Information processing system, storage medium, and information processing method

Abstract

An information processing system, a storage medium, and an information processing method are provided, and the information processing system includes a processor that performs a process of processing white pixels and color pixels in image data including the white pixels and the color pixels, when a sum of respective values of color pixels around a pixel of interest, which is a color pixel of interest, in a predetermined region including the pixel of interest is equal to or less than a threshold value that differs for each pixel of the image data, the correction unit corrects the pixel of interest.

Description

Technical Field

[0001] The invention relates to an information processing system, a storage medium and an information processing method. Background Art

[0002] Patent document 1 discloses a method for obtaining an output image with good granularity when converting a binary image into an image with a higher resolution. In this method, the ratio of color points to white points allocated to a color pixel of interest (hereinafter referred to as a pixel of interest) is changed according to the ratio of color pixels occupied in a predetermined area including the color pixel of interest (hereinafter referred to as a pixel of interest).

[0003] In the method of Patent Document 1, when observing in a second area having a size larger than a predetermined area (referred to as the first area), although the number of color pixels in the second area is almost the same, whether or not to correct the pixel of interest may be switched due to the difference in the number of color pixels in the first area. This may cause density unevenness.

[0004] In this regard, in the method disclosed in Patent Document 2, when the number of color pixels in the first area centered on the pixel of interest is less than a specified value, the number of color pixels in the second area wider than the first area is investigated, and the pixel of interest is corrected when the number is less than a threshold value. When the number is greater than the threshold value, the pixel of interest is not corrected. The correction performed here is called isolated point correction, which is a correction to reduce the concentration of the pixel of interest. That is, in this method, when the number of color pixels in the first area is less than a specified value and the number of color pixels in the second area is less than a threshold value, the pixel of interest is determined to be an isolated point, and the concentration of the isolated point is reduced.

[0005] In order to apply the methods of Patent Documents 1 and 2, the multi-valued image is binarized by a dithering method, an error diffusion method, or the like as pre-processing.

[0006] Patent Document 1: Japanese Patent Application Publication No. 2019-149786

[0007] Patent Document 2: Japanese Patent Application Publication No. 2021-114662

[0008] In a method of determining whether to correct a pixel of interest (i.e., a color pixel of interest) in a binary image based on whether the number of color pixels in an area including the pixel of interest (i.e., a color pixel of interest) is less than a specified value, whether to correct the pixel of interest is switched when a certain pixel value in a multi-valued image before binarization is a boundary. As a result, when the pixel value of the multi-valued image is the pixel value of the boundary or is greater than 1, the density of the corrected image changes significantly. Regarding this issue, refer to Fig.11 , Fig.12 and Fig.13 Provide explanation.

[0009] Here, the case of binarizing a multi-valued image by an ordered dithering method is described as an example. In the dithering method, each pixel of the multi-valued image is binarized according to the magnitude relationship with the threshold. The ordered dithering method is a type of dithering method, which divides the multi-valued image into small blocks, and compares each block with a threshold matrix having the same size as the block, thereby binarizing the multi-valued image. Fig.11 It is an example of a threshold matrix, which consists of 8 rows and 8 columns of thresholds.

[0010] Here, it is possible to consider performing isolated point correction on the binary image obtained by binarization. For example, if the number of color pixels in the area around the pixel of interest in the binary image is less than a threshold, the pixel of interest is determined to be an isolated point, and isolated point correction is performed. In addition, hereinafter, the threshold used in binarization based on the dithering method is referred to as a dithering threshold, and the threshold used in the isolated point correction determination is referred to as a correction threshold.

[0011] Fig.12 and Fig.13 This means that the image portion 100 of 16×16 pixels in the multi-valued image that is the source of the binary image is repeatedly applied two vertically and two horizontally. Fig.11 The threshold matrix is ​​shown in Figure 1. Fig.12 represents the case where the image portion 100 of a multi-valued image is composed entirely of pixels with a pixel value of 7, Fig.13 This shows a case where the image portion 100 is composed entirely of pixels having a pixel value of 8. Here, when the value of a pixel (i.e., pixel value) is greater than the dithering threshold, the pixel is binarized to 1 in the binary value, i.e., a color pixel, and when the pixel value is less than the dithering threshold, the pixel is binarized to 0 in the binary value, i.e., a white pixel.

[0012] exist Fig.12 , pixels with gray background colors (e.g., pixels 102 and 104) are pixels that are converted to color pixels by binarization, and pixels with white background colors (e.g., pixel 106) are pixels that are converted to white pixels by binarization. Fig.12 6 is shown. Fig.12 The pixel value of all pixels in is 7. Therefore, since the pixel value 7 of pixel 102 is greater than the jitter threshold 6, pixel 102 is binarized into a color pixel.

[0013] Here, the 5×5 pixel area 110 surrounded by the thick dotted line in the figure is an area for determining isolated points. If the number of color pixels in the area 110 is below the correction threshold, the focus pixel is determined to be an isolated point, and isolated point correction is performed on the focus pixel. Here, the correction threshold is set to 3. Area 110 is an area used to determine the focus pixel when the color pixel 108 is set as the focus pixel. There are three (this is the number of color pixels excluding the focus pixel itself) color pixels in the area 110, and since the number 3 is below the correction threshold 3, the color pixel 108 as the focus pixel is determined to be an isolated point, and isolated point correction is performed to reduce the concentration. Fig.12 In FIG. 1 , the color pixels (for example, the color pixel 108 ) to which the isolated point correction is applied are shown surrounded by a thick frame among the color pixels shown by the gray background color. Fig.12 In the example shown in FIG. 1 , 16 of the 30 color pixels within image portion 100 are reduced in density through isolated point correction.

