JP3203780B2 - Image processing method and image processing apparatus - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image processing method,
In particular, the present invention relates to an image processing method for processing using an error diffusion method.

[0002]

2. Description of the Related Art There is an error diffusion method for gradation expression when outputting multi-valued image data to a binary printer. Although this method is an excellent method to achieve both gradation and resolution, it further extracts the features of the original image and controls the error diffusion parameter according to the features to perform more accurate and high-quality image processing. The processing method is disclosed in JP-A-63-214073.

[0003]

In the above-described image processing method, the feature (edge, density, etc.) of the pixel of interest is extracted, and the amount of error feedback (for character, error feedback is set to 0 and simple 2 Is controlled, but such control alone has a problem in accuracy. For example, consider a case in which the feature of the target pixel is determined to be a character area, but the features of a plurality of surrounding pixels are determined to be a photograph area. In this case, in the case of the conventional image processing method, the target pixel is subjected to the binarization processing using the error diffusion parameter as the character area. However, although it is reasonable to think that this is the result of some noise on the target pixel, such processing is not performed.

SUMMARY OF THE INVENTION The present invention has been made to solve the above-described problem. Even when it is determined that the feature of a target pixel is a unique region, high-precision and high-quality image processing is performed. Is to provide an image processing method capable of performing the following.

[0005]

According to the first aspect of the present invention, there is provided an image processing method for sequentially reading M-value density data for each continuous pixel constituting an image, and applying an error diffusion method to the read density data. Extracting an image characteristic for each pixel based on the density data in an image processing method for obtaining N-valued (M ≧ N) image data.
N-value processing is performed on the eye pixels by applying error diffusion processing.
Steps and error data when N-value processing is performed
Calculating according to the image characteristics of the pixel of interest.
Correct the output error data and note the corrected error data.
Storing in association with the image characteristics of the eye pixel;
Pixel of interest to determine the image characteristics belonging region based on the image characteristics of 憶the peripheral pixels of the image characteristic and the target pixel of the target pixel, the error data stored in accordance with the determination result
Correcting and correcting the stored error data
Using the error data corrected in the step,
The error diffusion process related to the N-value conversion process of the target pixel is performed.

According to a second aspect of the present invention, in the image processing method of the first aspect, the stored error data
In the step of correcting
Before correction, determine the image characteristics of the area to which the pixel of interest belongs
Peripheral pixel errors reflected in error diffusion processing according to the result
Weight data. Image processing according to the invention of claim 3
The processing device calculates the density of the M value for each successive pixel forming the image.
Data is read sequentially and error diffusion is performed on the read density data
By applying the method, the N-valued (M ≧ N)
In an image processing apparatus that obtains image data, the
Pixel feature extraction means for extracting image characteristics for each pixel based on the
And N-value processing by applying error diffusion processing to the pixel of interest.
N-ary processing means for performing N-ary processing
Error calculating means for calculating error data, and an image of a pixel of interest
First correction for correcting error data calculated according to characteristics
And the error data corrected by the first correction means.
Error storage means for storing in association with the image characteristics of the eye pixel
And the image characteristics of the pixel of interest stored in the error storage means.
The pixel of interest belongs based on the image characteristics of the pixels surrounding the eye pixel.
Area feature extraction means for determining image characteristics of a region to be
The pixel of interest identified by the area feature extraction unit belongs
The error stored in the error storage means according to the image characteristics of the area
The data is corrected and the error related to the N-value processing of the next pixel of interest
Output to the N-value processing means as error data used for diffusion processing
And second correcting means for applying force.

