JP2023000217A - Image processing apparatus, image processing method, and program - Google Patents

Abstract

To convert color information on a part where a transmission designated object overlaps the other object into color information on monochrome image data.SOLUTION: An image processing apparatus uses color information on a transmission object designated to transmit the other object and color information on the other object overlapping the transmission object, of objects included in color image data, to calculate color information on a part where the transmission object overlaps the other object. The image processing apparatus converts the color information on the overlapping part into color information on the monochrome image data based on the calculated color information.SELECTED DRAWING: Figure 7

Description

The present disclosure relates to image processing technology for color-converting color image data into monochrome image data.

Documents and presentation documents may be printed in monochrome (single color) even when they are created in full color. When printing a document created in color in this way in monochrome, monochrome conversion processing for converting color image data into monochrome image data is required. However, when color image data is converted to monochrome image data, different colors may be converted to the same or similar gray values in the color image data, making the colors indistinguishable and less distinguishable.

In this regard, Patent Document 1 discloses a technique for creating a table for converting color image data into monochrome image data so that gray values converted from color image data are assigned to values separated from each other.

JP 2017-38242 A

By the way, in color image data, there are a plurality of objects, and among them, there is a case where a certain object is specified to be transparent and overlaps with another object. In such a portion where the transparency-designating object overlaps with another object, the color value of the overlapping portion may be expressed using the color information of the transparency-designating object and the color information of the other object on which it overlaps. The color value of the overlapping portion is determined only after the RIP processing, by synthesizing the color values of the transparency-designating object and another object that overlaps with the transparency-designating object. When converting such color image data into monochrome image data, even if the technique of Patent Document 1 is applied, there are cases where the overlapping portion cannot be converted into the color information of the monochrome image data.

An object of the present disclosure is to provide a technique for converting a portion where a transparency-designated object overlaps another object into color information of monochrome image data.

An image processing device according to an aspect of the present disclosure is an image processing device that converts color image data into monochrome image data, and includes color information of a transparent object specified to be transparent among objects included in the color image data, and a calculating means for calculating color information of a portion where the transparent object overlaps with the other object by using color information of another object which overlaps with the transparent object; conversion means for converting the monochrome image data into color information based on the color information.

According to the present disclosure, it is possible to convert a portion where a transparency-designated object overlaps another object into color information of monochrome image data.

1 is a block diagram showing a configuration example of an image processing system; FIG. FIG. 3 is a diagram showing an example of software configuration for performing print processing; FIG. 4 is a diagram showing an example drawing command and an example image thereof; FIG. 10 is a flowchart showing the flow of distinguishability improvement processing; FIG. FIG. 10 is a diagram showing an example of a color value list; FIG. 10 is a diagram showing an example of a color value list; 4 is a flowchart showing the flow of color value list creation processing; FIG. 10 is a diagram showing an example of a color value list; 4 is a flowchart showing the flow of color value list creation processing; 4 is a flowchart showing the flow of color value list creation processing; FIG. 10 is a diagram showing an example of detection of a background object;

Embodiments for implementing the present disclosure will be described below with reference to the drawings. However, the components described in this embodiment are merely examples, and are not intended to limit the scope of the invention. Moreover, not all combinations of the constituent elements described in the embodiments are essential to the means for solving the problems, and various modifications and changes are possible. The same configuration will be described with the same reference numerals.

<Image processing system>
An image processing system according to this embodiment will be described with reference to the drawings. FIG. 1 is a diagram showing a schematic configuration example of an image processing system according to this embodiment. The image processing system 117 has an image forming apparatus 101 and a host PC 119 .

The image forming apparatus 101 is an example of an image processing apparatus according to the present embodiment, and is, for example, a multi-function peripheral (MFP) in which multiple functions such as a scanning function and a printer function are integrated. The image forming apparatus 101 has a control unit 110 , an image reading unit 108 , an image output unit 109 and an operation unit 118 . Control unit 110 comprehensively controls image forming apparatus 101 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 . On the other hand, host PC 119 also has CPU 120 , ROM 121 , RAM 122 and HDD 123 .

A CPU (Central Processing Unit) 105 develops a program stored in a ROM 106 in a RAM 107 and controls the operation of the image forming apparatus 101 by executing the program. A RAM (Random Access Memory) 107 is a temporary storage memory, and can temporarily store image data, programs, and the like. A ROM (Read Only Memory) 106 stores parameters for controlling the image forming apparatus 101, applications, programs, an OS, and the like for realizing control according to the present embodiment. An HDD (hard disk drive) 111 stores scanned image data and the like.

An operation unit I/F 112 is an interface that connects the operation unit 118 and the control unit 110 . An operation unit 118 includes a display having a touch panel function, a keyboard, hardware keys, and the like. The operation unit 118 receives various input operations such as instructions and setting operations from the user, and has a user interface function of displaying device information of the image forming apparatus 101, job progress information, and various user interface screens. Setting information received by the operation unit 118 is stored in the RAM 107 via the operation unit I/F 112 . Similarly, a printer I/F 113 is an interface that connects the image output unit 109 and the control unit 110 . Image data to be output-processed by the image output unit 109 is input from the control unit 110 via the printer I/F 113 . The image output unit 109 is, for example, a printer. A scanner I/F 114 is an interface that connects the image reading unit 108 and the control unit 110 . The image reading unit 108 is, for example, a scanner, and reads an image of a document on a document platen (not shown) or a document fed from an ADF (automatic document feeder) to generate image data (scanned image data). The generated scan image data is input to control unit 110 via scanner I/F 114 .

