JP6765103B2 - Image processing device, image processing program and image processing method - Google Patents

Description

The present invention relates to an image processing apparatus, an image processing program, and an image processing method of a digital printing apparatus.

Conventionally, an inkjet digital printing apparatus that prints on printing paper by ejecting ink from a print head is widely known (Patent Document 1). In such a digital printing apparatus, printing is performed by turning on / off ink ejection on printing paper, so that the image data used for print control needs to be binary image data. Therefore, conventionally, as a pre-processing for digital printing, a gradation conversion process (halftone process) for converting multi-valued image data (shade image data) into binary image data is executed. Here, the binary image data refers to image data in which the gradation (luminance, density) of each pixel constituting the image is expressed by 1 bit (that is, 2 gradations). Further, the multi-valued image data refers to image data in which the gradation (luminance, density) of each pixel constituting the image is represented by 2 bits or more (for example, 8 bits = 256 gradations).

Various methods such as an error diffusion method and a dither method (organized dither method, random dither method, etc.) are known as gradation conversion processing, but an error is obtained because the reproducibility of smooth gradation is excellent. The diffusion method is widely adopted. Since the error diffusion method is a well-known method, a detailed description thereof will be omitted, but an outline thereof will be described with reference to FIG.

FIG. 11 is a diagram showing an example of binarization processing in 256-gradation multi-valued image data, and in particular, FIGS. 11 (a), 11 (c), 11 (e), and 11 (g). Shows an example of pixel arrangement of multi-valued image data, and FIGS. 11 (b), 11 (d), 11 (f) and 11 (h) show error diffusion tables, respectively. In the following description, the threshold value is set to an intermediate luminance value (that is, 128), pixels smaller than 128 are converted to 0, and pixels larger than 128 are converted to 256, but the present invention is not limited to this. , The threshold value can be set arbitrarily. Further, in the following description, the difference between the luminance value before the gradation conversion and the luminance value after the gradation conversion is defined as an "error (Err)", and as shown in FIG. 11B, the pixels represented by the "Err". The error is diffused at a rate of 7/16 to the right, 1/16 to the lower right, 5/16 to the lower, and 3/16 to the lower left, but the error diffusion is not limited to this. The values in the table can be set arbitrarily.

In FIG. 11A, the gradation conversion process is completed only in the upper left pixel (the portion where the brightness value is “0”). In the following figures, the pixels for which the gradation conversion process has been completed are indicated by hatches. In FIG. 11C, since the luminance value of the second pixel from the left in the upper row is 100 (<128), the luminance value after gradation conversion is “0”. At this time, since the error is Err = 100-0 = 100, the diffusion error of the error diffusion table shown in FIG. 11 (d) (44, 6, 31, 19 clockwise from the pixel on the right) is shown in FIG. It is added to the surrounding unprocessed pixels in (c) (144, 106, 131, 119). Subsequently, in FIG. 11 (e), since the luminance value of the third pixel from the left in the upper row is 144 (≧ 128), the luminance value after gradation conversion is “255”. At this time, the error becomes Err = 144-255 = −111, which is a negative value. As a result, the diffusion error of the error diffusion table shown in FIG. 11 (f) (-48, -7, -35, -21 clockwise from the pixel on the right) is added to the surrounding pixels in FIG. 11 (e). (52, 93, 71, 110). Subsequently, in FIG. 11 (g), since the value of the fourth pixel from the left in the upper row is 52 (<128), the value after gradation conversion is “0”. At this time, since the error is Err = 52-0 = 52, the diffusion error of the error diffusion table shown in FIG. 11 (h) (23, 3, 16, 10 clockwise from the pixel on the right) is shown in FIG. It is added to the surrounding pixels in (g) (123, 103, 109, 81). After that, similarly, for all the pixels constituting the multi-valued image data, the determination by comparison with the threshold value and the diffusion of the error to the surrounding unprocessed pixels are executed.

Then, according to such an error diffusion method, the error generated during binarization is diffused to the surrounding unprocessed pixels at a predetermined ratio, as compared with the case of simply binarizing with reference to the threshold value. , Smooth gradation can be reproduced, and the appearance of the image after the gradation conversion processing can be made closer to that before the gradation conversion processing.

However, as described above, the error diffusion method is a method of reproducing a smooth gradation by diffusing the error generated during binarization to the surrounding unprocessed pixels at a predetermined ratio, and thus is a multi-valued image. The edges of images and characters included in the data may be blurred. Further, for example, as shown in FIG. 12A, when the images and characters included in the multi-valued image data are composed of a small number of pixels (for example, in the case of characters represented by thin lines), As shown in FIG. 12B, there is a possibility that problems such as extremely lightening of colors and loss of images and characters may occur due to error diffusion.

Therefore, in recent years, by performing edge detection processing on multi-valued image data, after identifying the edge portion existing in the image, gradation conversion processing by the dither method is executed for the edge portion, and other portions (non-non-existent). For the edge portion), a method of executing gradation conversion processing by an error diffusion method has been proposed (Patent Document 2). According to the method of Patent Document 2 as described above, the edge portion is binarized by the dither method that does not diffuse the pixel error, so that the edge portion is theoretically suppressed from collapsing or color loss. It is possible.

Japanese Patent No. 5744360 Japanese Unexamined Patent Publication No. 2005-72748

However, in the method of Patent Document 2, since the edge detection process is limited to simply extracting the difference in brightness between adjacent pixels, there is a risk that a portion that should not be processed as an edge is processed as an edge, or as an edge. There is a problem that the part to be processed may not be extracted as an edge, and therefore it may not be possible to realize image generation with a flowing gradation expression. That is, in the conventional edge detection process including the edge detection process in Patent Document 2, a portion where the brightness difference between adjacent pixels is large is regarded as an “edge”. However, just because the brightness difference is large does not necessarily mean that it is an edge, and even if the brightness difference is small, it becomes an edge, for example, the boundary between red and blue having the same brightness. What you get. As a result, in the conventional edge detection process, for example, even though it is a continuous image portion, it may be erroneously detected as an edge portion, and inappropriate edge enhancement may be performed. Then, in the method of Patent Document 2, as a result of such erroneous detection, the gradation conversion is partially performed by the dither method for the portion to be uniformly gradation-converted by the error diffusion method, such as a continuous image portion. There is a risk of executing the process, which may make it impossible to reproduce a smooth gradation.

The present invention has been made in view of the above-mentioned problems of the prior art, and an object of the present invention is to provide an image processing apparatus, an image processing program, and an image processing method capable of accurately extracting an edge portion. With the goal.

In addition to the conventionally known feature that the brightness difference between adjacent pixels is large as a feature of the "edge portion", the present inventor always has different (or discrete) brightness around the pixel to be treated as the edge portion. We found a feature based on a new point of view that there are pixels with high homogeneity brightness and color around pixels that have color and do not (pixels that should be treated as non-edge parts), and we have been studying diligently. As a result, we have invented an image processing device, an image processing program, and an image processing method that can realize highly accurate edge extraction based on these features.

