JP5687560B2 - Image processing apparatus and program, and image forming apparatus - Google Patents

Description

  The image processing apparatus, the program, and the image forming apparatus, for example, are also referred to as an image (hereinafter referred to as “color image” or “multicolor image”) expressed by a plurality of color components (for example, three color components of RGB). ) To image processing that generates an image represented by a single color component (hereinafter also referred to as “monochrome image” or “monochromatic image”).

  Conventionally, there is a technique described in Patent Document 1 as an image processing apparatus that forms a monochrome image from a color image.

  In the technique described in Patent Document 1, a plurality of pixel patterns expressed in a monochrome format are prepared in advance, and for each pixel of a color image to be processed, a pattern corresponding to a combination of color component values of the pixel is selected. However, this was a method for forming a monochrome image.

JP-A-11-136208

  However, the image processing apparatus described in Patent Document 1 uses a common conversion method for each pixel constituting a color image to be processed. For this reason, in a monochrome image processed by the image processing apparatus described in Patent Literature 1, a bright color (for example, yellow) character disappears and may not be identified as a character. In addition, in the image processing apparatus described in Patent Document 1, even if a dark pixel pattern is applied so that a bright-colored character can be identified, the converted monochrome image has a difference in color and gradation. It will be lost. As a result, in the monochrome image processed by the image processing apparatus described in Patent Document 1, the discriminability of the image content other than the characters is lowered, and the identified information may be lost.

  Therefore, when a color image (multicolor image) is converted into a monochrome image (single color image), an image processing apparatus and program that can improve the discrimination of the converted monochrome image, and an image forming apparatus are desired. ing.

An image processing apparatus according to a first aspect of the present invention includes: (1) an area dividing unit that divides a multicolor image represented by a plurality of color component values into a plurality of types of object areas including at least a character area and an image area; 2) For each object area, weight coefficient deriving means for deriving the weight coefficient used for calculating the luminance value of the pixels constituting the object area by a method according to the type of the object area; and (3) for each object area Using a weighting factor derived by the weighting factor deriving unit, a luminance value calculating unit that calculates a luminance value of each pixel constituting the object area, and (4) each pixel calculated by the luminance value calculating unit. based on the luminance value, and a single-color image generating means for generating a monochrome image that can be represented by a single color component values, (5) the area division The stage further divides the character area into a character edge area constituting an edge portion of the character area and a character internal area surrounded by the character edge area, and divides the character area into different types of object areas, (6) The weighting factor deriving means derives a weighting factor that makes the discrimination property higher for the object region of the character edge region, and (7) obtains color information relating to the main color component for each of the object regions of the character edge region (8) the weighting factor deriving unit calculates the weight of the color component constituting the color indicated by the color information acquired by the color information acquiring unit for the object area of the character edge region. A lowered weighting factor is derived, and (9) the color information acquisition means is configured to determine the object area for the object area of the character edge. It determined the hue of each pixel constituting the color of the most frequency hue, and acquires the color information.

An image processing program according to a second aspect of the present invention provides a computer, (1) region dividing means for dividing a multicolor image expressed by a plurality of color component values into a plurality of types of object regions including at least a character region and an image region. (2) for each object region, weighting factor deriving means for deriving a weighting factor used for calculating the luminance value of the pixels constituting the object region by a method according to the type of the object region; and (3) an object For each region, using the weighting factor derived by the weighting factor deriving unit, a luminance value calculating unit that calculates the luminance value of each pixel constituting the object region, and (4) calculated by the luminance value calculating unit As monochromatic image generation means for generating a monochromatic image that can be expressed by one color component value based on the luminance value of each pixel Is ability, (5) the area dividing means, the character region, further, a text edge region constituting an edge portion of the character area, divided into a text edge region character inside area surrounded by the different types of the respective object (6) The weighting factor deriving means derives a weighting factor that makes the distinction of the object region of the character edge region higher. (7) The computer is divided into the object region of the character edge region. For each, it further functions as color information acquisition means for acquiring color information relating to the main color component, and (8) the weight coefficient derivation means is the color information acquired by the color information acquisition means for the object area of the character edge area. (9) The color information acquisition unit is configured to derive a weighting factor obtained by lowering the weight of the color component constituting the color indicated by Object region of Tsu di obtains the hue of each pixel constituting the object area, the color of the most frequency hue, and acquires the color information.

  According to a third aspect of the present invention, there is provided an image forming apparatus comprising: an image processing apparatus; and an image forming unit that forms an image processed by using the image processing apparatus on a recording medium. It is characterized by having applied.

  According to the present invention, when a multicolor image is converted into a single color image, the distinguishability of the converted single color image can be improved.

FIG. 2 is a block diagram illustrating a functional configuration of the printer according to the first embodiment. FIG. 2 is a block diagram illustrating a hardware configuration of the printer according to the first embodiment. It is explanatory drawing shown about the division process which the area | region division part which concerns on 1st Embodiment performs. 3 is a flowchart illustrating an operation of the entire printer according to the first embodiment. It is the flowchart shown about operation | movement of the weighting coefficient derivation | leading-out part which concerns on 1st Embodiment. It is explanatory drawing shown about the example of the image processing which the printer which concerns on 1st Embodiment performs. It is the block diagram shown about the functional structure of the printer which concerns on 2nd Embodiment. 10 is a flowchart illustrating an operation of the entire printer according to the second embodiment. It is the flowchart shown about operation | movement of the color information acquisition part which concerns on 2nd Embodiment. It is the flowchart shown about operation | movement of the weighting coefficient derivation | leading-out part which concerns on 2nd Embodiment. It is explanatory drawing shown about the example of the weighting coefficient table which the weighting coefficient derivation part concerning a 2nd embodiment uses.

(A) First Embodiment Hereinafter, an image processing apparatus, a program, and an image forming apparatus according to a first embodiment of the present invention will be described in detail with reference to the drawings. In the first embodiment, an example in which the image forming apparatus of the present invention is applied to a printer will be described.

