JP3574711B2 - Image data binarization method and apparatus - Google Patents

Description

[0001]
TECHNICAL FIELD OF THE INVENTION
The present invention relates to an image data binarization method and apparatus for converting multi-valued image data into binary image data.
[0002]
[Prior art]
2. Description of the Related Art Conventionally, when a multi-level image is pseudo-recorded by a binary recording apparatus such as an ink jet printer, an error diffusion method has been frequently used to binarize multi-level image data.
[0003]
FIG. 14 is a block diagram showing a conventional image data binarization device employing such an error diffusion method. As shown in FIG. 14, this image data binarization device includes an error memory 120, an adder 122, a comparator 124, a subtractor 126, a bit converter 128, and an error distribution circuit 130.
[0004]
The dynamic range of the image signal is 0 to 255, and the bold line in the figure indicates a multi-bit data line including a sign bit, an integer part bit, and a decimal part bit.
[0005]
Here, the error diffusion method means that when image data is binarized for a certain pixel, an error between the image data before binarization and the image data after binarization is determined by a predetermined ratio of the pixel. This is a method of distributing and diffusing each pixel to neighboring pixels. FIG. 15 is an explanatory diagram for explaining the principle of such an error diffusion method. In FIG. 15, reference numeral 230 indicates an image, an arrow 231 indicates a processing order when binarizing the image 230, and 232 indicates a pixel in the image 230.
[0006]
If the value of the image data before binarization for the binarization target pixel (hereinafter referred to as the pixel of interest) P is X, and the total error distributed from the binarized pixel to the pixel of interest P is Σe _P The correction data X ′ for the target pixel is expressed by the following equation.
X ′ = X + Σe _P (1)
[0007]
The target pixel P is binarized by comparing the correction data X ′ for the target pixel P with a threshold value (for example, 128). If the binarized image data is Y (0 or 255), the binarization is performed. The error e generated from the pixel P due to the binarization of the pixel P is expressed by the following equation.
e = X'-Y (2)
[0008]
The value of the error e is distributed and diffused to pixels near the target pixel P. That is, the value of the error e is divided at a predetermined ratio (error distribution ratio) and added to the image data of the neighboring pixels before binarization. As an example of the conventional diffusion process, the value of the error e is distributed to four pixels: a pixel A on the right, a pixel B on the lower right, a pixel C on the lower right, and a pixel D on the lower left with respect to the pixel of interest P; As an error distribution ratio, an example in which 7/16 is applied to the pixel A, 1/16 to the pixel B, 5/16 to the pixel C, and 3/16 to the pixel D is known.
[0009]
Pixel A: e _A = e × 7/16
Pixel B: e _B = e × 1/16 (3)
Pixel C: e _C = e × 5/16
Pixel D: e _D = e × 3/16
[0010]
In this way, the error diffused to the neighboring pixels is accumulated in the error memory 120 as error data for each pixel.
[0011]
Now, the image data binarization device shown in FIG. 14 will be described. When the target pixel to be binarized is P, multivalued image data for the pixel P is input to the adder 122. Further, from the error memory 120, the error data Δe _P stored in the pixel of interest _P is read and input to the adder 122. The adder 122 adds the error data to the input multi-valued image data to correct the image data of the target pixel.
[0012]
The comparator 124 receives the corrected image data from the adder 122, compares the corrected image data with a separately input reference value, and converts 1-bit data, that is, binary image data, according to the magnitude relationship between the values. Output. As a result, the image data output from the adder 122 is binarized. The bit converter 128 converts the 1-bit binary image data from the comparator 124 into 8-bit binary image data (0 or 255).
[0013]
The subtractor 126 receives the corrected image data (image data before binarization) from the adder 122 and the image data (image data after binarization) from the bit converter 128 and inputs the image data before binarization. The image data after the binarization is subtracted from the image data of (1) to derive an error e generated due to the binarization of the target pixel P.
[0014]
The error distribution circuit 130 distributes and diffuses the error e obtained by the subtractor 126 to the pixels A, B, C, and D in the vicinity of the target pixel P as described with reference to FIG. That is, the error distribution circuit 130 multiplies the error e by the coefficients corresponding to the pixels A, B, C, and D, as shown in Expression (2), for example, and then calculates each of the operation results e _A and e _B. , E _C , and e _D are added to the error data Σe _A , Σe _B , Σe _C , and Σe _D for the pixels A, B, C, and D in the error memory 120, respectively, so that each error data Σe _A , Σe _B , Σe _C and Σe _D are updated.
