Image processing device, image processing method, and program

Abstract

To solve the problem that when improving distinctiveness by allocating densities in accordance with the number of colors that are used in pages, if the number of colors to be used differs depending on the pages in printing the colors on a plurality of pages, gray values of the colors may substantially differ depending on the pages when the same colors are printed in different pages.SOLUTION: An image processing device comprises: determining means that determines whether or not a pixel of interest satisfies a predetermined condition, on the basis of a signal value corresponding to color information about the pixel of interest of inputted image data; and executing means that executes processing for highlighting the color information about the pixel of interest, on the basis of that fact that the pixel of interest does not satisfy the predetermined condition.SELECTED DRAWING: Figure 7

Description

[Technical field]

[0001] The present invention relates to an image processing apparatus, an image processing method, and a program for executing image processing. [Background technology]

[0002] In recent years, documents and presentations in general offices are created in color. However, when documents created in color are printed, they may be printed in monochrome (black only). When printing color documents in monochrome like this, the image processing device performs a process of converting the color data into grayscale data.

[0003] When the color data of an original is expressed in RGB, an image processing device usually performs conversion processing using a method called NTSC conversion. NTSC conversion performs a weighting calculation of 0.299R+0.587G+0.114B on the RGB values, and the resulting value is set as the gray value corresponding to the RGB value of the color. However, this method has the problem that multiple completely different colors in a color document become the same or similar gray value after NTSC conversion, reducing the discrimination of multiple different colors.

[0004] In addition to the NTSC conversion method, there are other methods for converting color data into gray data, such as converting RGB values ​​to gray values ​​with equal weighting, and other methods that use different weighting. However, as with NTSC, these methods have the problem of reducing the ability to distinguish between different colors.

[0005] Therefore, in Patent Document 1, when the number of colors used in the color data is equal to or less than a certain number, a table is created for converting color data into gray data so that the gray values ​​after converting the color data into gray data are assigned to values ​​that are distant from each other. For example, assuming 8-bit image data, a table is created in which possible gray values ​​from 0 to 255 are assigned at equal intervals. Then, a method is disclosed for improving the distinctiveness by converting the color data into gray data using this table. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] JP 2017-38242 A Summary of the Invention [Problem to be solved by the invention]

[0007] However, the above-mentioned method for improving distinctiveness changes the gray value depending on the number of colors in a page, so when printing multiple pages and the number of colors varies from page to page, the gray value of the same color may change significantly from page to page.

[0008] Therefore, the present invention provides Enter Based on the determination that a pixel adjacent to a pixel of interest in the input image data has color information different from the color information of the pixel of interest, an enhancement process is performed on the color information of the pixel of interest. After performing the enhancement process, the color information used for the object contained in the input image data is replaced with a signal value corresponding to an achromatic color, and image data expressed in achromatic colors is generated. The purpose of this document is to [Means for solving the problem]

[0009] The present invention relates to Enter a determining means for determining whether a pixel adjacent to a pixel of interest in the input image data has color information different from color information of the pixel of interest, and an executing means for executing an enhancement process of the color information of the pixel of interest based on the determination by the determining means that a pixel adjacent to the pixel of interest has color information different from color information of the pixel of interest; After performing the enhancement process, the color information used for the object included in the input image data is replaced with a signal value corresponding to an achromatic color to generate image data expressed in an achromatic color. do generating means; It is characterized by: Effect of the Invention

[0010] According to the present invention, Enter Based on the determination that a pixel adjacent to a pixel of interest in the input image data has color information different from the color information of the pixel of interest, an enhancement process is performed on the color information of the pixel of interest. After performing the enhancement process, the color information used for the object contained in the input image data is replaced with a signal value corresponding to an achromatic color, and image data expressed in achromatic colors is generated. It is possible to do so. [Brief description of the drawings]

[0011] [Figure 1] Block diagram showing the configuration of an image processing system [Diagram 2] Block diagram showing the flow of print processing [Diagram 3] Flowchart showing the flow of printing processing [Figure 4] An example of drawing commands and the resulting image [Diagram 5] UI example [Figure 6] UI example [Figure 7] Flowchart showing the process of improving discrimination [Figure 8] An example of a color value list [Figure 9] Flowchart performed in S302 [Figure 10] Drawings that require discrimination processing [Figure 11] Flowchart performed in S304 [Figure 12] A diagram showing a one-dimensional LUT [Figure 13] A diagram showing a one-dimensional LUT [Figure 14] Drawings that require discrimination processing [Figure 15] Example of an image after enhancement processing [Figure 16] Example of an image after enhancement processing [Figure 17] Example of an image after enhancement processing [Figure 18] An example of sharpness processing [Figure 19] An example of trapping processing [Figure 20] Flowchart showing the process of improving discrimination [Figure 21] Flowchart performed in S1802 [Figure 22] Flowchart showing the process of improving discrimination [Figure 23] Flowchart performed in S2002 [Figure 24]Flowchart showing the process of improving discrimination [Diagram 25] Flowchart performed in S1105 DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Each embodiment of the present invention will be described in detail with reference to the accompanying drawings. Note that the following embodiments do not limit the invention according to the claims, and not all of the combinations of features described in each embodiment are necessarily essential to the solution of the present invention. In this embodiment, an image processing device will be used as an example of an information processing device.

[0013] Example 1 <Image forming device> FIG. 1 shows an example of an image processing system 117 according to the present invention, which is made up of an image forming apparatus 101 and a host PC 119 .

[0014] Image forming apparatus 101 is an example of an image processing apparatus according to the present invention, and is, for example, a multi-function peripheral (MFP) that integrates multiple functions such as a scanning function, a printer function, etc. Control unit 110 controls image forming apparatus 101 in an integrated manner, and includes CPU 105, ROM 106, RAM 107, HDD 111, operation unit I / F 112, printer I / F 113, scanner I / F 114, and network I / F 115.