Network I/F 115 is an interface that connects control unit 110 (image forming apparatus 101 ) to LAN 116 . The network I/F 115 transmits image data and various information to the host PC 119 on the LAN 116 and an external device (for example, a cloud server) (not shown), and receives image data and various information from the host PC 119 on the LAN 116 and an external device. or

The CPU 105 expands the program stored in the ROM 106 into the RAM 107 and executes it, thereby realizing the following functions. That is, a scanning function of acquiring image data of a document read by the image reading unit 108 and an output function of outputting an image to a recording medium such as paper or a monitor via the image output unit 109 are realized.

<Software Configuration of Image Forming Apparatus>
A software configuration example of the image forming apparatus according to the present embodiment will be described with reference to the drawings. FIG. 2 is a block diagram showing a software configuration example of the image forming apparatus 101 that operates the print function from the host PC 119. As shown in FIG. The image forming apparatus 101 includes a command processing section 103 and an image processing section 104 . Each functional unit is controlled by CPU 105 of image forming apparatus 101 developing a program stored in ROM 106 or HDD 111 into RAM 107 and executing the program. Alternatively, some or all of the functions of the units in FIG. 2 may be realized by hardware such as ASICs or electronic circuits.

The command processing unit 103 determines, analyzes, and executes image data from the printer driver 202 of the host PC 119 (to 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 the raster image and the attribute information stored in the RAM 107, and performs image processing for optimizing the raster image according to the parameters.

Each processing unit in the image processing unit 104 will be described later. Image processing is also performed based on setting information notified from the operation unit 118 . Processing for converting a color raster image (color image) into a gray raster image (monochrome image) is also performed here.

<Print processing>
The flow of print processing by the image processing apparatus according to this embodiment will be described with reference to FIG. The host PC 119 has an application 201 and a printer driver 202 installed. The application 201 and the printer driver 202 are installed in the ROM 121 of the host PC 119, and are controlled by the CPU 105 loading a program stored in the ROM 106 into the RAM 107 and executing it.

The host PC 119 uses the application 201 to create electronic data such as documents and presentation documents. A printer driver 202 is a driver for outputting print data including, for example, color image data to the image forming apparatus 101 for printing. Print data created by the printer driver 202 is sent to the image forming apparatus 101 . The print data is processed by the command processing unit 103 of the image forming apparatus 101 , image-processed by the image processing unit 104 of the image forming apparatus 101 according to the processing result, and printed by the image output unit 109 .

Next, the configuration of the command processing unit 103 of the image forming apparatus 101 will be described. The command processing unit 103 has a command determination unit 203 , a command analysis unit 204 and a command execution unit 205 .

A command determination unit 203 determines the PDL type of print data output from the host PC 119 . PDL (Page Description Language) is an abbreviation for page description language. PDL types include PostScript (PS), PDF, Printer Command Language (PCL), and the like. The PDL type can be switched by switching the setting of the printer driver 202 or by switching the printer driver 202 itself to be used.

The command analysis unit 204 exists for each PDL type, and extracts and analyzes commands of the PDL type specified by the command determination unit 203 . Also, the command analysis unit 204 creates a color value list, which will be described later in detail.

A command execution unit 205 draws according to the analysis result of the command analysis unit 204, and generates a raster image and attribute information describing attribute information by executing RIP processing with a RIP (raster image processor).

A configuration example of the image processing unit 104 will be described. The image processing unit 104 has a color conversion processing unit 206 , a filter processing unit 207 , a gamma processing unit 208 and a dither processing unit 209 .

A color conversion processing unit 206 uses the raster image and attribute information generated by the command execution unit 205 to perform color conversion processing from the RGB color space to the CMYK color space. As a result, CMYK image data is generated.

A filter processing unit 207 performs edge enhancement such as sharpness processing on the CMYK image data. Note that the filter processing unit 207 may perform edge enhancement on RGB image data. In this case, after that, the color conversion processing unit 206 needs to perform color conversion processing to convert the RGB image data into CMYK image data.

A gamma processing unit 208 performs gamma processing on image data using a one-dimensional LUT (lookup table) according to printer characteristics.

A dither processing unit 209 dithers the gamma-processed image data.

FIGS. 3A and 3B are diagrams for explaining an example in which the command analysis unit 204 and the command execution unit 205 explained in FIG. 2 analyze, draw, and RIP a command to generate a raster image. Commands included in print data include drawing commands and control commands. FIG. 3(a) shows an example of drawing commands, and FIG. 3(b) shows an image drawn based on the drawing commands shown in FIG. 3(a). indicates Below, the command analysis unit 204 analyzes the command, and the command execution unit 205 draws according to the analysis result of the command analysis unit 204 and generates a raster image via a RIP (raster image processor) (not shown). An example will be given. First, commands include drawing commands and control commands, and examples of drawing commands will be described.

Drawing commands include a color mode setting command for setting the color mode of a job and a color setting command for setting colors. Further, there are object drawing commands for drawing objects, character size setting commands for setting character sizes, font setting commands for setting character fonts, and character drawing commands for drawing characters. The configuration of these 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 they are omitted. Note that the object is, for example, an area of an image other than characters, such as an illustration, a figure, or a photograph.

