JP2006129250A - Image processor - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To produce binary image data from document data at higher speed and with memory capacity less than that of the prior art. <P>SOLUTION: An image processor 10 describes image data 13 in an edge array form. A binary image producing unit 15 efficiently produces binary image data 17 in respective colors by performing processing of sorting edge body structures comprising the image data in accordance with color information, coordinates, attribute information and the like. The edge body structures are collected for each color and sorted so that the edge body structures in the same color become adjacent to each other, thereby specifying at high speed edge body structures to produce the binary image data 17. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to a technique for performing image processing on various objects such as characters, graphics, and images constituting a document.

  Computer networks such as the Internet and LAN (Local Area Network) are rapidly spreading. The user can attach the document data created using a computer to an e-mail and send it to another computer via a network. In addition, the user can easily realize storing document data in a server computer connected to the network and transmitting the document data from the server computer to another computer as necessary.

This type of document data is represented by a code unique to the document creation software installed in the transmission source computer. Therefore, the destination computer cannot display or edit the received document unless software of the same type as the document creation software is prepared. In addition, not only the type of document creation software but also the type and version of the operating system (OS) of the computer must be standardized, there is a risk that a document may not be displayed as intended by the creator. . Therefore, it is desirable that not only the document creation software, but also all other computer environments are unified.
Furthermore, in recent years, portable information terminals such as PDAs (Personal Digital Assistants) and mobile phones have become widespread, and these terminals are required to be able to display documents. In view of the diversification of software and hardware in this way, not all computers and portable information terminals are always equipped with a computer environment that can display and edit received documents. Conceivable.

  For such a problem, it is conceivable that the created document data is converted into general-purpose image data (for example, bitmap format) that is generally used and then transmitted to another computer. This is because if the document is expressed as an image by general-purpose image data, it can be displayed and edited by general image processing software or the like installed at the time of product shipment by the destination computer. However, since the size of image data such as bitmap format is usually very large, it must be compressed with a standard compression method such as JPEG (Joint Photographic Experts Group). It is inconvenient to send and receive with. For example, when image data is compressed by the JPEG method, high-frequency components of the image data are discarded, so that good results can be obtained in terms of compression rate and image quality for natural images such as photographs. However, the elements (objects) constituting the document include not only natural images but also characters (text) and line drawings / graphics (graphics). When these objects are uniformly compressed by an irreversible compression method such as the JPEG method, there arises a problem that, for example, the image quality is deteriorated at the boundary of the character region, and the characters are difficult to read.

  As a technique for solving this problem, there is a technique described in Patent Document 1. In Patent Document 1, an object (drawing object) such as text or graphics in an image data is separated from an object (image object) such as an image, and for example, a binary image is generated by performing lossless compression on the drawing object. However, different compression methods are applied to each object, such as irreversible compression for image objects to generate a multi-valued image. By generating image data in this way, it is possible to ensure a high compression ratio while clearly reproducing text and graphics.

  In the technique described in Patent Document 1, when generating binary image data representing a drawing object represented by a plurality of colors, it is necessary to generate binary image data for each color unit. . In such a case, the color information of each pixel of the image data before compression must be referred to one by one. Therefore, a load such as consuming a lot of physical resources such as a memory or increasing a processing time occurs. This type of problem is not particular to the technique described in Patent Document 1, and is a problem that has conventionally occurred when an image composed of a plurality of colors is to be expressed by binary image data.

JP 2003-189112 A

  The present invention has been made in view of the above-described problems, and an object of the present invention is to provide a technique for generating binary image data from document data at a higher speed and with a smaller memory capacity than before.

In order to achieve the above-described object, the present invention provides a storage unit that stores document data composed of a plurality of types of objects, and stores the stored document data for objects existing on each scan line of the document data. An analysis processing unit that converts image data composed of edge structures representing colors and positions, an edge storage unit that stores image data obtained by the analysis processing unit, and an image stored in the edge storage unit Of the edge structures that make up the data, the edge sort unit that sorts the edge structures that represent objects of the same color so that they are adjacent to each other, and the edge that represents the object of the same color among the sorted edge structures The structure is rearranged based on the position represented by the edge structure, and the rearranged edge structure To provide an image processing apparatus and a data output unit for generating and outputting the binary image data expressed in the object of the same color 2 value represented Te.
According to this image processing apparatus, since binary image data is output using image data configured by an edge structure, color information of each pixel is sequentially stored as in the case of referring to bitmap image data. There is no need to read. Therefore, binary image data can be generated at a higher speed and with a smaller memory capacity than in the past.