[0014] On the other hand, Fig.13 In , since the pixel value of all pixels in the image portion 100 is 8, there are 32 pixels in the image portion 100 that are converted into color pixels by binarization. Among them, when the color pixel 108 is set as the focus pixel, the number of color pixels in the surrounding 5×5 pixel area 110 is 4. Since the number 4 is greater than the correction threshold 3, the focus pixel will not become the object of isolated point correction. Fig.13 In the example shown in FIG. 1 , since all color pixels in the image portion 100 have four or more color pixels in the surrounding 5×5 pixel area, there are no color pixels in the image portion 100 that are the target of isolated point correction.

[0015] Thus, at pixel value 7 Fig.12 In the example, there are 16 color pixels whose density is reduced by isolated point correction. Fig.13 In the example shown in FIG. 1 , the number of color pixels whose density is reduced by isolated point correction is 0.

[0016] So, by Fig.12 and Fig.13 As can be seen from the comparison, the density difference of the corrected output image becomes significantly larger when the pixel value of the multi-valued image that is the source of the binary image is 7 and 8. In contrast, when other pixel values ​​are used as boundaries, for example, such a large difference does not occur between pixel values ​​9 and 10.

[0017] In the above, the case where the ordered dither method is used is described as an example, but the same problem occurs when other dither methods, error diffusion methods, or other binarization methods are used. Summary of the invention

[0018] An object of the present invention is to prevent the density of a corrected image of a binary image from being greatly changed due to a certain pixel value in a multi-valued image that is a source of the binary image being a boundary.

[0019] The invention involved in Scheme 1 is an information processing system, which includes a processor, which performs the following processing: in image data including white pixels and color pixels, when the sum of the values ​​of the color pixels around the focus pixel in a predetermined area including the focus pixel as the focus color pixel is below a threshold value, the focus pixel is corrected, and the threshold value is different for each pixel of the image data.

[0020] The invention according to claim 2 is the information processing system according to claim 1, wherein the threshold value is determined based on a random number for each of the pixels.

[0021] The invention according to claim 3 is the information processing system according to claim 2, wherein the threshold value is a value obtained by changing a predetermined reference value according to a random number for each pixel of the image data.

[0022] The invention according to claim 4 is the information processing system according to claim 3, wherein an average value of the threshold values ​​for each pixel of the image data is equal to the reference value.

[0023] The invention according to Scheme 5 is an information processing system as described in any one of Schemes 1 to 4, wherein each of the values ​​of the color pixels surrounding the pixel of interest is 1, and the sum represents the total number of the color pixels surrounding the pixel of interest in the area.

[0024] The invention according to claim 6 is an information processing system according to any one of claims 1 to 4, wherein each of the values ​​of the color pixels around the pixel of interest has a larger weight the closer the color pixel is to the pixel of interest.

[0025] The invention involved in Scheme 7 is a storage medium, characterized in that it stores a program that causes a computer to perform the following processing: in image data including white pixels and color pixels, when the sum of the values ​​of the color pixels around the focus pixel in a predetermined area including the focus pixel as the focus color pixel is below a threshold value, the focus pixel is corrected, and the threshold value is different for each pixel of the image data.

[0026] The invention involved in Scheme 8 is an information processing method, characterized in that it includes the following steps: in image data including white pixels and color pixels, when the sum of the values ​​of the color pixels around the focus pixel in a predetermined area including the focus pixel as the focus color pixel is below a threshold, the focus pixel is corrected, and the threshold is different for each pixel of the image data.

[0027] Effects of the Invention

[0028] According to the first, fifth, seventh or eighth aspect of the present invention, it is possible to prevent the density of the corrected binary image from being greatly changed due to a certain pixel value in the multivalued image that is the source of the binary image being a boundary.

[0029] According to the second aspect of the present invention, it is possible to make it difficult for artifacts to appear in a corrected image.

[0030] According to the third scheme of the present invention, it is possible to prevent the concentration of the corrected image of the binary image from being greatly changed due to a certain pixel value in the multi-value image that is the source of the binary image being used as a boundary, and at the same time obtain a result close to that of the case where all pixels are corrected using the same reference value as the threshold.

[0031] According to the fourth aspect of the present invention, the overall density of the image as a correction result can be made close to the density in a case where all pixels are corrected using the same reference value as a threshold value.

[0032] According to the sixth aspect of the present invention, compared with a method of controlling whether to correct a target pixel based on the number of color pixels in a predetermined area, an image with a good graininess can be obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Embodiments of the present invention will be described in detail with reference to the following drawings.

[0034] Figure 1 is a diagram illustrating an overall functional configuration of an image processing device;

[0035] Figure 2 is a diagram illustrating a functional configuration of a resolution conversion unit;

[0036] Figure 3 is a diagram illustrating the processing sequence of the resolution conversion unit;

[0037] Figure 4 is a diagram illustrating the distribution of correction threshold values ​​that become different values ​​for each pixel by applying random numbers;

[0038] Figure 5 This means that the binarization result of the multi-valued input image with pixel value 7 is applied. Figure 4A diagram of pixels subjected to isolated point correction for the illustrated distribution of correction thresholds;

[0039] Figure 6 This means that the binarization result of the multi-valued input image with pixel value 8 is applied. Figure 4 A diagram of pixels subjected to isolated point correction for the illustrated distribution of correction thresholds;

[0040] Figure 7 is a diagram showing an example of weight distribution;

[0041] Figure 8 is a diagram illustrating a functional configuration of a resolution conversion unit according to a modified example;

[0042] Fig. 9 is a diagram illustrating a processing sequence of a resolution conversion unit according to a modified example;

[0043] Fig.10 is a diagram illustrating the hardware configuration of a computer in which the information processing system is installed;

[0044] Fig.11 is a diagram showing an example of a threshold matrix used in the ordered dithering method;

[0045] Fig.12 It means that Fig.11 The result of binarizing a multi-value input image with a pixel value of 7 using a threshold matrix of ; a diagram showing isolated point correction for multiple pixels when a uniform correction threshold of 3 is used;

[0046] Fig.13 It means that Fig.11 The result of binarizing a multi-value input image with a pixel value of 8 using a threshold matrix of When a uniform correction threshold of 3 is used, there is no pixel for which isolated point correction is performed.