Next, the content of the drawing command 310 shown in FIG. 3A will be briefly described. It should be noted that the color value data in the subsequent rendering commands 310 is assumed to be an 8-bit image.

A color mode setting command 311 "Set Page Color (CL)", which is the second command of the drawing commands 310, indicates that the following commands are developed in color.

The third command, the color setting command 312 “Set Color (255, 128, 128)” indicates that the RGB values are red with R=255, G=128, and B=128. The fourth command, the object drawing command 313 "Draw Box (coordinates (X1, Y1), coordinates (X2, Y2) fill)" draws a rectangle from coordinates P11 (X1, Y1) to coordinates P12 (X2, Y2). Indicates to draw an object. As a result, as shown in FIG. 3B, an object 321 is drawn in red with R=255, G=128, and B=128 in an image 320. FIG.

Similarly, the fifth command, the color setting command 314 "Set Color (153, 153, 255)" indicates that the RGB values are blue with R=153, G=153, and B=255. The object drawing command 315 "Draw Box (coordinates (X2, Y1), coordinates (X3, Y2) fill)", which is the sixth command, indicates drawing a rectangular object 322 at the next position. That is, the object drawing command 315 indicates to draw the rectangular object 322 next to the object 321 from the coordinates P21 (X2, Y1) to the coordinates P22 (X3, Y2). As a result, as shown in FIG. 3B, an object 322 is drawn in blue with R=153, G=153, and B=255 next to an object 321, which is a red rectangle, in an image 320. .

Next, a raster image 320 generated by drawing and RIP by the command execution unit 205 according to the analysis of the drawing command 310 by the command analysis unit 204 will be described.

An object 321 representing a red rectangle is an image rendered and RIPped by the third and fourth commands 312 and 313 described above in the description of the rendering command 310 . The image drawn and RIPped by the fifth and sixth commands 314, 315 is an object 322 representing a blue rectangle.

The explanation of the command analysis unit 204 and the command execution unit 205 is finished with the above.

The description of the command analysis unit 204 and the command execution unit 205 is usually based on the user setting color. However, in the processing for improving the distinguishability according to the present embodiment, even if monochrome is specified, the same processing as color processing is performed, and then gray conversion is performed in the command execution unit 205 and thereafter to output in monochrome.

<Discriminability improvement processing>
Next, the distinguishability improvement processing S400 by the command analysis unit 204 and the command execution unit 205 will be described with reference to the drawings. FIG. 4 is a flowchart showing the flow of distinguishability improvement processing. A series of processes shown in the flowchart of FIG. 4 is performed by the CPU 105 of the image forming apparatus 101 reading program codes stored in the ROM 106 and developing them in the RAM 107 for execution. Also, some or all of the functions of the steps in FIG. 4 may be realized by hardware such as ASIC or electronic circuits. Note that the symbol "S" in the description of each process means a step in the flowchart, and the same applies to subsequent flowcharts.

In S401, the command analysis unit 204 executes color value list creation processing. Specifically, the command analysis unit 204 extracts the color value specified by the color setting command for each object in the image. The command analysis unit 204 adds the extracted color value to a color value list, for example, the color value list 501 shown in FIG. 5(a). That is, the color value list 501 holds the RGB values that are the extracted color values. Then, the command analysis unit 204 adds the gray values obtained by weighting the RGB values to the color value list, completing the color value list 501 indicating the color values and gray values of all the objects in the image. Then, the completed color value list 501 is saved in the RAM 107 . As a conversion method for converting RGB values into gray values, for example, NTSC weighted averaging, in which the values obtained by weighting and summing the RGB values such as 0.299R+0.587G+0.114B are regarded as the gray values corresponding to the RGB values. There is a transformation rule called law. Hereinafter, this will be called NTSC conversion. The conversion rule is not limited to NTSC conversion, and it is also possible to use a conversion rule that converts RGB values into gray values by evenly weighting them, or a conversion rule that changes weighting.

In S402, the command analysis unit 204 executes distinguishability improvement processing. Specifically, the command analysis unit 204 modifies all the gray values in the color value list 501 stored in the RAM 107 into distinguishable gray values. That is, based on the corrected color value list created in S401, the command analysis unit 204 creates a conversion table that converts each piece of color information associated with an object into gray density information corresponding to a single color. can also be said. Here, the distinguishability improvement processing will be described with reference to the drawings. FIGS. 5A and 5B show examples of color value lists stored in the RAM. FIG. 5A shows an example of the color value list before correction, and FIG. 5B shows an example of the color value list after correction. show. For example, in the color value list 501 shown in FIG. 5A, the gray value of object 321 is 166, while the gray value of object 322 is 165. judged to be unsuitable. Therefore, by performing a distinguishability improvement process on the gray values of these objects, for example, as shown in FIG. It will be modified to 120. Regarding the relationship between gray value difference and discrimination, it can be said that the larger the gray value difference, the higher the discrimination. However, if there is a difference of 15 to 20, for example, discrimination can be made.

In S403, the command execution unit 205 executes RIP processing. Specifically, the command execution unit 205 converts PDL data into raster image data. At this time, the command execution unit 205 converts the RGB values into gray values while referring to the color value list 501 .