(A-1) Configuration of the First Embodiment FIG. 2 is a block diagram illustrating a hardware configuration example of the printer 100 according to the first embodiment.

  FIG. 1 is a block diagram illustrating a functional configuration of the printer 100 according to the first embodiment.

  The printer 100 as the image forming apparatus of the present invention includes an input I / F 101, an HDD 106, an output unit 107, and a control unit 102 in terms of hardware.

  The input I / F 101 is an interface for connecting to an external PC 110, and obtains color image data represented by three RGB color component values for each pixel transmitted from the PC 110.

  The control unit 102 includes an implementation configuration of programs such as a RAM 103 that is a volatile memory, a ROM 104 that is a read-only memory, and a CPU 105. That is, in the printer 100, the CPU 105 of the control unit 102 executes a processing program stored in the ROM 104 using the RAM 103 as a working memory. In other words, the function of the printer 100 is realized by the control unit 102 executing the image processing program of the embodiment. The functional configuration of the printer 100 can be expressed as shown in FIG. The storage location of the program executed by the CPU 105 is not limited, and may be stored in the HDD 106 instead of the ROM 104. Further, the CPU 105 may use not only the RAM 103 but also the HDD 106 as a working memory.

  Since the hardware configuration of the printer 100 can be the same as that of an existing printer, FIG. 2 is illustrated based on the general hardware configuration of the existing printer. The printer 100 is characterized by the image processing performed by the image processing unit 21 described above, and the hardware configuration is not limited to the configuration in FIG. 2, and other various configurations can be applied.

  The HDD 106 is a readable / writable storage device, and is used for storing image data, setting items, and the like.

  When the image data is supplied, the output unit 107 outputs an image of the image data. A format for outputting the supplied image data by the output unit 107 is not limited. As the output unit 107, for example, the same output unit as that of an existing laser printer can be applied. Specifically, the output unit 107 has a configuration capable of forming (printing) an image on a recording medium (for example, a paper sheet) using one or a plurality of toner color materials. The output unit 107 forms a toner image on a recording medium when supplied with binary image data subjected to halftone processing that can be expressed by a toner color material.

  Next, the functional configuration of the printer 100 will be described.

  Functionally, the printer 100 includes an image processing unit 21 and an image forming unit 22 as shown in FIG. The image processing unit 21 includes an image acquisition unit 211, an area dividing unit 212 as an area dividing unit, a weight coefficient deriving unit 213 as a weight coefficient deriving unit, a luminance value calculating unit 214 as a luminance value calculating unit, and a single color A monochrome image generation unit 215 is provided as image generation means. Each functional block (component) constituting the image processing unit 21 shown in FIG. 1 can be realized by using the control unit 102 shown in FIG. Also, the image generation unit 215 illustrated in FIG. 1 can be realized using the control unit 102 and the output unit 107 illustrated in FIG.

  The image processing unit 21 is a part that functions as the image processing apparatus of the present invention in the printer 100. A program for realizing the image processing unit 21 is the image processing program of the embodiment. In other words, the printer 100 as the image forming apparatus of the present invention is equipped with the image processing unit 21 as the image processing apparatus of the present invention.

  The image acquisition unit 211 acquires color image data (hereinafter referred to as “RGB image data”) that is transmitted from the PC 110 through the input I / F 101 and expressed in the RGB format to be processed. In the first embodiment, the image acquisition unit 211 acquires RGB image data transmitted from the PC 110 through the input I / F 101. For example, the image acquisition unit 211 acquires RGB image data by connecting a scanner or a digital camera. Also good. That is, the method by which the image acquisition unit 211 holds RGB image data is not limited. As long as the RGB image data includes data of each component (each RGB component) for each pixel constituting the image, the specific format is not limited. As a specific format of RGB image data, for example, bitmap format (BMP format) data can be applied. The image acquisition unit 211 stores the acquired RGB image data in the RAM 103 and delivers it to the region division unit 212.

  The area dividing unit 212 performs a process of dividing the RGB image data acquired by the image acquiring unit 211 into at least two types of object areas: a character area and an image area. The “character area” is an area indicating a character image on the image indicated by the RGB image data. The “image area” is an area indicating an image image other than the character area.

  It should be noted that an identification ID (hereinafter referred to as “object ID”) is assigned to each object area divided by the area dividing unit 212. Here, when the number of object areas divided by the area dividing unit 212 is I, each object area is assigned an object ID of 1, 2, 3,. It will be described as a thing.

  The method of distinguishing and recognizing the character area and the image area for the image indicated by the RGB image data by the area dividing unit 212 is not limited, but here, as an example, the pixel corresponding to the character edge in the image is selected. A method of detecting as a character edge region and recognizing it as a character edge region, a region surrounded by the character edge region (hereinafter referred to as “character internal region”) and other regions (hereinafter referred to as “image region”). It will be described as being used. As a technique for detecting a character edge region in an image, for example, the technique described in Reference 1 (Japanese Patent Laid-Open No. 2002-354241) may be used. The image processing apparatus described in Reference 1 scans an image to be processed, determines an achromatic / chromatic color of a pixel, and switches an edge determination threshold based on the determination of the achromatic / chromatic color. When the difference between the density value of the target pixel and the density value of the adjacent pixel exceeds the edge determination threshold value, it is determined as a character edge pixel, thereby extracting a character edge region of a black character or a color character. Here, the region dividing unit 212 extracts a character edge region from the image indicated by the RGB image data using the technique described in Reference 1 described above, and recognizes the character edge region and the character internal region together as a character region. Other areas are recognized as image areas.

  FIG. 3 is an explanatory diagram showing a specific example in which the area dividing unit 212 divides an image into a character area and an image area.

  FIG. 3A illustrates an example of an image that is a target for which the region dividing unit 212 performs a division process. In the image shown in FIG. 3A, the characters “The” are represented.