Specifically, the image processing device according to the present invention is an image processing device that converts multi-valued image data into binary image data that can be used in a digital printing device, and edge extraction is performed on the multi-valued image data. By executing the gradation conversion process on the edge extraction unit that executes the process and the multi-value image data for which the edge extraction process has been executed by the edge extraction unit, the multi-value image data becomes binary image data. The edge extraction unit includes a gradation conversion processing unit for conversion, and the edge extraction unit includes a primary edge extraction unit that executes edge extraction processing on the multi-valued image data using a primary differential filter or a secondary differential filter, and the multi-value image data. For each of the pixels constituting the value image data, the variation in brightness is calculated for a plurality of pixels within a predetermined range as statistical targets, and the calculated variation is compared with a predetermined threshold to configure the multivalued image data. The mapping processing unit that maps the variation in the brightness of the pixels to be performed, the primary edge extraction data in which the edge portion is extracted by the primary edge extraction unit, and the map data indicating the variation in brightness generated by the mapping processing unit. It is characterized in that it includes an edge logic composition processing unit that generates secondary edge extraction data reflecting variations in brightness by logically synthesizing and.

In the image processing apparatus according to the present invention, the mapping processing unit calculates a luminance dispersion value for a plurality of pixels within a predetermined range centered on a pixel to be determined, and calculates the luminance dispersion value. By executing the process of determining whether or not is equal to or less than a predetermined threshold value for each of the pixels constituting the multi-valued image data, the variation in the brightness of the pixels constituting the multi-valued image data is mapped. It may be configured to be.

Further, in the image processing apparatus according to the present invention, the mapping processing unit targets a plurality of pixels within a predetermined range centered on a pixel to be determined as a statistical target, and pixels having a brightness equal to or lower than a predetermined threshold. By executing the process of calculating the number of pixels and determining whether or not the calculated number is equal to or less than a predetermined threshold value for each of the pixels constituting the multi-valued image data, the multi-valued image It may be configured to map the variation in the brightness of the pixels constituting the data.

Further, in the image processing apparatus according to the present invention, the mapping processing unit sets a plurality of pixels within a predetermined range centered on the pixel to be determined as statistical targets, and has a luminance dispersion value and a predetermined threshold value or more. Pixels constituting the multi-valued image data are subjected to a process of calculating the number of pixels having a brightness of less than or equal to and determining whether or not both the calculated dispersion value and the number of pixels are equal to or less than a predetermined threshold value. By executing each of the above, the variation in the brightness of the pixels constituting the multi-valued image data may be configured to be mapped.

In the image processing apparatus according to the present invention, the gradation conversion processing unit performs gradation conversion processing on a portion determined to be an edge portion by the edge extraction unit, and determines that the portion is a non-edge portion by the edge extraction unit. It is preferable that the gradation conversion process for the formed portion is executed under different conditions.

Specifically, the gradation conversion processing unit includes an edge portion quantization processing unit that executes gradation conversion processing for a portion determined to be an edge portion by the edge extraction unit, and the edge extraction unit. The non-edge portion quantization processing unit that executes the gradation conversion process, the quantization data processed by the edge portion quantization processing unit, and the non-edge portion for the portion determined to be the non-edge portion by It is preferable to include a quantization logic synthesis processing unit that generates the binary image data by logically synthesizing the quantization data processed by the partial quantization processing unit.

Further, in the image processing apparatus according to the present invention, the gradation conversion processing for the edge portion and the gradation conversion processing for the non-edge portion are gradation conversion processing based on an error diffusion method based on different threshold values. It is preferable to have.

The image processing program according to the present invention is an image processing program that causes an image processing device to execute image processing for converting multi-valued image data into binary image data that can be used in a digital printing device, and causes the image processing device to execute image processing. By executing the edge extraction process for executing the edge extraction process on the multi-valued image data and the gradation conversion process for the multi-valued image data on which the edge extraction process is executed by the edge extraction step. A gradation conversion processing step of converting the multi-valued image data into binary image data is executed, and the edge extraction step extracts edges of the multi-valued image data using a primary differential filter or a secondary differential filter. For each of the primary edge extraction step for executing the process and the pixels constituting the multi-valued image data, the variation in brightness is calculated for a plurality of pixels within a predetermined range as statistical targets, and the calculated variation is set as a predetermined threshold value. By comparing, the mapping processing step of mapping the variation in the brightness of the pixels constituting the multi-valued image data, the primary edge extraction data in which the edge portion is extracted by the primary edge extraction step, and the mapping process. It is characterized by including an edge logic synthesis processing step of generating secondary edge extraction data reflecting the variation in brightness by logically synthesizing the map data indicating the variation in brightness generated by the process.

In the image processing program according to the present invention, the mapping processing step calculates the luminance dispersion value for a plurality of pixels within a predetermined range centered on the pixel to be determined, and the calculated luminance dispersion value. By executing the process of determining whether or not is equal to or less than a predetermined threshold value for each of the pixels constituting the multi-valued image data, the variation in the brightness of the pixels constituting the multi-valued image data is mapped. It may include a step of converting.

Further, in the image processing program according to the present invention, in the mapping processing step, a plurality of pixels within a predetermined range centered on a pixel to be determined are set as statistical targets, and pixels having a brightness equal to or lower than a predetermined threshold. By executing the process of calculating the number of pixels and determining whether or not the calculated number is equal to or less than a predetermined threshold value for each of the pixels constituting the multi-valued image data, the multi-valued image It may include a step of mapping the variation in the brightness of the pixels constituting the data.

Further, in the image processing program according to the present invention, in the mapping processing step, a plurality of pixels within a predetermined range centered on the pixel to be determined are set as statistical targets, and the brightness dispersion value and a predetermined threshold value or more or Pixels constituting the multi-valued image data are subjected to a process of calculating the number of pixels having a brightness of less than or equal to and determining whether or not both the calculated dispersion value and the number of pixels are equal to or less than a predetermined threshold value. By executing each of the above, the step of mapping the variation in the brightness of the pixels constituting the multi-valued image data may be included.

In the image processing program according to the present invention, the gradation conversion processing step includes gradation conversion processing for a portion determined to be an edge portion by the edge extraction step and determination to be a non-edge portion by the edge extraction step. It is preferable to include a step of executing the gradation conversion process for the formed portion under different conditions.

Specifically, the gradation conversion processing step includes an edge portion quantization processing step for executing gradation conversion processing for a portion determined to be an edge portion by the edge extraction step, and the edge extraction step. The non-edge portion quantization processing step for executing the gradation conversion process, the quantization data processed by the edge portion quantization processing step, and the non-edge portion for the portion determined to be the non-edge portion by It is preferable to include a quantization logic synthesis processing step of generating the binary image data by logically synthesizing the quantization data processed by the partial quantization processing step.