As described above, a color value list for conversion from RGB to gray is created before RIP processing is performed. Fix the list. Then, when performing the RIP process, by converting the RGB values into gray values while referring to the corrected color value list, it is possible to improve the distinguishability.

<Transparent object>
Next, transparent objects will be described. There are roughly two methods of superimposing a background object (hereinafter referred to as a background object) by making another object transparent (hereinafter referred to as a transparent object).

The first method is a method of weighting a transparent object with a specified transparency with respect to a background object when performing RIP processing. For example, when the background object 1 is (R1, G1, B1) and the transparent object 2 whose transparency is specified by α is (R2, G2, B2), (R3, G3, B3) after synthesis is , can be expressed by the following transmission synthesis equation.
・R3=α×R1+(1−α)×R2
・G3=α×G1+(1−α)×G2
・B3=α×B1+(1−α)×B2

By this operation, the RGB values are calculated by mixing the color of the background object, the color of the transparent object, and the specified transparency, and the calculated RGB values are used to make the transparent object look transparent. be able to.

The second method is a method called ROP (Raster Operation) in which a logical operation is performed on a background object and a transparent object on a pixel-by-pixel basis when RIP processing is performed.

In this method, a mesh-like image is created as a transparent object according to the transparency, and the created image is logically synthesized with the background object. By this logic synthesis, it can be shown as follows. In other words, the color of the background object can be seen in the part where the mesh is missing, and the color of the transparent object is seen in the mesh part, so that when viewed from a distance, the color of the background object and the color of the transparent object are the same as in the first method. Can be made to appear blended with a specified transmittance. In the second method, after the ROP calculation, two RGB values are retained, such as the mesh portion, the white void portion, and the RGB values, but the apparent color value is calculated in the same way as in the first method. can be done. That is, when RIP processing is performed, instead of performing RIP processing as a mesh-like object according to the transmittance and merging it with the background object, it is combined with the background object at the transmittance and then RIP processing is performed. It can be treated in the same way as the first method. Therefore, in this embodiment, the above two transparent objects are handled in the same way.

In the above, the color values of transparent objects are determined only when performing RIP processing. On the other hand, in the above-described distinguishability improvement processing S400, the color values of the object are changed before performing the RIP processing. Therefore, even if the distinguishability is improved by the distinguishability improvement processing S400, if there is a transparent object, the color value will change further when the RIP process is performed, which may cause a problem such as a decrease in distinguishability. There is

Here, distinguishability improvement processing when a transparent object is included will be described with reference to FIGS. 3(c) and 3(d). Note that the second and third drawing commands 331 and 332 shown in FIG. 3C are the same as the second and third drawing commands 311 and 312 shown in FIG. .

The object drawing command 333 "Draw Box (coordinates (X1, Y1), coordinates (X4, Y2) fill)", which is the fourth command shown in FIG. It is shown that. That is, it indicates that a rectangular object is drawn from coordinates P31 (X1, Y1) to coordinates P32 (X4, Y2). As a result, according to the third and fourth commands 332 and 333 of the drawing command 330 in FIG. = 128, B = 128 red.

On the other hand, the fifth command, the color setting command 334 “Set Color (0, 0, 255)” indicates that the RGB values are blue with R=0, G=0, and B=255. A transparency command 335 "Set Composite (60%)", which is the sixth command of the drawing commands 330, indicates that the transparency is specified as 60%. The object drawing command 336 "Draw Box (coordinates (X5, Y1), coordinates (X3, Y2) fill)", which is the seventh command, draws a rectangle from coordinates P41 (X5, Y1) to coordinates P42 (X3, Y2). Indicates to draw an object. As a result, according to the fifth to seventh commands 334, 335, and 336 of the drawing command 330 in FIG. is rendered in blue with R=0, G=0, and B=255. Therefore, in a portion 343 where the object 341 and the object 342 overlap, the color value of the object 342 and the color value of the object 342 are synthesized with a transmittance of 60%.

In the case of such an image, the color value list created by the distinguishability improvement process has only two objects in terms of drawing commands. Therefore, as shown in FIG. Contains one color value. In image 341 , R=255, G=128, and B=128, resulting in a gray value of 166 in NTSC conversion. On the other hand, since R=0, G=0, and B=255 in image 342, the gray value is 29 in NTSC conversion. Therefore, on the color value list, the gray values are 166 and 29, respectively, and since the difference is greater than 20, it can be determined that they are already distinguishable.

However, in reality, since the image 342 is specified to be transparent, in the portion where the background is white (R, G, B)=(255, 255, 255), the calculation is performed as follows from the above formula for transparent synthesis. be done. However, each value of RGB shall be rounded off with respect to the decimal point.
R = 0.6 x 255 + 0.4 x 0 = 153
G = 0.6 x 255 + 0.4 x 0 = 153
B = 0.6 x 255 + 0.4 x 255 = 255
That is, (R, G, B)=(153, 153, 255). The gray value obtained by NTSC conversion is 165. Thus, since the gray value of image 342 is 165, which is approximately the same as the gray value of 166 for image 341, images 342 and 341 are not really distinguishable. Therefore, even though the distinguishability should be improved originally, the distinguishability cannot be improved.