[0047] Explanation of symbols

[0048] 10-image processing device, 11-information processing system, 12-image input unit, 14-error diffusion processing unit, 16-resolution conversion unit, 18-image output unit, 161-multi-valued unit, 162-counting unit, 163-weight accumulation unit, 164, 164A-threshold processing unit, 165, 165A-storage unit, 166-random number generation unit, 168-random number application unit. DETAILED DESCRIPTION

[0049] Hereinafter, embodiments of the present invention will be described with reference to the drawings.

[0050] <Overall device structure>

[0051] Figure 1 The overall functional structure of the image processing device 10 is illustrated in the figure. The image processing device 10 is a device having a printing function, such as a printer, a copier, a multifunction machine (i.e., a multifunctional device having the functions of a printer, a scanner, a copier, a fax machine, etc.). The image processing device 10 includes an information processing system 11 and an image output unit 18. The information processing system 11 is a functional module that performs information processing such as image processing on input image data. The image output unit 18 is a printing device that prints and outputs the input image data onto a recording medium such as paper (hereinafter referred to as "paper"). The printing method of the image output unit 18 is not particularly limited, and for example, it can be an electronic photography method or an inkjet method.

[0052] The information processing system 11 includes an image input unit 12 , an error diffusion processing unit 14 , and a resolution conversion unit 16 .

[0053] The image input unit 12 receives input of target image data. The image input unit 12 is, for example, a scanner that optically reads a manuscript and converts it into electronic image data. In addition, the image input unit 12 can receive a file of image data generated in an external device such as a personal computer and sent via a network or the like. When receiving an image data file, the image input unit 12 converts the image data included in the file into a bitmap image.

[0054] Furthermore, when the image data input to the image input unit 12 and the color space in the image output unit 18 are different, the image input unit 12 performs color conversion on the input image data to match the color space of the image output unit 18. For example, when the input image data is in RGB format and the image output unit 18 processes CMYK format, the image input unit 12 performs conversion from RGB to CMYK.

[0055] Therefore, the image data transmitted from the image input section 12 to the error diffusion processing section 14 is multi-valued (for example, 8 bits for each color) bitmap image data expressed in the color space of the image output section 18 .

[0056] The error diffusion processing unit 14 converts the multi-valued image data input from the image input unit 12 into binary image data by performing a known error diffusion process on the multi-valued image data. For example, when the input image data is CMYK multi-valued image data, the error diffusion processing unit 14 converts the multi-valued image of each CMYK color into binary image data. The binary image data of each color is an image represented by white pixels with a density of 0 and color pixels with a predetermined density.

[0057] The resolution conversion unit 16 converts the binary image data of each color output from the error diffusion processing unit 14 into image data of the resolution of the image output unit 18. The resolution of the binary image data of each color output from the error diffusion processing unit 14 is the same as the resolution of the image data input to the image input unit 12. On the other hand, the resolution of the image output unit 18 for printing may be higher than the resolution of the input image data. Therefore, the resolution conversion unit 16 converts the resolution of the binary image data after error diffusion.

[0058] Hereinafter, the resolution of the image data input to the resolution conversion unit 16, that is, the resolution of the binary image data output from the error diffusion processing unit 14, is referred to as the input resolution, and the resolution of the image data output from the resolution conversion unit 16 is referred to as the output resolution. The input resolution is the same as the resolution of the image data input to the image input unit 12. Furthermore, the output resolution is the same as the resolution of the image output unit 18. Furthermore, hereinafter, the image data input to the resolution conversion unit 16 is referred to as the input image data, and the image data output from the resolution conversion unit 16 is referred to as the output image data. Furthermore, the pixels of the input image data are referred to as the input pixels, and the pixels of the output image data are referred to as the output pixels.

[0059] Furthermore, the resolution conversion unit 16 also performs isolated point correction during resolution conversion. Isolated point correction is correction for reducing the density of a color pixel that is only isolated from one pixel. That is, if a color pixel corresponding to an isolated point is found in the input image data, the resolution conversion unit 16 performs correction for reducing the density of the color pixel.

[0060] The isolated point correction in the resolution conversion unit 16 is performed, for example, as follows.

[0061] Consider that the resolution conversion unit 16 converts one input pixel into m×n (m, n are integers greater than 1) matrix output pixels. The area of ​​the output pixel is one (m×n)th of the area of ​​the input pixel. If at least one of m or n is greater than 2, the resolution of the output image data is higher than the resolution of the input image data. For example, in the case of converting the resolution of one input pixel into an output pixel group of 2 rows and 4 columns, the number of pixels of the output image data is 8 times the number of pixels of the input image data.

[0062] Regarding input pixels other than isolated points, the resolution conversion section 16 converts the input pixels into an output pixel group of m rows and n columns having the same value as the input pixel. For example, in the case where the input pixel that is not an isolated point is a white pixel, the resolution conversion section 16 converts the input pixel into an m×n white output pixel group. Also, in the case where the input pixel that is not an isolated point is a color pixel, the resolution conversion section 16 converts the input pixel into an m×n color output pixel group. The density of each color output pixel is the same as the density of the color pixel that is the input pixel. Thus, regarding the input pixel that is not an isolated point, the density does not change in the resolution conversion.