  FIG. 3B shows a process in which the region dividing unit 212 performs the dividing process on the image of FIG. In FIG. 3B, for ease of explanation, the character edge area and the character internal area are distinguished from each other in the character area. The area dividing unit 212 first extracts a character edge area of an image to be divided (image in FIG. 3A). As a result, the region dividing unit 212 can extract the character edge region for each of the characters “T”, “h”, and “e” as shown in FIG. In FIG. 3B, the character edge region of the character “T” is represented as a region A101, the character edge region of the character “h” is represented as a region A102, and the character edge region of the character “e” is represented as a region A103. Further, in FIG. 3B, the character internal area of the character “T” is indicated as area A104, the character internal area of the character “h” is indicated as area A105, and the character internal area of the character “e” is indicated as area A106.

  The area dividing unit 212 recognizes the character edge area A102 indicating the character “T” and the character internal area A104 together as one object area. The area dividing unit 212 recognizes the character edge area A103 indicating the character “h” and the character internal area A105 together as one object area. Further, the region dividing unit 212 recognizes the character edge region A104 indicating the character “e” and the character internal region A106 as one object region (however, the portion of the region A107 that is outlined is a character region). Not included). Furthermore, the region dividing unit 212 recognizes the regions A107 and A108 that could not be recognized as character regions as image regions. As described above, when the region dividing unit 212 of the first embodiment performs the division processing of the image in FIG. 3A, the image is divided into three character region objects and two image region objects.

  As described above, the area dividing unit 212 divides an image of RGB image data into object area units.

  Then, the area dividing unit 212 stores the data representing the object area divided from the RGB image data in the RAM 103 and delivers it to the weight coefficient deriving unit 213. The specific format of data representing each object area is not limited. For example, the region dividing unit 212 stores the object ID of the object region to which the pixel belongs and flag information indicating the type of the object region in association with each piece of pixel data constituting the image of the RGB image data. Anyway. The content of the flag information indicating the type of the object area is not limited. For example, it may be expressed as “1” when indicating a character area and “2” when indicating an “image area”. it can.

  The weighting factor deriving unit 213 obtains a “weighting factor” for each object region recognized by the region dividing unit 212 as a parameter used in luminance calculation of the luminance value calculating unit 214 described later. In this embodiment, the weighting factor is a weighting factor applied to each RGB color component value for deriving a monochrome luminance value Y from each RGB color component value in the luminance value calculation unit 214 described later. Details of the processing by the weighting factor deriving unit 213 will be described in the section of the operation description described later.

  The luminance value calculation unit 214 calculates a luminance value for each pixel constituting the RGB data image using the weighting factor derived by the weighting factor deriving unit 213 described above. Details of the processing by the luminance value calculation unit 214 will be described in the section of operation description to be described later.

  The monochrome image generation unit 215 generates a monochrome image according to the luminance value of each pixel derived by the luminance value calculation unit 214. For example, the monochrome image generation unit 215 may generate a monochrome image such that a pixel having a higher luminance value calculated by the luminance value calculation unit 214 has a color with lower density. For example, the monochrome image generation unit 215 expresses the pixel having the maximum luminance value in white, expresses the pixel having the minimum luminance value in black, and other pixels having the luminance value according to the luminance value. It may be expressed in gray of gradation (density). The monochrome image generation unit 215 may generate an image expressed in gray scale for each pixel as a monochrome image.

  Further, the monochrome image generation unit 215 may simply output data in which each pixel is represented by a luminance value as monochrome image data. In this case, it is necessary to perform image formation on the image forming unit 22 side such that a pixel having a higher luminance value calculated by the luminance value calculating unit 214 has a color with a lower density.

  The image forming unit 22 converts the monochrome image generated by the monochrome image generating unit 215 into a format that can be output by the output unit 107, and causes the output unit 107 to output it.

  Here, the image forming unit 22 converts the monochrome image generated by the monochrome image generating unit 215 and expresses it with one color toner color material (one color (for example, K) of the four color toner colors CMYK). It is assumed that binary image data (hereinafter referred to as “monochrome binary image data”) subjected to possible processing (for example, halftone processing) is formed and supplied to the output unit 107. The processing method for converting from a monochrome image to monochrome binary image data is not limited. For example, in an existing laser printer, the supplied image data is converted into a format that can be expressed by a toner color material. The same processing may be applied.

(A-2) Operation of the First Embodiment Next, the operation of the printer 100 of the first embodiment having the above configuration will be described.

  FIG. 4 is a flowchart showing an outline of image processing by the printer 100.

  First, it is assumed that RGB image data is acquired by the image acquisition unit 211 via the input I / F 101 and stored in the RAM 103 (S101).

  Next, the image of the RGB image data acquired in step S101 described above is divided into two types of object areas, ie, a character area and an image area, by the area dividing process of the area dividing unit 212 (S102).

  Next, weighting factors WR, WG, WB (weighting factors multiplied by RGB color components) for calculating the monochrome luminance value Y for each of the object regions divided by the region dividing unit 212 by the weighting factor deriving unit 213. Is calculated (S103).

  FIG. 5 is a flowchart showing the processing performed by the weighting factor deriving unit 213.

In the flowchart of FIG. 5, i (1 ≦ i ≦ I) is a variable used for the iterative process. Note that the initial value of i is 1. In the following, an object area with an object ID i is represented as “object area i”. In the following, for the object region i, the R component weighting coefficient is represented as WR _i , the G component weighting coefficient is represented as WG _i , and the B component weighting coefficient is represented as WB _i .

  First, the weighting factor deriving unit 213 extracts and reads the data of the object area i from the RAM 103 (S201), and determines the type of the object area i (S202).

  If it is determined in step S202 that the type of the object area i is a character area, the process proceeds to step S203, which will be described later. Otherwise (if the type of the object area i is an image area), the process will be described later. The process proceeds to step S204.

If the type of the object region i is determined to be a character region in step S202 described above, the weighting factor deriving unit 213 determines the weighting factors WR _i , WG _i . WB _i is determined to a ratio suitable for the character area. Then, the weighting factor deriving unit 213 records the determined weighting factors (WR _i , WG _i .WB _i ) in the RAM 103 as data corresponding to the object area i (S203).