Further, in the image processing program according to the present invention, the gradation conversion processing for the edge portion and the gradation conversion processing for the non-edge portion are gradation conversion processing based on an error diffusion method based on different threshold values. It is preferable to have.

The image processing method according to the present invention is an image processing method that converts multi-valued image data into binary image data that can be used in a digital printing apparatus, and is an edge that executes edge extraction processing on the multi-valued image data. Gradation conversion that converts the multi-valued image data into binary image data by executing the gradation conversion process on the extraction step and the multi-valued image data for which the edge extraction process has been executed by the edge extraction step. The edge extraction step comprises a processing step, and the edge extraction step comprises a primary edge extraction step of executing an edge extraction process on the multivalued image data using a primary differential filter or a secondary differential filter, and the multivalued image data. For each of the pixels to be used, the variation in brightness is calculated for a plurality of pixels within a predetermined range as statistical targets, and the calculated variation is compared with a predetermined threshold to determine the brightness of the pixels constituting the multi-valued image data. The mapping processing step of mapping the variation, the primary edge extraction data in which the edge portion is extracted by the primary edge extraction step, and the map data showing the variation of the brightness generated by the mapping processing step are logically combined. This is characterized by including an edge logic synthesis processing step for generating secondary edge extraction data reflecting variations in brightness.

According to the present invention, it is possible to provide an image processing apparatus, an image processing program, and an image processing method capable of accurately extracting an edge portion.

It is a figure which shows schematic the image processing apparatus and digital printing apparatus which concerns on one Embodiment of this invention. It is a block block diagram which shows the schematic structure of the image processing apparatus which concerns on this embodiment. It is a block block diagram which shows the schematic structure of the edge extraction part. It is a figure which shows an example of the pixel arrangement of a multi-valued image data. It is a figure which shows an example of the edge extraction processing. It is a figure which shows another example of the edge extraction processing. It is a block composition diagram which shows the schematic structure of the gradation conversion processing part. It is a flowchart which shows the flow of the image processing method which concerns on this Embodiment. It is a flowchart which shows the flow of the mapping process which concerns on this embodiment. FIG. 10A is a diagram showing an original image, FIG. 10B is a diagram showing a binary image binarized by a conventional gradation conversion process, and FIG. 10C is a main image. It is a figure which shows the binary image which was binarized by the image processing method which concerns on embodiment. It is a figure which shows the example of the binarization processing by the error diffusion method in the multi-valued image data of 256 gradations, FIG. 11 (a), FIG. 11 (c), FIG. 11 (e) and FIG. 11 (g). It is a figure which shows an example of the pixel arrangement of the multi-valued image data, and FIG. 11 (b), FIG. 11 (d), FIG. 11 (f) and FIG. 11 (h) are the figures which show the error diffusion table respectively. FIG. 12A is a diagram showing an original image, and FIG. 12B is a diagram showing a binary image binarized by an error diffusion method.

Hereinafter, preferred embodiments for carrying out the present invention will be described with reference to the drawings. It should be noted that the following embodiments do not limit the invention according to each claim, and not all combinations of features described in the embodiments are essential for the means for solving the invention. ..

The image processing device 1 according to the present embodiment mainly performs various image processing on the submitted image data (original image data) input from another information processing terminal, storage medium, or the like, thereby performing the submitted image data. Has a function of converting the data into binary image data that can be used in the inkjet digital printing apparatus 100. Here, examples of the submitted image data include, but are not limited to, image data expressed in a format such as PDF (Portable Document Form) or PS (PostScript).

As shown in FIG. 1, the digital printing apparatus 100 includes a print head unit 102 that performs inkjet printing on the continuous paper W sent out from the take-up paper R, and a print head control unit 104 that controls the print head unit 102. .. Hereinafter, as the digital printing apparatus 100, a so-called single-pass type digital printing apparatus capable of four-color color printing will be described as an example, but the present invention is not limited to this, and for example, single-color printing or multicolor printing other than four-color printing. Various digital printing devices such as a digital printing device for printing and a so-called scanning type digital printing device can be used.

The printhead unit 102 includes a printhead group 102c capable of ejecting cyan (C) ink, a printhead group 102m capable of ejecting magenta (M) ink, and a printhead capable of ejecting yellow (Y) ink. It includes a group 102y and a print head group 102k capable of ejecting black (K) ink, and is configured to be capable of performing color printing on continuous paper W. In each of these printhead groups 102c, 102m, 102y, and 102k, a plurality of (for example, four) printheads (not shown) smaller than the width dimension of the continuous paper W are arranged in a staggered pattern in the width direction of the continuous paper W. The width dimension of the printhead groups 102c, 102m, 102y, 102k (the total length of a plurality of printheads arranged in a staggered pattern) is larger than the width dimension of the continuous paper W. .. Since the configuration of such a print head unit 102 is well known, detailed description thereof will be omitted.

The printhead control unit 104 is configured to be capable of data communication with the image processing device 1, and is configured to control the printhead unit 102 based on binary image data for four colors acquired from the image processing device 1. ing. Specifically, the printhead control unit 104 uses the printheads of the printhead groups 102c, 102m, 102y, and 102k corresponding to each color for each of the binary image data for four colors acquired from the image processing device 1. It is configured to be able to execute the process of sorting based on the arrangement. Further, the printhead control unit 104 turns on / off the ink ejection in each printhead of the printhead group 102c, 102m, 102y, 102k based on the rearranged binary image data in pixel units of the binary image data. It is configured to control. Since the configuration of such a printhead control unit 104 is well known, detailed description thereof will be omitted.

The image processing device 1 is, for example, a processing server including a control unit 10 such as a CPU, and as shown in FIG. 2, an input unit 12 that accepts input of submitted image data (original image data) and, if necessary, input. A raster conversion processing unit 14 that converts manuscript image data into multi-valued image data, an edge extraction unit 18 that executes edge extraction processing on the multi-valued image data, and a color separation plate that separates the multi-valued image data into each color. The processing unit 16, the gradation conversion processing unit (quantization processing unit) 20 that converts the multi-valued image data of each color into binary image data for printing, and the printhead control of each binary image data of the digital printing device 100. It includes an output unit 22 that outputs to unit 104 and the like, and a storage unit 24 that stores various data necessary for image processing in the image processing device 1 and various programs including an image processing program. Further, the image processing device 1 is configured to be capable of executing other processing (for example, imposition processing) necessary for providing print data to the digital printing device 100.

The input unit 12 is configured to receive input of submitted image data (original image data) in a format such as PDF or PS from another information processing terminal, storage medium, or the like. The output unit 22 has a cyan component binary image data, a magenta component binary image data, a yellow component binary image data, and a black component binary image data generated by the gradation conversion processing unit 20. It is configured to transfer the image data to the print head control unit 104 or the like of the digital printing device 100. Since various known interfaces can be adopted for the input unit 12 and the output unit 22, detailed description thereof will be omitted.

