JP2018133656A - Image forming apparatus - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an image forming apparatus capable of enhancing image quality.SOLUTION: The image forming apparatus includes: an image processing unit that performs a first correction process for gently changing a pixel value with respect to first image data corresponding to a first color in a boundary region including a boundary between a first region in which a second image corresponding to a second color is arranged among image regions in which a first image corresponding to a first color is arranged and a second region other than the first region among the image regions; and an image forming unit for forming an image on the basis of the second image data corresponding to the second color and the first image data subjected to the first correction processing.SELECTED DRAWING: Figure 21

Description

  The present invention relates to an image forming apparatus that forms an image on a recording medium.

  In an image forming apparatus, a so-called trapping process is often performed on image data in consideration of a shift in formation position when a plurality of color images having different colors are formed on a recording medium. For example, Patent Document 1 discloses an image forming apparatus that improves the image quality of a printed product by performing a trapping process.

JP 2008-141623 A

  In an image forming apparatus, for example, a white developer is often used in order to suppress the influence of the color of a recording medium on image quality. Even in such a case, high image quality is desired.

  It is desirable to provide an image forming apparatus that can improve image quality.

  An image forming apparatus according to an embodiment of the present invention includes an image processing unit and an image forming unit. The image processing unit includes: a first area in which a second image corresponding to the second color is arranged in an image area in which the first image corresponding to the first color is arranged; First in which the change of the pixel value is moderated with respect to the first image data corresponding to the first color in the boundary region including the boundary with the second region other than the first region. Correction processing is performed. The image forming unit forms an image on a recording medium based on the second image data corresponding to the second color and the first image data subjected to the first correction process.

  According to the image forming apparatus of one embodiment of the present invention, the first correction process for gradual change of the pixel value is performed on the first image data corresponding to the first color in the boundary region. So that the image quality can be improved

1 is a block diagram illustrating a configuration example of an image forming apparatus according to an embodiment. 3 is a flowchart illustrating an operation example of the image processing unit illustrated in FIG. 1. 3 is a flowchart illustrating an example of edge detection processing illustrated in FIG. 2. It is explanatory drawing showing an example of the binarization process shown in FIG. It is another explanatory drawing showing an example of the binarization process shown in FIG. It is another explanatory drawing showing an example of the binarization process shown in FIG. It is another explanatory drawing showing an example of the binarization process shown in FIG. It is a table | surface showing an example of the edge detection process shown in FIG. It is explanatory drawing showing an example of the edge detection process shown in FIG. 3 is a flowchart illustrating an example of white boundary detection processing illustrated in FIG. 2. FIG. 9 is an explanatory diagram illustrating an example of white boundary detection processing illustrated in FIG. 8. FIG. 9 is another explanatory diagram illustrating an example of the white boundary detection process illustrated in FIG. 8. FIG. 9 is another explanatory diagram illustrating an example of the white boundary detection process illustrated in FIG. 8. 3 is a flowchart illustrating an example of a white boundary correction process illustrated in FIG. 2. It is explanatory drawing showing an example of the white boundary correction process shown in FIG. FIG. 12 is another explanatory diagram illustrating an example of the white boundary correction process illustrated in FIG. 11. FIG. 12 is another explanatory diagram illustrating an example of the white boundary correction process illustrated in FIG. 11. FIG. 12 is another explanatory diagram illustrating an example of the white boundary correction process illustrated in FIG. 11. FIG. 12 is another explanatory diagram illustrating an example of the white boundary correction process illustrated in FIG. 11. FIG. 12 is another explanatory diagram illustrating an example of the white boundary correction process illustrated in FIG. 11. 3 is a flowchart illustrating an example of a trapping correction process illustrated in FIG. 2. It is a flowchart showing an example of the trapping process shown in FIG. It is explanatory drawing showing an example of the trapping correction process shown in FIG. FIG. 6 is an explanatory diagram illustrating an operation example of the image forming apparatus illustrated in FIG. 1. FIG. 10 is another explanatory diagram illustrating an operation example of the image forming apparatus illustrated in FIG. 1. FIG. 10 is another explanatory diagram illustrating an operation example of the image forming apparatus illustrated in FIG. 1. FIG. 10 is another explanatory diagram illustrating an operation example of the image forming apparatus illustrated in FIG. 1. FIG. 10 is another explanatory diagram illustrating an operation example of the image forming apparatus illustrated in FIG. 1. FIG. 10 is another explanatory diagram illustrating an operation example of the image forming apparatus illustrated in FIG. 1. FIG. 10 is a block diagram illustrating a configuration example of an image forming apparatus according to a modification.

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

[Configuration example]
FIG. 1 shows a configuration example of an image forming apparatus (image forming apparatus 1) according to an embodiment of the present invention. The image forming apparatus 1 functions as a printer that forms an image on a recording medium 9 such as plain paper using a plurality of developers including white. The image forming apparatus 1 includes a communication unit 11, a medium color sensor 12, an image processing unit 20, and an image forming unit 13.

  The communication unit 11 receives print data DP by communicating with a host computer (not shown) via a wired LAN (Local Area Network), for example. The communication unit 11 supplies the received print data DP to the image processing unit 20. In this example, a wired LAN is used. However, the present invention is not limited to this. For example, a wireless LAN may be used instead. In this example, the network is used. However, the present invention is not limited to this. For example, a USB (Universal Serial Bus) may be used instead.

  The medium color sensor 12 detects the color of the recording medium 9 on which the image forming unit 13 forms an image. The medium color sensor 12 supplies the detection result to the image processing unit 20 as medium color information C.

  The image processing unit 20 generates five color image data D (color image data DY, DM, DC, DK, DW) based on the print data DP, and performs a trapping process on each color image data D It is. For example, the image processing unit 20 may be configured by hardware, or may be configured by using a processor capable of executing a program. The image processing unit 20 includes a color image generation unit 21, an edge detection unit 22, a white boundary detection unit 23, and a correction unit 24.

  The color image generation unit 21 generates five color image data D (color image data DY, DM, DC, DK, DW) based on the print data DP. The color image data DY is image data corresponding to an image to be formed using a yellow developer, and the color image data DM is image data corresponding to an image to be formed using a magenta developer. The color image data DC is image data corresponding to an image to be formed using a cyan developer, and the color image data DK corresponds to an image to be formed using a black developer. The color image data DW is image data corresponding to an image to be formed using a white developer.

  The edge detector 22 detects the edge of the image based on the five color image data D. Specifically, as will be described later, the edge detection unit 22 based on the five color image data D, five edge direction data DIR indicating a map of edge direction information, and five edges indicating a map of edge distance information The distance data DIS and five width data DLW indicating a map of width information are generated.

  Based on the five color image data D, the white boundary detection unit 23 is a boundary between a white image area and an image area of a color other than white in an image to be formed on the recording medium 9 (hereinafter, white boundary (Also referred to as BW). Specifically, the white boundary detection unit 23 generates white boundary data DWR, which is a map indicating the location of the white boundary BW, based on the five color image data D, as will be described later.

  The correction unit 24 includes medium color information C, five edge direction data DIR, five edge distance data DIS, five width data DLW, and five color image data D (color image data DY, DM, DC, DK, DW). Then, based on the white boundary data DWR, correction processing (white boundary correction processing and trapping correction processing) is performed to generate five color image data E (color image data EY, EM, EC, EK, EW). Is. Specifically, the correction unit 24 generates the color image data EY by correcting the color image data DY, generates the color image data EM by correcting the color image data DM, and corrects the color image data DC. Thus, color image data EC is generated, color image data EK is generated by correcting color image data DK, and color image data EW is generated by correcting color image data DW.

  The image forming unit 13 forms an image on the recording medium 9 based on the five color image data E. The image forming unit 13 forms an image on the recording medium 9 using developers of five colors of yellow, magenta, cyan, black, and white. Specifically, the image forming unit 13 forms a yellow image using a yellow developer based on the color image data EY, and uses a magenta developer based on the color image data EM to form a magenta color. An image is formed, a cyan image is formed using a cyan developer based on the color image data EC, and a black image is formed using a black developer based on the color image data EK. A white image is formed using a white developer based on the image data EW. At that time, the image forming unit 13 forms a white image, a black image, a cyan image, a magenta image, and a yellow image on the recording medium 9 in this order. That is, a white image is formed at the bottom. As a result, the image forming apparatus 1 can suppress the influence of the color of the recording medium 9 on the image quality.