[0015] CPU 105 loads a program stored in ROM 106 into RAM 107 and executes it to control the operation of image forming apparatus 101. RAM 107 is a temporary storage memory and is capable of temporarily storing image data, programs, etc. ROM 106 stores parameters for controlling image forming apparatus 101, applications, programs, and an OS for implementing control according to the embodiment, etc. HDD 111 saves scanned image data, etc.

[0016] It also controls operation unit 118 via operation unit I / F 112, and similarly controls image output unit 109 via printer I / F 113 and image reading unit 108 via scanner I / F 114. It also controls reception of images and the like from host PC 119 and transmission of images and the like to PC 119 via network I / F 115 and LAN 116. Image reading unit 108 is, for example, a scanner, and image output unit 109 is, for example, a printer.

[0017] CPU 105 loads a program stored in ROM 106 into RAM 107 and executes the program. This realizes a scan function for acquiring image data of a document read by image reading unit 108 and an output function for outputting an image via image output unit 109 to a recording medium such as paper or a monitor.

[0018] 2 is a block diagram showing an example of a software configuration of the image forming apparatus 101 that operates a print function from the PC 119. The image forming apparatus 101 includes a command processing unit 103 and an image processing unit 104. Each functional unit is realized by a CPU 105 included in the image forming apparatus 101 executing a control program.

[0019] The command processing unit 103 identifies, analyzes, and executes image data from a printer driver 202 of the PC 119, which will be described later, creates a raster image and attribute information, and stores them in the RAM 107. Each processing unit in the command processing unit 103 will be described later. The image processing unit 104 reads out the raster image and attribute information stored in the RAM 107, and performs image processing to optimize the raster image according to parameters.

[0020] Each processing unit in the image processing unit 104 will be described later. Image processing based on setting information notified from the operation unit 118 is also performed. A process of converting a raster image expressed in color into a raster image expressed in gray (achromatic color) is also performed here. The operation unit 118 includes a touch panel, hardware keys, etc., and receives instructions and setting operations from a user, and displays device information of the image forming apparatus 101, job progress information, and various user interface screens. The setting information and the like received by the operation unit 118 are stored in the RAM 107 via the control unit 110.

[0021] <Print processing> The processing configuration of the image processing system 117 shown in FIG. 2 will be described along the flow of print processing.

[0022] 2, in the host PC 119, electronic data such as a document or a presentation document is created using an application 201. A printer driver 202 is a driver for outputting and printing print data (color image data) to the image forming apparatus 101. The print data created by the printer driver 202 is sent to the image forming apparatus 101.

[0023] Next, the flow of print data within the image forming apparatus 101 will be described using the flowchart in Fig. 3. Additionally, the process in which the CPU 105 loads a program stored in the ROM 106 into the RAM 107 and the loaded program is executed by the command processing unit 103 and the image processing unit 104 in Fig. 2 will be described.

[0024] First, in S2501, the CPU 105 receives print data. Here, the print data is received via the network I / F in FIG.

[0025] Next, in S2502, CPU 105 analyzes the print data. Here, CPU 105 causes command discrimination unit 203 in command processing unit 103 in FIG. 2 to discriminate the PDL type. This PDL type includes PostScript (PS) and Printer Command Language (PCL). CPU 105 then notifies command processing unit 204 of the judgment result of command discrimination unit 203. This command processing unit 204 exists for each PDL type, and executes extraction and analysis of commands of the PDL type identified by command discrimination unit 203. Note that RGB data acquisition processing in S301 and distinctiveness judgment processing in S302 in FIG. 3, which will be described later, are performed here.

[0026] Next, in S2503, the command processing unit 103 performs RIP processing.

[0027] Here, the CPU 105 executes a RIP (raster image processor) by drawing in accordance with the analysis result of the command analysis unit 204 through the command execution unit 205. As a result, for example, a raster image 407 in Fig. 4 and attribute information 415 that describes attribute information are generated.

[0028] Next, in S2504, image processing is performed. In S2504, the CPU 105 performs color conversion processing from the RGB color space to the CMYK color space by the color conversion processing unit 206 in the image processing unit 104, using the raster image and attribute information generated by the command execution unit 205.

[0029] Here, color conversion may be performed from the RGB color space to a different RGB color space. After that, the filter processing unit 207 performs edge enhancement and the like on the CMYK or RGB image. The edge enhancement process will be described later. Note that each image process from S304 to S306 in FIG. 5, which will be described later, is also performed in S2504.

[0030] Finally, in S2505, it is determined whether all pages of the received print data have been processed, and if there is more to be processed, the process proceeds to the next page and repeats the process from S2502 onwards. Also, if all pages have been processed, the process ends.

[0031] In this embodiment, the command discrimination unit 203, command analysis unit 204, command execution unit 205, and image processing unit 104 of the command processing unit 103 shown in Fig. 2 are realized by the CPU 105 executing the above-mentioned program. This concludes the explanation of the command processing unit 103 and the image processing unit 104.

[0032] Fig. 4 is a diagram for explaining an example in which the command analysis unit 204 and command execution unit 205 described in Fig. 2 analyze a command, draw, and perform RIP processing to generate a raster image and attribute information. Below, an example will be given to explain how the command analysis unit 204 analyzes a command, and the command execution unit 205 draws in accordance with the analysis result of the command analysis unit 204, and generates a raster image and attribute information via a RIP (raster image processor).

[0033] First, commands are classified into drawing commands and control commands. An example of a drawing command 400 will be described below.