In a more preferred aspect of the image processing apparatus of the present invention, the edge structure represents an object type in addition to the color and position of the object, and the edge sorting unit is stored in the edge storage unit. The edge structures representing the same kind and the same color of the edge structures constituting the image data are rearranged so that they are adjacent to each other, and the data output unit is rearranged by the edge sort unit Among the edge structures, the edge structures representing the same type and the same color object are rearranged based on the position represented by the edge structure, and the object represented by the rearranged edge structure is expressed in binary. Binary image data is generated and output.
In this way, binary image data of the same type of object can be generated for each color. Therefore, even if the object is the same color, each binary image data is generated as different binary image data. Is possible. Thereby, the binary image data desired by the user can be obtained more reliably, and the binary image data reflecting the user's intention faithfully can be obtained.

  The image processing apparatus according to the present invention is characterized in that binary image data is generated from document data. The image processing apparatus of the present invention describes image data in an edge array format, and performs a process of sorting edge structures constituting the image data according to color information, coordinates, or attribute information, whereby binary image data of each color Can be generated efficiently. By organizing the edge structures for each color and sorting the edge structures of the same color so as to be adjacent to each other, it is possible to specify the edge structures for which binary image data should be generated at high speed. In the following, an example of an image processing apparatus to which the present invention is applied will be shown, and embodiments of the present invention will be described in detail.

(1) Device Configuration FIG. 1 is a block diagram illustrating a hardware configuration of an image processing device 10 according to the present embodiment. The image processing apparatus 10 is a personal computer, for example, and includes a control unit 1, a storage unit 2, an input / output I / F 3, a display unit 4, and an operation unit 5. The control unit 1 includes an arithmetic device such as a CPU (Central Processing Unit) and various memories such as a ROM (Read Only Memory) and a RAM (Random Access Memory). The storage unit 2 is a large-capacity nonvolatile storage device such as a hard disk, for example, and stores a control program PRG1 describing a procedure for the control unit 1 to realize functions to be described later. The input / output I / F 3 is connected to a network (not shown), and exchanges data with external devices such as computers and printers connected to the network. The display unit 4 is a liquid crystal display, for example, and displays an image based on data supplied by the control unit 1. The operation unit 5 is, for example, a keyboard or a mouse, and supplies a signal corresponding to the operation content of the operator to the control unit 1.

FIG. 2 is a functional block diagram showing a functional configuration of the image processing apparatus 10. In this functional block diagram, the arrows in the figure represent the flow of data and various control information generated by processing executed by the image processing apparatus 10.
The control unit 1 executes functions of the analysis processing unit 12, the multi-value image generation unit 14, and the binary image generation unit 15 by executing the control program PRG1. These units cooperate with each other to perform operations described later.

  As shown in FIG. 2, document data 11 is input to the analysis processing unit 12. Here, the document data 11 is data created by a user using an application program such as document creation software, for example, and is described by a unique code defined in the application program. The text data 11 may be stored in advance in the storage unit 2 of the image processing apparatus 10 or input to the image processing apparatus 10 from an external computer or the like via the input / output I / F 3. There may be. Even when the text data 11 is acquired from the outside, the analysis processing unit 12 generates image data 13 based on the acquired document data 11 based on the document data 11. The function of the analysis processing unit 12 corresponds to a processing part that generates raster data or data equivalent to the raster data (interchangeable) in an image output device such as a printer. The image data 13 generated by the analysis processing unit 12 is supplied to a multi-value image generation unit 14 and a binary image generation unit 15 and is output as multi-value image data 16 and binary image data 17 respectively.

  As described above, the image processing apparatus 10 performs processing for generating the multi-value image data 16 and the binary image data 17 from the input document data 11. In the following, the function of each part of the image processing apparatus 10 in this processing will be described in detail. Before that, the document data 11 in the present embodiment, the image data 13 as intermediate data, and the finally generated multi-value image The data structure of the data 16 and the binary image data 17 will be described.