[0063] On the other hand, when it is determined that the input pixel is an isolated point (i.e., an isolated color pixel), the resolution conversion unit 16 reduces the density by changing one or more output pixels in the m-row n-column output pixel group to white pixels. That is, if it is a color pixel that is not an isolated point, it is converted to a color output pixel group of m rows and n columns, but in the case of a color pixel that is an isolated point, one or more of the color output pixel group of m rows and n columns is replaced by a white pixel, so the density is reduced. In addition, in the isolated point correction, which pixel in the m-row n-column output pixel group is replaced by a white pixel can be determined according to a predetermined rule. And the determination method of the isolated point in this embodiment will be described in detail later.

[0064] The output image data output from the resolution conversion section 16 is supplied to the image output section 18. The image output section 18 outputs an image on paper in accordance with the output image data.

[0065] <Example of resolution conversion unit>

[0066] In conventional isolated point correction, the correction is performed based on whether the number of color pixels in an area of ​​a predetermined size around a pixel of interest is equal to or smaller than a predetermined correction threshold. The correction threshold used in this case is the same value for all pixels.

[0067] In contrast, in the present embodiment, the correction threshold is set to a different value for each pixel. As an example, the correction threshold for each pixel is generated by changing the reference value by a random number for each pixel using a uniform correction threshold applied to all pixels by an existing method as a reference value.

[0068] An example of the configuration of the resolution conversion unit 16 having such a threshold value changing function is shown in FIG. Figure 2 middle.

[0069] Figure 2The resolution conversion section 16 illustrated in FIG. 1 includes a counting section 162 , a threshold processing section 164 , a storage section 165 , a random number generating section 166 , and a random number applying section 168 .

[0070] The counting section 162 counts the number of color pixels in a region of a predetermined size around each pixel of interest in the binary image data input from the error diffusion processing section 14 .

[0071] The threshold processing unit 164 determines whether to perform isolated point correction on the pixel of interest by comparing the total number of color pixels in the area obtained by the counting unit 162 with the correction threshold, and generates an output corresponding to the pixel of interest according to the determination. When generating the output, the threshold processing unit 164 converts one pixel of interest into a matrix of multiple output pixels. Through this action, the input image data is converted into higher-resolution output image data.

[0072] In this conversion, with respect to the pixel of interest for which isolated point correction is not performed, the threshold processing unit 164 outputs an output pixel group of m×n pixels having the same value as the value of the pixel of interest (i.e., 0 or 1). That is, if the pixel of interest is a white pixel, the threshold processing unit 164 outputs an m×n white output pixel group, and if the pixel of interest is a color pixel, an m×n color output pixel group is output. Furthermore, with respect to the pixel of interest for which isolated point correction is performed, the threshold processing unit 164 outputs pixels obtained by changing some of these output pixel groups to white pixels according to a predetermined rule.

[0073] The threshold processing unit 164 uses the threshold supplied from the random number application unit 168 for each pixel as the threshold for the above-mentioned threshold comparison.

[0074] The storage unit 165 stores a reference value of the correction threshold value. The reference value is a predetermined fixed value.

[0075] The random number generator 166 generates a random number for each pixel. The random number application unit 168 generates a correction threshold for each pixel by applying the random number generated for each pixel by the random number generator 166 to the reference value acquired from the storage unit 165. Thus, a different correction threshold is obtained for each pixel.

[0076] The method of applying the random number in the random number application unit 168 to the reference value may be, for example, a method of adding the reference value to the random number. In this case, for example, if the reference value is set to 3, the random number generation unit 166 randomly generates three values ​​of -1, 0, and 1 for each pixel, and the random number application unit 168 randomly outputs 2, 3, or 4 for each pixel. In this specific example, if a sufficient number of pixels (for example, all pixels of the input image data) are averaged, the correction threshold for each pixel randomly output by the random number application unit 168 is substantially equal to the reference value. If the average value of the correction threshold is equal to the reference value, the number of pixels in the entire image that are subjected to isolated point correction can be made close to the case where the reference value is used as the correction threshold and isolated point correction is performed on all pixels.

[0077] Furthermore, as another example, the random number application unit 168 may multiply the random number generated by the random number generation unit 166 by the reference value. For example, the random number generation unit 166 randomly outputs five real numbers, 0.5, 0.8, 1, 1.2, and 1.5, for each pixel. Then, the random number application unit 168 multiplies the output value of the random number generation unit 166 by the reference value (for example, 4) and rounds the multiplication result to an integer to obtain the correction threshold for each pixel. In this specific example, if the average is performed for a sufficient number of pixels, the correction threshold for each pixel output by the random number application unit 168 is also almost equal to the reference value.

[0078] An example of the processing sequence executed by the resolution conversion unit 16 is shown in Figure 3 middle.

[0079] exist Figure 3 In the order of the input image data, the counting unit 162 selects one pixel from the input image data in the prescribed order (S10). Next, the counting unit 162 determines whether the selected pixel is a dot (i.e., a color pixel) (S12). When the selected pixel is not a dot, that is, when the pixel is a white pixel, the threshold processing unit 164 outputs a white pixel group of m×n pixels as an output pixel group corresponding to the pixel (S24).

[0080] In S12, when it is determined that the pixel is a dot, the counting unit 162 counts the color pixels in an area of ​​a predetermined size around the pixel (which is a color pixel and is the focus pixel in the following order), and finds the total number of color pixels in the area (but excluding the focus pixel itself) (S14). Fig.12 As shown in the region 110 , the region can be set to a range of k×k pixels (k is an odd number greater than or equal to 3) centered on the pixel of interest.