[0015]
As described above, according to the image data binarization device shown in FIG. 14, when the multi-valued image data is binarized, the error diffusion method is used, so that even if the image is binarized, the image is pseudo-generated. Can be expressed in multiple gradations.
[0016]
[Problems to be solved by the invention]
However, in the above-described conventional image data binarization device, when image data having a relatively low gradation value is input, a pixel having a value “1” is binarized image data in the image. When image data having a relatively high gradation value is input in the form of a dotted line, pixels having a value “0” are arranged in the image in a dotted line as binarized image data. There is a problem that image quality is significantly impaired.
[0017]
Now, when the image data binarization device shown in FIG. 14 is employed in a printer or the like, the gradation value of the image data corresponds to the density in the recorded image. Therefore, when image data with a relatively low tone value is input, a print image with a relatively low density is obtained as a printing result, and when image data with a relatively high tone value is input, a comparative image is obtained. A recorded image with an extremely high density can be obtained.
[0018]
FIGS. 16 and 17 are explanatory views showing recording results by the printer when the image data binarization device of FIG. 14 is used. FIG. 16 shows a recording result for a low density area (0% to 15%), and FIG. 17 shows a recording result for a high density area (85% to 100%). 16 and 17, in the horizontal direction of the image, the density continuously changes in the above-described range. As shown in FIG. 15, the binarization process is performed by scanning one line horizontally from left to right and from left to right starting from the upper left corner of the image.
The same applies to the binarization processing described later.
[0019]
Also, FIG. 18 shows a recording result of the low-density area shown in FIG. 16, particularly for the density of 1%, and FIG. 19 shows a recording result of the high-density area shown in FIG. 17, particularly for the density of 99%. Show. That is, in FIGS. 18 and 19, the entire image has a uniform density. FIG. 20 is an explanatory diagram showing a printing result of an intermediate density area (15% to 85%) by a printer when the image data binarization apparatus of FIG. 15 is used. In FIG. 20, the density continuously changes in the above range in the horizontal direction of the image.
[0020]
As shown in FIG. 16 or FIG. 18, at a relatively low density of 0% to 3%, a black point (that is, a pixel having a value “1”) partially continues in a dotted line from upper right to lower left. Is arranged. Also, as shown in FIG. 17 or FIG. 19, at a relatively high density of 97% to 100%, a white point (that is, a pixel having a value “0”) partially becomes a dotted line from upper right to lower left. Are arranged continuously. Therefore, in each case, the black points or white points are not evenly scattered, thereby significantly deteriorating the image quality.
[0021]
In the intermediate density region (15% to 85%) shown in FIG. 20, a remarkable texture is partially observed, but such a texture can be removed by superimposing a random number on a threshold value in the binarization unit. It is.
[0022]
On the other hand, a texture generated in a low-density area or a high-density area cannot be removed even if a random number is superimposed on a threshold.
[0023]
The texture generated in such a low-density region or a high-density region is improved by distributing the error generated due to the binarization of the target pixel to more pixels before binarization. These methods have problems such as an increase in hardware scale and an increase in binarization processing time.
[0024]
SUMMARY OF THE INVENTION The present invention has been made in consideration of the above-described problems of the related art, and has as its object to provide an improved image data binarization device capable of performing binarization of image data. is there.
[0025]
[Means for Solving the Problems and Their Functions and Effects]
To achieve at least some of the above objectives,
The method according to claim 1 of the present invention comprises:
An image data binarization method for binarizing multi-valued image data,
(A) adding, to the image data of the target pixel, the error accumulated in the target pixel and the error accumulated in the non-target pixel before binarization located in the vicinity of the target pixel; Making image data correction data of the pixel of interest;
(B) updating the error accumulated in the target pixel and the non-target pixel;
(C) binarizing the image data of the pixel of interest based on the correction data and a threshold;
(D) deriving an error generated with the binarization of the target pixel based on the correction data and the binarization result;
(E) distributing the derived error to pixels before binarization;
(F) accumulating the distributed error as error data for each pixel;
The gist is to binarize the image data.
[0026]
According to the method of claim 1, in the image data of the target pixel, the error accumulated in the target pixel from the binarized pixel and the non-target pixel before binarization located near the target pixel are stored. By adding the obtained error, the image data of the target pixel is corrected.