[0034] The drawing command 400 includes a color mode setting command 401 for setting the color mode of the job, and a color setting command 402 for setting the color. It also includes an object drawing command 403 for drawing an object, a character size setting command 404 for setting the character size, a font setting command 405 for setting the character font, and a character drawing command 406 for drawing characters.

[0035] The configuration of this series of commands is the same for other objects and character strings. In addition, commands for setting coordinates and line thickness, commands for drawing images, etc. are also included, but these will be omitted.

[0036] Hereinafter, the content of the drawing command 400 will be briefly described. Note that the drawing command 400 and color value data described below are based on an 8-bit image.

[0037] "Set Page Color (CL)" indicates that the image will be expanded in color. The color setting command "Set Color (95,155,213)" indicates that the RGB values ​​are R=95, G=155, and B=213, which is green.

[0038] The text size setting command "SetText Size(16)" indicates that the text size is 16 points. The font setting command "Set Font (Arial)" indicates that the text font is Arial.

[0039] The object drawing command "Draw Polygon" indicates that a shape is to be drawn based on coordinate values ​​(not shown). The text drawing command "Draw Text("x")" indicates that the letter "x" is to be drawn. Thus, the third and fourth commands in Figure 4(a) indicate that a rectangle is to be drawn in blue.

[0040] Similarly, commands 5 through 10 specify that the string "ABC" should be drawn in orange with Arial font and a font size of 16 points, while commands 11 through 20 specify that five objects should be drawn in different colors.

[0041] Next, the raster image 407 (FIG. 4B) and attribute information 415 (FIG. 4C) that are drawn and generated by the command execution unit 205 through RIP processing in response to the analysis of the drawing command 400 by the command analysis unit 204 will be described.

[0042] The third and fourth commands described above in the description of the drawing command 400 are color graphics 408, in which a rectangle is drawn in blue, and the fifth to tenth commands are the character portion 409 of the character string ABC.

[0043] Furthermore, the 11th to 20th commands draw a pie graph made up of objects 410 to 414, and are converted into an 8-bit RGB three-channel raster image 407 by performing RIP.

[0044] The color values ​​of each object in the raster image 407 in Fig. 4(c) are (95, 155, 213) for the graphic 408 and (237, 125, 49) for the text 409. The color values ​​of the pie chart graphic object 410 are (237, 125, 49), 411 are (145, 145, 145), 412 is (255, 192, 0), 413 is (112, 173, 71), and 414 is (95, 155, 213).

[0045] Furthermore, the text portion 409 is generated from the text attribute 417, the graphic 408, and the objects 410 to 414. The pie chart is generated from 8-bit one-channel attribute information 415 indicating attribute information as the graphic attribute 416.

[0046] 4(c), attribute information is generated in which the graphic portion is indicated as (00100011), the text portion is indicated as (00100111), etc. This concludes the explanation of the command analysis unit 204 and the command execution unit 205.

[0047] The command analysis unit 204 and the command execution unit 205 are usually explained assuming that color has been set by the user. However, in the process of improving distinctiveness in this embodiment, as shown in Fig. 5 which shows a part of the UI of the printer driver 202, even if the color mode setting 1401 is black and white (improved distinctiveness) 1402, the same process as for color is performed. After that, in this embodiment, the method of improving distinctiveness can be set by setting edge emphasis and grayscale conversion for the raster image as shown in Fig. 6 which shows a part of the UI of the printer driver 202.

[0048] The distinctiveness improvement process 300 in this embodiment will now be described.

[0049] Before performing RIP processing, the distinctiveness improvement process 300 first analyzes the drawing command 400 to obtain the RGB color values ​​used in the document, and determines whether there are any color objects that cannot be distinguished when the R, G, and B values ​​are weighted and converted to gray.

[0050] Then, when there are colors that are difficult to distinguish, the distinctiveness improvement process 300 modifies the attributes of objects that use those colors, performs rendering as color data, and outputs a color raster image and attribute information. The distinctiveness improvement process 300 determines pixels to be subjected to edge enhancement or trapping processing from the color raster image and attribute information, and performs edge enhancement or trapping processing on the color raster image. Finally, the color raster image is converted into a gray raster image. The edge enhancement processing and trapping processing will be described later.

[0051] The above-mentioned distinctiveness improvement processing will be described in detail with reference to FIG.

[0052] The processes shown in these flowcharts are achieved by CPU 105 loading a program stored in ROM 106 onto RAM 107 and executing the loaded program via command processing unit 103 and image processing unit 104.

[0053] In RGB data acquisition processing S301, the image processing unit 104 acquires RGB color values ​​used in the document. Here, the image processing unit 104 checks the color setting command 402 in the command analysis unit 204 and extracts the color values ​​specified in the color setting command 402.

[0054] Next, the image processing unit 104 adds the extracted RGB color values ​​to a color value list 501 shown in Fig. 8(a). Note that the color value list 501 holds the extracted color values ​​for each color (RGB, Gray). The image processing unit 104 then adds to the list a gray value calculated by weighting the RGB values. After the color value list 501 for the raster image 407 is completed, the image processing unit 104 saves the color value list 501 in the RAM 107.

[0055] Next, in the distinctiveness determination process S302, the image processing unit 104 determines which colors are difficult to distinguish when converted to gray. Here, the image processing unit 104 determines whether or not the colors are difficult to distinguish based on the gray values ​​in the color value list 501 stored in the RAM 107 in the command analysis unit 204, and modifies the attributes of the object if it is determined that the colors are difficult to distinguish. Details will be described later. Note that difficult to distinguish refers to a state in which it is difficult for the user to distinguish between colors. The state in which it is difficult to distinguish between colors is determined using a threshold value.