[0081] In this case, the threshold processing unit 164 requests the random number generating unit 166 to generate a random number for the pixel of interest. The random number applying unit 168 generates a correction threshold value for the pixel of interest by applying the random number generated by the random number generating unit 166 in response to the request to the reference value stored in the storage unit 165 (S16).

[0082] Next, the threshold processing unit 164 determines whether the received counting result, that is, the total number of color pixels in the area around the pixel of interest is less than the correction threshold value for the pixel of interest generated by the random number application unit 168 (S18). When the counting result is less than the correction threshold value (the determination result of S18 is "yes"), the threshold processing unit 164 outputs a point group to which isolated point correction has been performed as an output pixel group corresponding to the pixel of interest (S20). That is, in this case, the threshold processing unit 164 determines that the pixel of interest is an isolated point and performs isolated point correction. When performing S20, since the pixel of interest is a point (that is, a color pixel), if it is assumed that isolated point correction is not performed, the threshold processing unit 164 outputs a point group of m×n pixels as a result of resolution conversion. However, since isolated point correction is performed in S20, the threshold processing unit 164 converts one or more points determined according to the isolated point correction rule in the point group of m×n pixels into white pixels and outputs them. As a result, the density of the output pixel group corresponding to the pixel of interest is lower than that in the case where isolated point correction is not performed.

[0083] In S20, the intensity of the isolated point correction can be changed according to the counting result. That is, since the smaller the value of the final counting result is, the lower the concentration around the pixel of interest is, the number of pixels converted to white pixels in the output pixel group can also be increased to further reduce the concentration of the pixel of interest through the isolated point correction.

[0084] If the result of determination in S18 is "No", that is, the count result is greater than the correction threshold, the threshold processing unit 164 directly outputs the point group of m×n pixels corresponding to the pixel of interest as the result of resolution conversion (S22). In other words, in this case, isolated point correction is not performed on the pixel of interest.

[0085] After S20, S22 or S24, the resolution conversion unit 16 determines whether the processing of S10 to S26 is completed for all pixels of the input image data (S26). If the result of this determination is "No", the resolution conversion unit 16 returns to S10, selects the next pixel of interest from the input image data, and performs the processing after S12 on the pixel of interest.

[0086] If the determination result of S26 is "Yes", the resolution conversion section 16 completes the resolution conversion process on the input image data.

[0087] As described above, in this embodiment, since the correction threshold used to determine whether to perform isolated point correction on a color pixel is made different for each pixel according to a random number, the problem that the density of the correction result differs significantly at the boundary of the pixel value at the stage of a multi-valued image is alleviated.

[0088] As a specific example, in the above reference Figure 11 to Figure 13 In the example described below, the case where the correction threshold is made different for each pixel by the method of this embodiment is shown. In the following example, the reference value of the correction threshold is set to be the same as Fig.12 and Fig.13 The correction threshold value for the case of is the same as 3. Then, the random number generation unit 166 randomly generates three values ​​of -1, 0, and 1 for each pixel, and the random number application unit 168 adds the randomly generated value to the reference value 3.

[0089] In this case, the correction threshold for each pixel becomes Figure 4 The distribution shown. Figure 4 In , the pixel that sets the background color to gray is Fig.12 and Fig.13 In the example of FIG. 1 , pixels with a gray background color that are classified as color pixels by binarization are also implemented. Figure 4 As shown, the correction threshold is different for each color pixel.

[0090] Figure 5 is based on Fig.11 The threshold matrix for all pixels in the 16×16 pixel is 7 (this is the same as Fig.12 The same situation) is binarized and it is determined whether it is necessary to use Figure 4 The correction threshold value of the pixel shown is the result of applying isolated point correction to each color pixel of the binarization result. Figure 5 In the example of FIG. 1 , a pixel group surrounded by a thick-line frame such as pixel 120 is a pixel to which isolated point correction is performed, and the total number of pixels is 14.

[0091] on the other hand, Figure 6 This means that all pixel values ​​for 16×16 pixels are 8 (this is consistent with Fig.13 The same situation) of the image part was carried out with Figure 5 The same binarization as in the example above and the result of determining whether isolated point correction is needed. Figure 6 In the example shown in FIG. 1 , the total number of pixels surrounded by the thick frame and subjected to isolated point correction is 11.

[0092] A uniform correction threshold of 3 was applied to all pixels. Fig.12 and Fig.13In the example of , if the pixel value changes from 7 to 8, the number of pixels to which isolated point correction is applied changes sharply from 16 to 0. Figure 5 and Figure 6 In the example of , under the same conditions, the number of pixels subjected to isolated point correction only changes from 14 to 11, which is a very small change. As can be seen, the problem of a large change in the density of the isolated point correction result between pixel values ​​7 and 8 is eliminated by this embodiment.

[0093] As described above, in this embodiment, since the correction threshold is different for each pixel, the situation in which the density after isolated point correction changes significantly as a boundary with a certain pixel value in the stage of the multi-value image before binarization, which occurs when a uniform correction threshold is used for all pixels, is reduced.

[0094] Furthermore, in the present embodiment, since the correction threshold is changed for each pixel according to a random number, the arrangement of the correction threshold is irregular. Therefore, artifacts are less likely to appear in the isolated point corrected image output from the resolution conversion unit 16 .

[0095] <Modification>

[0096] In the above-described embodiment, isolated point correction is controlled based on the number of color pixels in the area around the pixel of interest. In this method, whether the color pixels are clustered close to the pixel of interest or away from the pixel of interest, if the number of color pixels is the same, the result of determining whether to perform isolated point correction is also the same.