[0007]

According to the image processing method of the first aspect, an image characteristic is extracted for each pixel based on the density data, and
Then, N-value processing is performed by applying error diffusion processing,
Error data at the time of performing the N-value processing is calculated.
Error data calculated according to the image characteristics of the pixel is corrected.
The corrected error data is associated with the image characteristics of the pixel of interest.
The image characteristics of the pixel of interest and the note
The pixel of interest belongs based on the image characteristics of the pixels surrounding the eye pixel.
The image characteristics of the region to be determined are determined, and according to the determination result,
The stored error data is corrected. And corrected
Of the next pixel of interest using the error data
An error diffusion process is performed. For this reason, there are few misjudgments.
Error diffusion processing by highly accurate area discrimination becomes possible.

[0008] In the image processing method of the second aspect, the storage
Before correcting the corrected error data, the area to which the pixel of interest belongs
Reflected in error diffusion processing according to the result of judgment of the image characteristics of the area
Weighting of error data of peripheral pixels to be performed. For this reason, control of pixel data is performed finely. Contract
In the image processing apparatus according to claim 3, based on the density data
The image characteristics are extracted for each pixel by the
The N-value processing is performed by applying the diffusion processing.
Is calculated, and the image of the pixel of interest is calculated.
The error data calculated according to the characteristics is corrected and corrected.
Error data is associated with the image characteristics of the pixel of interest.
The stored image characteristics of the pixel of interest and the stored
The area to which the pixel of interest belongs based on the image characteristics of the peripheral pixels
Are determined. Then, the determined target pixel
Stored in the error data according to the image characteristics of the area to which the
Is corrected, and the error enlargement relating to the N-value processing of the next target pixel is performed.
It is output as error data used for the dispersion processing. others
Error diffusion by highly accurate area discrimination with less erroneous discrimination
Processing becomes possible.

[0009]

FIG. 1 is a system block diagram showing the configuration of an image processing apparatus according to one embodiment of the present invention.

The configuration and the function of each block will be described below with reference to the drawings. First, the image signal converted from the document image into multi-valued (M-value) data by an image reader (IR) or the like is corrected in advance by the γ correction unit 1 for the non-linearity of gradation expression due to dot collapse or the like. This correction is made because, when outputting N-valued data to a printer or a CRT, if the dots are hit at a too high density due to the characteristics of the output device, the dot collapse will result in area gradation. Is to prevent this.

FIG. 2 is a diagram showing characteristics of a ROM table used for the γ correction. In the figure, the broken line indicates the ideal characteristic, the solid line indicates the characteristic of the actually output data, and the dashed line indicates the γ correction characteristic. In other words, even if density data of 256 gradations is obtained in the input image, if the dots are hit in accordance with the data as they are in the output characteristics, the dots will not be separated in a high density portion. Because of the close contact, the gradation characteristics cannot be reproduced. Therefore, a process of performing a dynamic range conversion that does not use the data (high density data area) indicated by D in FIG. By performing the dynamic range conversion in this manner, the 8-bit data of the obtained image data becomes substantially 7-bit data, and the deterioration of about 1-bit data is inevitable.

Therefore, in FIG. 1, the output of the gamma correction unit 1 has a deterioration in accuracy as image data. For this reason, outputting the data to the pixel feature extraction unit 12 using the data cannot extract the pixel feature with high accuracy there. Therefore, in this embodiment, the image data (multi-valued input signal) before being input to the γ correction unit 1 is directly input to the pixel feature extraction unit 12, thereby enabling highly accurate extraction.

Next, the data γ-corrected by the γ-correction section 1 is input to the error correction section 2 where the N value (M ≧ N)
The error generated by the conversion is corrected using the error diffusion method, and is further converted into an N-value signal in the N-value processing unit 3.

Here, a method of setting a threshold value used for the N-value processing will be described.

When the recording system has a multilevel level D _i = i / (q−
1), (i = 0, 1, 2,..., Q-1: where q is the number of gradations), the target pixel Fm, n with the error corrected is set to the threshold value T _i for _i. = (D _i -D ₀ ) /
2, the multi-level output level Pm, n is determined as in the following equation.