Next, when storing the color values in the color value list, assuming that the background is a white background by looking at the transparency command, each value of RGB is set to (R, G, B )=(153, 153, 255). In that case, the gray value of the image 341 is 190, and the gray value of the image 342 is 120, as shown in FIG. However, since an image 343 where the background image 341 and the transparent image 342 overlap is synthesized after performing the RIP process, it is calculated as follows from the transparent synthesis equation. However, each value of RGB shall be rounded off with respect to the decimal point.
R = 0.6 x 255 + 0.4 x 0 = 153
G = 0.6 x 128 + 0.4 x 0 = 77
B = 0.6 x 128 + 0.4 x 255 = 179
That is, (R, G, B)=(153, 77, 179). The gray value obtained by NTSC conversion is 111. As described above, the gray value of the image 343 is 111, which is close to the gray value of 120 of the image 342, so that the images 342 and 343 have poor discrimination.

In this way, in the distinguishability improvement processing, even if the distinguishability is improved by the gray value of the color value before the transparent object is combined with the background object, the distinguishability deteriorates after the transparent object is combined. Sometimes I end up In addition, even if the background object is preliminarily determined to be white and the color value is calculated, if the background is not white, the distinguishability may be degraded in the same way as the technique for improving the distinguishability before synthesis. .

<Embodiment 1>
Therefore, in order to operate the distinguishability improvement processing as desired even if there is a transparent object as described above, if there is a transparent object when creating the color value list, the color value that combines the transparent object with the background is obtained in advance. need to keep

Therefore, in the color value list creation processing S401 of the distinguishability improvement processing, it is determined whether or not there is a transparent object. Then, a color value after composition is calculated from the transparent object, the background object, and the transmittance, and addition processing and correction processing are performed on the color value list based on the calculation result. After that, the distinguishability improvement processing S402 is performed to solve the problem.

<Color value list creation processing>
The color value list creating process according to this embodiment will be described below with reference to the drawings. FIG. 7 is a flowchart showing the flow of color value list creation processing. A series of processes shown in the flowchart of FIG. 7 is performed by the CPU 105 of the image forming apparatus 101 reading out the program code stored in the ROM 106, developing it in the RAM 107, and executing it. Also, some or all of the functions of the steps in FIG. 7 may be realized by hardware such as ASIC or electronic circuits. It is assumed that an object exists in the search target. Also, when the process of S401 is started, it is assumed that the object search for the document is started.

First, in S1001, the command analysis unit 204 extracts the color value, the position of the object, and information indicating whether or not the searched object is a transparent object. Color information indicating the color value of the object is extracted from the color setting command. Position information indicating the position of the object is extracted from the object drawing command. Further, transparency information indicating whether or not the object is a transparent object is extracted from the transparency command. Then, the command analysis unit 204 adds various types of information extracted regarding the object to be processed to a color value list indicating the correspondence between the object and the color information, position information, and transparency information of the object.

Next, in S1002, the command analysis unit 204 determines whether or not the color values/transparency information/positions of all objects in the document have been extracted. For example, if the processing target of S1001 is the command 336 shown in FIG. 3C and the next command line includes "Page End", it is determined that color information, position information and transparency information have been extracted for all objects. , the process proceeds to S1004. On the other hand, if the processing target of S1001 is the command 333 shown in FIG. 3C and the next command line does not include "Page End", color information, position information and transparency information have not yet been extracted for all objects. It is determined that there is not, and the process proceeds to S1003. Then, in S1003, the command analysis unit 204 sets the next object as the processing target, and then the process proceeds to S1001, where the next object, which is the new processing target, is processed. On the other hand, if it is determined that the color values/transparency information/positions of all objects have been extracted (YES in S1002), the process proceeds to S1004.

In S1004, it is determined whether or not there is a transparent object in the document. Here, the determination is made by referring to the transparency information in the color value list created in S1001. If it is determined that there is no transparent object (NO in S1004), the color value list creation process shown in FIG. 7 ends. On the other hand, if it is determined that there is a transparent object (YES in S1004), the process proceeds to S1005.

In S1005, the command analysis unit 204 executes processing for detecting the background. That is, the command analysis unit 204 searches for an object that serves as the background of the transparent object. First, the command analysis unit 204 compares the positions of transparent objects extracted in the color value list with the positions of other objects, and checks one by one whether or not they overlap. Then, when it is checked that there is an object that satisfies this condition and that there is another object that overlaps with the transparent object, the command analysis unit 204 extracts the corresponding object as a background object. On the other hand, when it is checked that there is no object that satisfies this condition and that there is no other object that overlaps the transparent object, the command analysis unit 204 determines that the white background of the document is the background. It is also assumed here that more than one object is the background.

In S1006, the command analysis unit 204 calculates a color value after composition from the transparency information of the background object and the transparent object extracted in S1005. If there are multiple background objects, multiple color values will be calculated.

In S1007, the command analysis unit 204 adds and corrects the value calculated in S1006 to the color value list to which the information about the object has been added in S1001.

In S1008, the command analysis unit 204 determines whether or not addition processing and correction processing have been executed for the color values of all transparent objects existing in the color value list to be processed. If the addition processing and correction processing for the color values of all transparent objects existing in the color value list to be processed have not been completed yet, and it is determined that there are unprocessed transparent object color values (S1008 NO), the process proceeds to S1009. Then, in step S1009, the command analysis unit 204 specifies the next transparent object to be processed from among the unprocessed transparent objects existing in the color value list to be processed. When the next processing target is identified, the process returns to S1005, and the processing of S1005 is performed on the new transparent object to be processed identified in S1009.