  Here, the image processing unit 20 corresponds to a specific example of an “image processing unit” in the present invention. The image forming unit 13 corresponds to a specific example of “image forming unit” in the present invention. The color image data DW corresponds to a specific example of “first image data” in the present invention. Any one of the color image data DY, DM, DC, and DK corresponds to a specific example of “second image data” in the present invention.

[Operation and Action]
Next, the operation and action of the image forming apparatus 1 according to the present embodiment will be described.

(Overview of overall operation)
First, an overall operation outline of the image forming apparatus 1 will be described with reference to FIG. The communication unit 11 receives the print data DP by communicating with the host computer. The medium color sensor 12 detects the color of the recording medium 9 on which the image forming unit 13 forms an image, and supplies the detection result to the image processing unit 20 as medium color information C. The color image generation unit 21 of the image processing unit 20 generates five color image data D (color image data DY, DM, DC, DK, DW) based on the print data DP. The edge detection unit 22 detects the edge of the image based on the five color image data D. Specifically, the edge detection unit 22 based on the five color image data D, five edge direction data DIR indicating a map of edge direction information, five edge distance data DIS indicating a map of edge distance information, and Five width data DLW indicating a map of width information is generated. Based on the five color image data D, the white boundary detection unit 23 is a boundary (white boundary BW) between a white image region and an image region of a color other than white in an image to be formed on the recording medium 9. Is detected. Specifically, the white boundary detection unit 23 generates white boundary data DWR, which is a map indicating the location of the white boundary BW, based on the five color image data D. The correction unit 24 includes medium color information C, five edge direction data DIR, five edge distance data DIS, five width data DLW, and five color image data D (color image data DY, DM, DC, DK, DW). Then, based on the white boundary data DWR, correction processing (white boundary correction processing and trapping correction processing) is performed to generate five color image data E (color image data EY, EM, EC, EK, EW). . The image forming unit 13 forms an image on the recording medium 9 based on the five color image data E.

(Detailed operation)
FIG. 2 illustrates an operation example of the image processing unit 20. The image processing unit 20 generates five color image data D (color image data DY, DM, DC, DK, DW) based on the print data DP, and performs correction processing (white boundary correction) on each color image data D. Processing and trapping correction processing). This operation will be described below.

  First, the color image generation unit 21 generates five color image data D (color image data DY, DM, DC, DK, DW) based on the print data DP received by the communication unit 11 (step S100).

  Next, the edge detection unit 22 performs an edge detection process based on the five color image data D (step S200). Specifically, as will be described later, the edge detection unit 22 based on the five color image data D, five edge direction data DIR indicating a map of edge direction information, and five edges indicating a map of edge distance information The distance data DIS and five width data DLW indicating a map of width information are generated.

  Next, the white boundary detection unit 23 performs white boundary detection processing based on the five color image data D (step S300). Specifically, the white boundary detection unit 23 generates white boundary data DWR, which is a map indicating the location of the white boundary BW, based on the five color image data D, as will be described later.

  In this example, the edge detection process (step S200) and the white boundary detection process (step S300) are performed in this order, but the present invention is not limited to this, and the order may be reversed.

  Next, the correction unit 24 performs white boundary correction processing on the white color image data DW among the five color image data D (step S400). Specifically, as will be described later, the correction unit 24 corrects a pixel value near the white boundary BW (a white boundary region RBW described later) in the white color image data DW based on the white boundary data DWR.

  Next, the correction unit 24 performs a trapping correction process on the five color image data D (step S500). Specifically, as will be described later, the correction unit 24 generates five color image data D based on the medium color information C, the five edge direction data DIR, the five edge distance data DIS, and the five width data DLW. 5 color image data E (color image data EY, EM, EC, EK, EW) is generated by performing a trapping process on.

  In this way, the image processing unit 20 generates five color image data E. The image forming unit 13 forms an image on the recording medium 9 based on the five color image data E.

(Edge detection processing)
Next, the edge detection process in step S200 shown in FIG. 2 will be described in detail.

  FIG. 3 shows an example of edge detection processing. In the edge detection processing, based on the five color image data D, five edge direction data DIR, five edge distance data DIS, and five width data DLW are generated. This operation will be described in detail below.

  First, the edge detection unit 22 selects one of the five color image data D (color image data DY, DM, DC, DK, DW) (step S201).

  Next, the edge detection unit 22 performs binarization processing on the selected color image data D (step S202). Specifically, the edge detection unit 22 compares the pixel value in each pixel with a predetermined threshold value TH1 in the selected color image data D. At this time, the edge detection unit 22 selects any one of the yellow color image data DY, the magenta color image data DM, the cyan color image data DC, and the black color image data DK as the selected color image data D. In the case where the pixel value is larger than the threshold value TH1, it is set to “1”, and when the pixel value is smaller than the threshold value TH1, it is set to “0”. Further, when the selected color image data D is white color image data DW, the edge detection unit 22 sets the pixel value to “0” when the pixel value is larger than the threshold value TH1. When it is smaller than the threshold value TH1, it is set to “1”.

  4A and 4B show an example of binarization processing for yellow color image data DY. FIG. 4A schematically shows part of the color image data DY. FIG. 4B shows binarization processing. Shows the results. The same applies to the magenta color image data DM, the cyan color image data DC, and the black color image data DK. In FIG. 4A, shaded pixels indicate pixels whose pixel values are larger than the threshold value TH1, and unshaded pixels indicate pixels whose pixel values are smaller than the threshold value TH1. The edge detection unit 22 generates binary data DBN indicating a map as illustrated in FIG. 4B by performing binarization processing on such color image data DY. In this example, since the selected color image data D is yellow color image data DY, the edge detection unit 22 sets “1” when the pixel value is larger than the threshold value TH1, and the pixel value is the threshold value. If it is smaller than the value TH1, it is set to “0”. That is, the pixels indicated by “1” in FIG. 4B correspond to the shaded pixels in FIG. 4A, respectively. Similarly, the pixels indicated by “0” in FIG. 4B correspond to the non-shaded pixels in FIG. 4A, respectively.

  5A and 5B show an example of binarization processing for the white color image data DW. FIG. 5A schematically shows a part of the white color image data DW, and FIG. The result of the digitization process is shown. In FIG. 5A, shaded pixels indicate pixels whose pixel values are larger than the threshold value TH1, and unshaded pixels indicate pixels whose pixel values are smaller than the threshold value TH1. The edge detection unit 22 generates binary data DBN indicating a map as shown in FIG. 5B by performing binarization processing on such color image data DW. In this example, since the selected color image data D is white color image data DW, the edge detection unit 22 sets “0” when the pixel value is larger than the threshold value TH1, and the pixel value is the threshold value. When it is smaller than the value TH1, it is set to “1”. That is, the pixels indicated by “0” in FIG. 5B correspond to the shaded pixels in FIG. 5A, respectively. Similarly, the pixels indicated by “1” in FIG. 5B correspond to the non-shaded pixels in FIG. 5A, respectively.

  In this way, the edge detection unit 22 generates binary data DBN by performing binarization processing on the selected color image data D.

  Next, the edge detection unit 22 generates edge direction data DIR and edge distance data DIS based on the binary data DBN generated in step S202 (step S203). This operation will be described in detail below.

  First, the edge detection unit 22 sequentially selects one of a plurality of pixels having a value “1” as the target pixel A in the binary data DBN. Then, the edge detection unit 22 includes a pixel above the target pixel A (upper pixel), a pixel below the target pixel A (lower pixel), a left pixel (left pixel) of the target pixel A, and a right side of the target pixel A. Based on the value (“0” or “1”) in the pixel (right pixel), edge direction information for the target pixel A is generated.