(2) Data Structure of Various Data FIG. 3 is a diagram conceptually showing the document data 11 and the multi-value image data 16 and the binary image data 17 generated from the document data 11 in this embodiment. . The document data 11 includes a “text” area in which characters are drawn, a “graphics” area in which graphics (here, stars) are drawn, and an “image” in which natural images such as photographs are drawn. ”Area. The “text” and “graphics” areas include an area drawn in “black” and an area drawn in “red”. Each pixel of the document data 11 has, for example, a color value of 3 bytes (24 bits) for each color of R (red), G (green), and B (blue) (hereinafter referred to as “RGB value”).
The multi-value image data 16 includes image information itself (multi-value image information) representing the “image” area in the document data 11, and position information and size information of the multi-value image information. As with the document data 11, the image information in the multi-value image data 16 has RGB values of 3 bytes for each RGB color. The position information indicates, for example, the coordinates of one point (for example, the upper left end) of the image information when the upper left end of the document data 11 is the origin. In the present embodiment, the main scanning direction (horizontal direction) in FIG. 2 is the x coordinate, and the sub scanning direction (vertical direction) is the y coordinate. The size information indicates the lengths of the multi-value image information in the x direction and the y direction.

On the other hand, the binary image data 17 is generated by image information representing a “text” region and image information representing a “graphics” region in the document data 11. The binary image data 17 is generated as one data for each color. That is, here, the binary image data 17k based on the “text” and “graphics” regions drawn in “black” and the binary image data 17r based on the “text” and “graphics” regions drawn in “red” are included. Generated. Therefore, the image information (binary image information) in the binary image data 17 is 1-bit information indicating whether or not an object is included in each pixel, and the binary image information itself identifies the color. Information to do is not included. Therefore, the binary image data 17 includes color information such as RGB values in order to identify the color of the binary image information. Further, each binary image data 17 has the same position information and size information as the multi-value image data 16.
The multi-value image data 16 and the binary image data 17 are not necessarily generated as described above. For example, the multi-value image data 16 is generated by image information representing a “graphics” area in the document data 11. May be.

Next, the image data 13 will be described in detail.
The image data 13 includes a plurality of edge data equivalent to raster data, tag information (attribute information) indicating attributes of each edge data, color information of each edge data, and a circumscribed rectangle that defines the shape of the object Information.
The edge data is data described in an edge arrangement format, and is data indicated in a format similar to so-called run length compression. Here, the “edge” is, for example, a line segment image existing on one scan line parallel to the x axis (main scanning direction) in the xy coordinate system defined in the image developed in the RAM (image memory). That is. The “edge arrangement format” means a format in which an image of the entire scan line is expressed by a plurality of edge rows existing on one scan line. When an image is described in an edge array format, it is possible to reduce the data capacity of the image in many cases while being reversibly converted to raster data. In particular, the edge data can efficiently compress an image including many areas (so-called “solid areas”) in which a certain area is drawn with the same color.
The tag information is information indicating the attribute of each edge included in the above edge data. Here, “attribute” refers to the type of object indicated by each edge. In this embodiment, the tag information includes a “text” object representing a “text” region, a “graphics” object representing a “graphics” region, an “image” object representing an “image” region, and other regions, Assume that there are four types of “background” objects representing background areas in which images are not drawn. In the actual image data 13, for example, the tag information of the “text” object is “1”, the tag information of the “graphics” object is “2”, the tag information of the “image” object is “3”, “background” As the tag information of “object”, a unique number is assigned to each object, such as “0”, and this is used as tag information.
The color information is information indicating the color in which each edge included in the above-described edge data is to be drawn, and is indicated by, for example, an RGB value.

  As for the “graphics” object, it is possible to analyze the shape and color of the object, the regularity and continuity of the color change, and specify more detailed attributes. For example, an object with a minute width such as a contour or ruled line of a figure is a “thin line”, an object in which a certain area is drawn in the same color is a “solid area”, and the color of a certain area is continuously in a certain direction. For example, the changing object is designated as “gradation”, and the object in which two or more minute areas having a certain shape are drawn in a regular pattern is designated as “pattern”. Therefore, for convenience of explanation, in the following, it is assumed that the “graphics” object is further subdivided into objects having “solid region” and “gradation” attributes, the former being a “solid region” object and the latter being “gradation”. Sometimes called an object. In this case, different tag information is assigned to each object, for example, “20” for the “solid region” object and “21” for the “gradation” object. At this time, the “solid region” object is a generation target of the binary image data 17, and the “gradation” object is a generation target of the multi-value image data 16.

The image data 13 is represented as a set of edge data constituting a plurality of scan lines. Each edge data is represented as a set of a plurality of edge images. A set of data constituting one edge image is hereinafter referred to as an “edge structure”.
FIG. 4 is a diagram showing the format of this edge structure. The edge structure includes start point coordinates SX, end point coordinates EX, tag information TG, and color information CL. In this embodiment, when a plurality of objects such as “characters” and “graphics” exist on one scan line, one edge structure is generated every time the color of the object or the type of the object changes. It has come to be.