[0056] Details of the distinctiveness determination process S302 will be described with reference to the flowchart in Fig. 9. First, in S901, the CPU 105 reads out the color value list 501 in Fig. 7 from the RAM 107, sorts the gray values ​​from smallest to largest, creates the color value list 502, and calculates the difference in gray values.

[0057] In the color value list 502 of FIG. 8A, the number of colors in the original is seven, but since 408 and 414, and 409 and 410 are the same color, there are different colors in the color value list 502, 413, 408, 411, 409, and 412, and the difference in gray value between them is four.

[0058] Next, in S902, the image processing unit 104 judges whether the difference in gray values ​​satisfies a predetermined condition. Specifically, a predetermined threshold value is read from the RAM 107, and it is judged whether the difference in gray values ​​is smaller than the threshold value. If the difference is equal to or greater than the threshold value, it is judged that discrimination is possible, and this flow is ended. On the other hand, if the difference in gray values ​​is smaller than the threshold value, it is judged that discrimination is difficult, and the flow proceeds to S903.

[0059] For example, in the color value list 502 in FIG. 8B, the difference between the gray value of the graphic 413 and the gray value of the graphic 408 is 1. Here, if the threshold value is 16, the difference in gray value is smaller than the threshold value. Therefore, the graphics 413 and 408 are determined to have colors that are difficult to distinguish.

[0060] Similarly, the image processing unit 104 performs these processes for the number of differences in gray value. For example, in the example of the color value list 502 in Fig. 8(b), it is determined whether the differences in gray values ​​between 408 and 414, 409 and 411, between 409 and 410, between 310 and 412, etc. are smaller than a threshold value.

[0061] Finally, in S903, the image processing unit 104 modifies the attribute of the color object to an attribute (with emphasis attribute) indicating that discrimination processing is required. For example, as described above, the attribute information 415 in Fig. 4 is (00100011) for the graphics portion. Here, there are bits 0 to 7, with bit 0 being 1, bit 1 being 1, bit 5 being 1, and the other bits being 0. Also, the text portion is (00100111).

[0062] If bit 3 is determined to be the bit that requires correction of distinctiveness, the graphic portion requiring discrimination is corrected to (00101011), and the text portion requiring discrimination is corrected to (00101111).

[0063] 10, the graphic portion (1601) that requires discrimination processing is represented as (00101011), and the text portion (1603) that requires discrimination processing is represented as (00101111). The graphic portion (1602) that does not require discrimination processing is represented as (00100011).

[0064] 7, if the threshold is 16, the colors with gray value differences smaller than 16 are 413, 408, 411, and 409. These colors are determined to be difficult to distinguish, and the attributes of the object are modified.

[0065] Returning to the explanation of Fig. 3, in the RIP process S2503, the image processing unit 104 creates a color raster image and attribute information from the document and attribute information in the PDL language in the command execution unit 205.

[0066] In the color conversion process S304, the image processing unit 104 performs color conversion processing. Here, the conversion is from RGB to RGB, but it may also be from RGB to CMYK.

[0067] In the edge enhancement process S305, the image processing unit 104 performs edge enhancement on the boundary between objects that are difficult to distinguish. The edge enhancement process is a process that performs edge enhancement for each pixel using a color raster image and attribute information.

[0068] The edge enhancement process S305 will be described in detail with reference to the flowchart of FIG.

[0069] The edge enhancement process S305 is a process performed on a pixel-by-pixel basis on the raster image, and the CPU 105 executes the flow shown in FIG. 11 for each pixel of interest by referencing attribute information at the same position as the raster image.

[0070] First, in S1001, CPU 105 determines whether or not the pixel of interest has an attribute indicating that discrimination processing is required. Here, if bit 3 of the attribute information at the same position as the pixel of interest is 1, it can be determined that discrimination processing is required. Here, if the attribute does not indicate that discrimination processing is required, the result is NO and this flow ends. On the other hand, if the attribute indicates that discrimination processing is required, the result is YES and the flow proceeds to S1002.

[0071] Next, in S1002, CPU 105 determines whether or not the pixels surrounding the pixel of interest have an attribute that indicates the need for discrimination processing corrected in the processing in S903. Here, as in S1001, it can be determined that discrimination processing is necessary if bit 3 of the attribute information at the same position as the pixels surrounding the pixel of interest is 1. Note that the surrounding pixels are, for example, pixels adjacent to the pixel of interest (adjacent pixels).

[0072] If the attribute does not indicate that discrimination processing is required, the result is NO and this flow ends. On the other hand, if the attribute indicates that discrimination processing is required, the result is YES and the flow proceeds to S1003.

[0073] Next, in S1003, CPU 105 determines whether the pixel of interest and its surrounding pixels are different colors. Here, it is possible to determine whether the pixels of interest and their surrounding pixels are the same color by comparing their color information. If the pixel of interest and its surrounding pixels are the same color, the result is NO and this flow ends. On the other hand, if the colors are different, the result is YES and the flow proceeds to S1004.

[0074] Finally, in S1004, the CPU 105 performs processing to enhance the pixel value of the pixel of interest. For example, when the input image data is RGB, the pixel value is multiplied by a one-dimensional LUT (lookup table) that draws a downward convex curve as shown in FIG. 12, to make the output value darker than the input value. Here, the input signal refers to one of the R, G, and B color plates, and the same LUT is applied to each of them. On the other hand, when the image data input to the edge enhancement processing in S305 is CMYK, the output value is made darker by using a one-dimensional LUT that draws an upward convex curve as shown in FIG. 13. This concludes the explanation of the edge enhancement processing S305.

