is corrected using the error diffusion method or the average error minimization method, and is converted into the two-tone image data 230 including only the first tone value and the second tone value that can be displayed in a halftone. Convert and output.

  The binary image output device 20 reproduces and outputs an original image based on the two-tone image data 230 output from the image processing device 30.

  In the present embodiment, the gradation image data output device 10 is formed using a computer. The computer is configured to output the multi-tone image data 200 stored in a hard disk or the like to the image processing device 30. The multi-tone image data 200 is represented as density data of 256 tones from 0 to 255. The gradation image data output device 10 may be formed so as to output, for example, multi-gradation image data of a computer graphic. May be used.

  Then, the image processing device 30 of the embodiment converts the input 256-gradation image data into two-gradation image data 230 consisting of only 0 (white) or 255 (black) by the error diffusion method.

  Further, the binary image output device 20 of the embodiment is formed by using a printer that cannot perform gradation control in units of pixels, and reproduces an original image to be capable of displaying halftones based on input two-gradation image data 230. Output. The binary image output device 20 may use a display, a facsimile machine, a digital copying machine, or the like, if necessary, other than the printer.

[Specific example of system]
In this embodiment, the image processing device 30 may be formed separately from the gradation image data output device 10 or the binary image output device 20, but may be integrated with these devices 10 and 20 as necessary. It may be formed.

  For example, as shown in FIG. 15, when the host computer 12 is used as the gradation image data output device 10 and the printer 20 is used as the binary image output device 20, the image processing device 30 of the present embodiment is replaced with the printer 22. It can be formed by being integrated into the inside. In this case, the printer 22 includes the data input unit 24 to which the multi-tone image data 200 output from the host computer 12 is input, the image processing device 30 of the present embodiment, and the binarized dot printing unit 26. It is comprised including.

  Further, as shown in FIG. 16, the image processing apparatus 30 according to the present embodiment may be formed integrally with the host computer 12. In this case, the host computer 12 is configured to include a gradation image file reading unit 14, a printer driver 16, and a data output unit 18. Then, the printer driver 16 receives the multi-gradation image data 200 from the gradation image file reading means 14, and outputs a printer control command based on the output of the image processing device 30. And a printer control command generating means 16a for generating, and is configured to control the printer 22 based on the printer control command.

  As shown in FIG. 17, when a scanner 50 is used as the gradation image data output device 10 and the multi-gradation image data read by the scanner 50 is output to the host computer 60 as binary data, the scanner 50 In this case, the image processing apparatus 30 of the present embodiment may be integrally incorporated. In this case, the scanner 50 includes a gradation image data reading unit 52 that optically reads an image, and an image processing apparatus according to the present embodiment that outputs the read multi-gradation image data 200 as two-gradation image data 230. 30 and a binarized data output unit 54 that outputs the output binary grayscale image data 230 to the host computer.

  The image processing apparatus according to the present embodiment can be formed integrally with other apparatuses as necessary.

  For convenience of description, in the following description, as shown in FIG. 1, the image processing device 30 of the present embodiment is formed separately from the gradation image data output device 10 and the binary image data output device 20. The description will be given.

[Image processing device]
FIG. 2 shows a functional block diagram of the image processing device 30.

  The image processing apparatus 30 according to the embodiment includes an optimum threshold value setting unit 32, an error correction unit 34, and a binarization unit 36.

  The data (i, j) of the pixel P [i, j] of the i-th row and the j-th column is input to the optimum threshold value setting means 32 and the error correction means 34 as the multi-tone image data 200 of the target pixel. Have been.

  The optimum threshold value setting means 32 converts slsh (i, j) used for binarizing the multi-tone image data 200 of the target pixel P [i, j] into the multi-tone image data of the target pixel. Set according to the following equation according to data (i, j).

slsh (i, j) = (data (i, j) * (K-1) +128) / K (1)
Here, K is a constant represented by an integer of 2 or more.

  The error correction means 34 corrects the multi-tone image data data (i, j) of the target pixel P [i, j] using an error diffusion method based on a binarization error generated by binarization of peripheral pixels. Then, the corrected pixel data is output to the binarizing unit 36 as corrected pixel data data c (i, j).