  FIG. 6 illustrates an example of an operation for generating edge direction information of the target pixel A. For example, when the values of the upper pixel, the lower pixel, the left pixel, and the right pixel are “1”, “0”, “0”, and “0”, respectively, the edge detection unit 22 Set the edge direction information to “up”. Similarly, the edge detection unit 22 pays attention when the values of the upper pixel, the lower pixel, the left pixel, and the right pixel are “0”, “1”, “0”, and “0”, respectively. When the edge direction information of the pixel A is “down” and is “0”, “0”, “1”, “0”, respectively, the edge direction information of the target pixel A is set to “left” and “0”, respectively. In the case of “,” “0”, “0”, “1”, the edge direction information of the target pixel A is set to “right”. Further, for example, the edge detection unit 22 determines that the pixel of interest when the values of the upper pixel, the lower pixel, the left pixel, and the right pixel are “1”, “1”, “0”, and “0”, respectively. When the edge direction information of A is “up and down” and are “1”, “0”, “1”, and “0”, respectively, the edge direction information of the target pixel A is set to “upper left” and “1”. , “0”, “0”, “1”, the edge direction information of the target pixel A is set to “upper right”. Further, for example, the edge detection unit 22 determines that the pixel of interest when the values of the upper pixel, the lower pixel, the left pixel, and the right pixel are “1”, “1”, “1”, and “0”, respectively. When the edge direction information of A is “left” and “1”, “1”, “0”, and “1” respectively, the edge direction information of the target pixel A is set to “right”. Further, for example, when the values of the upper pixel, the lower pixel, the left pixel, and the right pixel are all “1”, the edge detection unit 22 sets the edge direction information of the target pixel A to “none” and sets all “ If it is “0”, the edge direction information of the target pixel A is set to “up / down / left / right”.

  Then, when the edge direction information of the target pixel A is other than “none”, the edge detection unit 22 sets the edge distance information of the target pixel A to “1”.

  Thereby, the edge direction information and the edge distance information in the outermost pixel in the area of the area having the value “1” in the binary data DBN are generated.

  Next, the edge detection unit 22 sequentially selects one of the pixels whose edge direction information is “none” as the target pixel A. Then, the edge detection unit 22 includes a pixel above the target pixel A (upper pixel), a pixel below the target pixel A (lower pixel), a left pixel (left pixel) of the target pixel A, and a right side of the target pixel A. Based on the edge direction information in the pixel (right pixel), edge direction information in the target pixel A is generated.

  FIG. 7 illustrates an example of an operation for generating edge direction information of the target pixel A based on edge direction information in the upper pixel, the lower pixel, the left pixel, and the right pixel. For example, as illustrated in (a), when the edge direction information in the right pixel includes “left”, the edge detection unit 22 sets the edge direction information of the target pixel A to “left”. Specifically, for example, when the edge direction information in the right pixel is any one of “left”, “upper left”, “lower left”, “left / right”, and “up / down / left / right”, the edge direction information of the target pixel A Set to “Left”. Further, for example, as illustrated in (e), the edge detection unit 22 includes “left” in the edge direction information in the upper pixel and “down” in the edge direction information in the right pixel. The edge direction information of the target pixel A is set to “lower left”.

  By repeating this process, edge direction information is generated in order from the outside toward the inside in the area of the area having the value “1” in the binary data DBN. Then, the edge detection unit 22 sets the edge distance information in the pixel for which the edge direction information is generated when this processing is performed once to “2”, and the edge direction information is generated when this processing is performed twice. The edge distance information at the selected pixel is set to “3”. The same applies to the subsequent steps. Thereby, the edge distance information is set as “1”, “2”, “3”,... In order from the outermost side in the region of the value data “1” in the binary data DBN. .

  For example, the edge direction information of the target pixel A shown in FIG. 4B is “up”, and the edge distance information of the target pixel A is “2”. That is, the edge direction information and the edge distance information indicate that the target pixel A is “above” the nearest edge portion B and is located at the “2” pixel from the edge portion B.

  In this way, edge direction information and edge distance information are generated for all pixels having a value of “1” in the binary data DBN. Then, the edge detection unit 22 generates edge direction data DIR indicating a map of the edge direction information based on the edge direction information at each pixel, and also calculates the edge distance information based on the edge distance information at each pixel. Edge distance data DIS indicating a map is generated.

  Next, the edge detection unit 22 generates the width data DLW based on the edge direction data DIR and the edge distance data DIS generated in step S203 (step S204). Specifically, the edge detection unit 22 sequentially selects one of the pixels having a value “1” as the target pixel A in the binary data DBN. Then, based on the edge direction information of the target pixel A, the edge detection unit 22 checks how many pixels “1” continue in the direction indicated by the edge direction information and in the opposite direction. For example, when the edge direction information of the target pixel A is “upper”, “lower”, and “upper / lower”, the edge detection unit 22 determines how many pixels “1” continue in the vertical direction of the target pixel A. Investigate. For example, when the edge direction information of the target pixel A is “left”, “right”, and “left / right”, the edge detection unit 22 continues with “1” in the left / right direction of the target pixel A. Find out. For example, when the edge direction information of the target pixel A is “upper right”, “lower right”, “upper left”, and “lower left”, the edge detection unit 22 performs the vertical or horizontal direction of the target pixel A. , How many pixels “1” continues. Then, the edge detection unit 22 uses the value obtained in this way as the width information in the target pixel A.

  For example, the edge direction information of the target pixel A shown in FIG. 4B is “up”, and the edge distance information of the target pixel A is “2”. Therefore, the edge detection unit 22 checks how many pixels “1” continue in the vertical direction of the target pixel A. In this example, five “1” s follow in the vertical direction. Therefore, the edge detection unit 22 sets the width information at the target pixel A to “5”.

  In this manner, width information is generated for all pixels having a value of “1” in the binary data DBN. Then, the edge detection unit 22 generates width data DLW indicating a map of width information based on the width information in each pixel.

  Next, the edge detection unit 22 confirms whether or not all the color image data D has been selected (step S205). If all the color image data D have not been selected (“N” in step S205), the color image data D that has not been selected is selected (step S206), and the process returns to step S202. Then, steps S202 to S206 are repeated until all the color image data D are selected. When all the color image data D are selected (“Y” in step S205), the edge detection unit 22 ends this edge detection process (step S200 in FIG. 2).

(White border detection processing)
Next, the white boundary detection process in step S300 shown in FIG. 2 will be described in detail.

  FIG. 8 shows an example of white boundary detection processing. In the white boundary detection process, white boundary data DWR, which is a map indicating the location of the white boundary BW, is generated based on the five color image data D. This operation will be described in detail below.

  First, the white boundary detection unit 23 selects one of the pixels as the target pixel A (step S301).

  Next, the white boundary detection unit 23 has a pixel value at the target pixel A of the white color image data DW larger than the threshold value TH4, and four color image data DY, DM, DC, and other than the color image data DW. It is confirmed whether or not the pixel values of the target pixel A of DK are all “0” (zero) (step S302). If the condition of step S302 is not satisfied ("N" in step S302), the process proceeds to step S305.

  When the condition of step S302 is satisfied (“Y” in step S302), the white boundary detection unit 23 determines that the pixel value of the white color image data DW is “0” (zero) for the peripheral pixel A1 of the target pixel A. It is confirmed whether or not at least one pixel value of four color image data DY, DM, DC, DK other than the color image data DW includes a pixel larger than “0” (zero) (step S303). ). Here, the peripheral pixel A1 is, for example, eight pixels other than the target pixel A among nine (3 × 3) pixels centered on the target pixel A.

  When the condition of step S303 is satisfied (“Y” in step S303), the white boundary detection unit 23 sets the value of the white boundary data DWR at the target pixel A to “1” (step S304). That is, in this case, the white boundary detection unit 23 determines that the target pixel A is the white boundary pixel C1. Then, the process proceeds to step S306.

  If the condition of step S303 is not satisfied (“N” in step S303), the white boundary detection unit 23 sets the value of the white boundary data DWR at the target pixel A to “0” (step S305). That is, in this case, the white boundary detection unit 23 determines that the target pixel A is the non-white boundary pixel C2. Then, the process proceeds to step S306.

  9A and 9B show an example of white boundary detection processing. FIG. 9A schematically shows a part of the white color image data DW, and FIG. 9B schematically shows a part of the magenta color image data DM. Hereinafter, for convenience of explanation, the pixel value of the color image data D is normalized to 0 to 255 for explanation. In FIG. 9A, pixels that are darkly shaded indicate pixels having a large pixel value in the color image data DW (in this example, “255”), and pixels that are lightly shaded are pixel values in the color image data DW. Indicates a small pixel (in this example, “55”). In this example, the pixel value in the darkly shaded pixel and the pixel value in the lightly shaded pixel are both larger than the threshold value TH4. In FIG. 9B, the shaded pixels indicate pixels whose pixel value in the color image data DM is greater than “0” (zero) (in this example, “255”). A pixel having a pixel value “0” (zero) in the color image data DM is shown. Although not shown, in this example, the pixel values of the color image data DY, DC, and DK are all “0” (zero).