Pm, n = D _i : Fm, n ≧ T _i Pm, n = D _{i −1} : T _{i −1} ≦ Fm, n <T _i where N = 4, that is, multi-valued and output in four values In this case, a method of determining the multilevel output level will be described with reference to FIG.

Here, the output value is D ₃ = 255, D ₂ = 1
Assuming that 75, D ₁ = 85 and D ₀ = 0, the thresholds are T ₃ = 128, T ₂ = 85 and T ₁ = 43 based on the above equation.

By setting the threshold value in this manner, the range of input image values that can be taken by the multilevel output level Pm, n is as follows.

Pm, n = 255: 128 ≦ Fm, n ≦ 255 Pm, n = 175: 85 ≦ Fm, n <128 Pm, n = 85: 43 ≦ Fm, n <85 Pm, n = 0: 0 ≦ Fm, n <43. Accordingly, the range of the absolute value of the N-valued error Em, n is determined by the output value Pm, n = 255: 0 <| Em, n | ≦ 128 Pm, n = 175: 0 <| Em, n | <42 Pm, n = 85: 0 <| Em, n | <42 Pm, n = 0: 0 <| Em, n | <42. From this result, for the image data in which all the density data levels appear evenly, the N-value error E
This shows that the probability of occurrence of m and n differs depending on the output value. As a result, the error of each pixel is dispersed, and a pseudo gray level generated when the input image value Fm, n is close to the multilevel output level Pm, n for a halftone image such as a photographic image having a sharp change in density. Can be suppressed.

In the figure, the solid line corresponds to the output values of D _{0 to} D ₃ , and the broken line corresponds to the threshold values T _{1 to} T ₃ . For example, if the value of the input image value is 255
If, when such is between the threshold T ₃ i.e. 128, the value of the multi-level output level Pmn is D ₃ ie 2
The value is 55.

FIG. 4 shows an N-value processing unit 3 for the N (4) value.
FIG. 3 is a circuit diagram showing the specific contents of FIG.

This circuit converts 256-valued 8-bit data into 4-valued 2-bit data.

In the figure, input M-value image data is input to each terminal A of comparators 21, 22 and 23. Data “128” is input to the terminal B of the comparator 21, data “85” is input to the terminal B of the comparator 22,
Data “43” is input to the terminal B of No. 3. On the other hand, the outputs of the comparators 21, 22, and 23 are input to the terminals a, b, and c of the decoder 24, respectively. In the comparator, the data input to the terminal A and the data input to the terminal B are compared. When A> B, data “1” is output to the decoder 24. When A ≦ B, Data “0” is output to the decoder 24. FIG. 5 shows a decoder truth table of the decoder 24.

By using such a circuit configuration, the input image data of 256 gradations is output as quaternary data.

Next, based on the error-corrected image data output from the error correction unit 2 and the N-valued signal output from the N-value processing unit 3, an N-valued error at the time of N-value conversion is obtained. Is calculated by the error calculator 4.

FIG. 6 is a diagram showing a specific circuit configuration in the case where quaternary processing with N being 4 is performed as a specific circuit configuration of the error calculation section 4.

Note that this circuit specifically calculates N (4) -valued error = Fm, n-Pm, n.

In the figure, error-corrected image data Fm, n
Is input to the terminal A of the subtractor 32. The data "0", "85", "175", and "255" are input to the multiplexer 31, and the values of the 2-bit data D0 and D1 shown in FIG. (X1 to X
4) is selected. Data corresponding to the selected terminal is input to the terminal B of the subtractor 32. In the subtractor 32, a difference between the data input to the terminal A and the data input to the terminal B is obtained. And the subtractor 32
Based on the result of the difference, calculation error data including a sign indicating the sign of the difference value and a calculation error absolute value indicating the absolute value of the difference is output.

On the other hand, the multi-valued input signal before being input to the γ correction unit 1 is also input to the pixel feature extraction unit 12 to extract features (characters / photographs, edges, etc.) in pixel units. As this extraction method, a known technique such as a MAX-MIN detection circuit or a first-order differential filter may be used.