  FIG. 5 is a diagram conceptually showing the data structure of the image data 13 in this embodiment, and illustrates edge data E1 and E2 of a scan line indicated by certain y coordinates Y1 and Y2. Further, the image data 13 in the figure is an image diagram showing image data developed in, for example, a RAM (image memory), and regions T, G, I, and B are “text”, “graphics”, and “image”, respectively. , "Background" means each object. The image data 13 is assumed to have a width of 200 pixels in the main scanning direction.

The edge data E1 has four edge structures es1, es2, es3, and es4. The edge structure es1 having the start point coordinate SX = 0, the end point coordinate EX = 150, the tag information TG = 0 (background), and the color information CL = “undecided” is a part of the background object in the scan line Y1 (the region T On the left). The edge structure es2 having the start point coordinate SX = 151, the end point coordinate EX = 165, the tag information TG = 1 (character), and the color information CL = “black” constitutes a text object (region T) in the scan line Y1. ing. The edge structure es3 having the start point coordinate SX = 166, the end point coordinate EX = 170, the tag information TG = 2 (graphics), and the color information CL = “black” is a graphics object (right side of the region T) in the scan line Y1. It is composed. The edge structure es4 having the start point coordinates SX = 171, the end point coordinates EX = 199, the tag information TG = 0 (background), and the color information CL = “undecided” is a part (area) of the background object in the scan line Y1. G on the right side). Similarly, the edge data E2 includes three edge structures es5, es6, and es7, and each forms a background object and an image object in the scan line Y2.
The color information CL of the background object is “undecided”, which means that an arbitrary color may be designated. For example, the color information CL of the background object may be “white” or not particularly defined.

Since these edge structures es1, es2, es2, es3, es3, es4, es6, es6, and es7 are adjacent to each other, the start point coordinates SX and end point coordinates EX of the adjacent edge structures are It will be continuous. When the edge structure is a “text” or “graphics” object, one edge is colored as in the edge structures es1 to es5 and es7.
On the other hand, when the object constituted by the edge is an image like the edge structure es6, the memory address is described as the color information CL of the edge data. In the storage area designated by the memory address, actual image data, that is, image information is stored. As described above, how the color information CL should be interpreted differs depending on whether the object is a character, a graphic, or an image (that is, depending on the value of the tag information TG).

Next, circumscribed rectangle information included in the image data 13 will be described.
The circumscribed rectangle means a rectangle that touches the outer edge of the object, and the circumscribed rectangle information is information indicating the position and size of the circumscribed rectangle. In the present embodiment, the maximum number of circumscribed rectangle information generated can be set for each type of object. Further, the number of circumscribed rectangles to be generated can be changed for each type of object. For example, four circumscribed rectangles are generated only for a certain object, and one circumscribed rectangle is generated for other objects. The number of circumscribed rectangles can be designated by the user by inputting to the image processing apparatus 10 using the operation unit 5 or may be stored in the storage unit 2 in advance.

  FIG. 6 is a diagram conceptually showing circumscribed rectangle information generated for the document data 11 as shown in FIG. Here, a case is shown in which each circumscribed rectangle is set to be generated for each type of object, and rectangles C1, C2, and C3 indicated by dotted lines in the figure are “text”, “graphics”, It is a circumscribed rectangle of various objects of “image”. The circumscribed rectangle information is generated by the analysis processing unit 12, and details of the method of generating the circumscribed rectangle information will be described later.

(3) Description of Analysis Processing Unit 12 Next, the functional configuration and operation of the analysis processing unit 12 of the present embodiment will be described with reference to the functional block diagram of FIG. The analysis processing unit 12 analyzes the content of the input data using, for example, the technique disclosed in Japanese Patent Laid-Open No. 2002-334341 by the present inventor, and generates image data 13. The arrows shown in FIG. 7 represent the flow of data and various control information generated by the processing of the analysis processing unit 12. The input data analysis unit 120 analyzes the contents of the document data 11 and separates the data into object types such as “text”, “graphics”, and “image”, which are separated into a text processing unit 121 and a graphics processing unit, respectively. 122 and the image processing unit 123. Based on the analysis result by the input data analysis unit 120, tag information is added to the separated data. For example, tag information (that is, “1”) indicating that the data is a “text” object is added to the data supplied to the text processing unit 121.