  In this example, the pixel value of the white color image data DW at the target pixel A is larger than the threshold value TH4, and the pixel values of the four color image data DY, DM, DC, DK other than the color image data DW Are both “0” (zero) (“Y” in step S302). Among the peripheral pixels A1 of the target pixel A, the pixel value of the white color image data DW is “0” for the upper left pixel of the target pixel A, the left pixel of the target pixel A, and the lower left pixel of the target pixel A. “(Zero)” and the pixel value of the magenta color image data DM is larger than “0” (zero) (“Y” in step S303). Therefore, the white boundary detection unit 23 determines in step S304 that the target pixel A is the white boundary pixel C1.

  Next, the white boundary detection unit 23 checks whether or not all the pixels have been selected as the pixel of interest A (step S306). If all the pixels have not been selected (“N” in step S306), the white boundary detection unit 23 selects a pixel that has not been selected as the target pixel A (step S307), and returns to step S302. . Then, steps S302 to S307 are repeated until all the pixels are selected. When all the pixels are selected (“Y” in step S306), the white boundary detection unit 23 ends the white boundary detection process (step S300 in FIG. 2).

  FIG. 10 shows an example of the white boundary data DWR generated by the white boundary detection process in the example shown in FIGS. 9A and 9B. As shown in FIGS. 9A and 9B, in the left half image area, the value of the white color image data DW is “255” and the value of the magenta color image data is “255”. In the right half image area, the value of the white color image data DW is “55”, and the value of the magenta color image data is “0” (zero). That is, the left half image area is an image area in which magenta color and white color overlap, and the right half image area is an image area only in white, so the boundary between the two image areas is the white boundary BW ( FIG. 10). The white boundary detection unit 23 determines that the pixel in contact with the white boundary BW among the pixels of the image region of only white is the white boundary pixel C1.

(White border correction processing)
Next, the white boundary correction process in step S400 shown in FIG. 2 will be described in detail.

  FIG. 11 shows an example of white boundary correction processing. In the white boundary correction process, based on the white boundary data DWR, the pixel value near the white boundary BW (white boundary region RBW described later) in the white color image data DW is corrected. This operation will be described in detail below.

  First, the correction unit 24 selects one of the pixels as the target pixel A (step S401).

  Next, the correcting unit 24 confirms whether or not the target pixel A is the white boundary pixel C1 based on the white boundary data DWR (step S402). If the target pixel A is not the white boundary pixel C1 (“N” in step S402), the process proceeds to step S410.

  In step S402, when the target pixel A is the white boundary pixel C1 (“Y” in step S402), the correction unit 24 selects one of the peripheral pixels A1 of the target pixel A as the pixel A2. (Step S403). Specifically, the correcting unit 24 is, for example, any one of a pixel above the target pixel A, a pixel below the target pixel A, a left pixel of the target pixel A, and a right pixel of the target pixel A. Is selected as pixel A2.

  Next, the correction unit 24 determines whether the absolute value (difference value DIFF) of the difference between the pixel value at the target pixel A and the pixel value at the pixel A2 in the white color image data DW is greater than the threshold value TH5. It is confirmed whether or not (step S404). Here, the threshold value TH5 corresponds to a specific example of “first threshold value” in the present invention.

  If the difference value DIFF is smaller than the threshold value TH5 in step S404 (“N” in step S404), the correction unit 24 has selected all the pixels in the peripheral pixel A1 as the pixel A2. Is confirmed (step S405). When all the pixels have not been selected as the pixel A2 (“N” in step S405), the correction unit 24 selects a pixel that has not yet been selected from the peripheral pixels A1 as the pixel A2 (step S405). ). At this time, the correction unit 24 has not yet selected, for example, a pixel above the target pixel A, a pixel below the target pixel A, a left pixel of the target pixel A, and a right pixel of the target pixel A. The pixels are sequentially selected as the pixel A2, and after that, among the upper left pixel of the target pixel A, the upper right pixel of the target pixel A, the lower left pixel of the target pixel A, and the lower right pixel of the target pixel A Unselected pixels are sequentially selected as the pixel A2. Then, the process returns to step S404. In step S405, when all the pixels of the peripheral pixel A1 are selected (“Y” in step S405), the process proceeds to step S410.

  If the difference value DIFF is larger than the threshold value TH5 in step S404, the correction unit 24 sets the correction target region R (step S407). The correction target region R is a region including a plurality of pixels across the white boundary BW, as will be described later.

Then, the correction unit 24 corrects the pixel value of the color image data DW at each pixel in the correction target region R (step S408). Specifically, the correction unit 24 corrects the pixel value of each pixel in the correction target region R using the following equation.
XXi = Xi * (1-Wi) + Y * Wi (EQ1)
Here, XXi is a pixel value after correction of the color image data DW at the i-th pixel in the correction target region R, and Xi is a pixel value before correction of the color image data DW at the i-th pixel. Wi is a weighting coefficient of the i-th pixel, and Y is an average value of pixel values before correction of the color image data DW in all the pixels in the correction target region R. The weight coefficient Wi has a value of 0 or more and 1 or less, for example.

  FIG. 12 schematically illustrates an example of white boundary correction processing in the example illustrated in FIGS. 9A, 9B, and 10. As in FIG. 9A, pixels that are darkly shaded indicate pixels having a large pixel value in the color image data DW (in this example, “255”), and pixels that are lightly shaded are pixels in the color image data DW. This indicates a pixel having a small pixel value (in this example, “55”). In this example, the difference (in this example, “200”) between the pixel value of the darkly shaded pixel and the pixel value of the lightly shaded pixel is larger than the threshold value TH5.

  In this example, the target pixel A is the white boundary pixel C1 as shown in FIG. 10 (“Y” in step S402). When the correction unit 24 selects the pixel to the left of the target pixel A as the pixel A2, the absolute value (difference value DIFF) of the difference between the pixel value at the target pixel A and the pixel value at the pixel A2 in the color image data DW. ) Is larger than the threshold value TH5 (“Y” in step S404). Therefore, in this example, the correction unit 24 sets a correction target region R including four pixels arranged in the left-right direction, including the target pixel A and the pixel A2 that are adjacent to each other in the left-right direction (step S407). Then, the correction unit 24 corrects the four pixel values in the correction target region R (step S408).

  FIG. 13 shows an example of correction processing in the correction target region R shown in FIG. 12, (A) shows the weighting factor Wi, (B) shows the pixel value Xi before correction, and (C) Indicates a corrected pixel value XXi. In this example, the weight coefficient Wi is set to “0.2”, “0.7”, “0.7”, “0.2” in order from the left. That is, the weight coefficient Wi is set symmetrically in the arrangement direction (left-right direction) of the target pixel A and the pixel A2, and is set to a smaller value as it goes outward in the arrangement direction. The pixel values Xi before correction are “255”, “255”, “55”, and “55” in order from the left. By correcting these pixel values Xi using the equation (EQ1), a corrected pixel value XXi is obtained. The corrected pixel values XXi are “235”, “185”, “125”, “75” in order from the left. As shown in the equation (EQ1), the corrected pixel value XXi is closer to the average value Y as the weighting factor Wi is larger, and is closer to the pixel value Xi before correction as the weighting factor Wi is smaller. become. Accordingly, as shown in FIGS. 13B and 13C, the change in the pixel value XXi after correction in the direction intersecting the white boundary BW (in this example, the left-right direction) is the pixel value Xi before correction. Compared to changes in That is, the pixel value Xi before correction changes abruptly, but the pixel value XXi after correction changes gently and monotonously.

  In this example, since the white boundary BW extends in the vertical direction of FIG. 12, the correction target region R is elongated in the left-right direction. For example, when the white boundary BW extends in the horizontal direction, the correction target region R can be lengthened in the vertical direction. For example, when the white boundary BW forms a corner, the correction target region R can be set as follows.