On the other hand, if it is determined that the color values of all transparent objects in the color value list to be processed have been added and corrected (YES in S1008), the color value list shown in FIG. 8 is created. End the processing flow.

Here, the following three points will be described with reference to the drawings: searching for an object that serves as the background of the transparent object in S1005, calculation of the color value combined with the transparent object and the background object in S1006, and correction of the color value list in S1007. do. FIG. 8 is a diagram showing an example of a color value list based on information about an image corresponding to FIG. 3(c). FIG. 8(a) shows a color value list including the color information of the extracted object, FIG. 8(b) shows the color value list including the color information of the overlapping object, and FIG. 8(c) shows the color value list of FIG. 10 shows a color value list including color information after the color information in the color value list of 1 is subjected to discrimination improvement processing.

In S<b>1005 , the command analysis unit 204 refers to the color value list and determines an object for which a transmittance value greater than 0 is set as a transparent object. For example, referring to the color value list 501 shown in FIG. 8A, the command analysis unit 204 determines that an object (input image) 342 with a transmittance of 60 is a transparent object.

The start point of the transparent object 342 is (X5, Y1) and the end point is (X3, Y2), while the other object 341 has the start point (X1, Y1) and the end point (X4, Y2). be. Regarding the X coordinates indicating the horizontal direction, there is a relationship of X1<X5<X4<X3, and the Y coordinates in the vertical direction are the same, so the overlap is between X5 and X4, and the white background is the background between X4 and X3. I understand. An object 343 is located between X5 and X4.

Next, in S1006, the command analysis unit 204 combines the color values of the transparent object 342 and the background object.

First, where the background object for the transparent object 342 is the object 341, (R, G, B)=(153, 77, 179) is calculated from the previous example. Where the background object for the transparent object 342 is white, (R, G, B)=(153, 153, 255) is calculated from the previous example. Here, at both locations, the transmittance is changed to 0 because it has been synthesized.

In S1007, the command analysis unit 204 uses the information obtained in S1005 and S1006 to perform addition/modification processing on the color value list. Based on the information obtained in steps S1005 and S1006, the command analysis unit 204 performs, for example, addition/modification processing on the color value list 501, and as shown in FIG. Generate list 501 .

This concludes the description of the color value list creation processing.

If there is a transparent object in the color value list, an object corresponding to the background is searched, and the color value after composition is calculated before performing the RIP process. Therefore, the density information indicating the gradation of the gray value can be appropriately adjusted (corrected) so as to have distinctiveness in subsequent distinguishability improvement processing S402. The gray value after adjustment is 210 for object 341, 160 for object 342, and 100 for object 343, as shown in FIG. 8(c). Therefore, it can be seen that the difference in gray value between all objects is 20 or more, and the distinguishable color value list 501 can be generated. This can be said to be conversion into color information of monochrome image data (grayscale image data) so that colors in monochrome image data after conversion can be identified.

That is, in this embodiment, the object corresponding to the background of the transparent object is searched for the object corresponding to the background from among the objects in the document by checking the position of each object, and the color value that appears after compositing from the transparent object and the background object is calculated. . For example, if there is a transparent object in S1004 of FIG. 8, two rectangles are used as an example in S1005 to search for the background object of the transparent object.・Fixed.

As described above, according to this embodiment, the following color value list can be generated when color data (color image data) is converted into gray data (monochrome image data). That is, when there is an object to be combined with a background object, such as an object designated as transparent, in the document, a color value list with improved discrimination can be generated by performing the following processing. That is, before performing the RIP process, the color value synthesized with the background object is calculated in advance. Then, it is possible to improve the distinguishability by adding the color values and adding shading. Accordingly, the image output unit 109 can perform monochrome printing with improved distinguishability based on the print data.

As described above, if there is a transparent object in the document, the object corresponding to the background is searched for. In addition, assuming that 8-bit image data is used, gray values can only take values from 0 to 255. Therefore, if there are many objects of different colors, there is a limit to the improvement in distinguishability. For example, if there are 256 different colors in the document, each color has a gray value of 0, 1, 2, . I can not say.

However, when the number of objects with different colors is large and is equal to or greater than a predetermined number, there are countless different colors in the document even if it is output in color, and the user has little intention of increasing the distinguishability in the first place. it is conceivable that.

Therefore, when the number of transparent objects is equal to or greater than a predetermined number, the process ends without adding or modifying the color value list, thereby preventing the process from taking too much time to improve the distinguishability. , will be described with reference to the drawings. FIG. 9 is a flowchart showing the flow of color value list creation processing. Note that S2001 to S2004 and S2006 to S2010 are the same processes as S1001 to S1004 and S1005 to S1009, respectively, and detailed description thereof will be omitted. Since the configuration is the same as that of the steps described above and the only difference is S2005, only S2005 will be described. A series of processes shown in the flowchart of FIG. 9 is also performed by the CPU 105 of the image forming apparatus 101 reading out the program code stored in the ROM 106, developing it in the RAM 107, and executing it. Also, some or all of the functions of the steps in FIG. 9 may be realized by hardware such as ASIC or electronic circuits. It is assumed that an object exists in the search target.