  The binarizing means 36 binarizes the input corrected pixel data data c (i, j) of the target pixel P [i, j] by comparing it with a threshold value slsh (i, j). The binarization result result (i, j) is output as two-tone image data 230. That is, the correction pixel data is binarized and output as follows.

If data c (i, j) ≧ slsh (i, j), result (i, j) = 255
If data c (i, j) <slsh (i, j), result (i, j) = 0 (2)
The error correction means 34 of the embodiment includes a data correction means 38, an error diffusion means 40, and a diffusion error storage means 42.

The diffusion error storage means 42 stores a diffusion error integrated value total err for each pixel of the original image.
(N, m) is stored.

Then, the error diffusion means 40 first calculates a binarization error err based on the binarization result result (i, j) and the correction data data c (i, j).
err (i, j) = data c (i, j) −result (i, j) (3)
Ask as follows. Next, the binarization error err (i, j) is distributed and diffused to neighboring non-binarized pixels P [m, n] (P [i, j + 1], P [i + 1, j], etc.). Specifically, the diffusion error from the target pixel P [i, j] is added to the diffusion error integrated value total err (n, m) for each pixel stored in the diffusion error storage means 40. . It is assumed that an error diffusion weight matrix as shown in FIG. * In FIG. 3 indicates a target pixel. Since the total value of the weights is 16, the value obtained by multiplying the binarization error at the pixel of interest by the weight value corresponding to the pixel position to be distributed and dividing the result by 16 as follows is total err (m, n) ).

total err (i, j + 1) = total err (i, j + 1) + err (i, j) * 3/16
total err (i, j + 2) = total err (i, j + 2) + err (i, j) / 16
total err (i + 1, j-2) = total err (i + 1, j-2) + err (i, j) / 16
total err (i + 1, j-1) = total err (i + 1, j-1) + err (i, j) * 2/16
total err (i + 1, j) = total err (i + 1, j) + err (i, j) * 3/16
total err (i + 1, j + 1) = total err (i + 1, j + 1) + err (i, j) * 2/16
total err (i + 1, j + 2) = total err (i + 1, j + 2) + err (i, j) / 16
total err (i + 2, j-1) = total err (i + 2, j-1) + err (i, j) / 16
total err (i + 2, j) = total err (i + 2, j) + err (i, j) / 16
total err (i + 2, j + 1) = total err (i + 2, j + 1) + err (i, j) / 16… (4)
The error diffusion accompanying the binarization of the target pixel P [i, j] is completed by the above steps.

  The above steps are repeated each time a binarization result is output from the binarization means 36. In addition, as examples of the weight matrix of the error diffusion method, various types such as, for example, FIGS. 3B and 3C can be adopted as needed.

When the multi-gradation image data data (i, j) of the target pixel P [i, j] is input, the data correction unit 38 calculates the diffusion error integrated value corresponding to the target pixel P [i, j]. The total err (i, j) is read from the diffusion error storage means 42, and is added to the multi-gradation image data data (i, j) of the pixel of interest based on the following equation to obtain corrected image data data
Find c (i, j).

data c (i, j) = data (i, j) + total err (i, j)… (5)
By repeating such an operation for all pixels, binarization of the entire screen is performed.

[Solution of first and second problems]
In this way, the image processing apparatus 30 of the embodiment converts the input multi-tone image data 200 of the target pixel from only the 0 tone values and the 255 tone values that can be displayed in a half tone using the error diffusion method. Is converted and output into two-tone image data 230.

  The feature of the image processing apparatus 30 of the present embodiment is that the large amount of binarization errors accumulated in the low-density region or the high-density region is eliminated by using the optimum threshold value setting means 32. It is an object of the present invention to solve problems such as delay of dot generation at a rising portion of a low-density area or a high-density area and tailing after the end of a low-density area or a high-density area.

  Hereinafter, the reason why both the first problem (the delay of dot generation) and the second problem (the problem of tailing) described above are solved by the image processing apparatus of this embodiment will be described.