  The text processing unit 121, the graphics processing unit 122, and the image processing unit 123 perform processing according to the type of each object. For example, the text processing unit 121 sets the binary image data 17 output from the image processing apparatus 10 in the coordinate space based on the supplied data and font outline data stored in the storage unit 2 in advance. Perform processing such as generating contour data for matched characters. In addition, the graphics processing unit 122 performs processing such as converting supplied data into data suitable for the coordinate space of the binary image data 17. In addition, the image processing unit 123 performs processes such as image enlargement / reduction and color space adjustment on the supplied data, and generation of contour data for expressing the contour shape of the image.

  The intermediate code generation unit 124 converts the data processed by the text processing unit 121, the graphics processing unit 122, and the image processing unit 123 into an intermediate code suitable for development in a raster format. The intermediate code generated by the intermediate code generation unit 124 is sequentially stored in the storage unit 2. When the intermediate code for one page is accumulated, the rendering processing unit 126 generates the edge data described above from the intermediate code for each scan line.

  The circumscribed rectangle generating unit 125 generates the circumscribed rectangle information described above while the intermediate code is generated by the intermediate code generating unit 124 or after the intermediate code is generated. For example, when the tag information represents the type of an object such as “text”, “graphics”, and “image”, the circumscribed rectangle generation unit 125 generates circumscribed rectangle information as follows. In the following description, an example in which one piece of circumscribed rectangle information is specified for each object type will be described.

  FIG. 8 is a diagram showing a process of generating circumscribed rectangle information of star-shaped graphics as an example. The circumscribed rectangle generation unit 125 prepares one drawing flag for one circumscribed rectangle in advance. This drawing flag is stored in an appropriate storage area of the storage unit 2 and its initial value is “0 (OFF)”. Therefore, in the initial state of FIG. 8A, the value of the drawing flag is “0 (OFF)”. Then, the circumscribed rectangle generation unit 125 sequentially scans in units of scan lines from the upper end to the lower end of the page. When an object is detected, the value of the drawing flag is changed to “1 (ON)”. . In the initial state shown in FIG. 8A, the circumscribed rectangle generating unit 125 sets a circumscribed rectangle in an appropriate area such as an area other than the printable area of the page.

  Next, as shown in FIG. 8B, when a star-shaped graphics object to be processed is detected for the first time in the page, the circumscribed rectangle generating unit 125 makes a circumscribed rectangle that touches the outer edge of the object. And circumscribing rectangle information including the coordinate values of the two vertices on the diagonal in the circumscribing rectangle is generated. Here, the circumscribed rectangle generating unit 125 generates circumscribed rectangle information including the vertex h1 closest to the origin O and the furthest vertex h2 as the origin O at the upper left corner of the page in FIG. To do. The position and size of the circumscribed rectangle are represented by the coordinate values of the two vertices h1 and h2. Then, the circumscribed rectangle generation unit 125 changes the drawing flag from “0 (OFF)” to “1 (ON)”.

  Assume that the processing further proceeds and a second object is detected in the page as shown in FIG. 8C. At this time, since the drawing flag has already been set to “1 (ON)”, the circumscribed rectangle generation unit 125 performs a process of expanding the circumscribed rectangle that has already been generated, instead of newly generating the circumscribed rectangle. . That is, the circumscribed rectangle generation unit 125 circumscribes the two coordinate values included in the already generated circumscribed rectangle information and the coordinate values of the circumscribed rectangle that touches the outer edge of the newly generated object (circumscribes the black star-shaped graphics). (Rectangular) two vertex coordinates are compared, and a circumscribed rectangle that encloses both of these two objects is formed. Then, the circumscribed rectangle generating unit 125 generates and stores circumscribed rectangle information including the coordinate values of the two vertices (h1, h3) of the circumscribed rectangle obtained in this way. Thereafter, similarly when the third graphics object is detected, the circumscribed rectangle generating unit 125 generates one circumscribed rectangle including all three objects.

  Next, a case where two or more circumscribed rectangle information is generated for each type of object will be described with reference to FIG. Here, as an example, a case where a maximum of four circumscribed rectangle information is generated for one object type will be described. First, the circumscribed rectangle generation unit 125 equally divides the entire area of the page into four partial areas as shown in FIG. Next, the circumscribed rectangle generation unit 125 generates a circumscribed rectangle in each partial region by the same method as described with reference to FIGS. 8 (A), (B), and (C). This is equivalent to performing the circumscribed rectangle generation process described above on each page, with each of the partial areas divided into four equals one page (that is, a total of four pages). In this case, as many drawing flags as the number of partial areas are prepared. By this processing, four circumscribed rectangles as shown in FIG. 9B are generated.