  FIG. 14 schematically illustrates another example of the white boundary correction process. As in FIG. 12, pixels that are darkly shaded indicate pixels having a large pixel value in the color image data DW (in this example, “255”), and pixels that are lightly shaded are pixels in the color image data DW. This indicates a pixel having a small pixel value (in this example, “55”).

  Also in this example, the target pixel A is the white boundary pixel C1 (“Y” in step S402). When the upper left pixel of the target pixel A is selected as the pixel A2, the absolute value (difference value DIFF) of the difference between the pixel value at the target pixel A and the pixel value at the pixel A2 in the color image data DW is the threshold. It is larger than the value TH5 (“Y” in step S404). Accordingly, in this example, the correction unit 24 sets a correction target region R including eight pixels including the target pixel A and the pixel A2 that are adjacent to each other in the oblique direction (step S407). Then, the correction unit 24 corrects the eight pixel values in the correction target region R (step S408).

  FIG. 15 illustrates an example of correction processing in the correction target region R illustrated in FIG. The weight coefficient Wi is set symmetrically in the arrangement direction (diagonal direction) of the target pixel A and the pixel A2, and is set to a smaller value as it goes outward in the arrangement direction. As a result, as shown in FIGS. 15B and 15C, the change in the corrected pixel value XXi in the direction intersecting the white boundary BW (in this example, the oblique direction) is the pixel value Xi before correction. Compared to changes in That is, the pixel value Xi before correction changes abruptly, but the pixel value XXi after correction changes gently and monotonously.

  For example, the size of the correction target region R may be fixed. In this case, the width of the correction target region R in the direction intersecting with the white boundary BW is, for example, that the image forming unit 13 forms yellow, magenta, cyan, black, and white images on the recording medium 9, respectively. It can be set in advance according to the relative shift amount of the image formation position. Specifically, in the example of FIGS. 12 and 13, when the relative shift amount of each image formation position is large, the number of pixels arranged in the left-right direction in the correction target region R can be increased. . Further, the size of the correction target region R may be changed according to the difference value DIFF, for example. Specifically, for example, it is desirable to increase the width in the direction intersecting the white boundary BW of the correction target region R as the difference value DIFF increases. For example, by setting a plurality of threshold values and comparing the difference value DIFF with each threshold value, the size of the correction target region R can be set according to the difference value DIFF. Here, any one of the plurality of threshold values corresponds to a specific example of “second threshold value” in the present invention. In this correction processing, if there is a pixel whose color image data DW has a pixel value “0” (zero) in the correction target region R, the correction target region R is set by omitting this pixel. Also good.

  Next, the correction unit 24 excludes pixels that have not yet been selected as the target pixel A among the pixels in the correction target region R from the selection candidates for the target pixel A (step S409).

  Next, the correcting unit 24 checks whether or not all the pixels have been selected as the target pixel A (step S410). If not all the pixels have been selected (“N” in step S410), the correction unit 24 selects a pixel that has not been selected as the target pixel A (step S411), and returns to step S402. Then, steps S402 to S411 are repeated until all the pixels are selected. When all the pixels are selected (“Y” in step S410), the correction unit 24 ends the white boundary correction process (step S400 in FIG. 2).

  FIG. 16 schematically shows an example of the white color image data DW generated by the white boundary correction process in the examples shown in FIGS. FIG. 17 schematically shows an example of the white color image data DW generated by the white boundary correction process in the examples shown in FIGS. In FIGS. 16 and 17, shaded shading corresponds to the size of the pixel value in the color image data DW, as in FIGS. In the white boundary correction process, correction is performed by sequentially setting the correction target region R, thereby correcting the pixel value in the band-like region (white boundary region RBW) including the white boundary BW. In the white boundary region RBW, the change in the pixel value in the direction intersecting with the white boundary BW is gentler than before correction (FIGS. 12 and 14). The white boundary region RBW corresponds to a specific example of “boundary region” in the present invention.

(Trapping correction process)
Next, the trapping correction process in step S500 shown in FIG. 2 will be described in detail.

  18 and 19 show an example of the trapping correction process. In the trapping correction process, the trapping process is performed on the five color image data D based on the medium color information C, the five edge direction data DIR, the five edge distance data DIS, and the five width data DLW. This operation will be described in detail below.

  First, the correction unit 24 selects one of the pixels as the target pixel A (step S501), and selects one of the five color image data D (step S502).

  Next, the correcting unit 24 checks whether or not the selected color image data D is white color image data DW (step S503). If it is not the white color image data DW (“N” in step S503), the process proceeds to step S505.

  In step S503, when the selected color image data D is white color image data DW (“Y” in step S503), the correction unit 24 corrects the target pixel A in step S408 of the white boundary correction process. It is confirmed whether or not the pixel has been subjected to (step S504). In other words, the correction unit 24 checks whether or not the target pixel A is a pixel in the white boundary region RBW. If the target pixel A is a pixel that has been corrected in step S408 of the white boundary correction process (“Y” in step S504), the process proceeds to step S510.

  In step S504, when the selected color image data D is not white color image data DW (“N” in step S504), the correction unit 24 uses the target pixel A in the edge direction data DIR of the selected color image data D. On the basis of the edge direction information, one of the edge directions included in the edge direction information is selected (step S505). That is, for example, when the edge direction information is “upper left”, one of them (for example, “left”) is first selected.

  Next, the correction unit 24 determines two pixels B1 and B2 that sandwich the edge (step S506).

  FIG. 20 illustrates an example of the determination operation of the pixels B1 and B2. FIG. 20 shows the binary data DBN of the selected color image data D. The shaded pixels indicate the pixels having the value “1”, and the pixels not shaded have the value “0”. "Indicates a pixel. In this example, the edge direction information of the target pixel A is “left”, and the edge distance information of the target pixel A is “2”. The correcting unit 24 identifies an edge based on the edge direction information and the edge distance information. That is, the edge portion B is located on the right side of the target pixel A at a distance of one pixel from the target pixel A, and on the right side of the target pixel A at a distance of two pixels from the target pixel A. It is between the pixel B2 on the back side. That is, the pixel B1 is located at a distance corresponding to one smaller value than the value indicated by the edge distance information from the target pixel A in the opposite direction to the edge direction indicated by the edge direction information of the target pixel A. Is located at a distance corresponding to the value indicated by the edge distance information from the target pixel A in a direction opposite to the edge direction indicated by the edge direction information of the target pixel A.

  In this way, the correction unit 24 identifies the pixels B1 and B2 that sandwich the edge portion B based on the edge direction information and the edge distance information at the target pixel A.

  Next, the correction unit 24 performs a trapping process (step S507).

  First, as illustrated in FIG. 19, the correction unit 24 confirms whether or not the target pixel A in the selected color image data D is a trapping target pixel (step S <b> 521). Specifically, the correction unit 24 determines that the target pixel A is a trapping target pixel when the following two conditions are satisfied.

  The first condition is that the edge distance information at the target pixel A is equal to or less than a predetermined threshold value TH2. That is, when this condition is satisfied, it indicates that the target pixel A is close to the edge portion B in the selected color image data D. This threshold value TH2 is the relative shift amount of each image forming position when the image forming unit 13 forms yellow, magenta, cyan, black, and white images on the recording medium 9, respectively. It is preset according to For example, when the shift amount is 2 pixels, the threshold value TH2 is set to “2”. In this case, for example, if the value indicated by the edge distance information in the target pixel A is “2” or less, the first condition is satisfied, and if the value indicated by the edge distance information in the target pixel A is “3”, This first condition is not satisfied.

  The second condition is that one of the pixels B1 and B2 has “1” in the binary data DBN of only one of the color image data DY, DM, DC, and DK, The binary data DBN of only the color image data DW has “1”. That is, when this condition is satisfied, only one of the color image data DY, DM, DC, and DK and the pixel value of the color image data DW are greater than the threshold value TH1 in one of the pixels B1 and B2. It shows that the pixel value of the color image data DY, DM, DC, DK, DW is smaller than the threshold value TH1 on the other of the pixels B1, B2. For example, the second condition is satisfied when the pixel B1 has a magenta pixel value and a white pixel value, and no color has a pixel value in the pixel B2. For example, when the pixel B1 has a magenta pixel value and a yellow pixel value, the second condition is not satisfied.