  The inventor first clarified the causes of the first and second problems. For this reason, in the image processing device 30 shown in FIG. 2, the binarization threshold value set by the optimum threshold value setting means 32 is fixed at 128, and all the pixels have a constant gradation value as the image data 200. Entered such image data. Then, it was examined how the average value of the binarization error err (i, j) added to the original image data in the above equation (5).

  Specifically, the optimum threshold value setting means 32 was removed from the image processing apparatus of the embodiment shown in FIG. 2, and the binarization threshold value was almost fixed at 128. Then, as shown in FIG. 4, an original image 160 having an original image size of 600 × 400 pixels and all pixels having a constant gradation value was subjected to a binarization process with the upper left corner as a starting point. Then, for a region 170 of 200 pixels × 100 pixels at the lower right corner where it is considered that dot formation has reached a stable state, the binarization error err (i, j) added to the original image data by the above equation (5) is obtained. The average was determined as the average binarization error. However, the threshold value is not completely fixed at 128, and a small amount of random noise in the range of plus or minus 6 is added for the purpose of avoiding the occurrence of a specific regular pattern. This noise is added in order to prevent a pattern from occurring regularly when the original image data is artificial data generated by a computer as in this example.

  FIG. 5 shows the results of the above experiment performed on the original images 160 having different gradation values. As is clear from the experimental results, the average binarization error of the original image 160 is low when the tone value is as low as 1 to 4 or when the tone value is as high as 251 to 254. It can be seen that, instead of being zero, the absolute value is as large as 100. The average binarization error corresponds to the expected value of the amount of error diffusion and accumulation in a steady state. Since the error diffusion method is considered to be a method of minimizing the local average value of the binarization error, the average binarization error is not zero in a low density region or a high density region. Taking a large value was a very interesting finding.

  From the experimental data shown in FIG. 5, the mechanism that causes the first and second problems described above can be analyzed as follows.

  (1) When the gradation value of the original image 160 takes a value near 0 or 255, such as 1 to 8, or 247 to 254, if the binarization threshold is fixed at 128, the dot becomes A large amount of error accumulation needs to be performed so that the absolute value of the average binarization error reaches 80 or more before reaching a stable and steady state. In particular, in the original image data 160 having a gradation value of 1 to 4 or 251 to 254, a large amount of error of 100 or more needs to be accumulated before the dot settles in a steady state in which dots are formed stably. . The error accumulation amount increases as the density value of the multi-tone image data 200 approaches the binarized density values 0 and 255.

(2) Accumulation time of error When the density of the image data is a value near 0, even if it is binarized to 0, only a small binarization error occurs. Therefore, a considerable accumulation period is required until the binarization error diffuses and accumulates to reach a value of about 80 to 100. Even when the density of the image data is a value near 255, a considerable accumulation period is similarly required until the binarization error diffuses and accumulates and reaches a value around 80 to 100. . Moreover, the accumulation speed becomes slower as the density value of the image data approaches the binarized density values of 0 and 255.

(3) First Problem Dots are not formed during the accumulation period in which errors accumulate and reach the accumulation amount in a steady state. For this reason, if a large amount of error accumulation is required for dot formation, and a considerable accumulation period is required until the required error accumulation amount is reached, dot generation will be delayed. . This causes the first problem.

(4) Second Problem If a large amount of error accumulation is required for dot formation, a large amount of accumulated error is diffused to the outside of the area to distort the surrounding image data. This causes the second problem, and the above-described problem of tailing occurs.

  As described above, as described in (1) to (4), when the image data takes values near the binarized density values 0 and 255, the average binarization error takes an extremely large absolute value, The "accumulation of error" phenomenon occurs. This "accumulation of errors" phenomenon is the cause of the first and second problems.

  In the image processing apparatus of the present embodiment, the optimum threshold value setting means 32 solves the root cause of "accumulation of errors" and essentially solves the first and second problems. That is, when the original image data 200 has low density, the binarization threshold value is small, and when the original image data 200 has high density, the threshold value is increased. This makes it possible to perform dot generation without “accumulation of errors” while eliminating “accumulation of errors” itself.