Thereafter, the circumscribed rectangle generation unit 125 generates a circumscribed rectangle that includes circumscribed rectangles that are in contact with each other across adjacent partial areas, among the circumscribed rectangles included in each partial area. This is called “joining of circumscribed rectangles”. In the example of FIG. 9C, the circumscribed rectangle C11 and the circumscribed rectangle C12 are combined to generate the circumscribed rectangle C10, and the circumscribed rectangle C13 and the circumscribed rectangle C14 are combined to generate the circumscribed rectangle C20. In this way, when generating a maximum of N circumscribed rectangles for one object type, the circumscribed rectangle generating unit 125 first divides the entire area for one page into N partial areas, A circumscribed rectangle is generated in the area of, and finally adjacent circumscribed rectangles are joined.
Note that, unlike the state shown in FIG. 9, when the circumscribed rectangles included in the respective partial areas do not contact each other, the above-described combining process is not performed. At this time, if one circumscribed rectangle is generated in each partial area, the number of circumscribed rectangles in the entire page is four.

  The above is an example of a circumscribed rectangle generation process for graphics. The circumscribed rectangle generation unit 125 can generate circumscribed rectangle information for other objects such as images and characters by the same processing as described above. Then, the circumscribed rectangle information generated by the circumscribed rectangle generating unit 125, the raster data generated by the rendering processing unit 126, and the tag information generated by the input data analyzing unit 120 are combined to generate the image data 13. Is done. The image data 13 is supplied from the analysis processing unit 12 to the multi-value image generation unit 14 and the binary image generation unit 15.

(4) Description of Multilevel Image Generation Unit 14 Next, the operation of the multilevel image generation unit 14 of the present embodiment will be described. As described above, the multi-value image generation unit 14 generates multi-value image data 16 including multi-value image information, position information, and size information. First, the multi-value image generation unit 14 acquires tag information and circumscribed rectangle information belonging to an object to be generated of the multi-value image data 16 from the image data 13 when generating the multi-value image data 16 in page units. Here, it is assumed that the objects for which the multi-value image data 16 is generated are an “image” object and a “gradation” object. Using these data, the multi-value image generation unit 14 calculates position information and size information of the multi-value image data 16.

FIG. 10 is a diagram conceptually illustrating a process in which the multi-value image generation unit 14 calculates position information and size information. For example, when tag information indicating an “image” object and a “gradation” object is acquired as the generation target of the multi-value image data 16 from the image data 13, the multi-value image generation unit 14 specifies the circumscribed rectangle of each object. . At this time, as shown in FIG. 10A, if there is a region where a plurality of circumscribed rectangles overlap, the multi-value image generation unit 14 newly creates a circumscribed rectangle that includes these circumscribed rectangles. Define. This process is performed in the same manner as the above-described “joining rectangles”. FIG. 10B shows circumscribed rectangles generated as a result of this processing. In FIG. 10A, there are two circumscribed rectangles (C1, Cg2, Ci1, Ci2) (C1). , C2). Then, the multi-value image generation unit 14 recalculates the position information and the size information for these two circumscribed rectangles C1 and C2, analyzes the edge data contained in these circumscribed rectangles, and uses this to calculate the multi-value image. Convert to raster data, which is information.
As described above, the multi-value image generation unit 14 obtains multi-value image information, position information, and size information from the object for which the multi-value image data 16 is to be generated, and outputs this as multi-value image data 16.

(5) Description of Binary Image Generation Unit 15 Next, the configuration and operation of the binary image generation unit 15 of the present embodiment will be described. As shown in FIG. 2 described above, the binary image generation unit 15 generates one binary image data 17 for each color. The binary image data 17 includes 1-bit binary image information and color information, position information, and size information of the binary image information. That is, as shown in FIG. 3, when “black” and “red” “text” objects and “graphics” objects (here, “solid regions”) exist, the binary image generation unit 15 at least Two or more binary image data 17 are generated.

FIG. 11 is a functional block diagram showing a functional configuration of the binary image generation unit 15. Further, FIG. 12 shows image data 13 in which binary image data 17 is generated by the binary image generation unit 15 (only the object that becomes the binary image data 17 is displayed, and the object that becomes the multi-value image data 16 is displayed. (Not shown) conceptually. In the following, a process in which the binary image generation unit 15 generates the binary image data 17 from the image data 13 shown in FIG. 12 under the configuration shown in FIG. 11 will be described. As shown in FIG. 12, hereinafter, the image data 13 will be described as 10 × 7 pixel data.
The image data 13 in FIG. 12 includes a “black” text object Tx1, a “blue” text object Tx2, a “red” graphics object Gr1, a “black” graphics object Gr2, and a “black” graphics object. Gr3. All areas other than these objects are background objects. The color information of the background object is “white”.