[0027]
The error accumulated in the non-target pixel is an error generated and distributed from a binarized pixel located near the target pixel.
[0028]
Therefore, the target pixel is corrected based on errors generated from a number of binarized pixels located in the vicinity of the target pixel, so that the image quality after binarization is significantly improved.
[0029]
According to the method of claim 2, instead of adding the error accumulated in the non-target pixel to the image data of the target pixel, the non-target pixel is multiplied by a coefficient determined according to the image data of the target pixel. The obtained value is added to the image data of the target pixel to obtain correction data.
[0030]
Therefore, it is possible to determine to what extent the error accumulated in the pixel before binarization is added to the image data of the target pixel in accordance with the image data of the target pixel. And the effect of shortening the binarization processing time can be obtained.
[0031]
The invention according to claim 3 is:
An image data binarization device for binarizing multi-valued image data,
Correction data for the pixel of interest is obtained by adding the error accumulated in the pixel of interest to the image data of the pixel of interest and the error accumulated in the non-target pixel before binarization located in the vicinity of the pixel of interest. Correction data deriving means for deriving
Updating means for updating the error accumulated in the target pixel and the non-target pixel,
Binarizing means for binarizing the image data of the pixel of interest based on the correction data and a threshold value;
Error distributing means for distributing an error generated due to binarization of the target pixel based on the correction data and the binarization result to pixels before binarization;
And an error accumulating means for accumulating the distributed error as error data for each pixel.
[0032]
According to the third aspect of the present invention, the target pixel is corrected based on errors generated from a large number of binarized pixels located near the target pixel. Therefore, the image quality after the binarization is corrected. Is significantly improved.
[0033]
BEST MODE FOR CARRYING OUT THE INVENTION
Hereinafter, embodiments of the present invention will be described based on examples. FIG. 1 is an explanatory diagram for explaining an error diffusion method according to the first embodiment of the present invention. In FIG. 1A, reference numeral 220 indicates an image, an arrow 221 indicates the order of the binarization processing on the image 220, and 222 indicates a target pixel in the image 220 to be binarized. ing.
[0034]
FIG. 1B is an explanatory diagram for describing a method for deriving correction data for the target pixel P to be binarized in the present embodiment.
[0035]
In the present invention, the multi-value data of the target pixel is obtained by adding the error Δe _P accumulated in the target pixel and the error accumulated in the pixel before binarization located near the target pixel to the target pixel. The correction data for P is derived.
[0036]
In the present embodiment, the pixel E is selected as a pre-binarized pixel located in the vicinity of the pixel of interest, and 1/8 of the error Δe _E accumulated in the pixel E is added to the image data of the pixel of interest. All of the accumulated errors are added to the image data of the target pixel.
[0037]
The image data before binarization for the pixel of interest P and X, if the total of distributed errors from binarized pixels to the pixel of interest P and Sigma] e _P, the correction data X with respect to the pixel of interest '' the following It is derived according to the formula.
X '' = X + Σe _P + ／ Σe _E (4)
[0038]
When the correction of the image data of the target pixel P is completed, the error value for the pixels P and E in the error memory 20 is updated according to the following equation.
Σe _P → 0
Σe _E → 7 / 8Σe _E ……… (5)
[0039]
The binarization of the pixel of interest P is performed by comparing the correction data X ″ for the pixel of interest P with a threshold value (eg, 128). If the image data after binarization is Y (0 or 255), An error e generated from the pixel of interest P due to binarization of the pixel is expressed by the following equation.
e = X ''-Y (6)
[0040]
The derived error is distributed at a predetermined ratio to the pixels A, B, C, and D before binarization in the vicinity of the target pixel as shown in FIG.
[0041]
The error distributed to the neighboring pixels in this manner is stored in the error memory as error data for each pixel.
[0042]
Thus, the binarization process for the pixel P is completed, and the binarization process for the next pixel A starts.
[0043]
FIG. 2 is a block diagram showing an image data binarization device as a first embodiment of the present invention. As shown in FIG. 2, the image data binarization device includes an error memory 20, a data correction circuit 22, a comparator 24, a subtractor 26, a bit converter 28, an error distribution circuit 30, and an error update circuit 32. I have.
[0044]
The error memory 20 has a storage capacity for only two lines of the image as shown in FIG. 2, but when the correction for the pixel of interest is completed, the error for the pixel of interest in the error memory 20 is updated to 0. The storage area for lines can be used by toggle, and error data for each pixel of one image can be accumulated in order.