[0075] Returning to the explanation of Fig. 3, in color conversion process S306, CPU 105 converts the color to gray for each pixel in image processing unit 104. In this process, if the input image to color conversion process S306 is RGB, R, G, and B are weighted, a gray value is calculated, and this is inverted to convert it to a density signal and sent to image output unit 109. On the other hand, if the input image to color conversion process S306 is CMYK, it is converted from CMYK to K and sent to image output unit 109.

[0076] This concludes the explanation of the process flow for the distinctiveness improvement process 300 of this embodiment.

[0077] Here, the effect of the distinctiveness improvement process 300 will be described with reference to FIGS.

[0078] First, a conventional configuration will be described. If the edge enhancement process of S305 is not performed, when the color raster image 407 shown in Fig. 4 is converted into a gray raster image, it changes to an image 607 shown in Fig. 14. In other words, the title bar 608 and the character string 609 have almost the same gray value, making it difficult to distinguish them. Also, since the objects 610, 611, 613, and 614 in the pie chart have almost the same gray value, it is difficult to tell where the boundaries are, making it difficult to distinguish them.

[0079] On the other hand, when the distinctiveness improvement process 300 is performed on a color raster image 407 as in this embodiment, the boundaries of objects are emphasized, resulting in an image 707 as shown in FIG. 15, and distinctiveness is improved.

[0080] By performing the distinctiveness improvement process 300, it is possible to prevent a decrease in distinctiveness when a color image is converted to gray. In this embodiment, in the edge enhancement process S305, enhancement processing is performed by making pixel values ​​at the portion where the edge is to be enhanced darker using a one-dimensional LUT, but this is not limited to this. For example, by making the pixel values ​​lighter using a one-dimensional LUT, the distinctiveness between objects may be improved as shown in FIG. 16. In addition to the one-dimensional LUT, sharpness processing by the filter processing unit 207 in the image processing unit 104 and trapping processing by the trapping processing unit 208 may also be used.

[0081] When sharpness processing is used here, emphasis processing is performed to add a border around the object to be made distinctive, as shown in Fig. 17. If the attribute determination processing of the reference pixels in S1002 is not performed, the edges of the boundaries of any color object that is a different color from an object that is not distinctive will be emphasized.

[0082] An example of the sharpness processing performed by the filter processing unit 207 will now be described with reference to Fig. 18. Here, only the R color plate among the RGB data will be described.

[0083] In image 2101 in Fig. 18(a), the shaded area indicates that the R signal value is 200, and the white area indicates that the R signal value is 255. Fig. 18(b) shows an enlarged view of edge portion 2102 of 3 x 3 pixels in image 2101. Fig. 18(b) shows that the pixel at the center of edge portion 2102 is the pixel that is subjected to image processing in this embodiment. Fig. 18(c) shows the weight of each pixel of the 3 x 3 pixel edge portion 2102.

[0084] Next, the calculation of the sharpness processing will be explained. In the sharpness processing, the signal value of the central pixel is obtained by multiplying each pixel by the weight of the 3×3 pixels shown in the image 2103 shown in FIG. 18(b) and the 3×3 weight of the edge part 2102 shown in FIG. 18(c), and adding them up. Therefore, the equation (1) is obtained. Formula (1) (Signal value of center pixel) = 200 x 0 + 200 x (-1) + 255 x 0 + 200 x (-1) + 200 x 5 + 255 x (-1) + 200 x 0 + 200 x (-1) + 255 x 0 = 145. Then, as shown in image 2103 in Fig. 18(d), the signal value of the central pixel of edge portion 2102 shown in Fig. 18(a) before the sharpness processing becomes darker, i.e., (signal value of central pixel) = 145. By performing this processing on the entire image, the edge portions of image 2101 in Fig. 18(a) become darker, as shown in image 2104 in Fig. 18(e).

[0085] The above has been described with respect to the R color plate, but similar processing can also be performed for G and B, and similar processing can also be performed for other color spaces such as CMYK data.

[0086] This concludes the explanation of the sharpness processing.

[0087] Next, an example of the trapping process similarly performed by the trapping processing unit 208 will be described with reference to Fig. 19. Generally, trapping is performed after conversion to CMYK, so the RGB data is converted to CMYK before the trapping process. Here, a case where the C and M color plates of CMYK are adjacent to each other will be described.

[0088] In image 2201 in Fig. 19(a), the shaded portion indicates that the C pixel value is 128, and the dotted portion indicates that the M pixel value is 128. An edge portion 2202 of 3 x 3 pixels in image 2201 is enlarged to form edge portion 2202 shown in Fig. 19(b). In this example, the pixel at the center of 2202 is subjected to image processing. Of the enlarged edge portion 2202, edge portion 2202_1 shows only the C plate, and edge portion 2202_2 shows only the M plate. Edge portion 2203 shown in Fig. 19(c) indicates the weighting when performing trapping processing.

[0089] Trapping processing is a process in which a color plate not present at the pixel of interest is taken from the surroundings. Therefore, when the pixel of interest has a C plate as in this example, trapping processing is not performed to take the C pixel from the surroundings of the pixel of interest.

[0090] On the other hand, since the pixel of interest does not have an M plate, a trapping process is performed to obtain M plate pixels from the periphery of the pixel of interest. The calculation method for the trapping process to obtain the M plate pixels from the periphery is to obtain the central pixel value by multiplying the 3×3 image shown in the edge portion 2202_2 by the weighting for trapping shown in 2203.

[0091] Therefore, the pixel value of the central pixel M is given by Equation 2. (Formula 2) (pixel value of center pixel M) = 128 x 100% = 128 When combined with the C version of 2202_1, the pixel values ​​of the central pixel become (C,M,Y,K)=(128,128,0,0), as shown in image 2204.