  The correction unit 24 determines that the target pixel A is a trapping target pixel when these two conditions are satisfied. If the target pixel A is not a trapping target pixel (“N” in step S521), the trapping process is terminated.

  In step S521, when the target pixel A is a trapping target pixel (“Y” in step S521), the correction unit 24 determines that both the width information in the pixels B1 and B2 are greater than the predetermined threshold value TH3. It is confirmed whether it is larger (step S522). Specifically, for example, the binary data DBN of the magenta color image data DM has “1” in the pixel B1, and the binary data DBN of the white color image data DW has “1” in the pixel B2. When the correction unit 24 includes the width information at the pixel B1 in the width data DLW of the magenta color image data DM and the width information at the pixel B2 in the width data DLW of the white color image data DW, In addition, it is confirmed whether or not both are larger than the threshold value TH3.

  In step S522, when both the width information in the pixels B1 and B2 are larger than the threshold value TH3 (“Y” in step S522), the correction unit 24 determines that the lightness in the pixel B2 is equal to that in the pixel B1. It is confirmed whether or not the brightness is higher (step S523). Specifically, for example, the binary data DBN of the magenta color image data DM has “1” in the pixel B1, and the binary data DBN of the white color image data DW has “1” in the pixel B2. In this case, the correction unit 24 checks whether or not the lightness obtained based on the medium color information C is larger than the lightness obtained from the pixel value at the pixel B1 of the magenta color image data DM. . That is, in this example, for example, none of the colors in the pixel B2 has a pixel value, so the brightness is obtained based on the medium color information C indicating the color of the recording medium 9, and this brightness is used as the brightness in the pixel B2. Here, the lightness corresponds to a specific example of “brightness degree” in the present invention. If the brightness at pixel B2 is higher than the brightness at pixel B1 ("Y" in step S523), the process proceeds to step S527, and if the brightness at pixel B2 is lower than the brightness at pixel B1 (in step S524). In “N”), the trapping process ends.

  In step S522, when at least one of the width information in the pixels B1 and B2 is smaller than the threshold value TH3 (“N” in step S522), the correction unit 24 uses the width information in the pixel B1 as a threshold. It is confirmed whether or not the value is larger than the value TH3 (step S524). If the width information at the pixel B1 is larger than the threshold value TH3 in step S524 (“Y” in step S524), the process proceeds to step S527.

  In step S524, when the width information at pixel B1 is smaller than threshold value TH3 ("N" at step S524), correction unit 24 determines whether the width information at pixel B2 is larger than threshold value TH3. It is confirmed whether or not (step S525). When the width information at the pixel B2 is larger than the threshold value TH3 (“Y” in step S525), the trapping process is terminated.

  In step S525, when the width information at the pixel B2 is smaller than the threshold value TH3 (“N” in step S525), the correction unit 24 sets the edge distance information of the pixel of interest A to half the threshold value TH3 (TH3). / 2) is checked (step S526). When the edge distance information of the target pixel A is smaller than half of the threshold value TH3 (“Y” in step S526), the process proceeds to step S527, and the edge distance information of the target pixel A is smaller than half of the threshold value TH3. If larger (“N” in step S526), the trapping process is terminated.

  Next, the correction unit 24 sets the pixel value of the target pixel A to the same pixel value as the pixel value of the pixel B2 (step S527).

  Specifically, for example, the binary data DBN of the white color image data DW has “1” in the pixel B1, and the binary data DBN of the magenta color image data DM has “1” in the pixel B2. , The pixel value of the target pixel A in the magenta color image data DM is set to the same pixel value as the pixel value in the pixel B2 in the magenta color image data DM. As a result, the magenta image area is widened.

  For example, when the binary data DBN of the magenta color image data DM has “1” in the pixel B1, and the binary data DBN of the white color image data DW has “1” in the pixel B2. The correction unit 24 sets the pixel value of the target pixel A in the white color image data DW to the same pixel value as the pixel value in the pixel B2 in the white color image data DW. That is, in this case, in the white color image data DW, the pixel value in the pixel B2 is lower than the threshold value TH1. Therefore, the pixel value in the target pixel A is also set to such a sufficiently low value. This narrows the white image area.

  Thus, the trapping process ends.

  Next, as illustrated in FIG. 18, the correction unit 24 confirms whether or not all edge directions have been selected for the target pixel A in the selected color image data D (step S508). If not all edge directions have been selected (“N” in step S508), the correction unit 24 selects an edge direction that has not yet been selected (step S509), and returns to step S506. Steps S506 to S509 are repeated until all edge directions are selected. If all edge directions have been selected (“Y” in step S508), the process proceeds to step S510.

  Next, the correcting unit 24 checks whether or not all the color image data D has been selected (step S510). If all the color image data D has not yet been selected (“N” in step S510), the correction unit 24 selects the color image data D that has not yet been selected (step S511), and returns to step S503. . Then, steps S503 to S511 are repeated until all the color image data D are selected. If all the color image data D are selected (“Y” in step S510), the process proceeds to step S512.

  Next, the correction unit 24 checks whether or not all the pixels have been selected as the target pixel A (step S512). When all the pixels have not been selected (“N” in step S512), the correction unit 24 selects a pixel that has not been selected as the target pixel A (step S513), and returns to step S502. Then, steps S502 to S513 are repeated until all the pixels are selected. When all the pixels are selected (“Y” in step S512), the correction unit 24 ends the trapping correction process (step S500 in FIG. 2).

  In this way, the correction unit 24 generates five color image data E by correcting the five color image data D. The image forming unit 13 forms an image on the recording medium 9 based on the five color image data E.

  Next, the operation of the image forming apparatus 1 will be described with some examples.

(Specific example of white boundary correction processing)
21 and 22 schematically illustrate an image formation example when a magenta color image PM is formed on the recording medium 9. FIG. 21A shows a cross-sectional view when the white boundary correction process is not performed, and FIG. 21B shows a cross-section when a relative shift occurs in the image formation position in FIG. The figure is shown. FIG. 21C shows a cross-sectional view when white boundary correction processing is performed, and FIG. 21D is a cross-sectional view when a relative shift occurs in the image formation position in FIG. 21C. Indicates. 22A shows a cross-sectional view when the white boundary correction process is not performed, and FIG. 22B shows a cross-section when a relative shift occurs in the image formation position in FIG. 22A. The figure is shown. 22C is a cross-sectional view when white boundary correction processing is performed, and FIG. 22D is a cross-sectional view when a relative shift occurs in the image formation position in FIG. 22C. Indicates. 21 and 22, for convenience of explanation, the gradations of the magenta color image PM and the white image PW are represented by, for example, the thickness of the developer layer. Note that the gradation expression method in the image forming unit 13 is not limited to the density gradation, and may be, for example, an area gradation.

  As shown in FIGS. 21A and 22A, when the white boundary correction processing is not performed, the image forming apparatus 1 has a large pixel value in the region R1 on the left side of the white boundary BW on the recording medium 9. The white image PW is formed based on (for example, “255”), and the white image PW is formed based on a small pixel value (for example, “55”) in the region R2 on the right side of the white boundary BW. Then, the image forming apparatus 1 forms a magenta image PM on the white image PW in the region R1. In this manner, by forming the white image PW under the magenta color image PM in the region R1, the possibility that the magenta color appears to be mixed with the color of the recording medium 9 when the user observes the image is reduced. Can do.

  However, when the image forming unit 13 forms the white image PW and the magenta image PM on the recording medium 9, for example, the magenta image PM is formed to be shifted to the left relative to the white image PW. In this case, as shown in FIG. 21B, a part of the white image PW is exposed in the region R11 near the white boundary BW of the region R1. In particular, in this example, the density of the developer is high in the white image PW in the area R1, and the density of the developer is low in the white image PW in the area R2, and the difference between these densities is large. May stand out unnaturally. For example, when the magenta color image PM is formed to be shifted to the right relative to the white image PW, as shown in FIG. 22B, the region R21 near the white boundary BW of the region R2. In addition, the magenta color image PM protrudes. In particular, in this example, since the density of the developer is low in the white image PW in the region R2, the magenta image PM in the region R21 is affected by the color of the recording medium 9, and may reproduce the original color. It may not be possible.