In S2005, the command analysis unit 204 determines whether or not there are a predetermined number N or more of transparent objects in the document. For example, the predetermined number N may be 12 when the upper limit value of the gray value difference is “20”, or it may be 17 when the lower limit value of the gray value difference is “15”. Good, but not limited to this. Here, the number of transparent objects in the created color value list is counted, and it is determined whether or not the number is equal to or greater than a predetermined number N. If it is determined that the number of transmissive objects is equal to or greater than the predetermined number N (YES in S2005), the flow of color value list creation processing shown in FIG. 9 ends. On the other hand, if it is determined that the number of transmissive objects is less than the predetermined number N (NO in S2005), the process proceeds to S2006.

As described above, based on the number of transparent objects in the original, the process of searching for the background object by switching whether or not to modify the color value list in consideration of the combined color value of the transparent object and the background object. time can be reduced.

Also, although the number of transparent objects alone is used for determination this time, the number of other objects may be added to the number of transparent objects.

<Embodiment 2>
In this embodiment, it is not possible to determine from the drawing position whether or not the transparent object and each object in the document overlap. explain about.

First, when searching for an object to be the background of a transparent object, it is determined from the position information of the object that it is not the background if it is clearly not in contact with the object. On the other hand, other than that, it is determined that there is a high possibility that it is a background object that partially overlaps with the transparent object, and all possible color combinations are added to the color value list. Details will be described below using the processing flow of FIG. 10 . FIG. 10 is a flowchart showing a detailed flow of another example of color value list creation processing. Note that the flow of FIG. 10 is the same as the steps up to S1003 in FIG. 7, so it is the step after YES in the determination of whether or not there is a transparent object in S1004, and is the step corresponding to the processing after S1005 in FIG. Explain the flow. Also, the description of the same configuration as that of the first embodiment is omitted. A series of processes shown in the flowchart of FIG. 10 is also performed by the CPU 105 of the image forming apparatus 101 reading out the program code stored in the ROM 106, developing it in the RAM 107, and executing it.

First, in S3001, the command analysis unit 204 extracts position information indicating the rendering position of the target transparent object. For example, if the transparent object is a rectangle, the start point and end point of the transparent object are extracted as position information, and if the transparent object is a circle, the center and radius of the transparent object are extracted as position information.

In S3002, the command analysis unit 204 extracts position information indicating the rendering position of the object to be compared with the target transparent object.

In S3003, the command analysis unit 204 determines whether the position of the target transparent object is close to the position of the object extracted in S3002. Here, a method of detecting a background object will be described with reference to the drawings. FIG. 11 is a diagram showing an example of background object detection. In S3003, instead of strictly determining whether or not they overlap, for example, as shown in FIG. A simple verification is sufficient. The L may be, for example, 10 pixels to 20 pixels in size, but is not limited to this. In FIG. 11, rectangle A (start point, end point) of transparent object = ((X1, Y1), (X2, Y2)), and circle B (center, radius) of object = ((X3, Y3), r) , and the dotted line indicates that the rectangle A is larger by L in the X and Y directions. Here, it is only necessary to check whether the distance from the center (X3, Y3) of circle B to (X2+L, Y2+L) is greater than r.

Here, it suffices if it can be judged that objects that are clearly separated do not overlap, and other objects are judged to overlap. Then, if the result of the judgment is that it is close (YES in S3003), the process proceeds to S3004. On the other hand, if it is determined that they do not overlap and the determination result is that they are not close (NO in S3003), the process proceeds to S3009.

In S3004, the command analysis unit 204 determines whether the object to be compared includes a transparent object. That is, it is determined whether the object to be compared includes a transparent object and the object to be compared is larger than the transparent object. If it is determined that it is included (YES in S3004), the process proceeds to S3007. On the other hand, if the determination result is that it is not included (NO in S3004), the process proceeds to S3005.

In S3005, all color combinations of target transparent objects and background objects are calculated. Here, since there are two objects of interest, a transparent object and a background object, two possible combinations are conceivable: a portion where the background of the transparent object is the white color of the background, and a portion where the background overlaps the object. Note that the color of the background object is already listed in the color value list, so we won't cover it here. Therefore, the color values for these two cases are calculated here.

In S3006, the command analysis unit 204 deletes the color value of the transparent object described in the color value list, and executes correction processing of adding the two assumed color values calculated in S3005 to the color value list. Note that after executing the correction process, the process proceeds to S3009.

In S3007, the command analysis unit 204 calculates a color value obtained by synthesizing the transparent object and the background object. Here, since the target transparent object is completely included in the background object, a color value is calculated by overlapping the transparent object and the object to be compared. Note that after the calculation, the process proceeds to S3008.

In S3008, the command analysis unit 204 executes processing for correcting the color value of the transparent object described in the color value list to the color value calculated in S3007. Note that after executing the correction process, the process proceeds to S3009.

In S3009, the command analysis unit 204 determines whether all objects have been compared. If it is determined that all objects have not been compared (NO in S3009), the process proceeds to S3010. At S3010, command analysis unit 204 sets the next object as a processing target. After the processing of S3010 is finished, the processing moves to S3002, and the processing of S3002 is executed for the next object newly set as the processing target. On the other hand, if it is determined that all objects have been compared (YES in S3009), the process proceeds to S3011.