When the submitted image data input via the input unit 12 is non-raster data such as vector data (line image data expressed by a vector amount), the raster conversion processing unit 14 uses this non-raster data. It is configured to perform raster conversion processing (RIP) and convert it into raster data, which is a set of dots (pixels). The raster data generated by the raster conversion processing unit 14 is multi-valued image data in which each of the pixels constituting the image has gradation information of 2 bits or more (for example, each pixel has 8 bits (= 256 gradations). ) Full-color image data having gradation information). When the submitted image data is a multi-valued image, the raster conversion process by the raster conversion processing unit 14 does not have to be executed. Further, since various known processing methods can be adopted for the raster conversion processing (RIP) executed by the raster conversion processing unit 14, detailed description thereof will be omitted.

The edge extraction unit 18 is configured to execute edge extraction processing for each of the multi-value image data input via the input unit 12 or the multi-value image data raster-converted by the raster conversion processing unit 14. Specifically, as shown in FIG. 3, the edge extraction unit 18 includes a primary edge extraction unit 18a that executes an edge extraction process on the multi-valued image data using an edge extraction filter, and pixels in the multi-valued image data. Logical composition (AND, NOR, etc.) of the mapping processing unit 18b that maps the variation in brightness, the primary edge extraction data obtained by the primary edge extraction unit 18a, and the map data obtained by the mapping processing unit 18b. It is provided with an edge logic synthesis processing unit 18c that generates secondary edge extraction data. In the edge extraction unit 18 according to the present embodiment, when each pixel has luminance information for each of a plurality of color components, the weighted average of the luminance of each color component is treated as the luminance of the pixel, and the edge extraction process is executed. However, the present invention is not limited to this, and various methods such as a method of treating the total brightness of each color component as the brightness of a pixel and a method of treating the brightness of an arbitrary color component as the brightness of a pixel are adopted. It is possible.

Similar to the conventional edge detection process, the primary edge extraction unit 18a is configured to extract the edge portion included in the image by a method of regarding a portion having a large brightness difference between adjacent pixels as an “edge”. Specifically, the primary edge extraction unit 18a extracts the edge portion included in the image by a primary differential filter such as a Roberts filter, a Sobel filter, and a Prewitt filter, or an edge extraction filter such as a secondary differential filter such as a Laplacian filter. It is configured to do. Further, the primary edge extraction unit 18a stores data indicating the position information (coordinates) of the pixels extracted as the edge unit as primary edge extraction data (data shown in FIGS. 5 (b) and 6 (b)). It is configured to be stored in 24. When a first-order differential filter is used, the edge region is obtained only in one direction. Therefore, for example, processing in the x direction (horizontal direction of the pixel array) and processing in the y direction (vertical direction of the pixel array). It is preferable to extract the edge portion by logically synthesizing (AND, NOR, etc.) these processes after executing each of the above. Since various known edge extraction processes can be adopted for the edge extraction process in the primary edge extraction unit 18a, detailed description thereof will be omitted. Further, the edge extraction filter is not limited to the various filters described above, and various filters can be adopted.

The mapping processing unit 18b calculates the variation in luminance for each of the pixels constituting the multi-valued image data, using a plurality of pixels within a predetermined range as statistical targets, and compares the calculated variation with a predetermined threshold value. , It is configured to map the variation in the brightness of the pixels that make up the multi-valued image data. Further, the mapping processing unit 18b is configured to store the map data in which the variation in the brightness of the pixels is mapped in the storage unit 24.

In the mapping processing unit 18b according to the present embodiment, as the mapping process using the variation in luminance as a determination criterion, for example, (1) the first mapping process using the dispersion value of luminance as a determination criterion (FIGS. 4 and FIG. 5), (2) a second mapping process (see FIGS. 4 and 6) that uses the number of pixels (number of pixels) having a predetermined brightness as a criterion, and (3) a variance value and a predetermined brightness. It is configured to be able to execute three patterns of a third mapping process (not shown) using both of the number of pixels having the brightness of the above as a determination criterion. Hereinafter, the first to third mapping processes will be described.

First, the first mapping process will be described. In the first mapping process, the dispersion value of the brightness is calculated for a plurality of pixels within a predetermined range centered on the pixel to be determined (the pixel with the reference numeral A in FIG. 4) as the statistical target. By executing the process of determining whether or not the calculated dispersion value is equal to or less than (or less than) a predetermined threshold value for each of the pixels constituting the multi-valued image data, the pixels constituting the multi-valued image data This is a process for mapping the variation in brightness. The range to be statistically measured, the threshold value, and the like can be arbitrarily set.

Explaining in more detail using the example shown in FIG. 4, in the first mapping process, first, 81 pixels in the range of 9 × 9 centered on the determination target pixel A are set as statistical targets, and the luminance is increased. Calculate the variance value. The variance value is a value generally used as an index of variation in brightness, and is the "square average" and "average square" of the gradation (brightness value) of all pixels to be statistically measured. It can be calculated by the difference. In the example shown in FIG. 4, the "squared average" of the 81 pixel gradation is "14713" and the "average squared" is "8831", therefore the difference is "5882". In the first mapping process, it is then determined whether or not the calculated variance value (“5882”) is greater than or equal to (or less than) the threshold value. For example, when the threshold value is set to "4000" and a pixel having a dispersion value of 4000 or more is determined as an edge portion, in the example shown in FIG. 4, the dispersion value is the threshold value (4000) or more, so the determination target pixel. A is determined as an edge portion. Then, by executing these processes for all the pixels constituting the multi-valued image data, the pixels determined to be the edge portion (the pixels whose dispersion value is equal to or more than the threshold value) and the other pixels (the dispersion value is less than the threshold value). Map data (data shown in FIG. 5C) showing the distribution with (pixels of) is generated. For pixels for which 9 × 9 cannot be statistically targeted (for example, pixels at the image edge), for example, the size of the filter used for the mapping process extends out of the image only when the pixels at the image edge are processed. The process is executed by a conventionally known method such as a method of changing so as not to be performed or a method of complementing pixels outside the image with the same value as the value of the nearest pixel in the image.

Next, the second mapping process will be described. In the second mapping process, a plurality of pixels within a predetermined range centered on the pixel to be determined (pixel with reference numeral A in FIG. 4) are set as statistical targets, and the value is equal to or less than a predetermined threshold. The process of calculating the number of pixels having the brightness of and determining whether or not the calculated number is equal to or less than a predetermined threshold is executed for each of the pixels constituting the multi-valued image data. This is a process for mapping the variation in the brightness of the pixels constituting the multi-valued image data. The range to be statistically targeted, the brightness threshold value, the number threshold value, and the like can be arbitrarily set.