  Here, "If the threshold value in the low-density region is reduced arbitrarily, may the number of pixels binarized to 255 (black dots) increase, resulting in a significant increase in density?" Such a question may arise. Certainly, in a general dither method in which error diffusion is not performed, a change in the threshold immediately leads to a change in density. However, the present inventor has confirmed that in the error diffusion method, the total output density hardly fluctuated even if the threshold value was changed. That is, the output density hardly changes between the case where the threshold is fixed at 128 and binarization and the case where the threshold is fixed at 64 or 192. This is because in the error diffusion method, the binarization error is diffused to surrounding non-binarized pixels without being discarded. For example, even if a pixel that was previously binarized to 0 due to a reduced threshold value is binarized to 255, the pixel has a negative binarization error having a larger absolute value. Occurs. It is diffused to the peripheral non-binarized pixels, and works in the direction of lowering the peripheral pixel gradation level to adjust the balance.

  FIG. 6 shows the effect of eliminating “accumulation of errors” according to this embodiment. That is, FIG. 6 clarifies how the average binarization error examined in the same manner as in FIG. 5 when the optimum threshold value setting means 32 of the present embodiment is used. In addition to the case where the threshold value shown in FIG. 5 is fixed at 128, the result when the value of K in the equation (1) of the present embodiment is set to 2, 4, 8, and ∞ is plotted. If K = ∞, slsh = data. However, in the case of FIG. 6, as in the case of FIG. 5, a small amount of random noise of at most ± 6 is added to the threshold value determined by the equation (1).

  According to FIG. 6, if K = 2, that is, slsh = (data + 128) / 2, the average binarization error is reduced to 50 or less and 50% or less at the maximum, and there is an effect of greatly reducing the accumulated amount of error. I understand. Further, when K = 4, the average binarization error approaches 0 as a whole, and when K = 8, the gradation value of the image data density is very close to 0 or 255, such as 1, 2, 253, 254. Even in this case, the average binarization error becomes almost zero.

Based on the two-tone image data 23 output from the image processing device 30 shown in FIG. 1, an experiment was performed to actually print using a printer and evaluate the effect of the “tailing” of the second problem described above. went. As a result of this evaluation experiment, it was confirmed that by suppressing the absolute value of the average binarization error to 50 or less, the influence of the “tailing” of the second problem can be significantly reduced. From this, by setting the value of K in the equation (1) to a range of K = 2 to ∞, that is, by setting the threshold value slsh in accordance with the gradation value of the image data data,
When data <128, data ≦ slsh ≦ (128 + data) / 2
When data> 128, the absolute value of the average binarization error can be reduced to 50 or less by setting the range of (128 + data) / 2 ≦ slsh ≦ data (6). Can be solved.

  Next, an experiment was conducted to evaluate the effect of the first problem, that is, the "dot generation delay". As a result of this experiment, it was confirmed that the dot generation delay was improved as the average binarization error was reduced. If K in the above equation (1) is too large, the average binarization error exceeds 0, and the "overcorrection state" occurs in which the sign is reversed. However, in the subjective evaluation of the actual print result, the higher the dot generation speed up to a slightly overcorrected state, the more the print emphasis effect was obtained, and the preferable image quality was obtained. According to the subjective evaluation result that emphasizes the reproducibility of the data area where the data value has the largest delay of dot generation such as 1, 2, 253, and 254, the range of about K = 8 to 24 is very good. It was optimal.

  FIG. 7 shows a binarization threshold value at which the average binarization error is always zero. The binarization threshold value was obtained as an estimated value by an interpolation operation based on FIG.

  If the optimum threshold value setting means 32 of the image processing apparatus according to the embodiment is formed so as to determine the binarization threshold value from the original image data based on FIG. 7, the "tailing" of the second problem can be solved. The best optimal threshold value setting means for which the expected value of becomes 0 can be realized. Note that FIG. 7 also shows a characteristic diagram when the value of K in Expression (1) is 2, 8, and ∞. From these characteristic curves, it can be seen that when the value of K is set to around K = 8, a suitable approximate value for setting the average binarization error to 0 can be obtained. Also, as described above, from the viewpoint of improving the dot generation delay, which is the first problem, the subjective evaluation is better in the "overcorrected state" where the average binary error exceeds 0 and the sign is reversed. May be obtained. In the subjective evaluation, the best result was obtained when an approximate expression of K = 16 was used. When K = ∞, the edge of the data change portion is considerably enhanced, but this is also sufficiently useful depending on the use purpose of the image.