The content of the weighting factor applied to the object region of the character region by the weighting factor deriving unit 213 is not limited, but here, as an example, a ratio that is easy to discriminate in any wavelength range is applied. In other words, it is desirable that the weighting factor deriving unit 213 applies a weighting factor with a ratio that can be easily discriminated in any wavelength region to an object region of a type that emphasizes discrimination, such as a character region. Specifically, the weighting factor deriving unit 213 sets the ratio of the weights of the three components to about 1: 1: 1 (for example, WR _i = 0.33, WG _i = 0.34, WB) for the object area of the character area. _i = 0.33).

If it is determined in step S202 described above that the type of the object area i is an image area, the weight coefficient derivation unit 213 sets the weight coefficients WR _i , WG _i , and WB _i of the object area i to be suitable for the image area. Decide on a percentage. Then, the weighting factor deriving unit 213 records the determined weighting factors (WR _i , WG _i .WB _i ) in the RAM 103 as data corresponding to the object area i (S204).

The content of the weighting factor applied to the object region of the image region by the weighting factor deriving unit 213 is not limited, but here, as an example, it is assumed that a ratio of a person close to the sensitivity to the light intensity is applied. . The sensitivity to human visual light is highest in the Green wavelength range and low in the Blue wavelength range. Therefore, the weighting factor deriving unit 213 applies a weighting factor that increases the ratio of WG _i and decreases the ratio of WB _i to an object region that emphasizes color difference and gradation, such as an image region. It is desirable. Specifically, the weight coefficient deriving unit 213 sets the ratio of the three component weights to about 3: 6: 1 (for example, WR _i = 0.30, WG _i = 0.59, WB _i = 0.11). It shall be.

  When the weighting coefficient of the object area i is determined and recorded in the above-described step S203 or S204, the weighting coefficient deriving unit 213 determines whether the weighting coefficient has been derived for all the object areas (whether i = I). (S205).

  If the weighting factor deriving unit 213 determines in step S205 described above that the weighting factors have been derived for all object regions (that is, i = I), the weighting factor deriving unit 213 determines that the RGB image Terminates processing related to data. On the other hand, in the above-described step S205, when the object region for which the weighting factor is not yet derived by the weighting factor deriving unit 213 remains (that is, when i <I), the weighting factor deriving unit 213 The variable i is incremented (i = i + 1) (S206), and the operation starts from step S202 described above.

  As described above, the weighting factor deriving unit 213 derives a weighting factor for each object region constituting the RGB image data.

Next, the luminance value calculation unit 214 calculates the luminance value Y of each pixel based on the weighting factor derived in step S103 described above (S104). For the luminance value Y of each pixel, the luminance value calculating unit 214 follows the following equation (1) and assigns the weighting factors WR _i , WG _i , WB _{i of the} object region i to which each pixel belongs to the RGB color component value of each pixel. It is obtained by multiplying.

Y = WR _i × R + WG _i × G + WB _i × B (1)
Next, the monochrome image generation unit 215 generates monochrome image data obtained by converting the RGB color component value of the input color image into the luminance value Y based on the luminance value Y of each pixel calculated in step S104 described above (S105). ).

  Next, the image forming unit 22 acquires the monochrome image data generated in step S105. Then, the image forming unit 22 converts the monochrome image data into monochrome binary image data that has been subjected to halftone processing that can be expressed by one color (for example, K) of the CMYK four color toner color materials. Then, the output unit 107 is controlled by the image forming unit 22, and a toner image is formed on a recording medium (for example, a paper sheet) based on the converted monochrome binary image (S106).

  Next, an example of a result when the printer 100 processes RGB image data according to the processing of the above-described flowchart will be described.

  FIG. 6 is an explanatory diagram illustrating an example of a result when the image processing unit 21 processes RGB image data. In the first embodiment, the image processing unit 21 outputs monochrome image data formed from RGB image data. In FIG. 6, for the convenience of illustration, monochrome binary data subjected to halftone processing is output. This is explained using images.

  FIG. 6A shows an example of a monochrome binary image based on the monochrome image processed by the image processing unit 21.

  In the image of FIG. 6A, a character string “seasonal vegetable” (hereinafter, a character area constituting this character string is referred to as “area A201”) and a character “to here → XXXX”. A string (hereinafter, a character area constituting the character string is referred to as “area A202”) is shown. Each character in the region A201 is represented in green in the RGB image data before processing. Further, each character in the area A202 is represented in yellow in the RGB image data before processing.

  In the image of FIG. 6A, a lettuce image (hereinafter, this image region is referred to as “region A203”) and a lemon image (hereinafter, this image region is referred to as “region A204”). Suppose). In addition, the image of the area A203 is mainly represented using green in the RGB image data before processing. Further, the image of the area A204 is mainly represented in yellow in the RGB image data before processing.

  6B to 6D are enlarged images (LG11 to LG13, LG21 to LG23, LG31) in which a part of the areas A201 to A204 is enlarged when monochrome binary image data is formed under different conditions. To LG33, LG41 to LG43).

  FIG. 6B shows a monochrome binary image formed when the weighting factors are set to WR: WG: WB = 3: 6: 1 for all pixels constituting the image before processing. FIG. 6C shows a monochrome binary image formed when the weighting factors are set to WR: WG: WB = 1: 1: 1 for all the pixels constituting the image before processing. In FIG. 6D, the weighting factor is set to WR: WG: WB = 1: 1: 1 for the character region of the image before processing, and the weighting factor is set to WR: WG: WB = for the image region of the image before processing. It represents a monochrome binary image formed when 3: 6: 1.

  6B to 6D, enlarged images LG11 to LG13 are enlarged images obtained by enlarging a part of the region A201. In FIGS. 6B to 6D, enlarged images LG21 to LG23 are enlarged images obtained by enlarging a part of the region A202. Further, in FIGS. 6B to 6D, enlarged images LG31 to LG33 are enlarged images obtained by enlarging a part of the region A203. Furthermore, in FIGS. 6B to 6D, enlarged images LG41 to LG43 are enlarged images obtained by enlarging a part of the region A204.