FIG. 8 is a diagram showing a specific configuration of the MAX-MIN detection circuit. In the figure, the pixel data in the peripheral area of the target pixel is indicated by the MAX detection circuit 41 and the MI
The signal is input to the N detection circuit 42. The MAX detection circuit 41
This is a circuit that detects the maximum pixel value MAX in the peripheral area of the target pixel, and the MIN detection circuit is a circuit that detects the minimum pixel value MIN in the peripheral area of the target pixel. The pixel value MAX output from the MAX detection circuit 41 is input to a terminal A of the subtractor 43, and the pixel value MIN output from the MIN detection circuit 42 is input to a terminal B of the subtractor 43. Subtractor 43
In, the difference between the pixel value input to the terminal A and the pixel value input to the terminal B is calculated, and the difference value (MAX−
MIN) is output.

FIG. 9 discriminates the characteristics of the original in pixel units based on each of the maximum pixel value MAX, the minimum pixel value MIN, and the difference value (MAX-MIN) obtained by the circuit of FIG. FIG. 6 is a diagram showing the characteristics of basic types of originals.

That is, based on the values of the maximum pixel value MAX and the minimum pixel value MIN, it is determined that the document is a text area, a photographic area, a text / photo intermediate area, a dark text / photo intermediate area, or a light text / photo intermediate area. can do.

The calculated error data calculated by the error calculating section 4 is transmitted to the first
The gain calculator 5 performs a gain calculation process according to a rule corresponding to the feature of the pixel of interest. At this time, as shown in FIG. 6, since the calculated error data is known to be a positive component or a negative component, high-precision control is performed by performing gain calculation separately for these positive and negative components. . Specifically, this rule is based on the following rules: (1) If the pixel of interest is a character area, it is preferable to place importance on resolution, so that gain = 0 and calculation error data for both positive and negative components are used for error diffusion processing. Avoid feedback.

(2) If the pixel of interest is a photographic area, it is better to emphasize the gradation, so that the gain is set to 1, and the calculated error data is completely fed back to the error diffusion processing for both the positive and negative components.

(3) If the pixel of interest is a text / photograph intermediate area, it is preferable to balance contrast and gradation, so that gain = 0.5 and calculation error data is only half for both positive and negative components. This is fed back to the error diffusion process.

(4) If the pixel of interest is a dark text / photograph intermediate area, it is preferable to emphasize the resolution in the high-density portion and emphasize the gradation in the low-density portion. = 0.5, negative component gain = 0, and as a result, only the error data of the positive component is fed back to the error diffusion process.

(5) If the pixel of interest is a light text / photograph intermediate area, it is preferable to emphasize the gradation in the high-density part and emphasize the resolution in the low-density part. = 0, negative component gain = 0.5, and as a result, only the error data of the negative component is fed back to the error diffusion process.

By doing so, it is possible to perform an appropriate error diffusion process according to the characteristics of the document of the pixel of interest, and it is possible to obtain a high-precision, high-quality image.

Next, an attribute flag indicating the feature of the image extracted by the pixel feature extracting unit 12 is added to the storage error data output from the first gain calculating unit 5. The reason for this is to clarify what kind of document caused the error by an attribute flag and to perform highly accurate error correction in the subsequent processing.

The storage error data itself is composed of 8-bit data, and is handled as 9-bit data as shown by E in FIG. 10 by using one sign bit. However, even if the accuracy of the image data itself is originally 8 bits, the accuracy of the data is degraded by about 1 to 2 bits due to shaling correction and other processing as described above. About 7 bits including the sign bit is sufficient. Therefore, in the 8-bit memory area, the remaining 1
It is possible to assign the attribute flags described above to bits. The attribute flag is calculated by the following equation.