Here, FIG. 13 is a diagram for explaining a process in which the binary image generation unit 15 generates the binary image data 17 from the image data 13 shown in FIG. 14 shows the format of the edge structure in FIG. 13. The edge structure in FIG. 13 shows color information CL, tag information TG, start point coordinates SX, and end point coordinates EX (from the left). Means.
When generating the binary image data 17 in page units, the edge storage unit 150 of the binary image generation unit 15 first acquires edge data from the image data 13 and stores it in, for example, a RAM. When the image data 13 shown in FIG. 12 is input, the edge data is stored as shown in FIG. For example, the edge data of the scan line with y = 0 is composed of an edge structure which is a background object (B) with SX = 0 and EX = 9. The edge data of the scan line with y = 1 includes an edge structure which is a background object (B) with SX = 0 and EX = 0 and an edge with a graphics object (G) with SX = 1 and EX = 3. An edge structure that is a background object (B) of SX = 4, EX = 6, an edge structure that is a text object (T) of SX = 7, EX = 7, and SX = 8, EX = 9 background object (B).
Subsequently, the edge storage unit 150 identifies an edge structure that belongs to the object that is the generation target of the binary image data 17 and deletes the edge structure that is not the generation target of the binary image data 17. For example, if this processing is performed on the image data 13 shown in FIG. 12, the edge structure having tag information indicating the background object is deleted. This is shown in FIG. The processing unit in which the edge storage unit 150 performs these processes may be, for example, a page unit, or may be for each predetermined one or a plurality of scan lines.

  Subsequently, the edge sort unit 151 performs sort (rearrangement) processing on the edge data shown in FIG. Here, the sort processing is performed using the color information CL of each edge structure as a sort key. Although the sort order of the color information CL is arbitrary, for example, it is preferable to sort from a color with a small RGB value of the color information CL to a large color. For edge structures having the same color information, the edge sorting unit 151 refers to the start point coordinates SX and arranges them in order from, for example, the edge structures having the smallest start point coordinates SX. This sort processing will be specifically described as follows. That is, when the color information CL of each edge structure is expressed by N-byte data and the start point coordinate is expressed by M-byte data, the numerical values of (N + M) bytes obtained by connecting these are compared. Sort. FIG. 13C shows an example of the processing result. Referring to the figure, it can be understood that edge structures of the same color are grouped and arranged adjacent to each other. In the figure, the data structures are sorted in ascending order of numerical values of (N + M) bytes, and in each scan line, they are sorted in the order of “black”, “blue”, and “red”.

  Subsequently, the data output unit 152 outputs the edge structure shown in FIG. 13C for each color. Specifically, the data output unit 152 scans the edge structures sequentially from y = 0, and sequentially specifies edge structures having the same color information CL as the edge structure detected first. In FIG. 13C, since the edge structure detected first is a graphics object with y = 1, SX = 1, and EX = 3, and its color information CL is “black”, the data output unit 152 Sequentially detects and specifies edge structures having color information CL of “black”. That is, the data output unit 152 displays a graphics object (G) with y = 1, SX = 1, EX = 3, a text object (T) with y = 1, SX = 7, EX = 7, y = 2, SX = 3, EX = 3 graphics object (G), y = 2, SX = 7, EX = 7 text object (T), y = 4, SX = 3, EX = 6 graphics object (G), y = Five edge structures are specified in the order of graphics objects (G) of 5, SX = 3 and EX = 6, and these are output together. The image information (binary image information) of these edge structures, the color information, the position information, and the size information constitute binary image data to be drawn in “black”. At this time, the data output unit 152 Will output binary image data to be drawn in this "black". The edge structures are rearranged by the edge sort unit 151, but by referring to the position information included in the binary image data, the data output unit 152 restores each binary image information to an appropriate position. It is possible to arrange.