[0092] By performing these processes on the entire image, the boundary between the C and M plates in image 2201 will overlap the C and M plates, as shown in image 2205. Then, color conversion of CMYK data to K data can be done by C+M+Y+K=K', and the C plate portion other than the boundary between the C and M plates becomes (C,M,Y,K)=(128,0,0,0)⇒(128). And the boundary between the C and M plates becomes (C,M,Y,K)=(128,128,0,0)⇒(256).

[0093] In other words, the color becomes darker at the boundary between the C and M plates. This completes the explanation of the trapping process.

[0094] According to the configuration of this embodiment, when performing color reduction processing such as converting a color image to a gray image, the attributes of colors that were different colors become similar grays and cannot be distinguished from each other are changed, and edge enhancement can be performed on the boundary between those colors by referring to the changed attributes. Then, by performing edge enhancement, it is possible to improve the distinguishability of the boundary.

[0095] Example 2 In the first embodiment, the attributes of colors that were determined to be difficult to distinguish were corrected, and the corrected portions were subjected to edge enhancement to improve the distinguishability. In this embodiment, instead of correcting the attributes, a method of performing edge enhancement on the boundary portion where colors that are difficult to distinguish are adjacent by changing the color conversion processing and edge enhancement processing will be described with reference to Fig. 20. Note that the description of the same processing as in the first embodiment will be simplified.

[0096] The distinctiveness improvement process 1800 will be described with reference to FIG.

[0097] In step S1801, color values ​​used in the document are obtained in the same manner as in step S301 described with reference to FIG.

[0098] In step S1802, a distinctiveness determination process is performed. In the first embodiment shown in FIG. 9, the attribute is corrected in step S903 according to the determination of whether or not distinctiveness exists in step S902. However, in this embodiment, the color conversion process is changed according to the determination of whether or not distinctiveness exists.

[0099] The details will be explained using FIG.

[0100] First, in S1901, the same process as S901 described in the first embodiment is performed. Next, in S1902, CPU 105 reads a predetermined threshold value from RAM 107 and determines whether the difference in gray value is equal to or greater than the threshold value. If the difference in gray value is equal to or greater than the predetermined threshold value, CPU 105 determines that there is discrimination and proceeds to color conversion process 1-1 in S1903. If the difference is smaller than the threshold value, CPU 105 determines that discrimination is difficult and proceeds to color conversion process 1-2 in S1904.

[0101] Here, in color conversion process 1-1, a process of converting RGB to R'G'B' is performed. This R'G'B' is converted so that the R, G, and B signal values ​​are all the same. Here, for example, the CPU 105 assigns the same values ​​to R, G, and B as in the method of calculating gray values ​​by weighting RGB (R', G', B') = (Gray, Gray, Gray).

[0102] On the other hand, in the color conversion process 1-2, a process of converting RGB to R"G"B is carried out, where R"G"B" is converted so that the R, G, and B signal values ​​do not all become the same signal value.

[0103] Returning to the explanation of Fig. 20, in step S1803, the RIP process of step S303 described in the first embodiment is performed. In step S1804, color conversion process 2 is performed. Here, color conversion process is performed from RGB space to CMYK space. Here, R'G'B' values ​​that are determined to be distinctive are converted to color values ​​of only the K plate, such as (C,M,Y,K) = (0,0,0,K), and R"G"B" values ​​that are determined to be difficult to distinguish are converted to color values ​​other than the K plate, such as (C,M,Y,K) = (C,M,Y,0).

[0104] Here, (R',G',B')=(Gray,Gray,Gray), and when converting to (C,M,Y,K)=(0,0,0,K), the gray value is inverted, that is, K=255-Gray.

[0105] On the other hand, when (R”,G”,B”)=(C,M,Y,0), the value of C+M+Y is converted to the inverted gray value, that is, C+M+Y=255-Gray.

[0106] In step S1805, edge enhancement is performed. Here, edge enhancement is performed for each of the C, M, Y, and K color plates, and edge enhancement is performed only for the C, M, and Y plates, and edge enhancement is not performed for the K plate.

[0107] As a result of the above processing, colors whose gray value difference is equal to or greater than a predetermined threshold in the processing of S1902 are color converted to the K color plate by color conversion processing 1-1 and color conversion processing 2, and edge emphasis is not applied. On the other hand, colors whose gray value difference is less than the predetermined threshold in the processing of S1902 are color converted to the C, M, Y color plates by color conversion processing 1-2 and color conversion processing 2, and edge emphasis is applied.

[0108] In step S1806, color conversion is performed from CMYK to K. Here, for example, conversion is performed from (C,M,Y,K) to (C+M+Y+K). As a result, the values ​​of the areas that have been edge-emphasized in step S1805 are increased and output darker.

[0109] Furthermore, colors that are determined to be distinctive in areas where edge enhancement has not been performed are converted to (C,M,Y,K) = (0,0,0,K) in color conversion process 2 in S1804, where K is inverted gray and is the same as the gray value weighted by RGB, and in the process in S1806 (C,M,Y,K) → (C+M+Y+K) = (255-Gray), which becomes the same value as the gray value weighted by RGB.

[0110] On the other hand, colors that are determined to be distinctive are also converted in color conversion process 2 in S1804 so that (C,M,Y,K)=(C,M,Y,0) becomes C+M+Y=255-Gray. Therefore, in the process in S1806, (C,M,Y,K)=(C,M,Y,0) becomes (C+M+Y+K)=(255-Gray), which is the same value as the RGB-weighted gray value.

[0111] In the configuration of this embodiment as well, the distinctiveness improvement process can be performed by changing the color conversion process and the edge emphasis process without using attributes.

[0112] Instead of the two processes of color conversion process 1-1 in S1903 and color conversion process 1-2 in S1904, it is also possible to perform similar processing in color conversion process 2 in S1804.