  In the case of FIG. 6B (when the weighting coefficients of all the pixels in the image are set to WR: WG: WB = 3: 6: 1), the green character region (LG11) and the yellow character region (LG21) The difference in color is obvious as the difference in density. In the case of FIG. 6B, the difference in color between the green image region (LG31) and the yellow image region (LG41) is also clear as a difference in density. However, in the case of FIG. 6B, the yellow character region (LG21) is very thin and difficult to distinguish.

  In the case of FIG. 6C (when the weighting coefficients of all the pixels in the image are set to WR: WG: WB = 1: 1: 1), the yellow character region LG22 is the case of FIG. 6B. Compared to the above, the density increases and it becomes easier to distinguish. However, in the case of FIG. 6C, the green character region LG12 and the green image region (LG32) are slightly thinner, and the difference from the yellow character region (LG12) and the yellow image region (LG42) is small. Therefore, the impression of the image area in particular changes from the original image.

  Next, in the case of FIG. 6D (when processing is performed using the image processing unit 21 of the first embodiment), the green character region (LG13) and the yellow character region (LG23) also have densities. It is easy to identify, and the difference in color between the green image region (LG33) and the yellow image region (LG43) is reflected in the density difference, so it is necessary to have high character discrimination and a difference in image color Can keep a lot of information.

(A-3) Effects of First Embodiment According to the first embodiment, the following effects can be achieved.

  In the image processing unit 21 of the first embodiment, the luminance value is calculated using different weighting factors for the character area and the image area, and a monochrome image based on the luminance value is generated. Thereby, in the image processing unit 21 of the first embodiment, it is possible to improve the distinguishability of the content of the generated monochrome image and the content of the image region, and prevent the loss of necessary information.

(B) Second Embodiment Hereinafter, an image processing apparatus, a program, and an image forming apparatus according to a second embodiment of the present invention will be described in detail with reference to the drawings. In the second embodiment, an example in which the image forming apparatus of the present invention is applied to a printer will be described.

(B-1) Configuration of Second Embodiment Hereinafter, a difference between the printer 100A of the second embodiment and the first embodiment will be described.

  The hardware configuration of the printer 100A of the second embodiment can be shown in FIG.

  Next, a functional configuration of the printer 100A according to the second embodiment will be described.

  FIG. 7 is a block diagram illustrating a functional configuration of the printer 100A according to the second embodiment. The same and corresponding parts as those in FIG. 1 are denoted by the same reference numerals.

  The printer 100A according to the second embodiment is different from the first embodiment in that the image processing unit 21 is replaced with the image processing unit 21A. Further, the image processing unit 21A of the second embodiment differs from the first embodiment in that the region dividing unit 212 and the weighting factor deriving unit 213 are replaced with the region dividing unit 212A and the weighting factor deriving unit 213A. ing. Further, the image processing unit 21A of the second embodiment is different from the first embodiment in that a color information acquisition unit 216 as a color information acquisition unit is added. Since other configurations are the same as those of the first embodiment, detailed description thereof is omitted.

  The area dividing unit 212 of the first embodiment recognizes a character area and an image area from an image of RGB image data, and performs a process of dividing each recognized area as an object area. On the other hand, the area dividing unit 212A according to the second embodiment processes the character edge area and the character internal area surrounded by the character edge area as separate object areas for the character area. That is, the area dividing unit 212A according to the second embodiment recognizes three types of areas, that is, a character edge area, a character internal area, and an image area, from the image of RGB image data, and sets each recognized area as an object area. Process to divide.

  The method in which the region dividing unit 212A recognizes three types of regions, that is, a character edge region, a character internal region, and an image region, from an image of RGB image data is not limited. Also in the second embodiment, the technique described in Reference 1 described above can be used for the area dividing process of the area dividing unit 212A. In the technique described in Reference 1, as described above, the character edge region and the character internal region are extracted in the process of extracting the character region. Therefore, the region dividing unit 212A can recognize three types of regions, that is, a character edge region, a character internal region, and an image region, from the image of the RGB image data by using the technique described in Reference Document 1 described above. .

  Next, the dividing process performed by the area dividing unit 212A will be described using the example of FIG. In the area dividing unit 212 of the first embodiment, the area A101 and the area A104 are recognized as an object area of one character area. However, in the area dividing unit 212A according to the second embodiment, the area A101 is divided into a character edge area object area, and the area A104 is divided into a character internal area object area and a separate object area.

  The color information acquisition unit 216 determines a color that is a main component in the object region of at least the character edge region among the object regions divided by the region dividing unit 212A. Then, the color information acquisition unit 216 acquires the determination result as color information. Here, the “color information” is information indicating a color that is a main component of each object area, and is represented by any one of seven colors of Red, Yellow, Green, Cyan, Blue, Magenta, and Gray. In other words, the color information acquisition unit 216 determines which of the above seven colors corresponds to the color that is the main component in the object region. Here, the color information is described as being represented by the above seven colors, but the number of colors is not limited.

  The weighting factor deriving unit 213A derives a different weighting factor for each object region based on the color information in the region acquired by the color information acquiring unit 216 for the object region divided by the region dividing unit 212A.

  Here, it is assumed that the weighting factor deriving unit 213A includes a table in which weighting factors for each color information are registered (hereinafter referred to as “weighting factor table”). Then, the weighting factor deriving unit 213A obtains, for each object region, a weighting factor corresponding to the color information of the object region from the weighting factor table.

  FIG. 11 is an explanatory diagram showing an example of the contents of the weighting coefficient table.

  In the weighting coefficient table, as shown in FIG. 11, weighting coefficients (WR, WG, WB) corresponding to each color of the color information (the above-mentioned seven colors) are registered.

  As shown in FIG. 11, each weight coefficient in the weight coefficient table is a coefficient that functions as a color catching filter for each color of the color information. In the weighting coefficient table shown in FIG. 11, the brightness value calculated for the character edge region is set lower (higher density in a monochrome image) than in the first embodiment.