  On the other hand, in the image forming apparatus 1, as shown in FIGS. 21C and 22C, the density of the developer in the white image PW gradually changes in the white boundary region RBW by the white boundary correction process. Thus, in the image forming apparatus 1, when the image forming unit 13 forms an image, even when the magenta color image PM is formed relatively shifted to the left as compared with the white image PW, FIG. As shown in FIG. 6, since the developer density in the exposed white image PW changes gradually, the possibility that the white image PW becomes unnaturally conspicuous in the white boundary region RBW can be reduced. Further, for example, even when the magenta color image PM is formed to be shifted to the right relative to the white image PW, as shown in FIG. 22D, the white image under the protruding magenta color image PM. Since the density of the developer in PW is higher than the density of the developer in the white image PW in the region R21 when the white boundary correction processing is not performed (FIG. 22B), the influence of the color of the recording medium 9 is affected. The risk of receiving can be reduced. Further, since the change in the density of the white image PW is gentle, the possibility that the image looks unnatural can be reduced. As a result, the image forming apparatus 1 can improve the image quality.

(Specific example 1 of trapping process)
FIG. 23 schematically illustrates an image formation example in the case where a magenta color image PM having a lightness higher than the lightness is formed on the recording medium 9 having a low lightness. FIG. 23A shows a cross-sectional view when the trapping process is not performed, and FIG. 23B shows a cross-sectional view when a relative shift occurs in the image formation position in FIG. 23A. Show. FIG. 23C shows a cross-sectional view when the trapping process is performed, and FIG. 23D shows a cross-sectional view when a relative shift occurs in the image formation position in FIG. 23C. . In this example, the width WP1 and the intervals WS1 and WS2 of the magenta color image PM when the trapping process is not performed are made wider than the width WTH corresponding to the threshold value TH3.

  As shown in FIG. 23A, in the case where the trapping process is not performed, the image forming apparatus 1 forms a white image PW on the recording medium 9 and a magenta image PM on the recording medium 9 in this example. Form. In this way, by forming the white image PW under the magenta color image PM, it is possible to reduce the possibility that the magenta color appears to be mixed with the color of the recording medium 9 when the user observes the image.

  However, when the image forming unit 13 forms the white image PW and the magenta color image PM on the recording medium 9, if a relative shift occurs in the formation position of these images, FIG. ), A part of the white image PW having high brightness is exposed. As a result, a white image PW with high brightness appears in the vicinity of the magenta color image PM.

  On the other hand, in the image forming apparatus 1, as shown in FIG. 23C, the magenta color image PM is expanded by the trapping process. That is, when the white color image data DW is selected in step S502, the width WP1 and the intervals WS1 and WS2 are wider than the width WTH corresponding to the threshold value TH3 (“Y” in step S522), and the magenta image PM Is higher than the brightness of the color of the recording medium 9 (“Y” in step S523), the image forming apparatus 1 corrects the magenta color image data DM in step S527, thereby correcting the magenta color image PM. spread. Specifically, as shown in FIG. 23C, the image forming apparatus 1 moves the left edge of the magenta color image PM to the left and moves the right edge to the right, thereby moving the magenta Widen the color image PM.

  Thus, in the image forming apparatus 1, even when a relative shift occurs in the image forming position when the image forming unit 13 forms an image, the brightness is high as shown in FIG. The possibility that the white image PW is exposed can be reduced. As a result, the possibility that a white image PW with high brightness can be seen can be reduced, and the image quality can be improved.

(Specific example 2 of trapping process)
FIG. 24 schematically illustrates an image formation example when a magenta color image PM having a lightness lower than the lightness is formed on the recording medium 9 having a high lightness. FIG. 24A shows a cross-sectional view when the trapping process is not performed, and FIG. 24B shows a cross-sectional view when a relative shift occurs in the image formation position in FIG. 24A. Show. FIG. 24C shows a cross-sectional view when the trapping process is performed, and FIG. 24D shows a cross-sectional view when a relative shift occurs in the image formation position in FIG. 24C. .

  Even in this case, when the image forming unit 13 forms the white image PW and the magenta color image PM on the recording medium 9, if a relative shift occurs in the formation positions of these images, FIG. As shown to (B), a part of white image PW with high brightness will be exposed. As a result, a white image PW with high brightness appears in the vicinity of the magenta color image PM.

  As a method of making the white image PW difficult to be exposed in this manner, a method of widening the magenta color image PM can be considered as in FIG. However, in this case, the object (for example, a magenta line) looks thick. That is, in this example, since the brightness of the color of the recording medium 9 is high, the widened magenta color image PM is conspicuous and the object looks thick.

  Therefore, in the image forming apparatus 1, as shown in FIG. 24C, the white image PW is narrowed by the trapping process. That is, when the magenta color image data DM is selected in step S502, the width WP1 and the intervals WS1 and WS2 are wider than the width WTH corresponding to the threshold value TH3 (“Y” in step S522), and the recording medium 9 Is lighter than the magenta color image PM (“Y” in step S523), the image forming apparatus 1 corrects the white color image data DW in step S527 to narrow the white image PW. . Specifically, as shown in FIG. 24C, the image forming apparatus 1 moves the left edge of the white image PW to the right and moves the right edge of the white image PW to the left. As a result, the white image PW is narrowed.

  Accordingly, in the image forming apparatus 1, even when a relative shift occurs in the image forming position when the image forming unit 13 forms an image, the brightness is high as shown in FIG. The possibility that the white image PW is exposed can be reduced, and the width of the magenta color image PM can be maintained. As a result, it is possible to reduce the possibility that the white image PW with high brightness can be seen without causing the object to appear thick, and the image quality can be improved.

(Specific example 3 of trapping process)
FIG. 25 schematically shows an image formation example in the case where a magenta color image PM having a lightness higher than the lightness is formed on the recording medium 9 having a low lightness. In this example, the width WP1 and the interval WS1 of the magenta image PM when the trapping process is not performed are made narrower than the width WTH corresponding to the threshold value TH3, and the interval WS2 is set to the width WTH corresponding to the threshold value TH3. Than wider.

  Even in this case, when the image forming unit 13 forms the white image PW and the magenta color image PM on the recording medium 9, if a relative shift occurs in the formation positions of these images, FIG. As shown to (B), a part of white image PW with high brightness will be exposed. At this time, for example, as in FIGS. 23C and 23D, when the magenta color image PM is widened, the magenta color image PM may spread even in the narrow interval WS <b> 1, thereby degrading the image quality. .

  On the other hand, in the image forming apparatus 1, as shown in FIG. 25C, in the trapping process, both the left edge of the magenta color image PM and the left edge of the white image PW are moved in opposite directions. As a result, the magenta image PM is widened and the white image PW is narrowed. That is, when the white color image data DW is selected in step S502, the width WP1 and the interval WS1 are narrower than the width WTH corresponding to the threshold value TH3 (“N” in steps S522, S524, and S525). In steps S526 and S527, the forming apparatus 1 corrects the magenta color image data DM to widen the magenta color image PM. Specifically, the image forming apparatus 1 widens the magenta color image PM by moving the left edge of the magenta color image PM to the left by half of the case of FIG. Further, when the magenta color image data DM is selected in step S502, the width WP1 and the interval WS1 are narrower than the width WTH corresponding to the threshold value TH3 (“N” in steps S522, S524, and S525). In steps S526 and S527, the image forming apparatus 1 corrects the white color image data DW to narrow the white image PW. Specifically, the image forming apparatus 1 narrows the white image PW by moving the left edge of the white image PW to the right by half of the case of FIG.

  Thus, in the image forming apparatus 1, even when a relative shift occurs in the image forming position when the image forming unit 13 forms an image, the brightness is high as shown in FIG. The possibility that the white image PW is exposed can be reduced, and the possibility that the magenta color image PM spreads in the narrow interval WS1 can be reduced. As a result, the image quality can be improved.

(Specific example 4 of trapping process)
FIG. 26 schematically illustrates an image formation example when a magenta color image PM having a lightness lower than the lightness is formed on the recording medium 9 having a high lightness.