[0097] However, if the distance of the color pixel relative to the pixel of interest is different, the point generated by printing the color pixel and the point generated by printing the pixel of interest may overlap or may not overlap. When these points do not overlap, it can be said that the pixel of interest is isolated, and when these points overlap, it is difficult to say that the pixel of interest is isolated. It can be considered that in the control of isolated point correction, for example, it is preferable to consider the distance of the color pixel relative to the pixel of interest (in other words, whether it is far away or close). The following is a description of a modification in which the distance is taken into consideration.

[0098] In this variation, the color pixels around the pixel of interest are given a weight that decreases as they are farther from the pixel of interest, and whether the pixel of interest is an isolated point is determined based on the sum of the weights of the color pixels around the pixel of interest. Here, the weight is a value greater than 0.

[0099] More specifically, in this modified example, when the sum of the weights of the color pixels around the pixel of interest is less than a predetermined threshold, the pixel of interest is determined to be an isolated point. Since the weights of the color pixels farther away from the pixel of interest are smaller, in other words, the weights of the color pixels closer to the pixel of interest are larger, the more color pixels there are near the pixel of interest, the greater the sum of the weights is likely to be, making it difficult to determine that the pixel of interest is an isolated point.

[0100] On the contrary, when there are no color pixels near the pixel of interest, it is easy to determine that the pixel of interest is an isolated point. For example, even if there are many color pixels at a position far from the pixel of interest, but there are no color pixels near it, in the printed result, the points of the surrounding color pixels sometimes do not overlap with the point of the pixel of interest at all. In this case, since the pixel of interest is easy to be conspicuous as an isolated point, it is desirable to set it as an isolated point correction object, for example.

[0101] Based on this idea, in this modification, the sum of the weights of the color pixels around the pixel of interest is calculated, and the sum is compared with a threshold value to determine whether or not to perform isolated point correction on the pixel of interest.

[0102] Figure 7 An example of assigning weights to a group of pixels surrounding a pixel of interest is shown in FIG. Figure 7 represents the weights of the pixel positions 210, 220, etc. in the 7×7 pixel area 300 centered on the pixel of interest 200. In this example, the weight W of each pixel position (i, j) is ij It is calculated by the following formula.

[0103] [Formula 1]

[0104] W ij =1 / (i 2 +j 2 ) 1 / 2 ×3

[0105] Here, i and j are the x-coordinate and y-coordinate of each pixel position when the position of the pixel of interest 200 is set to the origin (0, 0). For example, the right side of the origin is positive and the left side is negative in the x-coordinate, and the upper side of the origin is positive and the lower side is negative in the y-coordinate. In this formula, the weight W ij It is inversely proportional to the distance from the pixel of interest, and the proportionality constant is 3. The relationship in which the weight is inversely proportional to the distance and the proportionality constant 3 are merely examples.

[0106] exist Figure 7In the example, since the focus pixel 200 is not the object of the weight sum, the weight of the position of the focus pixel 200 is 0, the weight of the pixel position 210 of the adjacent pixel below the focus pixel 200 is 3.0000, and the weight of the pixel position 220 of the lower right adjacent pixel is 2.1213. In addition, the weight of the pixel position 230 of the lower left corner pixel in the region 300, which is farthest from the focus pixel 200 in the region 300, is 0.7071.

[0107] The overall structure of the image processing device 10 in this modification is different from Figure 1 The structure shown is the same as that shown in FIG. 1 , but the internal structure of the resolution conversion unit 16 is the same as that shown in FIG. Figure 2 The above-described embodiments shown have different structures. Figure 8 An example of the internal structure of the resolution conversion unit 16 in this modification is shown in FIG. Figure 8 In Figure 2 The same components as those shown in the figure are denoted by the same reference numerals, and duplicate description is omitted.

[0108] Figure 8 The resolution conversion section 16 illustrated in FIG. 1 includes a multi-valued section 161 , a weight accumulation section 163 , a threshold processing section 164A, a storage section 165A, a random number generation section 166 , and a random number application section 168 .

[0109] The multi-valued unit 161 converts the value of each pixel of the binary image data input from the error diffusion processing unit 14 into a multi-value. This conversion is to set the pixel value to the same real value as the weight so that it can be multiplied by the weight by the weight accumulation unit 163. For example, in the case of converting the binary image into an 8-bit multi-value, the multi-valued unit 161 converts the value of the white pixel (i.e., the value is 0) of the binary image into an 8-bit value representing the real value 0.0, and converts the value of the color pixel (i.e., the value is 1) of the binary image into an 8-bit value representing 1.0 among the real values. That is, the multi-valued unit 161 converts the binary image of 0 and 1 into a binary image of 0.0 and 1.0, although it is multi-bit.

[0110] The weight accumulation unit 163 regards each pixel of the multi-valued image data output from the multi-valued unit 161 as a pixel of interest, and calculates the sum of the weights of the color pixels surrounding the pixel of interest for each pixel of interest.

[0111] The threshold processing unit 164A determines whether to perform isolated point correction on the pixel of interest by comparing the sum of the weights calculated by the weight accumulation unit 163 with the threshold value, and generates an output corresponding to the pixel of interest according to the determination. When generating the output, the threshold processing unit 164A converts one pixel of interest into a matrix of multiple output pixels. Through this action, the input image data is converted into output image data with a higher resolution.

[0112] In this conversion, with respect to the pixel of interest for which the isolated point correction is not performed, the threshold processing unit 164A outputs an output pixel group of m×n pixels having the same value as the value of the pixel of interest (i.e., 0 or 1). That is, if the pixel of interest is a white pixel, the threshold processing unit 164A outputs an m×n white output pixel group, and if the pixel of interest is a color pixel, an m×n color output pixel group is output. Furthermore, with respect to the pixel of interest for which the isolated point correction is performed, the threshold processing unit 164A outputs pixels in which some of these output pixel groups are changed to white pixels according to a predetermined rule.