[0045]
In FIG. 2, when the target pixel to be binarized is P, multivalued image data of the pixel P is input to the data correction circuit 22. The multi-valued image data is, for example, 8-bit data, and can take a value from “0” to “255” as a data value (that is, a gradation value).
[0046]
The error memory 20 distributes and accumulates the error data Δe _P from the binarized neighboring pixels for the pixel of interest P, and distributes and accumulates the pixel E from the binarized neighboring pixels for the pixel E. The error data Δe _E is read and input to the data correction circuit 22.
[0047]
Incidentally, when the error data Sigma] e _P and error data Sigma] e _E is read, the error data Sigma] e _P 0 accumulated in the error memory 20 by the error update circuit 32, error data Sigma] e _E is the 7 / 8Σe _E Update Is done.
[0048]
The data correction circuit 22 adds a part of the error Δe _P accumulated in the pixel of interest _P and the error Δe _E accumulated in the pixel E, that is, ΣΔE _E, to the image data of the pixel of interest P, Is corrected.
[0049]
The comparator 24 receives the corrected image data from the data correction circuit 22, compares the corrected image data with a separately input reference value, and outputs 1-bit data, that is, binary image data according to the magnitude relation between the values. Is output. Here, "128" which is an intermediate value between "0" and "255" is adopted as the reference value. That is, the comparator 24 outputs “1” when the value of the image data from the data correction circuit 22 is “128” or more, and outputs “0” when the value is smaller than “128”. Thus, the image data output from the data correction circuit 22 is binarized.
[0050]
The bit converter 28 receives the 1-bit binary image data from the comparator 24, converts it into 8-bit binary image data, and outputs it. That is, when the value of the image data from the comparator 24 is “0”, data of a value “0” is output as 8-bit image data, and when the value of the image data from the comparator 24 is “1”. Outputs data of a value “255” as 8-bit image data. Note that such a bit converter 28, for example, branches a 1-bit image data line branched from the output of the comparator 24 into eight lines, and connects it to the input of the subtracter 26 as an 8-bit image data line. This can be easily realized.
[0051]
The subtractor 26 receives the image data (image data before binarization) from the data correction circuit 22 and the image data (image data after binarization) from the bit converter 28, and inputs the image data before binarization. The binarized image data is subtracted from the image data to derive an error e generated by binarization of the pixel of interest.
[0052]
The derived error e is divided by the error distribution circuit 30 into e _A , e _B , e _C , and e _D according to the equation (3) and distributed to pixels A, B, C, and D located near the target pixel. Is done. That is, by adding e _A , e _B , e _C , and e _D to the error values Δe _A , Δe _B , Δe _C , and Δe _D for the pixels A, B, C, and D in the error memory 20, Δe _{A is obtained.} , Σe _B , Σe _C , and Σe _D are updated.
[0053]
In this way, when a series of processing is completed for the target pixel P, the binarization processing shifts from the pixel P to the next pixel A, and the same processing is performed using the pixel A as the target pixel for binarization. Repeated.
[0054]
FIG. 3 is an explanatory diagram showing a printing result by a printer in a low density area (0% to 15%) according to the present embodiment, and corresponds to FIG. 16 of the conventional example.
[0055]
FIG. 4 is an explanatory diagram showing a printing result of a high density area (85% to 100%) by the printer according to the present embodiment, and corresponds to FIG. 17 of the conventional example.
[0056]
FIG. 5 is an explanatory diagram showing a printing result of a low density area (1% constant) by a printer according to the present embodiment, and corresponds to FIG. 18 of the conventional example.
[0057]
FIG. 6 is an explanatory diagram showing a printing result of a high density area (99% constant) by the printer according to the present embodiment, and corresponds to FIG. 19 of the conventional example.
In each case, the black point and the white point are arranged evenly, and the image quality is remarkably improved.
[0058]
FIG. 7 is an explanatory diagram showing a printing result of the intermediate density area (15% to 85%) by the printer according to the present embodiment, and corresponds to FIG. 20 of the conventional example.
In this region, no remarkable difference is observed as compared with the conventional method.
[0059]
In the above-described embodiment, when correcting the image data of the target pixel, the error amount of a fixed ratio (に 対 す る) to the error Δe _E accumulated in the pixel E regardless of the value of the image data of the target pixel. Although the image data is distributed to the pixel image data, by setting this distribution ratio to a preferable value corresponding to the image data of the target pixel, the image quality after the binarization is further improved, or the binarization processing time is reduced. Effects such as shortening can be obtained.