  Further, in FIG. 7, the optimum threshold value when the original image data 200 is 0 or 255 is set to 128, but how the threshold value is set when the data gradation value and the binarization result value are equal is set. Even if there is no big difference. Therefore, even when the data value is 0 or 255, the optimum threshold value setting means 32 of this embodiment may set the threshold value in any manner.

  As described above, according to the present embodiment, the multi-tone image data 200 is converted into two-tone image data 230 capable of halftone display using the error diffusion method, and is used for the binarization processing. By setting the binarization threshold value in the range of the expression (6) based on the gradation value of the multi-gradation image data 200, the first and second problems can be solved without side effects that lead to image quality deterioration. Could be solved fundamentally.

  Moreover, according to the present embodiment, a binarized output image having desired characteristics can be obtained by setting the constant K in Expression (1).

  Note that the correction data shown in FIGS. 6 and 7 is an example in the case where error diffusion is performed using the error diffusion weight matrix shown in FIG. When different weight matrices are used, quantitatively slightly different results are obtained, but the qualitative tendency is almost the same. Thus, the present invention is effective even when different error diffusion weight matrices are used.

[Comparison between the present embodiment and the conventional example]
As in the present embodiment, an image processing apparatus and image processing method disclosed in Japanese Patent Application Laid-Open No. 4-154370 is one that changes the binarization threshold value according to the gradation value of the original image data 200. Was. However, this method has the main purpose of suppressing noise in the character / line image portion, and is the opposite of the present embodiment, such that "the larger the original image density data, the smaller the binarization threshold value is made to correspond". The threshold value was changed in the direction of, and there was almost no improvement effect on the first and second problems. It should be noted that Japanese Patent Application Laid-Open No. 4-154370 may be referred to for an example in which the average error minimum method is used for the binarization method.

  FIG. 12 is a diagram illustrating an output example when the original image 100 illustrated in FIG. 3 is formed using the image processing apparatus 30 according to the present embodiment illustrated in FIG. It can be seen that the problems such as the delay in dot generation and the disappearance of the center of the straight line 130 due to the tailing from the low density region 120, which were problems in FIG. 14, have been completely solved.

  As described above, according to the present embodiment, it was confirmed that the first and second problems can be ideally solved. Furthermore, according to the present example, it was confirmed that the first and second problems could be solved without any harmful side effects.

[Another Embodiment of Optimal Threshold Setting Means]
In the above embodiment, the optimal threshold value setting means 32 sets the binarization threshold value based on the equation (1). The present invention is not limited to this, and the binarization threshold may be set using another method as needed.

  FIG. 8 shows another embodiment of the optimum threshold value setting means 32.

  The optimum threshold value setting means 32 of this embodiment sets a threshold value as shown in the following equation based on data data [i, j] input as the multi-tone image data 200 of the target pixel. Is formed.

If 0 ≦ data (i, j) <128-L1, slsh (i, j) = data (i, j) + L1
If 128-L1 ≦ data (i, j) ≦ 128 + L2, slsh (i, j) = 128
If 128 + L2 <data (i, j) ≦ 255, slsh (i, j) = data (i, j) -L2 (7)
Here, L1 and L2 may be appropriate values of 0 to 64, but are optimal if they are in the range of 8 to 16. Note that L1 and L2 may be set to the same value. In the case of the present embodiment, when the data value is around 128, it is expressed by the equation (6).
When data <128, data ≦ slsh ≦ (128 + data) / 2
When data> 128, (128 + data) / 2 ≦ slsh ≦ data
However, the first and second problems to be solved by the present invention are particularly remarkable when data is a value near 0 or 255 (excluding 0 and 255). It is. Therefore, Expression (6) does not need to be satisfied in the entire data area, and Expression (6) only needs to be satisfied when data is 0 or a value near 255.

  Therefore, the first and second problems can be solved by setting the binarization threshold as shown in the equation (7), and the binarization threshold can be improved as in the first embodiment. Images can be obtained.