[0113] The processing sequence executed by the weight accumulation unit 163 and the threshold processing unit 164A is shown below. Fig. 9 In. Fig. 9 In Figure 3 The same steps as those in the processing sequence of the above-described embodiment are marked with the same reference numerals. Figure 8 .

[0114] exist Fig. 9 The weight accumulation unit 163 selects a pixel from the input image data in the prescribed order (S10). Next, the weight accumulation unit 163 determines whether the selected pixel is a dot (i.e., a color pixel) (S12). When the pixel selected by the weight accumulation unit 163 is not a dot, that is, when the pixel is a white pixel, the threshold processing unit 164A outputs a white pixel group of m×n pixels as an output pixel group corresponding to the pixel (S24).

[0115] If it is determined in S12 that the selected pixel is a dot (i.e., a color pixel), the weight accumulation unit 163 clears the value of the accumulation result held as the internal variable to zero (S140). Next, the weight accumulation unit 163 investigates the pixel in a predetermined order, i.e., focuses on an area 300 of a predetermined size around the pixel 200 (see Figure 7 ), if a color pixel is found, it is distributed by weight (e.g., Figure 7 ) to find the weight of the color pixel position. Then, the weight integration unit 163 multiplies the value of the color pixel and its weight, and adds the multiplication result to the integration result (S142). Next, the weight integration unit 163 determines whether the investigation of all pixels in the area 300 has been completed (S144). If there are uninvestigated pixels, it returns to S142 and processes the uninvestigated pixels.

[0116] In S144, when the weight integration unit 163 determines that the investigation of all pixels is completed, the total value of the multiplication result of the value of the color pixel existing in the area 300 and the weight of the color pixel is maintained in the integration result as an internal variable. Since the value of the color pixel is a real value 1.0, the final integration result represents the total of the weights of the color pixels in the area 300. The weight integration unit 163 transmits the final integration result to the threshold processing unit 164A.

[0117] When the threshold processing unit 164A receives the final cumulative result of the pixel of interest from the weight accumulation unit 163, it performs processing for obtaining the correction threshold used in determining the pixel of interest (S16A). That is, the threshold processing unit 164A requests the random number generation unit 166 to generate a random number for the pixel of interest. The random number application unit 168 generates a correction threshold for the pixel of interest by applying the random number generated by the random number generation unit 166 in response to the request to the reference value stored in the storage unit 165A. In addition, the reference value stored in the storage unit 165A is a reference value for the correction threshold to be compared with the cumulative result of the weight. The reference value is compared with the reference value stored in the storage unit 165A. Figure 2 A value different from the reference value of the threshold value for comparison with the total number of color pixels in the area in the storage unit 165.

[0118] Next, the threshold processing unit 164A determines whether the received final integration result, that is, the sum of the weights of the color pixels in the area around the pixel of interest is less than or equal to the correction threshold for the pixel of interest generated by the random number application unit 168 ( S18A).

[0119] When the cumulative result is below the correction threshold (the judgment result of S18A is "yes"), the threshold processing unit 164A outputs the point group to which the isolated point correction is applied as the output pixel group corresponding to the pixel of interest (S20). When the judgment result of S18A is "no", that is, when the cumulative result is greater than the correction threshold, the threshold processing unit 164A directly outputs the point group of m×n pixels corresponding to the pixel of interest as the result of resolution conversion (S22).

[0120] After S20, S22 or S24, the resolution conversion unit 16 determines whether the processing of S10 to S26 is completed for all pixels of the input image data (S26). If the result of this determination is "No", the resolution conversion unit 16 returns to S10, selects the next pixel of interest from the input image data, and performs the processing after S12 on the pixel of interest.

[0121] If the determination result of S26 is "Yes", the resolution conversion section 16 completes the resolution conversion process on the input image data.

[0122] As described above, in this variation, a weight is assigned to a color pixel that is farther from the pixel of interest, and when the sum of the weights of the color pixel group surrounding the pixel of interest is less than a threshold, isolated point correction is performed on the pixel of interest. Compared with the above embodiment in which whether isolated point correction is required is determined based on the number of color pixels, this method can make a more precise determination.

[0123] The above-described embodiments are merely illustrative, and various modifications may be made within the technical scope of the present invention. For example, the image processing device 10 binarizes the input multi-valued image data by error diffusion, but the binarization method is not limited thereto. Of course, binarization may also be performed by other methods such as a dithering method.

[0124] And, in Figure 1 In the example of , the image processing device 10 has the image output unit 18, but it may also be a structure without the image output unit 18. In this case, the image processing device 10 can receive an image printed by an external printer from the host, binarize and convert the resolution of the image (including isolated point correction) by the method of this embodiment, and supply the resulting image to the printer.

[0125] The information processing system 11 of the image processing device 10 described above is constituted by, for example, a general-purpose computer. Fig.10 As shown in the example, the computer has a controller for controlling a processor 1002, a memory (main storage device) 1004 such as a random access memory (RAM), an auxiliary storage device 1006 as a non-volatile storage device such as a flash memory or SSD (solid state drive), HDD (hard disk drive), etc., an interface with various input and output devices 1008, and a network interface 1010 for controlling connection with a network such as a local area network, etc., and a circuit structure connected via a data transmission path such as a bus 1012. A program describing the content of the processing of the above-mentioned embodiment is installed in the computer via a network or the like, and is stored in the auxiliary storage device 1006. The information processing mechanism of this embodiment is configured by the processor 1002 executing the program stored in the auxiliary storage device 1006 using the memory 1004. In addition, the information processing system 11 may include an image processing processor for performing general-purpose or special-purpose image processing at high speed.