Explaining in more detail using the example shown in FIG. 4, in the second mapping process, first, 81 pixels in the range of 9 × 9 centered on the determination target pixel A are set as statistical targets, and a predetermined value is specified. Calculate the number of pixels with brightness above (or below) the threshold. For example, when the brightness threshold is set to "50", in the example shown in FIG. 4, the number of pixels having brightness (gradation) equal to or higher than the threshold is "29". Hereinafter, for the sake of simplicity, a pixel having a brightness (gradation) equal to or higher than a threshold value is referred to as a "black pixel" regardless of its color. In the second mapping process, it is next determined whether or not the calculated number (“29”) is equal to or greater than (or less than) a predetermined threshold value. For example, when the threshold value of the number is set to "16 or more" and the pixels having 16 or more surrounding black pixels are determined as the edge portion, the surrounding black pixels are the threshold value (16) in the example shown in FIG. Since the number is equal to or more than the above, the determination target pixel A is determined as the edge portion. Then, by executing these processes for all the pixels constituting the multi-valued image data, the pixels determined to be the edge portion (the number of surrounding black pixels is equal to or larger than the threshold value) and the other pixels (periphery). Map data (data shown in FIG. 6C) showing the distribution with (pixels in which the number of black pixels is less than the threshold value) is generated. The processing for pixels whose statistical target cannot be 9 × 9 (for example, pixels at the edge of an image) is the same as the first mapping processing described above.

Next, the third mapping process will be described. In the third mapping process, a plurality of pixels within a predetermined range centered on the pixel to be determined are used as statistical targets, and the brightness dispersion value and the number of pixels having brightness equal to or higher than (or less than) a predetermined threshold value are used. By executing the process of determining whether or not both the calculated variance value and the number are equal to or less than a predetermined threshold value for each of the pixels constituting the multi-valued image data. This is a process for mapping the variation in the brightness of the pixels constituting the multi-valued image data. That is, new map data is generated by logically synthesizing (AND, NOR, etc.) the map data generated by the above-mentioned first mapping process and the map data generated by the above-mentioned second mapping process. It is a process. The process using the variance value of the luminance as the determination criterion is the same as the first mapping process described above, and the process using the number of surrounding black pixels as the determination criterion is the same as the second mapping process described above. Since they are the same, detailed description thereof will be omitted.

The edge logic synthesis processing unit 18c logically synthesizes the primary edge extraction data in which the edge portion is extracted by the primary edge extraction unit 18a and the map data indicating the variation in brightness generated by the mapping processing unit 18b. It is configured to generate secondary edge extraction data that reflects the variation in brightness. Specifically, as shown in FIGS. 5 and 6, the edge logic synthesis processing unit 18c includes primary edge extraction data (FIGS. 5 (b) and 6 (b)) in which the edge portion is extracted by a normal edge extraction process. ) And the map data (data shown in FIGS. 5 (c) and 6 (c)) generated by the first to third mapping processes are logically combined (AND, NOR, etc.). Secondary edge extraction that takes into account both the difference in brightness between adjacent pixels and the spatial distribution characteristics of brightness (continuity and discreteness of brightness, etc.) and the spatial distribution characteristics of tint (ratio of tint, etc.). It is configured to generate data (data shown in FIGS. 5 (d) and 6 (d)). In this way, by reflecting the information of the map data showing the variation in luminance in the primary edge extraction data, not only the simple luminance difference but also the spatial distribution characteristics of luminance (continuity and discreteness of luminance, etc.) and color Since it is possible to specify the edge portion in consideration of the spatial distribution characteristics of the taste (ratio of hue, etc.), it is possible to suppress erroneous detection of the edge portion and accurately extract the edge portion. .. Further, the edge logic synthesis processing unit 18c is configured to store the generated secondary edge extraction data in the storage unit 24 as edge extraction data commonly used in the multi-valued image data for four colors described later. ing.

Here, as the logic synthesis process in the edge logic synthesis processing unit 18c, for example, a method of logic synthesis in pixel units and a method of logic synthesis in pixel group units composed of a plurality of adjacent pixels can be appropriately adopted. Is. As a method of logic synthesis in pixel units, for example, each pixel of the primary edge extraction data and each pixel of the map data are compared in pixel units, and a pixel determined to be an edge portion (or non-edge portion) in both data. It is possible to adopt a method in which only the edge portion (or non-edge portion) is used. Further, as a method of logically synthesizing in pixel group units, for example, pixels connected in the vicinity of 4 in the primary edge extraction data are regarded as one pixel group (cluster), and the pixels constituting the pixel group are collectively logically synthesized. It is possible to adopt the method of doing. In the description of the present embodiment, the pixel group consisting of pixels connected in the vicinity of four means a pixel group consisting of one central pixel and four peripheral pixels located in the vicinity of the four neighborhoods (left, right, top and bottom). There is. Further, as a method of performing logical composition in a batch, for example, the average value of the pixel group in the map data corresponding to the pixel group in the primary edge extraction data (the average value of the variance values in the first mapping process, the first In the mapping process of 2, the average value of the number of pixels is calculated, and in the third mapping process, the dispersion value and / or the average value of the number of pixels) is calculated, and the calculated average value and an arbitrary threshold value set in advance are set. By comparing, it is possible to adopt a method of determining the edge portion (or non-edge portion) for each pixel group. In the above description, the pixels connected in the vicinity of 4 are regarded as one pixel group, but the present invention is not limited to this, and the pixel group is a pixel group having an arbitrary number of pixels and a pixel arrangement. It is possible to

The color separation processing unit 16 uses the multi-value image data input via the input unit 12 or the multi-value image data raster-converted by the raster conversion processing unit 14 as the multi-value image data of the cyan component and the magenta component. It is configured to be divided into a multi-value image data, a multi-value image data of a yellow component, and a multi-value image data of a black component. Since various known processing methods can be adopted for the color separation processing executed by the color separation processing unit 16, detailed description thereof will be omitted.

The gradation conversion processing unit 20 uses the secondary edge extraction data generated by the edge extraction unit 18 for the multi-valued image data for four colors color-coded by the color separation processing unit 16 to each floor. By executing the key conversion process, each multi-valued image data is converted into binary image data (bitmap data) for printing. Further, the gradation conversion processing unit 20 is configured to store the generated binary image data for printing in the storage unit 24.

Here, in the gradation conversion processing unit 20 according to the present embodiment, the gradation conversion processing for the portion determined to be the edge portion by the edge extraction unit 18 and the non-edge portion (not the edge portion) by the edge extraction unit 18 are performed. It is configured to execute the gradation conversion process for the portion determined to be the portion) under different conditions. Specifically, as shown in FIG. 7, the gradation conversion processing unit 20 performs an edge portion quantization process for executing a gradation conversion process on a portion determined to be an edge portion by the edge extraction unit 18. The non-edge portion quantization processing unit 20b and the edge portion quantization processing unit 20a that execute the gradation conversion process target the portion 20a and the portion determined to be the non-edge portion by the edge extraction unit 18. Quantization logic synthesis processing to generate binary image data for printing by logically synthesizing (AND, NOR, etc.) the quantized data and the quantized data processed by the non-edge part quantization processing unit 20b. It is provided with a part 20c.