Attribute flag = 1: Th ≦ MAX-MIN (character image area) Attribute flag = 0: Th> MAX-MIN (photo image area) (Th: threshold value) By defining the attribute flag in this manner, The attribute flag-added error data in the example of FIG. 10 represents data of the character image area, and the sign bit is stored in the last eighth bit data.

FIG. 11 is a diagram showing a specific example of a circuit in which the first gain calculating section 5 and the attribute flag adding section 6 are combined.

As shown in the figure, since such an operation has a one-to-one output with respect to a condition, by storing all combinations in the ROM table 51,
It can be easily realized. Note that the ROM table 5
1 and the absolute value of the calculation error are shown in FIG.
In the case of the example, the portion corresponds to the portion indicated by the data E.

The storage error data with the attribute flag output from the attribute flag adding unit 6 is held for several lines in the error storage unit 7, and further stored by the attribute flag separating unit 8.
It is separated into error data and attribute flags. The attribute flags separated by the attribute flag separation unit 8 are input to the area feature extraction unit 11, where the area features are determined based on the characteristics of the target pixel by the attribute flags and the characteristics of the peripheral pixels by the attribute flags. Is extracted.

The extraction operation will be described below with reference to a specific example. As shown in FIG. 13, the error data with the attribute flag is stored in the FIFO memory of the error storage unit 7 for several lines, and its output is separated by the attribute flag separation unit 8. It is input to the feature extraction unit 11. FIG. 12 shows the specific contents of this digital filter. That is, the feature amount of the attribute flag of the target pixel and its surrounding pixels is weighted by the attribute flag weighting digital filter, and the result is output as a multi-value (0 to 33) as the area attribute determination result. As is clear from FIG. 12, the weight of the digital filter is set larger as the pixel is closer to the pixel of interest (corresponding to the coefficient surrounded by a circle), thereby suppressing erroneous determination of the area feature. Since the attribute flag “1” indicates a character image area, the closer the area attribute determination is to 33, the closer the document image is to the character image area, and the closer the value is to 0, the closer to the photographic image area. I have.

On the other hand, the error data separated by the attribute flag separating section 8 is obtained by weighting the error data of the peripheral pixels by the error weighting digital filter of the coefficient corresponding to each attribute based on the above-mentioned area attribute determination result. Is done. The selection criteria for this digital filter are as follows: 21 ≦ area attribute determination result → digital filter coefficient = for character area 11 ≦ area attribute determination result <21 → digital filter coefficient = for text / photo intermediate area 0 ≦ area attribute determination result <11 → Digital filter coefficient = photo area. FIG. 14 shows a specific example of the coefficients of the weighted digital filter.

That is, FIG. 14 shows specific coefficients of the photographic region error filter. Note that * is a portion corresponding to the target pixel, and in this manner, in the photographic region, error data in a wide area around the pixel is considered in order to emphasize the gradation.

FIG. 14 shows an example of specific coefficients of the error filter for the character / photograph intermediate area. Here, since it is a character / photograph intermediate area, error data of peripheral pixels in a narrower range than the error filter for the photo area is considered in order to balance gradation and resolution.

FIG. 14 shows an example of specific coefficients of the character area error filter. Here, since it is a character area, only the error data of the peripheral pixels in a very narrow range is considered in order to emphasize the resolution.

FIG. 15 is a block diagram showing a specific configuration of the error weighting filter 9.

In the figure, the error data output from the attribute flag separation unit 8 is
1. After being input to each of the character / photograph intermediate area error weighting filter 62 and the character area error weighting filter 63 and subjected to the filter processing, the respective outputs are input to the filter selection circuit 64. The area selection determination result output from the area feature extraction unit 11 is input to the filter selection circuit 64, and based on the result, the output of one of the weighting filters is selected. It is output to 10.