  For example, in FIG. 11, the weighting factors for the color information RED are WR = 0.00, WG = 0.50, and WB = 0.50. When the color information acquisition unit 216 determines that the main color component of the character edge region is RED, it means that each pixel of the character edge region includes a large amount of R component of RGB. Therefore, if the ratio of the weighting factor WR among the weighting factors applied to the character edge region is lowered, the luminance value of the character edge region is lowered (the density in the monochrome image is increased). For this reason, in FIG. 11, the WR weight is reduced to 0.00 in the weight coefficient of the color information RED. Similarly, the weight of the WG is lowered in the weight coefficient corresponding to the color information Green. In addition, the weight of the WB is lowered in the weight coefficient corresponding to the color information Blue. Furthermore, in the weighting coefficient corresponding to the color information Yellow, the weights (WR and WG) of the components indicating Yellow are lowered. Furthermore, in the weighting coefficient corresponding to the color information Cyan, the weights of the components indicating Cyan (WG and WB) are lowered. Further, in the weighting coefficient corresponding to the color information Magenta, the weights (WR and WB) of the components indicating Magenta are lowered. Since the color information Gray is an achromatic color, WR, WG, and WB are set to have the same weight in FIG.

  If the luminance value calculated for the character edge region is a weighting factor that is lower than that of the first embodiment, the content of the weighting factor table is limited to the content of FIG. 11 described above. Is not.

(B-2) Operation of Second Embodiment Next, the operation of the printer 100A of the second embodiment having the above configuration will be described.

  FIG. 8 is a flowchart showing an outline of the operation of the printer 100A.

  First, it is assumed that RGB image data is acquired by the image acquisition unit 211 via the input I / F 101 and stored in the RAM 103 or the HDD 106 (S301). Since the process of step S302 is the same as that of the first embodiment (the above-described step S101), detailed description thereof is omitted.

  Next, by the region dividing process of the region dividing unit 212, the image of the RGB image data acquired in step S301 described above is divided into three types of object regions: a character edge region, a character internal region, and an image region (S302). ).

  Then, the color information acquisition unit 216 acquires color information in the area for each object area divided in step S302 (S303). In the second embodiment, the attribute of the color that is the main component in each object region is determined. Then, the color information acquisition unit 216 acquires the determination result as color information of the object area. Specifically, the color information acquisition unit 216 determines that each pixel is red, yellow, green, cyan, blue, based on the color component value (value of each component of RGB) of each pixel in the object region. It is determined which of the seven types of color information classified into Magenta and Gray belongs. Then, the color information acquisition unit 216 assumes that the information on the most pixels in the area is the color information of the area.

  FIG. 9 is a flowchart illustrating an example of specific processing of the color information acquisition unit 216.

In the flowchart of FIG. 9, i (1 ≦ i ≦ I) and j (1 ≦ j ≦ J) are variables used for the iterative process. In addition, the initial value of i is 1, and in the following, an object area with an object ID i is represented as “object area i”. Further, in the following, it is assumed that the total number of pixels constituting the object area i is represented as J. Furthermore, the initial value of j is 1, and hereinafter, the j-th pixel constituting the object area i is represented as “pixel j”. Hereinafter, the color information of the object region i is represented as “C _i ”, and the color information of the pixel j is represented as “C _i−j ”.

  First, the color information acquisition unit 216 extracts data of each pixel constituting the object area i from the RGB image data (S401).

  Next, the color information acquisition unit 216 extracts data of the pixel j constituting the object area i (S402).

  Next, the color information acquisition unit 216 obtains a hue value H in an HSV (Hue Saturation Value) color space for the pixel j indicated in the RGB format (S403).

  The method of obtaining the formula phase value H in the HSV color space for the pixel j shown in the RGB format is not limited, but here it is assumed that conversion is performed using the following formulas (2) to (6). To do.

  First, the color information acquisition unit 216 obtains the maximum value (MAX) and the minimum value (MIN) among the R, G, and B values of the pixel j using the following equations (2) and (3).

Then, when MAX = MIN is not satisfied, the color information acquisition unit 216 obtains the hue value H of the pixel j using the following equations (4) and (5). In the following formulas (4) and (5), hue values are represented by values of 0 to 359 in the order of Red, Yellow, Green, Cyan, Blue, Magenta, and Red. On the other hand, when MAX = MIN, since the pixel j is an achromatic color (Gray), the color information acquisition unit 216 has a value indicating the hue indefinite as the hue value H as shown in the following equation (6) ( NULL).

Then, the color information acquiring unit 216, based on the hue value H obtained in step S403 described above, it is determined by Equation (7) below the color information _{C i-j} of the pixel j (S404).

  Next, the color information acquisition unit 216 determines whether the color information of all the pixels in the object region i has been determined (that is, whether j = J) (S405). When the color information of all the pixels in the object area i is determined (that is, when j = J), the color information acquisition unit 216 initializes j (j = 1) and performs step S407 to be described later. Work from processing. On the other hand, when there are still pixels for which color information is not determined in the object area i (that is, when j <J), the color information acquisition unit 216 increments j (j = j + 1) (S406). ), The operation returns to the process of step S402 described above.

On the other hand, if it is determined in step S405 described above that the color information of all the pixels in the object area i has been determined (that is, if j = J), the color information acquisition unit 216 Among the color information (C _{i−1 to} C _i−j ) of all the pixels, the most numerous color information is determined as the color information C _i of the object area i.

  Next, the color information acquisition unit 216 confirms whether or not the color information has been determined for all the object areas constituting the RGB image data (that is, whether i = I) (S407). When the color information is determined for all the object areas constituting the RGB image data (that is, when i = I), the color information acquisition unit 216 ends the process. On the other hand, if there is an object area for which color information is not determined in the RGB image data (that is, if i <I), the color information acquisition unit 216 increments i (i = i + 1) (S409). Then, the operation returns to the above-described step S401.