  FIG. 8 shows still another embodiment of the optimum threshold value setting means 32.

  The optimum threshold value setting means 32 of the embodiment sets the binarization threshold value stepwise, not continuously, according to the gradation value of the original image data 200. Even in this case, the first and second problems can be solved in the same manner as in the above embodiment, and a good binary image can be obtained.

  FIG. 10 shows still another embodiment of the optimum threshold value setting means 32. The optimum threshold value setting means 32 of the embodiment is formed so that the threshold value optimizing operation characteristic of the present invention operates only in the low density region. That is, when the threshold value of the image data 200 is on the high density area side, the binarization threshold value is fixed at 128, and when the image data 200 is near 0 on the low density area side, the optimum The threshold value is set so as to satisfy Expression (6).

  For example, in a printer device having a large amount of dot bleeding, isolated white dots in a high-density portion are almost completely crushed, so that the first problem does not appear remarkably in a high-density portion. Therefore, the present invention is intended to improve only the low-density portion where the first problem is conspicuous.

  It should be noted that, depending on the characteristics of the image output device, the threshold value optimization operation of the present invention may be performed only in the high-density portion region.

  As described above, the optimum threshold value setting means 32 of the present invention may be formed so as to set the optimum threshold value only in a necessary original image density area according to the image output device.

[Specific example of threshold setting means]
The optimum threshold value setting means 32 in each of the above-described embodiments may be formed so as to perform an operation such as the above-described formula (1) each time to set a binarization threshold value from original image data. Alternatively, the correspondence between the gradation value of the original image data and the binarization threshold may be stored in a conversion table in advance, and the conversion table may be referred to.

  FIG. 11 shows a specific example in which the optimum threshold value setting means 32 is configured using a ROM. When 8-bit original image data is input to the address buses A0 to A7 of the ROM, the corresponding 8-bit binarization threshold is output to the data buses D0 to D7.

  Further, in each embodiment, there is an example in which a small amount of random noise is added to the binarization threshold value set by the optimum threshold value setting means 32. This is done to prevent a specific regular pattern from being generated due to binarization by the error diffusion method when the original image data is very ordered data drawn by a computer or the like. is there. Therefore, when the original image data is a natural image having an appropriate variation in gradation values, such a process of adding random noise is unnecessary.

  Also, as a result of adding random noise, the threshold value may deviate from the range shown in Expression (6), but it is sufficient that the expected value of the threshold value falls within the range of Expression (6). Shall be.

  Similar effects can be obtained by adding noise to the original image data instead of the threshold.

[Other Examples]
Note that the present invention is not limited to the above embodiments, and various modifications can be made within the scope of the present invention.

  For example, in the above-described embodiment, the case where the original image data is density data such that it is 0, white, and black at 255 is described as an example. It is needless to say that the present invention can be similarly applied to brightness data.

In the case where the original image data takes a value in the range of AB (A> B) and the original image data is binarized to a or b (a> b), the first embodiment Equation (1)
slsh (i, j) = (data (i, j) * (K-1) + (a + b) / 2) / K… (1) '
And convert equation (2) to
If dataC (i, j) ≧ slsh (i, j), result (i, j) = a
If dataC (i, j) <slsh (i, j), result (i, j) = b ... (2) '
It can be changed as follows. Generally, A = a and B = b. However, when the outputable density of the output device is significantly different from the range of the original image data, A and a, and B and b may not match.

In this case, the conditional expression for setting the threshold value of Expression (6) is m = (a + b) / 2,
When data <m, data ≦ slsh ≦ (m + data) / 2
(m + data) / 2 ≦ slsh ≦ data when data> m (8)
become that way.

  Further, in the above-described embodiment, the case where the multi-tone image data 200 is corrected using the error diffusion method has been described as an example. However, the present invention is not limited to this. Is also applicable.

  FIG. 18 shows a preferred embodiment of the image processing apparatus 30 using the minimum average error method. The members corresponding to those in the embodiment shown in FIG. 2 are denoted by the same reference numerals, and the description thereof will be omitted.