Next, the error data for correction calculated by the error weighting filter 9 is subjected to a gain calculation of the error in the second gain calculator 10 based on the area attribute determination result based on the output of the area feature extractor 11. It is. That is, if the pixel of interest is included in the character image area in area, simple N-value processing is performed with the gain = 0 and the correction error data set to 0. On the other hand, if the pixel of interest is areaally included in the character / photograph intermediate image area, the gain =
The error diffusion process is performed using only half of the correction error data. Further, if the pixel of interest is areaally included in the photographic image area, the gain is set to 1 and the error diffusion process is performed using all the correction error data.

In summary, the gain calculation at the time of storing the error is performed according to the characteristics of each pixel of interest. On the other hand, the output data of the error weighting filter for calculating the error data for correction of the pixel of interest based on the error data of the peripheral pixels is used. The gain calculation is performed based on the area attribute determination result in which the characteristics of the pixels are also considered. By doing so, it is possible to perform an error diffusion process by highly accurate area determination with less erroneous determination.

FIG. 16 is a diagram showing a specific configuration of the second gain calculator 10. In the figure, the output of the error weighting filter and the area attribute discrimination result are input to the ROM table 71, and the error data calculated based on the above gain is output to the error correction unit 2.

The error correction unit 2 corrects the error based on the error data output from the second gain calculation unit 10 as described above.

In the above-described embodiment, the area feature is extracted based on the attribute flag. Instead, the feature of one pixel is first extracted and stored in the FIFO memory. To extract or
A configuration may be employed in which the feature of one pixel and the area feature are separately extracted.

In the above embodiment, the attribute flag is 1-bit data. However, if the attribute flag is data of several bits, the control can be performed with higher precision.

[0058]

According to the first aspect of the present invention, as described above,
Error data at the time of performing the N-value processing is calculated.
Error data calculated according to the image characteristics of the pixel is corrected.
The corrected and corrected error data is
Note determined based on the image characteristics of the pixels surrounding the eye pixel
The correction is made according to the image characteristics of the region to which the eye pixel belongs. This
Therefore, it is possible to control the error data with less erroneous discrimination and high accuracy, and it is possible to obtain a higher quality image at the boundary where the region characteristics change.

[0059] As claimed in the invention of claim 2 is described above, the serial
Before correcting the memorized error data, the pixel of interest belongs
Reflected in error diffusion processing according to the result of discriminating the image characteristics of the area
Weighting of error data of peripheral pixels to be performed is performed.
For this reason, a more accurate error diffusion process can be performed. Contract
The invention of claim 3 performs the N-value processing as described above.
Error data at the time of the
The error data is corrected according to the corrected error data
Are the image characteristics of the pixel of interest and the image characteristics of the pixels surrounding the pixel of interest.
Image of the area to which the pixel of interest belongs based on the gender
Corrected according to the characteristics. For this reason, accurate
Error diffusion processing by highly discriminating areas becomes possible,
Obtain higher-quality images at boundaries where frequency characteristics change
be able to.

[Brief description of the drawings]

FIG. 1 is a block diagram illustrating an overall configuration of an image processing apparatus to which an embodiment of the present invention is applied.

FIG. 2 is a graph showing a correction characteristic in the γ correction unit 1 of FIG.

FIG. 3 is a diagram showing the contents of a quaternary processing shown as a specific example in an N-ary processing unit 3 of FIG. 1;

FIG. 4 is a diagram showing a specific configuration of an N-ary processing unit 3 corresponding to the specific example of FIG. 3;

FIG. 5 is a decoder truth table of the decoder shown in FIG. 4;

FIG. 6 is a diagram showing a configuration of a specific example of an error calculator 4 in FIG. 1;

FIG. 7 is a multiplexer truth table for the multiplexer of FIG. 6;

FIG. 8 is a diagram showing specific contents of a pixel feature extraction unit 12 of FIG. 1;

FIG. 9 is a diagram showing characteristics of an image area for determining the characteristics of a document image based on the output of FIG. 8;

FIG. 10 is a diagram illustrating a method of generating error data with attribute flags in the attribute flag adding unit of FIG. 1;

11 is a diagram showing a specific configuration of an attribute flag adding unit 6 of FIG.