[0126] Furthermore, the information processing system 11 may be composed of one computer or a plurality of computers. For example, a part of the functions of the information processing system 11 may be provided in another computer outside the image processing apparatus 10 .

[0127] In the above-mentioned embodiments, the processor refers to a processor in a broad sense, including a general-purpose processor (for example, CPU: Central Processing Unit, etc.) or a dedicated processor (for example, GPU: Graphics Processing Unit, ASIC: Application Specific Integrated Circuit, FPGA: Field Programmable Gate Array, programmable logic device, etc.).

[0128] Furthermore, the actions of the processors in the above-mentioned embodiments can be performed not only by one processor, but also by a plurality of processors located in physically separate positions. Furthermore, the order of the operations of the processors is not limited to the order described in the above-mentioned embodiments, and can be changed appropriately.

[0129] (Note) (1)

[0131] An information processing system includes a processor, wherein the processor performs the following processing:

[0132] In image data including white pixels and color pixels, when the sum of the values ​​of the color pixels around the focus pixel in a predetermined area including the focus pixel as the focus color pixel is less than a threshold value, the focus pixel is corrected,

[0133] The threshold value is different for each pixel of the image data. (2)

[0135] The information processing system according to (1), wherein:

[0136] The threshold value is determined for each of the pixels according to a random number. (3)

[0138] The information processing system according to (2), wherein:

[0139] The threshold value is a value obtained by changing a predetermined reference value according to a random number for each pixel of the image data. (4)

[0141] The information processing system according to (3), wherein:

[0142] An average value of the threshold values ​​for each pixel of the image data is equal to the reference value. (5)

[0144] The information processing system according to any one of (1) to (4), wherein:

[0145] The respective values ​​of the color pixels surrounding the pixel of interest are 1, and the sum represents the total number of the color pixels surrounding the pixel of interest in the area. (6)

[0147] The information processing system according to any one of (1) to (4), wherein:

[0148] The values ​​of the color pixels around the pixel of interest have a greater weight the closer the color pixel is to the pixel of interest. (7)

[0150] A program, characterized in that it causes a computer to execute the following processing:

[0151] In image data including white pixels and color pixels, when the sum of the values ​​of the color pixels around the focus pixel in a predetermined area including the focus pixel as the focus color pixel is less than a threshold value, the focus pixel is corrected,

[0152] The threshold value is different for each pixel of the image data.

[0153] According to the information processing system described in (1) or (5) and the program described in (7), it is possible to prevent the density of the corrected image of the binary image from being greatly changed due to a certain pixel value in the multi-valued image that is the source of the binary image being used as a boundary.

[0154] According to the information processing system described in (2), it is possible to make it less likely that artifacts will appear in the corrected image.

[0155] According to the information processing system described in (3), it is possible to prevent the concentration of the corrected image of the binary image from changing significantly due to a certain pixel value in the multi-value image that is the source of the binary image being used as a boundary, and at the same time obtain a result close to that of the case where all pixels are corrected using the same reference value as the threshold.

[0156] According to the information processing system described in (4), the overall density of the image as a correction result can be made close to the case where all pixels are corrected using the same reference value as a threshold value.

[0157] According to the information processing system described in (6), compared with a method of controlling whether to correct the target pixel based on the number of color pixels in a predetermined area, an image with good graininess can be obtained.

[0158] The above-mentioned embodiments of the present invention are provided for the purpose of illustration and description. In addition, the embodiments of the present invention do not fully and exhaustively include the present invention, and do not limit the present invention to the disclosed methods. Obviously, various modifications and changes are self-evident to those skilled in the art to which the present invention belongs. The present embodiment is selected and described in order to most easily understand the principles of the present invention and its application. Thus, other technicians in the art can understand the present invention through various variations optimized for specific uses assumed to be various embodiments. The scope of the present invention is defined by the above claims and their equivalents.

Claims

1. An information processing system comprising a processor, wherein the processor performs the following processing: In image data including white pixels and color pixels, when the sum of the values ​​of the color pixels around the focus pixel in a predetermined area including the focus pixel as the focus color pixel is less than a threshold value, the focus pixel is corrected, The threshold value is different for each pixel of the image data.

2. The information processing system according to claim 1, wherein: The threshold value is determined for each of the pixels according to a random number.

3. The information processing system according to claim 2, wherein: The threshold value is a value obtained by changing a predetermined reference value according to a random number for each pixel of the image data.

4. The information processing system according to claim 3, wherein: An average value of the threshold values ​​for each pixel of the image data is equal to the reference value.

5. The information processing system according to any one of claims 1 to 4, wherein: The respective values ​​of the color pixels surrounding the pixel of interest are 1, and the sum represents the total number of the color pixels surrounding the pixel of interest in the area.

6. The information processing system according to any one of claims 1 to 4, wherein: The values ​​of the color pixels around the pixel of interest have a larger weight as the color pixel is closer to the pixel of interest.

7. A storage medium, characterized in that: A program is stored which causes the computer to execute the following processing: In image data including white pixels and color pixels, when the sum of the values ​​of the color pixels around the focus pixel in a predetermined area including the focus pixel as the focus color pixel is less than a threshold value, the focus pixel is corrected, The threshold value is different for each pixel of the image data.

8. An information processing method, characterized in that: The steps include: In image data including white pixels and color pixels, when the sum of the values ​​of the color pixels around the focus pixel in a predetermined area including the focus pixel as the focus color pixel is less than a threshold value, the focus pixel is corrected, The threshold value is different for each pixel of the image data.

Images