[0113] In this case, based on the result of the determination in S1902 in color conversion process 2 of S1804, if the difference in gray value is equal to or greater than a predetermined threshold, CPU 105 performs RGB→K color conversion, and if it is less than the threshold, performs RGB→CMY color conversion.

[0114] Furthermore, in color conversion process 2 of S1804, the CPU 105 may perform RGB→C color conversion if the difference in gray value is equal to or greater than a predetermined threshold based on the result of S1902, and perform RGB→MYK color conversion if it is less than the threshold. In that case, the same result can be obtained by not performing edge emphasis on the C plate in edge emphasis process S1805, but by performing edge emphasis on the MYK plate.

[0115] Example 3 As described above, there are several methods for performing edge enhancement, but in this embodiment, edge enhancement using a trapping process will be described.

[0116] As described above in the first embodiment, the trapping process overlaps adjacent different color plates to provide an edge emphasis effect. However, there are cases where the trapping process is not effective depending on the adjacent colors.

[0117] For example, when (C,M,Y,K)=(100,50,0,0) and (50,100,0,0) are adjacent to each other, no whiteout occurs because both adjacent colors are made up of C and M. In such a case, no trapping process is performed, and therefore no distinctiveness improvement process is performed.

[0118] Therefore, (C,M,Y,K) = (100,50,0,0) is converted to (100,0,0,0) beforehand, and (50,100,0,0) is converted to (0,100,0,0). When color conversion is performed in this way, adjacent colors become only the C and M plates, which may result in white spots. Therefore, trapping processing is performed to improve distinctiveness.

[0119] In this embodiment, an example of solving the problem by intentionally changing the color configuration and performing color conversion so as to facilitate trapping processing will be described with reference to Fig. 22. Note that the same description as in the second embodiment will be omitted.

[0120] In step S2001, similar to step S1801 described in the second embodiment, color values ​​used in the document are obtained.

[0121] In step S2002, a process for improving distinctiveness is performed. This process will be described with reference to FIG.

[0122] First, S2301 performs the same process as S1901 described in the second embodiment. Next, S2302 performs the same process as S1902 described in the second embodiment, but here, if the difference in gray values ​​is equal to or greater than a predetermined threshold, it is determined that there is discrimination and the process proceeds to S2303, which is color conversion process 1-1, the same as S1903. Then, if the difference in gray values ​​is smaller than the predetermined threshold, it is determined that discrimination is difficult and the process proceeds to color conversion process 1-3 in S2304.

[0123] Here, in the color conversion process 1-1 of this embodiment 2, conversion is performed from RGB to R'G'B in the same manner as in S1903, where R'G'B' is the same signal value for all of the R, G, and B signals.

[0124] On the other hand, in color conversion process 1-3, a process of converting RGB to R"G"B is performed, where R"G"B" is converted so that two of the R, G, and B signal values ​​become 255.

[0125] For example, (R,G,B)=(R,255,255).

[0126] In S2003, the CPU 105 performs the RIP process of S303 described in the first embodiment. In S2004, the CPU 105 performs color conversion process 2'. When performing color conversion process from RGB space to CMYK space, the CPU 105 converts the R'G'B' values ​​to color values ​​of only the K plate, such as (C,M,Y,K)=(0,0,0,K), as in the second embodiment. On the other hand, the R"G"B" values ​​are converted so that one of the CMY plate is 0. For example, the values ​​are converted to a configuration of only the C plate, such that color values ​​other than the C plate are 0, such as (C,M,Y,K)=(C,0,0,0).

[0127] In this case, the trapping process is performed by changing the configuration for each indistinguishable color, such as a configuration of only the C plate, a configuration of only the M plate, a configuration of only the Y plate, a configuration of only the C plate and the M plate, etc. Here, the color conversion method for two color plates, for example, when converting to a configuration of only the C plate and the M plate, converts (R,G,B) to (R,G,255) in color conversion process 1-3 in S2304. Then, in color conversion process 2' in S2004, this is realized by converting (R,G,255) to (C,M,0,0).

[0128] In step S2005, edge emphasis is performed. Here, similarly to the second embodiment, distinctive colors are converted to K-series monochrome, and trapping processing is not performed on the K-series. On the other hand, non-distinguishable colors are converted to have different plate configurations, and therefore trapping processing is performed.

[0129] S2006 performs color conversion from CMYK to K. Here, the conversion is from (C,M,Y,K) to (C+M+Y+K). This converts the color to monochrome, and when edges are emphasized in S2005, the value is increased and the output is darker.

[0130] As described above, when performing trapping processing, the distinctiveness improvement processing can be performed by changing the color conversion processing and edge emphasis processing without using attributes.

[0131] In this embodiment, color conversion process 1-3 in S2304 converts RGB to R"G"B, where R"G"B is converted so that two of the R, G, and B signal values ​​become 255. Changing the RGB values ​​here changes the original values, so when they are converted to gray, there is a possibility that the values ​​may change even before this discrimination process is performed.

[0132] Therefore, when performing color conversion from RGB to R"G"B in color conversion process 1-3, CPU 105 calculates gray values ​​in advance and sets (R,G,B) = (Gray, 255, 255). Then, when performing color conversion from R'G'B' to CMYK in color conversion process 2', the color is converted to (C,M,Y,K) = (Gray, 0,0,0). This makes it possible to preserve the value in color conversion process 3 as (Gray, 0,0,0) → (C+M+Y+K) = (Gray).

[0133] Example 4 In the first, second and third embodiments, the RIP process S303 was performed on the color data as is, the edge emphasis process S305 was performed on the color raster image, and the color raster image was then subjected to the color conversion process S306 to convert it into a gray raster image. However, this method requires handling of color, which results in large memory and hardware configurations and is expensive. Therefore, in this embodiment, the memory and hardware configurations are reduced by improving the attribute correction process (S903) performed in the distinctiveness determination process S302.