FIG. 12 is a diagram showing a specific example of coefficients of an attribute flag weighting digital filter used in the area feature extraction unit of FIG. 1;

FIG. 13 is a diagram showing a specific configuration of an error storage unit 7, an attribute flag separation unit 8, and an area feature extraction unit 11 of FIG.

FIG. 14 is a diagram showing a specific example of an error filter corresponding to each region characteristic of the error weighting filter 9 of FIG. 1;

FIG. 15 is a diagram showing a specific configuration of an error weighting filter 9 of FIG. 1;

FIG. 16 is a diagram showing a specific configuration of a second gain calculator 10 of FIG. 1;

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 1 γ correction part 2 Error correction part 3 N value processing part 4 Error calculation part 5 First gain calculation part 6 Attribute flag addition part 7 Error storage part 8 Attribute flag separation part 9 Error weighting filter 10 Second gain calculation part 11 Area Feature extraction unit 12 Pixel feature extraction unit In the drawings, the same reference numerals indicate the same or corresponding parts.

────────────────────────────────────────────────── ─── Continuation of front page (56) References JP-A-3-187677 (JP, A) JP-A-2-103684 (JP, A) JP-A-3-109868 (JP, A) JP-A-4- 81073 (JP, A) JP-A-6-46263 (JP, A) JP-A-6-46266 (JP, A) (58) Fields investigated (Int. Cl. ⁷ , DB name) H04N 1/40 G06T 5 / 00 200 H04N 1/405

Claims (3)

(57) [Claims] 1. N-valued (M ≧ N) image data by sequentially reading density data of M values for each successive pixel constituting an image and applying an error diffusion method to the read density data. Extracting image characteristics for each of the pixels based on the density data.
And applying an error diffusion process to the pixel of interest to perform an N-value conversion process.
And a step for calculating error data when the N-value processing is performed.
And the error data calculated according to the image characteristics of the pixel of interest.
Data of the target pixel.
A step of associating and storing the image characteristics, image characteristics of the area to which the pixel of interest belongs to determined based on the image characteristics of surrounding pixels of the pixel of interest and image characteristics of the stored target pixel, on the determination result According to the memory
And correcting the error data, in the step of correcting the stored error data
Use the corrected error data to convert the next pixel of interest to N-value
Performing an error diffusion process related to the process. 2. A process for correcting the stored error data.
Correcting the stored error data in step
Before, the determination result of the image characteristics of the area to which the pixel of interest belongs
Error data of peripheral pixels reflected in the error diffusion process according to
The image processing method according to claim 1, wherein data weighting is performed . 3. The M value of each successive pixel constituting an image
The density data is read sequentially, and there is an error in the read density data.
By applying the diffusion method, N values (M ≧ N) are obtained.
An image processing apparatus for obtaining image data, extracting image characteristics for each of the pixels based on the density data.
Pixel feature extraction means for performing error-diffusion processing on a pixel of interest to perform N-value processing
Means for performing the N-value processing, and an error for calculating error data when the N-value processing is performed.
Difference calculating means, and the calculated error data according to the image characteristics of the pixel of interest.
A first correction unit for correcting the data, and the error data corrected by the first correction unit.
Error storage means for storing in association with the image characteristics of the eye pixel
And image characteristics of the pixel of interest stored in the error storage means.
And the image characteristics of the pixels surrounding the pixel of interest.
Area features that determine the image characteristics of the area to which the pixel of interest belongs
Extraction means, and the pixel of interest determined by the area feature extraction means
In the error storage means according to the image characteristics of the area to which the
The stored error data is corrected, and the N-value processing of the next pixel of interest is performed.
The error data used in the error diffusion process
Having a second correction unit for outputting to the value processing unit
Processing equipment.