Here, as different conditions, for example, the threshold value at the time of binarization by the error diffusion method may be set to a different value between the edge portion and the non-edge portion. For example, if the threshold value for binarizing the edge portion by the error diffusion method is set lower than the threshold value for binarizing the non-edge portion by the error diffusion method, the pixel that should originally be the edge portion Since it is possible to suppress the value conversion (disappearance) of "0", it is possible to clearly reproduce the edge portion. In addition to the example of gradation conversion processing (binarization processing) by the error diffusion method using different threshold values, for example, the edge portion is processed by the dither method (for example, the systematic dither method), and the non-edge portion is errored. The gradation conversion processing may be performed by different methods for the edge portion and the non-edge portion, such as processing by the diffusion method. In the gradation conversion processing unit 20 according to the present embodiment, the edge portion and the non-edge portion can be subjected to the gradation conversion processing independently of each other in this way, so that each of the edge portion and the non-edge portion can be processed. Since it is possible to execute the gradation conversion process under the optimum conditions, it is possible to generate binary image data that has both the smoothness of the image in the non-edge portion and the sharpness of the image in the edge portion. Become.

Next, an image processing method using the image processing apparatus according to the present embodiment will be described with reference to FIGS. 8 and 9. The image processing method described below is executed by an image processing program stored in the storage unit 24 of the image processing device 1.

In the image processing method according to the present embodiment, as shown in FIG. 8, first, input of submitted image data (original image data) is received from another information processing terminal, storage medium, etc., and the submitted image data is large. If it is not value image data, the raster conversion processing unit 14 converts it into multi-value image data (S1).

Subsequently, in the image processing method according to the present embodiment, edge extraction processing is executed for the binary image data input via the input unit 12 or the binary image data raster-converted by the raster conversion processing unit 14 ( Edge extraction process). Specifically, first, an edge extraction process is executed on the multi-valued image data using a first-order differential filter or a second-order differential filter (first-order edge extraction step S2). Further, before and after that or in parallel with the primary edge extraction step, for each of the pixels constituting the multi-valued image data, a variation in brightness is calculated for each of a plurality of pixels within a predetermined range as statistical targets, and the calculated variation is determined. By comparing with the threshold value of, the variation in the brightness of the pixels constituting the multi-valued image data is mapped (mapping processing step S3). Then, after the primary edge extraction step and the mapping processing step are completed, the primary edge extraction data in which the edge portion is extracted by the primary edge extraction step and the map data showing the variation in brightness generated by the mapping processing step are obtained. By logically synthesizing the above, secondary edge extraction data reflecting the variation in brightness is generated (edge logical synthesis processing step S4).

Here, the mapping processing step (S3) will be described in detail with reference to FIG. The mapping process step is a step of executing any of the above-mentioned first mapping process, second mapping process, and third mapping process. Specifically, first, the number of pixels having a brightness dispersion value and / or a brightness equal to or higher than (or less than) a predetermined threshold value is set as a statistical target for a plurality of pixels within a predetermined range centered on the pixel to be determined. (In the case of the first mapping process, the brightness dispersion value, in the case of the second mapping process, the number of pixels equal to or greater than the predetermined brightness, and in the case of the third mapping process, the brightness dispersion value and the predetermined brightness. Both of the above number of pixels) are calculated (S3-1). Subsequently, it is determined whether or not the calculated dispersion value and / and the number of pixels having a predetermined brightness or more is equal to or less than (or less than) a predetermined threshold value (S3-2). Then, the pixels determined to be equal to or higher than the predetermined threshold value are determined to be the pixels constituting the edge portion (S3-3), and the pixels determined to be less than the predetermined threshold value are non-edge. It is determined that it is a pixel that constitutes a portion (a pixel that does not constitute an edge portion) (S3-3'). After that, if there are unprocessed pixels, the process shifts to the unprocessed pixels and the processing of S3-1 to S3-3 and S3-3'is executed (S3-4, S3-5). Then, when the processing is completed for all the pixels constituting the multi-valued image data, the distribution information (position) of the pixels determined to be the edge portion or the non-edge portion by any of the first to third mapping processes. Information) is stored in the storage unit 24 as map data in which the variation in the brightness of the pixels is mapped (S3-4, S3-6). The mapping processing step is executed by the above steps S3-1 to S3-6.

Further, in the image processing method according to the present embodiment, the multi-valued image data or raster conversion processing input via the input unit 12 in the color separation processing unit 16 before or after or in parallel with the edge extraction step. The multi-valued image data rasterized by Part 14 is divided into multi-valued image data of cyan component, multi-valued image data of magenta component, multi-valued image data of yellow component, and multi-valued image data of black component. Print (S4).

Subsequently, in the image processing method according to the present embodiment, as shown in FIG. 8, gradation is obtained for each of the four-color multi-valued image data based on the common secondary edge extraction data generated by the edge logic synthesis processing step. By executing the conversion process, each multi-valued image data is converted into binary image data (gradation conversion processing step). Specifically, a gradation conversion process (quantization process) is performed on a pixel that is an edge portion in the secondary edge extraction data (edge portion quantization process step S6), and before, after, or in parallel with this. , The gradation conversion process (quantization process) for the pixel which is regarded as the non-edge portion in the secondary edge extraction data is executed (non-edge portion quantization process step S7). Since the gradation conversion processing for the edge portion and the gradation conversion processing for the non-edge portion have different suitable processing conditions, in the image processing method according to the present embodiment, these processings have different conditions. (For example, different thresholds and binarization methods suitable for each process) are executed. Then, after the edge portion quantization processing and the non-edge portion quantization processing are completed, the quantization data processed by the edge portion quantization processing and the quantization data processed by the non-edge portion quantization processing are logically processed. By synthesizing, binary image data for printing is generated (quantization logic synthesis processing step S8).

Finally, in the image processing method according to the present embodiment, the binary image data for printing generated by the above steps S1 to S8 is stored in the storage unit 24 (S9), and the binary image is stored in the digital printing apparatus 100. After executing various processes (for example, imposition processing) necessary for providing data, the data is output to the printhead control unit 104 of the digital printing apparatus 100 (S10).