  As described above, the color information acquisition unit 216 selects a primary color (Red, Green, Blue), a secondary color (Yellow, Cyan, Magenta) or none as the main color in each object area constituting the RGB image data. A result classified by coloring (Gray) is obtained as color information for each region.

  In FIG. 9, the color information is obtained for all the object areas, but it may be obtained only for the object area of the character edge area. For example, the color information acquisition unit 216 may skip the acquisition of color information for an object area other than a character edge area.

  Next, the weighting factor deriving unit 213A derives the weighting factors WR, WG, and WB for calculating the monochrome luminance value Y for each object region acquired in step S303 described above.

  FIG. 10 is a flowchart illustrating an operation example of the weighting factor deriving unit 213A.

In the flowchart of FIG. 10, i (1 ≦ i ≦ I) is a variable used for the iterative process. The initial value of i is 1, and an object area with an object ID i is represented as “object area i”. In the following, for the object region i, the R component weighting coefficient is represented as WR _i , the G component weighting coefficient is represented as WG _i , and the B component weighting coefficient is represented as WB _i .

  First, the weighting factor deriving unit 213A extracts the data of the object area i from the RAM 103 (S501), and determines whether the type of the object area i is a character edge area (S502).

  If it is determined in step S502 described above that the type of the object area i is a character edge area, the weighting factor deriving unit 213A proceeds to step S503 described later, and otherwise (the type of the object area i is a character edge In the case of an internal area or an image area), the process proceeds to step S505 described later.

  If it is determined in step S502 described above that the type of the object area i is a character edge area, the weight coefficient deriving unit 213A obtains the color information of the object area i by referring to the RAM 103 (S503).

  Next, the weighting factor deriving unit 213A refers to the weighting factor table and acquires a weighting factor corresponding to the color information of the object area i. Then, the weighting factor deriving unit 213A records the acquired weighting factor in the RAM 103 as data corresponding to the object area i (S504).

  On the other hand, if the type of the object area i is not determined to be the character edge area in step S502 described above, the weighting factor deriving unit 213A determines whether the type of the object area i is the character internal area. (S505).

  If it is determined in step S505 described above that the type of the object area i is a character internal area, the weighting factor deriving unit 213A proceeds to step S506 described below, and otherwise (the type of the object area i is an image. In the case of an area), the process proceeds to step S507 described later.

If it is determined in step S502 that the type of the object area i is a character internal area, the weight coefficient deriving unit 213A determines the weight coefficient (WR _i , WG _i .WB _i ) of the object area i as the character area. (For example, WR _i = 0.33, WG _i = 0.34, WB _i = 0.33). Then, the weighting factor deriving unit 213A records the determined weighting factor in the RAM 103 as data corresponding to the object area i (S506).

When it is determined in step S502 that the type of the object area i is an image area, the weight coefficient deriving unit 213A uses the weight coefficients (WR _i , WG _i , WB _i ) of the object area i as image areas. A suitable ratio (for example, WR _i = 0.30, WG _i = 0.59, WB _i = 0.11) is determined. Then, the weighting factor deriving unit 213A records the determined weighting factor in the RAM 103 as data corresponding to the object area i (S504).

  When the weighting coefficient of the object area i is determined by any of the above-described steps S504, S506, and S507, the weighting coefficient deriving unit 213A determines whether or not the weighting coefficient has been derived for all the object areas (that is, i = I (S508).

  When it is determined in step S508 described above that weighting factors have been derived for all object regions (that is, i = I), the weighting factor deriving unit 213A ends the processing relating to the RGB image data. On the other hand, in the above-described step S508, when an object region for which the weighting factor has not yet been derived remains (that is, when i <I), the weighting factor deriving unit 213A increments the variable i (i = i + 1) (S509), the process proceeds to step S501 described above.

  As described above, the weighting factor deriving unit 213A derives a weighting factor for each object region constituting the RGB image data.

  Next, the luminance value calculation unit 214 calculates the luminance value Y of each pixel based on the weighting factor derived in step S304 described above (S305). Since the process of step S305 is the same as the process of step S104 of the first embodiment, detailed description thereof is omitted.

  Next, the monochrome image generation unit 215 generates monochrome image data in which the RGB color component value of the input color image is converted into the luminance value Y based on the luminance value Y of each pixel calculated in step S305 described above (S306). ). Then, the image forming unit 22 converts the monochrome image data generated in step S306 into monochrome binary image data, and forms a toner image on the recording medium (S307). Note that the processes in steps S306 and S307 are the same as those in steps S105 and S106 of the first embodiment, and thus detailed description thereof is omitted.

(B-3) Effects of Second Embodiment According to the second embodiment, the following effects can be achieved.

  In the image processing unit 21A of the second embodiment, the density of the pixels constituting the character edge region is increased by adjusting the weighting coefficient applied to the character edge region. Thereby, in the image processing unit 21A of the second embodiment, it is possible to generate a monochrome image with higher character discrimination as compared with the first embodiment.

(C) Other Embodiments The present invention is not limited to the above-described embodiments, and may include modified embodiments as exemplified below.

(C-1) In each of the above embodiments, the printer as the image forming apparatus of the present invention receives RGB image data supplied from an external PC, scanner, or the like, and converts the RGB image data into a monochrome image. Had gone. However, in the image forming apparatus of the present invention, the method for holding the RGB image data to be processed is not limited. For example, the image forming apparatus according to the present invention may be applied to a copying machine, a facsimile machine, a multifunction machine, or the like that incorporates a scanner.

(C-2) In each of the above embodiments, the example in which the image forming apparatus of the present invention is applied to a printer has been described. However, the method of forming a monochrome image generated by the image forming apparatus of the present invention is not limited. . For example, a monochrome image generated by the image forming apparatus of the present invention may be displayed on a display or the like.