[0060]
FIG. 8 is an explanatory diagram for explaining a method for deriving correction data for a target pixel P to be binarized according to the second embodiment of the present invention.
[0061]
In this embodiment, the error Δe _P accumulated in the pixel of interest _P , the error Δe _E accumulated in the pixel _E , and the error Δe _F accumulated in the pixel F are distributed to the image data of the pixel of interest. The correction data of the pixel is derived. The distribution ratios of the errors Δe _E and Δe _F accumulated in the pixels E and F to the image data of the pixel P are not constant, and as shown in FIGS. 8B and 8C, the values of the image data of the pixel P depending on, it is respectively _W E, and _{W F} determined.
[0062]
Thus it is determined the distribution ratio W _E, based on the W _F, the correction data X with respect to the image data X of the pixel of interest P '''is derived according to the following equation.
X '''= X + Σe P + W E · Σe E + W F Σe F ......... (7)
[0063]
When the correction of the image data of the target pixel P is completed in this way, the error values for the pixels P, E, and F in the error memory are updated according to the following equation.
Σe _P → 0
_{Σe E → (1-W E} ) · Σe E ......... (8)
_{Σe F → (1-W F} ) · Σe F
[0064]
The subsequent operation is exactly the same as in the first embodiment.
[0065]
FIG. 9 is a block diagram of an image data binarization device according to a second embodiment of the present invention.
The multi-level image data X for the pixel P is input to the data correction circuit 22. Data correction circuit 22, first, Sigma] e _P from the error memory 20, Sigma] e _E, reads Sigma] e _F, then the distribution ratio _W E corresponding to the value of the image data X of the pixel of interest P, to determine the _{W F,} the formula According to (7), the correction data X ′ ″ for the image data of the target pixel P is derived.
[0066]
Distribution ratio W _E corresponding to the image data X of the pixel of interest _P, as a method of determining the W _F, for example, receives image data X as an address in the memory, W _E as memory contents corresponding to the _address, W _F Should be stored. Alternatively, the image data X and the error value Sigma] e _E of the pixel P, enter the Sigma] e _F as an address in the memory, if storing the image data _{X + W E · Σe E +} W F · Σe F as memory contents corresponding to the address , Correction data X ′ ″ is quickly derived.
[0067]
When the correction of the image data of the target pixel P is completed in this way, the error data Σe _P , Σe _E , and Σe _F in the error memory 20 are updated by the error update circuit 32 according to the equation (8).
[0068]
Update data _{(1-W E) · Σe} E, (1-W F) · Σe as a method of deriving _F, for example, the image data X and the error value Sigma] e _E of the target pixel P or image data of the pixel of interest P, X and error values Sigma] e _F input to the memory as an address, as a memory content corresponding to this _{address, (1-W E) ·} Σe E or _{(1-W F) · Σe} F
Should be stored.
The subsequent operation is exactly the same as in the first embodiment.
[0069]
FIG. 10 is an explanatory diagram showing a printing result by a printer in a low density area (15% to 0%) according to the present embodiment, and corresponds to FIG. 16 of the conventional example.
[0070]
FIG. 11 is an explanatory diagram showing a printing result of a high density area (85% to 100%) by the printer according to the present embodiment, and corresponds to FIG. 17 of the conventional example.
[0071]
FIG. 12 is an explanatory diagram showing a printing result of a low density area (1% constant) by the printer according to the present embodiment, and corresponds to FIG. 18 of the conventional example.
[0072]
FIG. 13 is an explanatory diagram showing a printing result of a high density area (99% constant) by the printer according to the present embodiment, and corresponds to FIG. 19 of the conventional example.
In each case, the black point and the white point are arranged evenly, and the image quality is remarkably improved.
[0073]
According to the present embodiment, in the low-density region (1%) and the high-density region (99%), a slight improvement compared to the first embodiment is recognized.
[0074]
Further, in the present embodiment, the processing for the portion of the density region of 10% to 90% is not different from the conventional method, so that the time required for the binarization processing is not different from the conventional method.
[Brief description of the drawings]
FIG. 1 is an explanatory diagram for explaining an error diffusion method according to a first embodiment of the present invention.
FIG. 2 is a block diagram showing an image data binarization device as a first embodiment of the present invention.