[0134] Here, description of the same configuration as in the embodiment 1 will be omitted. Also in this embodiment, the CPU 105 loads a program stored in the ROM 106 into the RAM 107, and the loaded program is executed by the command processing unit 103 and the image processing unit 104, thereby achieving the above.

[0135] The distinctiveness improvement process 1100 according to the fourth embodiment will be described with reference to FIG.

[0136] First, in RGB data acquisition processing S1101, color values ​​used in the document are acquired. This is the same processing as S301.

[0137] Next, the distinctiveness determination process S1102 is the same as S302 in the flow shown in Fig. 7, but the attribute correction process S903 for correcting attributes is different, so an explanation of the attribute correction process will be added. In S1102, in the attribute correction process in the command analysis unit 204, the CPU 105 assigns a number to colors in the color value list 502 in Fig. 8 whose gray value difference is smaller than a predetermined threshold value.

[0138] Taking color value list 502 in FIG. 8 as an example, graphics 413, 408, ..., 410 are colors with no distinctiveness because the difference in gray value is smaller than a predetermined threshold. Therefore, 0 is assigned to graphic 413, 1 to graphic 408, ..., and 5 to graphic 410. These numbers are then added to bits 4, 6, and 7 of the attribute information. Then, graphic 413 is (00101011) = (43), and graphic 408 is (00111011) = (59). Here, graphic 414 is the same as graphic 408, so (00111011) = (59). Furthermore, by doing this, graphic 411 becomes (10111011) = (187).

[0139] Next, in color conversion processing S1103, the CPU 105 converts the color data into gray data in the command execution unit 205.

[0140] Then, in the RIP process S1104, the CPU 105 converts the gray data into a gray raster image and attribute information in the command execution unit 205.

[0141] Finally, edge enhancement processing S1105 is processing performed for each pixel on the gray raster image, and the CPU 105 executes the flowchart shown in FIG. 24 for each pixel of interest.

[0142] The edge enhancement process S1105 flow chart shown in FIG. 25 will now be described.

[0143] First, S1701 is the same as S1001 in the first embodiment, so a description thereof will be omitted. Also, S1702 is the same as S1002 in the first embodiment, so a description thereof will be omitted. Next, in S1703, the CPU 105 determines whether the pixel of interest and its surrounding pixels have different color information (are different color objects). In other words, it determines whether the attributes corrected in the distinctiveness determination process S1102 are different between the pixel of interest and its surrounding pixels.

[0144] Here, the combinations of bits 4, 6, and 7 of the attribute information obtained by the attribute correction process described above are checked to see if they are the same. If the combinations of bits 4, 6, and 7 of the pixel of interest and its surrounding pixels are different, the result is YES and the process proceeds to S1704, and if the combinations are the same, the result is NO and the process ends.

[0145] Finally, in S1704, the CPU 105 performs edge enhancement processing on the gray raster image. Here, the edge enhancement method may use the one-dimensional LUT or sharpness processing described in the first embodiment. This concludes the description of the flowchart in FIG.

[0146] According to this embodiment, the RIP process S1104 and the edge emphasis process S1105 are performed in gray, so that the memory and hardware configuration can be reduced whether this embodiment is implemented by hardware or software.

[0147] (Other embodiments) Although various examples and embodiments of the present invention have been shown and described, the spirit and scope of the present invention is not limited to the specific descriptions within this specification.

[0148] The present invention can also be realized by a process in which a program for implementing one or more of the functions of the above-described embodiments is supplied to a system or device via a network or a storage medium, and one or more processors in a computer of the system or device read and execute the program. The present invention can also be realized by a circuit (e.g., ASIC) that implements one or more of the functions. [Explanation of symbols]

[0149] 105 CPU 107 RAM 108 Image reading unit 109 Image Processing Unit 110 Control section

Claims

1. A determination means for determining whether or not a pixel adjacent to a pixel of interest in input image data has color information different from color information of the pixel of interest; an execution means for executing an enhancement process for the color information of the pixel of interest based on the determination by the determination means that there is color information different from the color information of the pixel of interest; and a generating means for, after performing the enhancement processing, replacing color information used for the object contained in the input image data with a signal value corresponding to an achromatic color, thereby generating image data expressed in an achromatic color.

2. 2. The image processing device according to claim 1, further comprising: a creating means for creating information for executing the enhancement processing on attribute information of the pixel of interest based on the determination by the determining means that there is color information different from the color information of the pixel of interest.

3. 3. The image processing device according to claim 1, wherein when the determining means determines that there is no color information different from the color information of the pixel of interest, the executing means does not execute the enhancement process of the color information of the pixel of interest.

4. 4. The image processing apparatus according to claim 1, wherein the enhancement processing is a trapping processing.

5. 4. The image processing device according to claim 1, wherein the enhancement processing is a sharpness processing.

6. 6. The image processing apparatus according to claim 1, further comprising an output unit that outputs onto a sheet of paper based on the generated image data expressed in achromatic colors.

7. A step of determining whether or not a pixel adjacent to a pixel of interest in the input image data has color information different from color information of the pixel of interest; A method for controlling an image processing device, comprising: a step of executing an enhancement process for the color information of the pixel of interest based on a determination that there is color information different from the color information of the pixel of interest; and a step of replacing color information used for an object contained in the input image data with a signal value corresponding to an achromatic color after the enhancement process, thereby generating image data expressed in achromatic colors.

8. A program for causing a computer to execute the control method according to claim 7.

9. A computer-readable storage medium storing the program according to claim 8.

Images