  Even in this case, when the image forming unit 13 forms the white image PW and the magenta color image PM on the recording medium 9, if a relative shift occurs in the formation positions of these images, FIG. As shown to (B), a part of white image PW with high brightness will be exposed. At this time, for example, as in FIG. 24C, when the white image PW is narrowed, the white image PW is further narrowed in the narrow width WP1, so that the color of the recording medium 9 and the magenta color are mixed. Since the visible area increases, the color may change and the image quality may deteriorate.

  On the other hand, in the image forming apparatus 1, as shown in FIG. 26C, in the trapping process, the right edge of the magenta color image PM is moved to widen the magenta color image PM. That is, when white color image data DW is selected in step S502, width WP1 is narrower than width WTH corresponding to threshold value TH3 (“N” in step S522), and interval WS2 corresponds to threshold value TH3. The image forming apparatus 1 widens the magenta color image PM by correcting the magenta color image data DM in step S527 because it is wider than the width WTH to be performed (“Y” in step S524). Specifically, the image forming apparatus 1 widens the magenta color image PM by moving the right edge of the magenta color image PM to the right.

  Thus, in the image forming apparatus 1, even when a relative shift occurs in the image forming position when the image forming unit 13 forms an image, the brightness is high as shown in FIG. The possibility that the white image PW is exposed can be reduced, and the possibility that the color of the recording medium 9 and the magenta color are mixed and discolored in the narrow width WP1 can be reduced. As a result, the image quality can be improved.

[effect]
As described above, in this embodiment, the white boundary correction process is performed so that the pixel value gradually changes in the vicinity of the white boundary, so that the image quality can be improved.

  In the present embodiment, since the color of the recording medium is detected and the trapping process is performed based on the detected color, the image quality can be improved.

  In the present embodiment, the width of the white image is narrowed when the brightness of the recording medium is higher than the brightness of the image when the width and interval of the image are wide, thereby reducing the possibility that the object becomes thick. Image quality can be improved.

  In this embodiment, since the trapping process is performed based on the width and interval of the image, the image quality can be improved.

[Modification 1]
In the above-described embodiment, an example of the generation method of the edge direction data DIR, the edge distance data DIS, the width data DLW, and the white boundary data DWR has been described. It may be used.

[Modification 2]
In the above embodiment, in step S408, the pixel value of the color image data DW at each pixel in the correction target region R is corrected using the equation (EQ1), but the present invention is not limited to this. For example, various methods can be used in which the pixel value in the white boundary region RBW is gradually changed in the direction intersecting the white boundary BW.

[Modification 3]
In the above embodiment, the medium color sensor 12 is provided and the color of the recording medium 9 is detected. However, the present invention is not limited to this. Instead, for example, a medium color setting unit 12B is provided as in the image forming apparatus 1B shown in FIG. 27, and the user inputs the color of the recording medium 9 by operating the operation panel, for example, to set the medium color. The unit 12B may generate the medium color information C based on the input result. Further, for example, the recording medium information regarding the recording medium 9 may be received together with the print data DP from a host computer (not shown), and the medium color information C may be generated based on the recording medium information. Further, for example, a table indicating the correspondence relationship between the type of the recording medium and the medium color information C is stored in the apparatus of the image forming apparatus 1 or in an external database, and the recording medium information supplied from the host computer is stored in the table. Based on this table, the medium color information C may be generated by referring to this table.

  The present technology has been described above with some embodiments and modifications. However, the present technology is not limited to these embodiments and the like, and various modifications are possible.

  For example, in the above-described embodiment and the like, the present invention is applied to the use of forming an image on the recording medium 9 such as plain paper, but the present invention is not limited to this. The recording medium may be a so-called iron print transfer sheet, or may be an OHP (Over Head Projector) film, for example. In this case, the image forming unit 13 forms, for example, a yellow image, a magenta image, a cyan image, a black image, and a white image in this order. That is, a white image is formed on the top. For example, in the case of iron printing, a white image is formed at the bottom of a non-transferred material by transferring it from a transfer sheet to an object to be transferred such as cloth.

  Further, for example, in the above-described embodiment and the like, the white developer is used. However, the present invention is not limited to this. For example, a developer having a color close to white may be used.

  Further, for example, in the above-described embodiment, the present invention is applied to the image forming apparatus, but the present invention is not limited to this, and instead, a multifunction peripheral having functions such as a copy, a facsimile, and a scanner. You may apply to an apparatus (MFP; Multi Function Peripheral).

  Further, for example, in the above-described embodiment and the like, brightness is used as a specific example of “brightness degree” in the present invention, but the present invention is not limited to this. Instead of this, for example, luminance may be used.

  DESCRIPTION OF SYMBOLS 1,1B ... Image forming apparatus, 9 ... Recording medium, 11 ... Communication part, 12 ... Medium color sensor, 12B ... Medium color setting part, 13 ... Image forming part, 20 ... Image processing part, 21 ... Color image generation part, 22 ... Edge detection unit, 23 ... White boundary detection unit, 24 ... Correction unit, A ... Target pixel, A1 ... Peripheral pixel, B ... Edge part, B1, B2 ... Pixel, C ... Medium color information, C1 ... White boundary pixel , C2 ... non-white boundary pixels, D, DY, DM, DC, DK, DW, E, EY, EM, EC, EK, EW ... color image data, DBN ... binary data, DIR ... edge direction data, DIS ... Edge distance data, DLW ... width data, DWR ... white boundary data, PM ... magenta image, PW ... white image, R ... correction area, RBW ... white boundary area, R1, R11, R2, R21 ... area, TH1, TH2, TH3, TH4 ... Threshold WP1 ... width, WS1, WS2 ... interval.

Claims (15)

Of the image area in which the first image corresponding to the first color is arranged, the first area in which the second image corresponding to the second color is arranged, and the first of the image areas. First correction processing for gradual change in pixel value with respect to first image data corresponding to the first color in a boundary region including a boundary with a second region other than the first region An image processing unit for performing
An image forming unit configured to form an image on a recording medium based on the second image data corresponding to the second color and the first image data subjected to the first correction process. Forming equipment. The image processing unit includes a first pixel value of a first pixel included in the first region and a second value of a second pixel included in the second region in the first image data. The image forming apparatus according to claim 1, wherein the boundary region is set based on a difference from a pixel value. The image processing unit sets the boundary region when a difference value that is an absolute value of a difference between the first pixel value and the second pixel value is larger than a first threshold value. The image forming apparatus according to 2. The image processing unit
When the difference value is larger than the first threshold value and smaller than a second threshold value larger than the first threshold value, the boundary region in a direction intersecting the boundary Set the region width to the first width,
The image forming apparatus according to claim 3, wherein when the difference value is larger than a second threshold value, the area width is set to a second width wider than the first width. The image forming apparatus according to claim 2, wherein the first pixel and the second pixel are adjacent to each other. The image processing unit includes pixels in the plurality of pixels based on pixel values of the plurality of pixels in the boundary region and weight values respectively associated with the plurality of pixels in the first image data. The image forming apparatus according to claim 2, wherein the first correction process is performed by correcting a value. The image forming apparatus according to claim 6, wherein the plurality of pixels include the first pixel and the second pixel. The image processing unit performs the first correction processing so that pixel values of a plurality of pixels arranged in a direction intersecting the boundary in the boundary area in the first image data change monotonously. The image forming apparatus according to claim 1. The image processing unit includes a first edge portion of the first image and a first edge portion of the second image in an area other than the boundary area based on a medium color of the recording medium. The second correction processing for correcting the first image data and the second image data is further performed by separating the second edge portion corresponding to the first edge data. 9. The image forming apparatus described in 1. The image processing unit compares the first brightness level of the medium color with the second brightness level near the second edge portion in the second image, and based on the comparison result, The image forming apparatus according to claim 9, wherein the second correction process is performed. When the first brightness level is higher than the second brightness level, the image processing unit moves the first edge portion so that the area of the first image is reduced. The image forming apparatus according to claim 10, wherein the second correction process is performed. When the second brightness level is higher than the first brightness level, the image processing unit moves the second edge portion so that the area of the second image is increased. The image forming apparatus according to claim 10, wherein the second correction process is performed. The image forming apparatus according to any one of claims 1 to 12, wherein the first color is a color other than a basic color forming an image. The image forming apparatus according to claim 1, wherein the first color is white. The image forming apparatus according to claim 1, wherein the second color is any one of yellow, magenta, cyan, and black.

Images