  In this embodiment, the error correction unit 34 includes a data correction unit 38, an error calculation unit 44, and an error storage unit 46.

  The error calculating means 44 is formed so as to calculate the binarization error err of the pixel of interest based on the formula (3), and write the value to an address of the error storage means 46 corresponding to the pixel of interest. As a result, the binarization errors of the binarized pixels are sequentially written and stored in the storage area corresponding to each pixel address of the error storage unit 46.

  When the multi-tone image data data (i, j) of the target pixel P [i, j] is input, the data correction unit 38 determines the binarized pixels near the target pixel P [i, j]. The error is read from the error storage means 46. Then, a predetermined weight is given to the read error data to obtain an average error, the average error is added to the multi-gradation image data data (i, j) of the pixel of interest, and this is corrected image data data c (i , J) to the binarizing means 36.

  Since the other configuration is the same as that of the first embodiment, the description is omitted here.

  Even when such an average error minimizing method is used, the same effect as in the first embodiment can be obtained.

FIG. 1 is a schematic explanatory diagram of an image processing system to which the present invention has been applied. FIG. 2 is a functional block diagram of an image processing device used in the image processing system shown in FIG. 1. FIG. 5 is an explanatory diagram of a specific example of a diffusion weight matrix used in the embodiment. FIG. 9 is an explanatory diagram showing a relationship between original image data used for obtaining an average binarization error and a region for obtaining the average binarization error. FIG. 5 is an explanatory diagram showing a relationship between a tone value of original image data and an average binarization error. FIG. 4 is an explanatory diagram showing how an average binarization error occurs in the first embodiment of the present invention. FIG. 9 is an explanatory diagram of a binarization threshold value at which an average binarization error is always zero. FIG. 11 is an explanatory diagram of another embodiment of the threshold value setting means used in the present invention. FIG. 11 is an explanatory diagram of another embodiment of the threshold value setting means used in the present invention. FIG. 11 is an explanatory diagram of another embodiment of the threshold value setting means used in the present invention. FIG. 3 is an explanatory diagram of an example in which a threshold setting unit used in the present invention is configured by hardware. FIG. 9 is an explanatory diagram of a binarization result obtained by using the image processing device of the present invention. FIG. 15 is an explanatory diagram of an original image used to obtain the binarized images shown in FIGS. FIG. 11 is an explanatory diagram of a binarization result obtained by a conventional error diffusion method. FIG. 1 is an overall schematic explanatory diagram of an image processing system incorporating an image processing apparatus of the present invention. FIG. 16 is an explanatory diagram of another embodiment of the system in FIG. 15. FIG. 16 is an explanatory diagram of another embodiment of the system in FIG. 15. FIG. 2 is a block diagram of an embodiment of an image processing apparatus that uses a minimum average error method for correcting multi-tone image data.

Explanation of reference numerals

30 image processing apparatus, 32 threshold value setting means, 34 error correction means,
36 binarization means, 38 data correction means, 40 error diffusion means,
42 diffusion error storage means, 200 multi-gradation image data

Claims (10)