As described above, the image processing device 1 according to the present embodiment is an image processing device 1 that converts multi-valued image data into binary image data that can be used in the digital printing device 100, and is used for the multi-valued image data. Then, the edge extraction unit 18 that executes the edge extraction process and the multi-value image data for which the edge extraction process is executed by the edge extraction unit 18 are subjected to the gradation conversion process to convert the multi-value image data into binary. The primary edge extraction unit 18a is provided with a gradation conversion processing unit 20 for converting into image data, and the edge extraction unit 18 executes edge extraction processing on the multi-valued image data using a primary differential filter or a secondary differential filter. And, for each of the pixels constituting the binary image data, the variation in brightness is calculated with a plurality of pixels within a predetermined range as statistical targets, and the calculated variation is compared with a predetermined threshold to obtain the binary image data. The mapping processing unit 18b that maps the variation in the brightness of the constituent pixels, the primary edge extraction data in which the edge portion is extracted by the primary edge extraction unit 18a, and the variation in the brightness generated by the mapping processing unit 18b are shown. It includes an edge logic composition processing unit 18c that generates secondary edge extraction data reflecting variations in brightness by logically synthesizing the map data.

According to the image processing apparatus 1 according to the present embodiment configured in this way, the information of the map data indicating the variation in brightness is reflected in the primary edge extraction data in which the edge portion is extracted by the normal edge extraction process. Therefore, the edge portion should be specified by considering not only the simple brightness difference but also the spatial distribution characteristics of brightness (continuity and discreteness of brightness, etc.) and the spatial distribution characteristics of tint (ratio of tint, etc.). Therefore, it is possible to realize highly accurate edge extraction. That is, in the image processing apparatus 1 according to the present embodiment, pixels having a large brightness difference from adjacent pixels are not immediately set as "edge portions", but are retained as candidates for "edge portions", and these "edges" are retained. Of the candidates for the "part", only the pixels having different (or discrete) brightness and color to some extent around them can be specified as the "edge part". Therefore, in particular, FIG. 5 (b) and FIG. As is clear from the comparison with 5 (d), it is possible to suppress erroneous detection of the edge portion and accurately extract the edge portion. Further, according to the image processing apparatus 1 according to the present embodiment, it is possible to extract, for example, boundaries of different colors having the same brightness as edge portions.

Further, in the image processing device 1 according to the present embodiment, as described above, the gradation conversion processing unit 20 performs gradation conversion processing on a portion determined by the edge extraction unit 18 to be an edge portion, and the edge extraction unit 18 It is configured to execute the gradation conversion process for the portion determined to be the non-edge portion under different conditions. According to the image processing apparatus 1 according to the present embodiment configured in this way, the edge portion and the non-edge portion can be subjected to gradation conversion processing independently of each other, so that the edge portion and the non-edge portion can be processed respectively. Since it is possible to execute the gradation conversion process under the optimum conditions, as is clear from the comparison between FIGS. 10 (b) and 10 (c), the flow of the image in the non-edge portion is flowing. It is possible to generate binary image data that has both the sharpness of the image at the edge portion and the sharpness of the image at the edge portion. Note that FIG. 10A shows an original image which is multi-valued image data, and FIG. 10B shows binary image data obtained by gradation-converting the entire original image data by a conventional error diffusion method. FIG. 10 (c) shows binary image data that has been gradation-converted by the image processing device 1 according to the present embodiment.

In particular, the image processing apparatus 1 according to the present embodiment separately executes the gradation conversion processing for the edge portion and the gradation conversion processing for the non-edge portion as described above, and then the quantum obtained by these processes. It is configured to combine the quantized data to generate binary image data. According to the image processing apparatus 1 configured in this way, the influence of the quantization performed on the non-edge portion (error diffusion, etc.) does not extend to the edge portion, and the quantum performed on the edge portion. Since the influence of the conversion (error diffusion, etc.) does not extend to the non-edge portion, it is possible to eliminate mutual interference of color and brightness between the edge portion and the non-edge portion. By eliminating the mutual interference of color and brightness between the edge portion and the non-edge portion in this way, the reproducibility of the color of the small character portion, which should be considered in the multitone processing, is maintained even after quantization. It becomes sustainable.

Although the preferred embodiments of the present invention have been described above, the technical scope of the present invention is not limited to the scope described in the above-described embodiments. Various changes or improvements can be made to each of the above embodiments.

For example, in the above-described embodiment, the first to third mapping processes are exemplified as the mapping process in the mapping processing unit 18b, but the present invention is not limited to these, and the pixels constituting the multi-valued image data are not limited thereto. Various processing methods can be adopted as long as the processing can map the variation in the brightness of the above.

Further, in the above-described embodiment, the edge extraction process is executed on the multi-valued image data before color separation, and the secondary edge extraction data obtained by this is subjected to gradation conversion of the multi-valued image data of each color after color separation. Although it has been described as being commonly used in processing, it is not limited to this. For example, edge extraction processing is executed for each of the multi-valued image data of each color after color separation, and the obtained secondary edge extraction data is used for gradation conversion of the multi-valued image data of each color. It may be configured to perform processing.

Further, in the above-described embodiment, in the gradation conversion process, the edge portion quantization process for the pixel determined to be the edge portion in the secondary edge extraction data and the non-edge portion are determined in the secondary edge extraction data. Binary image data was generated by executing the non-edge part quantization processing for the pixels separately and logically synthesizing the quantization data obtained by these processing. It is not limited. For example, in the process of scanning along the pixel array, it is determined whether the pixel is an edge portion or a non-edge portion by referring to the secondary edge extraction data for each pixel to be processed, and the edge is determined according to the determination result. Either one of the partial quantization process and the non-edge portion quantization process may be selected and executed. In this case, although the arithmetic processing of the error diffusion method becomes slightly complicated, there is an advantage that the processing of logically synthesizing the two quantized data becomes unnecessary.

It is clear from the description of the claims that the above-mentioned modifications are included in the scope of the present invention.

1 Image processing device, 18 edge extraction unit, 18a primary edge extraction unit, 18b mapping processing unit, 18c edge logic synthesis processing unit, 20 gradation conversion processing unit, 20a edge part quantization processing unit, 20b non-edge part quantization Processing unit, 20c quantization logic synthesis processing unit, 100 digital printing device

Claims (15)