(C-3) In each of the above embodiments, the example in which the image processing apparatus of the present invention is mounted on a printer has been described. However, the image processing apparatus may be constructed as a single apparatus. For example, the image processing program of the present invention is installed in an information processing apparatus such as a personal computer to construct an image processing apparatus, and the image processing apparatus is used to generate and output monochrome image data from RGB image data. May be. Further, the format in which the image processing apparatus of the present invention outputs monochrome image data is not limited. For example, the image processing apparatus may be stored in a storage medium such as an HDD or a CD-ROM, or may be transmitted to another computer by communication. May be.

  In each of the above embodiments, the example in which the image processing unit is mounted on the printer side has been described. However, the image processing unit may be mounted on the PC side illustrated in FIG. 2 (that is, an image processing program is installed in the PC).

(C-4) In each of the above embodiments, the image processing unit outputs monochrome image data generated from RGB image data. However, the image processing unit may perform conversion up to monochrome binary image data. That is, the image processing performed by the image processing unit may include the content performed by the image forming unit.

(C-5) In each of the above embodiments, the example in which the image processing program of the present invention is stored in the ROM has been described. However, the medium for storing the image processing program is not limited. For example, the image processing program of the present invention may be stored in the HDD shown in FIG.

(C-6) In each of the above embodiments, it has been described that all images formed (printed) by the printer are images processed by the image processing unit. For example, image processing is performed according to an operation from the user. It is also possible to stop the processing of the image forming unit and pass the RGB image data as it is to the image forming unit. The configuration in which the printer accepts an operation from the user is not limited. For example, the printer may accept an operation from the input I / F via a PC, or a separate operation switch may be provided on the printer itself. Also good.

(C-7) In the image processing unit of each of the above embodiments, a common weighting factor is applied to all image regions, but the weighting factor applied to a part of the image region is changed. You may do it. For example, the image processing unit may apply a weighting factor derived by a method similar to that for a character region to a region selected by a user operation. The method by which the image processing unit allows the user to select a part of the image area is not limited. For example, RGB image data is displayed on a display (not shown) mounted on the PC, and an input device (not shown) (for example, You may make it select using a mouse | mouth etc.).

(C-8) In the image processing unit of the second embodiment, the weighting factor is adjusted using the weighting factor table (see FIG. 11) so that the luminance value of the character edge region is lowered. The luminance value itself of the area may be adjusted. For example, the luminance value calculation unit may adjust the calculated luminance value to be lower only for the character edge region (for example, adjustment to reduce the constant value). In addition, for example, the luminance value calculation unit may apply a preset fixed value (which may be a value according to color information) without performing the luminance value calculation process only for the character edge region. .

  DESCRIPTION OF SYMBOLS 100 ... Printer (image forming apparatus), 110 ... PC, 101 ... Input I / F, 106 ... HDD, 107 ... Output part, 102 ... Control part, 103 ... RAM, 104 ... ROM, 105 ... CPU, 21 ... Image processing Reference numeral 211 (image processing device), 211... Image acquisition unit, 212... Region dividing unit, 213.

Claims (5)

Area dividing means for dividing a multicolor image represented by a plurality of color component values into a plurality of types of object areas including at least a character area and an image area;
For each object region, weighting factor deriving means for deriving a weighting factor used for calculating the luminance value of the pixels constituting the object region by a method according to the type of the object region;
For each object region, using the weighting factor derived by the weighting factor deriving unit, a luminance value calculating unit that calculates the luminance value of each pixel constituting the object region;
Monochrome image generation means for generating a monochrome image that can be expressed by one color component value based on the brightness value of each pixel calculated by the brightness value calculation means ;
The area dividing means further divides the character area into a character edge area that constitutes an edge portion of the character area and a character internal area surrounded by the character edge area, and divides them into different types of object areas,
The weighting factor deriving means derives a weighting factor that is more discriminating for the object region of the character edge region,
For each of the object regions of the character edge region, further comprising color information acquisition means for acquiring color information relating to the main color component,
The weighting factor deriving unit derives a weighting factor obtained by reducing the weight of the color component constituting the color indicated by the color information acquired by the color information acquiring unit for the object region of the character edge region,
The color information acquisition means obtains the hue of each pixel constituting the object area of the character edge object area, and acquires the color of the hue with the highest appearance frequency as color information. .   The image processing apparatus according to claim 1, wherein the weighting factor deriving unit derives a weighting factor that equally weights each color component value for an object region of a type that emphasizes discrimination.   The weighting factor deriving means derives a weighting factor that weights each color component value with a ratio in accordance with human visual sensitivity for an object region of a type that emphasizes color difference and / or gradation. The image processing apparatus according to claim 1. Computer
Area dividing means for dividing a multicolor image represented by a plurality of color component values into a plurality of types of object areas including at least a character area and an image area;
For each object region, weighting factor deriving means for deriving a weighting factor used for calculating the luminance value of the pixels constituting the object region by a method according to the type of the object region;
For each object region, using the weighting factor derived by the weighting factor deriving unit, a luminance value calculating unit that calculates the luminance value of each pixel constituting the object region;
Based on the luminance value of each pixel and the brightness value calculation means has calculated, to function as a single-color image generating means for generating a monochrome image that can be represented by a single color component values,
The area dividing means further divides the character area into a character edge area that constitutes an edge portion of the character area and a character internal area surrounded by the character edge area, and divides them into different types of object areas,
The weighting factor deriving means derives a weighting factor that makes the object region of the character edge region more distinctive.
Further causing the computer to function as color information acquisition means for acquiring color information related to the main color component for each of the object areas of the character edge area,
The weighting factor deriving unit derives a weighting factor obtained by reducing the weight of the color component constituting the color indicated by the color information acquired by the color information acquiring unit for the object region of the character edge region,
The color information acquisition means obtains the hue of each pixel constituting the object area of the object area of the character edge, and acquires the color of the hue with the highest appearance frequency as color information. Image processing program. An image forming apparatus comprising: an image processing apparatus; and an image forming unit that forms an image processed using the image processing apparatus on a recording medium.
Wherein the image processing apparatus, an image forming apparatus, characterized in that the application of the image processing apparatus according to any one of claims 1-3.

Images