FIG. 3 is an explanatory diagram showing a recording result in a low density area (0% to 15%) according to the first embodiment of the present invention.
FIG. 4 is an explanatory diagram showing a recording result in a high density area (85% to 100%) according to the first embodiment of the present invention.
FIG. 5 is an explanatory diagram showing a recording result for a density of 1% according to the first embodiment of the present invention.
FIG. 6 is an explanatory diagram showing recording results for a density of 99% according to the first embodiment of the present invention.
FIG. 7 is an explanatory diagram showing a recording result in an intermediate density area (15% to 85%) according to the first embodiment of the present invention.
FIG. 8 is an explanatory diagram for explaining an error diffusion method according to a second embodiment of the present invention.
FIG. 9 is a block diagram illustrating an image data binarization device according to a second embodiment of the present invention.
FIG. 10 is an explanatory diagram showing a recording result in a low density area (0% to 15%) according to the second embodiment of the present invention.
FIG. 11 is an explanatory diagram showing a recording result in a high density area (85% to 100%) according to the second embodiment of the present invention.
FIG. 12 is an explanatory diagram showing a recording result for a density of 1% according to a second embodiment of the present invention.
FIG. 13 is an explanatory diagram showing a recording result for a density of 99% according to the second embodiment of the present invention.
FIG. 14 is a block diagram showing a conventional image data binarization device.
FIG. 15 is an explanatory diagram for explaining the principle of the error diffusion method.
FIG. 16 is an explanatory diagram showing a printing result in a low density area (0% to 15%) by a printer when a conventional image data binarization device is used.
FIG. 17 is an explanatory diagram showing a printing result in a high density area (85% to 100%) by a printer when a conventional image data binarization device is used.
FIG. 18 is an explanatory diagram showing a printing result for a density of 1% by a printer when a conventional image data binarization device is used.
FIG. 19 is an explanatory diagram showing a printing result of a printer at a density of 99% when a conventional image data binarization device is used.
FIG. 20 is an explanatory diagram showing a printing result of an intermediate density area (15% to 85%) by a printer when a conventional image data binarization device is used.
[Explanation of symbols]
Reference Signs List 20 error memory 22 data correction circuit 24 comparator 26 subtractor 28 bit converter 30 error distribution circuit 32 error update circuit 120 error memory 122 adder 124 comparator 126 subtractor 128 Bit converter 130 Error distribution circuits 220 and 230 Images 221 and 231 Scan lines 222 and 232 Pixels A, B, C, D, E and F Pixel P Target pixels P100 to P111 Pixels P200 to P211 pixel _{e A, e B, e C} , e D, e E, e F ... distribution error e ... error _{Σe A, Σe B, Σe C} , Σe D, Σe E, Σe F ... error data Sigma] e P _... error data

Claims (3)

An image data binarization method for binarizing multi-valued image data,
(A) adding, to the image data of the target pixel, the error accumulated in the target pixel and the error accumulated in the non-target pixel before binarization located in the vicinity of the target pixel; Making image data correction data of the pixel of interest;
(B) updating the error accumulated in the target pixel and the non-target pixel;
(C) binarizing the image data of the pixel of interest based on the correction data and a threshold;
(D) deriving an error generated with the binarization of the target pixel based on the correction data and the binarization result;
(E) distributing the derived error to pixels before binarization;
(F) accumulating the distributed error as error data for each pixel;
Image data binarization method comprising: A step of multiplying the error accumulated in the non-target pixel by a coefficient of 0 to 1 determined according to image data of the target pixel,
In the step (a), a value obtained by multiplying the error accumulated in the non-target pixel by the coefficient is added to the image data of the target pixel instead of the error accumulated in the non-target pixel. 2. The image data binarization method according to claim 1, wherein: An image data binarization device for binarizing multi-valued image data,
Correction data for the pixel of interest is obtained by adding the error accumulated in the pixel of interest to the image data of the pixel of interest and the error accumulated in the non-target pixel before binarization located in the vicinity of the pixel of interest. Correction data deriving means for deriving
Updating means for updating the error accumulated in the noted pixel and the noted pixel,
Binarizing means for binarizing the image data of the pixel of interest based on the correction data and a threshold value;
Error distributing means for distributing an error generated due to binarization of the target pixel based on the correction data and the binarization result to pixels before binarization;
An image data binarizing device comprising: an error accumulating means for accumulating the distributed error as error data for each pixel.

Images