An image processing device that converts multi-valued image data into binary image data that can be used in digital printing devices.
An edge extraction unit that executes edge extraction processing for multi-valued image data,
A gradation conversion processing unit that converts the multi-valued image data into binary image data by executing a gradation conversion process on the multi-valued image data for which the edge extraction process has been executed by the edge extraction unit. Prepare,
The edge extraction unit
A primary edge extraction unit that executes edge extraction processing using a primary differential filter or a secondary differential filter on the multi-valued image data.
For each of the pixels constituting the multi-value image data, the variation in brightness is calculated for each of a plurality of pixels within a predetermined range as statistical targets, and the calculated variation is compared with a predetermined threshold to obtain the multi-value image data. A mapping processing unit that maps the variation in brightness of the pixels that make up the
The variation in brightness is reflected by logically synthesizing the primary edge extraction data in which the edge portion is extracted by the primary edge extraction unit and the map data indicating the variation in brightness generated by the mapping processing unit. An image processing device including an edge logic synthesis processing unit that generates next edge extraction data. The mapping processing unit calculates a luminance dispersion value for a plurality of pixels within a predetermined range centered on a pixel to be determined as statistical targets, and the calculated variance value is equal to or less than a predetermined threshold value. By executing the process of determining whether or not the multi-valued image data is performed for each of the pixels constituting the multi-valued image data, it is configured to map the variation in the brightness of the pixels constituting the multi-valued image data. The image processing apparatus according to claim 1. The mapping processing unit calculates the number of pixels having brightness equal to or less than a predetermined threshold with a plurality of pixels within a predetermined range centered on the pixel to be determined as statistical targets, and the calculated number of pixels. By executing the process of determining whether or not is equal to or less than a predetermined threshold value for each of the pixels constituting the multi-valued image data, the variation in the brightness of the pixels constituting the multi-valued image data is mapped. The image processing apparatus according to claim 1, wherein the image processing apparatus is configured to be the same. The mapping processing unit calculates the dispersion value of brightness and the number of pixels having brightness equal to or less than a predetermined threshold, with a plurality of pixels within a predetermined range centered on the pixel to be determined as statistical targets. Then, by executing the process of determining whether or not both the calculated dispersion value and the number are equal to or less than a predetermined threshold value for each of the pixels constituting the multi-valued image data, the multi-valued value is obtained. The image processing apparatus according to claim 1, wherein the variation in brightness of pixels constituting the image data is configured to be mapped. The gradation conversion processing unit includes gradation conversion processing for a portion determined to be an edge portion by the edge extraction unit, and gradation conversion processing for a portion determined to be a non-edge portion by the edge extraction unit. The image processing apparatus according to any one of claims 1 to 4, wherein the image processing apparatus is configured to be executed under different conditions. The gradation conversion processing unit
An edge portion quantization processing unit that executes gradation conversion processing for a portion determined to be an edge portion by the edge extraction unit,
A non-edge portion quantization processing unit that executes gradation conversion processing for a portion determined to be a non-edge portion by the edge extraction unit, and
Quantization logic that generates the binary image data by logically synthesizing the quantization data processed by the edge portion quantization processing unit and the quantization data processed by the non-edge portion quantization processing unit. The image processing apparatus according to claim 5, further comprising a compositing processing unit. Claim 5 or 6 is characterized in that the gradation conversion process for the edge portion and the gradation conversion process for the non-edge portion are gradation conversion processes based on an error diffusion method based on different threshold values. The image processing apparatus according to. An image processing program that causes an image processing device to execute image processing that converts multi-valued image data into binary image data that can be used in a digital printing device.
In the image processing device
An edge extraction process that executes edge extraction processing for multi-valued image data,
A gradation conversion processing step of converting the multi-valued image data into binary image data by executing a gradation conversion process on the multi-valued image data for which the edge extraction process has been executed by the edge extraction step. Let it run
The edge extraction step
A primary edge extraction step of executing an edge extraction process using a primary differential filter or a secondary differential filter on the multi-valued image data, and
For each of the pixels constituting the multi-value image data, the variation in brightness is calculated for each of a plurality of pixels within a predetermined range as statistical targets, and the calculated variation is compared with a predetermined threshold to obtain the multi-value image data. A mapping process that maps the variation in brightness of the pixels that make up the
The variation in brightness is reflected by logically synthesizing the primary edge extraction data in which the edge portion is extracted by the primary edge extraction step and the map data indicating the variation in brightness generated by the mapping processing step. An image processing program characterized by including an edge logic synthesis processing step for generating next edge extraction data. In the mapping processing step, the brightness dispersion value is calculated for a plurality of pixels within a predetermined range centered on the pixel to be determined as statistical targets, and the calculated dispersion value is equal to or less than a predetermined threshold value. It is characterized by including a step of mapping the variation in brightness of the pixels constituting the multi-valued image data by executing the process of determining whether or not the multi-valued image data is performed for each of the pixels constituting the multi-valued image data. The image processing program according to claim 8. In the mapping processing step, the number of pixels having a brightness equal to or less than a predetermined threshold is calculated for a plurality of pixels within a predetermined range centered on the pixel to be determined, and the calculated number of pixels is calculated. By executing the process of determining whether or not is equal to or less than a predetermined threshold value for each of the pixels constituting the multi-valued image data, the variation in the brightness of the pixels constituting the multi-valued image data is mapped. The image processing program according to claim 8, further comprising a step of converting. In the mapping processing step, the dispersion value of the brightness and the number of pixels having the brightness equal to or less than the predetermined threshold are calculated by setting a plurality of pixels within a predetermined range centering on the pixel to be determined as statistical targets. Then, by executing the process of determining whether or not both the calculated dispersion value and the number are equal to or less than a predetermined threshold value for each of the pixels constituting the multi-valued image data, the multi-valued value is obtained. The image processing program according to claim 8, further comprising a step of mapping variations in brightness of pixels constituting image data. The gradation conversion processing step includes a gradation conversion process for a portion determined to be an edge portion by the edge extraction step, and a gradation conversion process for a portion determined to be a non-edge portion by the edge extraction step. The image processing program according to any one of claims 8 to 11, further comprising a step of executing the above under different conditions. The gradation conversion processing step is
An edge part quantization processing step of executing a gradation conversion process for a part determined to be an edge part by the edge extraction step, and
A non-edge portion quantization processing step for executing a gradation conversion process for a portion determined to be a non-edge portion by the edge extraction step, and a non-edge portion quantization processing step.
Quantization logic that generates the binary image data by logically synthesizing the quantization data processed by the edge quantization processing step and the quantization data processed by the non-edge quantization processing step. The image processing program according to claim 12, wherein the image processing program includes a synthesis processing step. Claim 12 or 13 characterized in that the gradation conversion process for the edge portion and the gradation conversion process for the non-edge portion are gradation conversion processes based on an error diffusion method based on different threshold values. The image processing program described in. An image processing method that converts multi-valued image data into binary image data that can be used in digital printing equipment.
An edge extraction process that executes edge extraction processing for multi-valued image data,
A gradation conversion processing step of converting the multi-valued image data into binary image data by executing a gradation conversion process on the multi-valued image data for which the edge extraction process has been executed by the edge extraction step. Prepare,
The edge extraction step
A primary edge extraction step of executing an edge extraction process using a primary differential filter or a secondary differential filter on the multi-valued image data, and
For each of the pixels constituting the multi-value image data, the variation in brightness is calculated for each of a plurality of pixels within a predetermined range as statistical targets, and the calculated variation is compared with a predetermined threshold to obtain the multi-value image data. A mapping process that maps the variation in brightness of the pixels that make up the
The variation in brightness is reflected by logically synthesizing the primary edge extraction data in which the edge portion is extracted by the primary edge extraction step and the map data indicating the variation in brightness generated by the mapping processing step. An image processing method characterized by including an edge logic composition processing step for generating next edge extraction data.