The multi-tone image data is converted into two-tone image data including a first tone value and a second tone value (first tone value <second tone value) by using an error diffusion method or an average error minimum method. In an image processing apparatus for performing binarization conversion to
Error correction means for correcting the multi-tone image data of the pixel of interest by adding an error diffused from neighboring binarized pixels, and outputting as corrected pixel data;
Threshold setting means for setting a different threshold value for binarization in accordance with the gradation value of the multi-gradation image data of the pixel of interest;
A binarizing unit that binarizes and converts the corrected pixel data into the binary image data based on the set threshold value;
Has,
In a predetermined multi-tone image data area, a threshold value set for a tone value of the predetermined multi-tone image data is a threshold set for a tone value larger than the predetermined tone value. The relationship below the value holds,
The binarizing means includes:
An image processing apparatus that performs a binarization process after adding random noise to a set threshold value or the corrected pixel data. The area of the predetermined multi-tone image data is
The image processing apparatus according to claim 1, wherein substantially the entire range is from the first gradation value to the second gradation value. The area of the predetermined multi-tone image data is
The image processing apparatus according to claim 1, wherein at least one of the vicinity of the first gradation value and the vicinity of the second gradation value. Assuming that an intermediate value between the first gradation value and the second gradation value is m,
The area of the predetermined multi-tone image data is near the first tone value, and a tone value larger than the predetermined tone value is near m, or
The predetermined multi-tone image data area is near the m, and a tone value larger than the predetermined tone value is near the second tone value.
2. The image processing apparatus according to claim 1, wherein at least one of the conditions is substantially satisfied. The multi-tone image data is converted into two-tone image data including a first tone value and a second tone value (first tone value <second tone value) by using an error diffusion method or an average error minimum method. In an image processing apparatus for performing binarization conversion to
Error correction means for correcting the multi-tone image data of the pixel of interest by adding an error diffused from neighboring binarized pixels, and outputting as corrected pixel data;
Threshold setting means for setting a different threshold value for binarization in accordance with the gradation value of the multi-gradation image data of the pixel of interest;
A binarizing unit that binarizes and converts the corrected pixel data into the binary image data based on the set threshold value;
Where data is a gradation value of multi-gradation image data of the pixel of interest, m is an intermediate value between the first and second gradation values, and slsh is the threshold value.
The threshold setting means,
If data is a value near the first gradation value, data ≦ slsh ≦ (m + data) / 2
When data is a value near the second gradation value, (m + data) / 2 ≦ slsh ≦ data
Set the threshold slsh to satisfy any of
The binarizing means includes:
An image processing apparatus that performs a binarization process after adding random noise to a set threshold value or the corrected pixel data. The threshold setting means,
slsh = (data * (K-1) + m) / K (where K is a constant represented by an integer of 2 or more)
6. The image processing apparatus according to claim 5, wherein the threshold value slsh is set so as to satisfy the following condition. The threshold setting means,
6. The image processing apparatus according to claim 5, wherein the threshold value slsh is set stepwise according to the value of the gradation value data. The threshold setting means,
If data <m−L1, slsh = data + L1
If m−L1 <data <m + L2, slsh = m
When m + L2 <data, slsh = data−L2
(However, L1 and L2 are constants represented by integers between 0 and m)
6. The image processing apparatus according to claim 5, wherein the threshold value slsh is set so as to satisfy the following condition. The multi-tone image data is converted into two-tone image data including a first tone value and a second tone value (first tone value <second tone value) by using an error diffusion method or an average error minimum method. In an image processing method for performing binarization conversion to
An error correction step of correcting the multi-tone image data of the pixel of interest by adding an error diffused from neighboring binarized pixels and outputting the corrected pixel data;
A threshold setting step of setting a threshold for binarization;
A binarizing step of binarizing and converting the corrected pixel data into the binary image data based on the set threshold value;
Wherein the threshold value set in the threshold value setting step for the gradation value of the predetermined multi-tone image data is a threshold value set for a gradation value larger than the predetermined gradation value. Lower relationship holds in a given multi-tone image data area,
In the binarization step,
An image processing method of performing a binarization process after adding random noise to a set threshold value or the corrected pixel data. The multi-tone image data is converted into two-tone image data including a first tone value and a second tone value (first tone value <second tone value) by using an error diffusion method or an average error minimum method. In an image processing method for performing binarization conversion to
An error correction step of correcting the multi-tone image data of the pixel of interest by adding an error diffused from neighboring binarized pixels and outputting the corrected pixel data;
A threshold setting step of setting a threshold for binarization;
A binarizing step of binarizing and converting the corrected pixel data into the binary image data based on the set threshold value;
Where the grayscale value of the multi-grayscale image data of the target pixel is data, the intermediate value between the first grayscale value and the second grayscale value is m, and the threshold is slsh,
In the threshold setting step,
determining whether data is a value near the first tone value or a value near the second tone value,
If data is a value near the first gradation value, data ≦ slsh ≦ (m + data) / 2
When data is a value near the second gradation value, (m + data) / 2 ≦ slsh ≦ data
Set the threshold slsh to satisfy
In the binarization step,
An image processing method of performing a binarization process after adding random noise to a set threshold value or the corrected pixel data.

Images