In S<b>3011 , the command analysis unit 204 determines whether all transparent objects have been combined with the background object. If it is determined that all transparent objects have not been combined with the background object (NO in S3011), the process proceeds to S3012. In S3012, command analysis unit 204 sets the next transparent object as a processing target. Note that when the processing of S3012 ends, the processing shifts to S3001, and the processing of S3001 is executed for the next transparent object newly set as the processing target. On the other hand, if it is determined that all the transparent objects have been combined with the background object (YES in S3011), the process of extracting overlapping objects and calculating overlapping color values shown in FIG. 10 ends.

A method for simply searching for an object that is the background of a transparent object has been described above.

In this embodiment, a color value is calculated that can be assumed assuming that a transparent object and another object may overlap if they are located close to each other. Therefore, if the expected color values do not appear because they do not actually overlap, it means that the discriminability interval will be reduced accordingly. For example, when it is desired to distinguish three colors with gray, three values of 0, 128, and 255 can be taken as gray values when the distinguishability is enhanced most in the distinguishability improvement processing, and the gray value difference is 128. . On the other hand, if we add color values that can be assumed to overlap, we can discriminate with four colors, so four values of 0, 85, 170, and 255 can be taken as gray values. The difference is 85. Therefore, it can be said that the smaller the number of colors, the higher the distinguishability can be. On the other hand, checking whether the transparent object and the background object exactly overlap leads to a heavy load.

As described above, according to the present embodiment, even if there is an object such as a transparent object whose color value is known by compositing it with a background object during RIP processing in the document, the processing speed is not lowered and the color value can be determined before RIP processing. Background objects can be searched. Based on this search result, the color value is calculated from the transparent object and the background object, the color value list is updated, and the gradation is added by the distinguishability improvement processing S402. . In addition, even if the object that becomes the background of the transparent object has various shapes such as a star, the time required for searching can be reduced.

[Other embodiments]
In the above description, the image data represented by the RGB color space is used as the color data.

Although a mode of converting color image data into monochrome image data has been described above, the present invention is not limited to this. For example, each color information of color image data can be converted into color information of monochrome image data such as magenta.

The present disclosure provides a program that implements one or more functions of the above-described embodiments to a system or device via a network or storage medium, and one or more processors in a computer of the system or device reads and executes the program. It can also be realized by processing to It can also be implemented by a circuit (for example, ASIC) that implements one or more functions.

Claims (13)

An image processing device for converting color image data into monochrome image data,
Of the objects included in the color image data, color information of a transparent object designated to be transparent and color information of another object overlapping with the transparent object are used to determine a portion where the transparent object overlaps with the other object. a calculation means for calculating color information;
conversion means for converting the overlapping portion into color information of the monochrome image data based on the color information calculated by the calculation means;
An image processing device comprising: Based on the color information calculated by the calculation means and the color information of an object included in the color image data, the conversion means performs the 2. The image processing apparatus according to claim 1, wherein the image processing apparatus converts the color information of monochrome image data. If the overlapping portion of the transparent object and the other object is a part of the other object, the calculation means calculates all of the combined colors of the transparent object and the other object, which are assumed to be combined. Calculate the color information,
The conversion means is configured to make the colors in the converted monochrome image data identifiable based on all the color information calculated by the calculation means and the color information of each object included in the color image data. 3. The image processing apparatus according to claim 2, wherein the color information of the monochrome image data is converted into color information. further comprising generating means for analyzing the color image data and generating a list showing correspondence between objects included in the color image data and color information of the objects;
4. The image processing apparatus according to claim 2, wherein the calculation means calculates color information of a portion where the transparent object overlaps the other object based on the list. The calculation means does not calculate color information of a portion where the transparent object overlaps with the other object when there are a predetermined number or more of the transparent objects,
3. The image processing apparatus according to claim 2, wherein the converting means converts the color information of the object included in the color image data into the color information of the monochrome image data. further comprising generating means for analyzing the color image data and generating a list showing correspondence between objects included in the color image data and color information of the objects;
6. The image processing apparatus according to claim 5, wherein said converting means converts the color information of said monochrome image data based on the color information included in said list. 7. The image processing apparatus according to claim 1, wherein the monochrome image data is grayscale image data. 8. The image processing apparatus according to claim 7, wherein said conversion means converts said grayscale image data into color information according to a predetermined conversion rule. wherein the color image data is data expressed in an RGB color space;
9. The image processing apparatus according to claim 8, wherein said predetermined conversion rule is a conversion rule based on the NTSC weighted average method. further comprising creating means for creating a conversion table for converting each color information into color information of the monochrome image data;
10. The image processing apparatus according to claim 1, wherein the converting means converts the monochrome image data into color information using the conversion table. The color image data is included in print data,
11. The image processing apparatus according to claim 1, further comprising printing means for performing monochrome printing based on said print data. An image processing method for converting color image data into monochrome image data,
Of the objects included in the color image data, color information of a transparent object designated to be transparent and color information of another object overlapping with the transparent object are used to determine a portion where the transparent object overlaps with the other object. a calculation step of calculating color information;
a converting step of converting the overlapping portion into color information of the monochrome image data based on the color information calculated in the calculating step;
An image processing method comprising: A program for causing a computer to function as each means of the image processing apparatus according to any one of claims 1 to 11.

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