Here, “rearrangement” refers to processing in which the data output unit 152 determines the position and size of a binary image from a plurality of detected edge structures (hereinafter referred to as “edge structure group”). That is. Various methods are conceivable for this processing. For example, there is a method of determining a binary image rectangle (hereinafter referred to as “image rectangle”) for each edge while temporarily copying the detected edge structure to another storage area such as a memory. . 13C will be described as an example. In this case, the image rectangle when the first edge structure, that is, the graphics object (G) with y = 1, SX = 1, and EX = 3 is copied is the upper left corner. The coordinates of (x, y) = (1, 1) and the coordinates of the lower right corner are (x, y) = (3, 1). Next, when the edge structure of the text object (T) with y = 1, SX = 7, and EX = 7 is copied, the coordinates of the upper left corner of the image rectangle are (x, y) = (1, 1), The coordinates of the lower right corner are (x, y) = (7, 1). Hereinafter, when all the six edge structures are calculated in the same manner, the coordinates of the upper left corner are (x, y) = (1, 1), and the coordinates of the lower right corner are (x, y) = (7, 5 ) Therefore, the generated binary image has a width of 6 pixels and a height of 5 pixels from the position of the upper left (x, y) = (1, 1). In this method, after the image rectangle is determined, the copied edge structure group is converted into an actual binary image.
As another example, for example, in the edge data of FIG. 13C, there is a method in which the data output unit 152 detects the edge structure having the “black” color information CL twice. In this case, the image rectangle is determined in the first detection, and the data is actually output in the second detection. In this method, in order to efficiently perform the second process, the edge reading position can be stored in another storage area such as a memory. That is, in FIG. 13C, as the position where the edge structure having the color information CL of “black” starts, y = 1 is the first edge, y = 2 is the first edge, and y = 3 is no edge. , Y = 4 is the first edge, and y = 5 is the first edge. A table describing the information is created, and in the second reading, this table is referred to and the position indicated in the table is created. The data is acquired by reading from.
There are various other methods for determining the position and size of a binary image when generating a binary image from edge data. That is, in the present invention, the method for determining the position and size of the binary image is not limited. In the present invention, the binary image region is not limited to a rectangle with the minimum area including the target edge structure group. For example, a certain amount of surroundings is used for various image processing in the subsequent stage. It may be generated as an image with a margin.

  As described above, if the data output unit 152 performs sorting in order from the edge structure with the smallest color information CL, the data output unit 152 has a different color from the specific target when scanning each scan line. When the edge structure of the information CL is scanned, the scanning of the scanning line may be terminated and the scanning of the next scanning line may be started. As a result, it is possible to skip edge structures that do not need to be read, and the output time of the binary image data 17 can be shortened and the processing can be made more efficient.

  As described above, when the data output unit 152 outputs binary image data to be rendered in “black”, the data output unit 152 subsequently deletes the edge structure having the “black” color information CL from the RAM. This is shown in FIG. 13 (D). Then, the data output unit 152 scans the edge structure in order from y = 0 in the same manner as when the edge structure having the “black” color information CL is specified, and detects the edge structure detected first. The edge structures having the same color information CL are sequentially identified. In FIG. 13D, the edge structure detected first is a graphics object (G) with y = 2, SX = 1, and EX = 2, and its color information CL is “red”. The output unit 152 outputs graphics objects (G) with y = 2, SX = 1, and EX = 2, graphics objects (G) with y = 3, SX = 1, and EX = 2, y = 4, SX = 1, and EX = 3 graphics objects (G) in the order of 2 graphics objects (G) are identified and output together. That is, at this time, the data output unit 152 outputs the binary image data 17 to be drawn in “red”.

  Thereafter, the data output unit 152 deletes the edge structure having the color information CL of “red” from the RAM, as in the case where the binary image data to be drawn with “black” is output. And the process which specifies and outputs an edge structure is similarly performed with respect to the edge data of the state shown by FIG.13 (E). Here, since the specified edge structure is only a text object with y = 4, SX = 7, and EX = 7, the data output unit 152 outputs the binary image data 17 to be drawn in “blue”.

  As described above, the image processing apparatus 10 according to the present embodiment can generate the binary image data 17 from the document data 11. Since the binary image generation unit 15 of the image processing apparatus 10 outputs binary image data using edge data described in the edge array format, each pixel is referred to as in the case of referring to bitmap format image data. There is no need to read the color information of each. Therefore, binary image data can be generated at a higher speed and with a smaller memory capacity than in the past.

  In the above-described embodiment, the binary image generation unit 15 generates the binary image data 17 for each color, and the binary image data 17 of a certain color can include both graphics objects and text objects. It was an aspect. However, the binary image generation unit 15 of the present embodiment is not limited to such a mode, and it is of course possible to generate the binary image data 17 for each object. In order to generate the binary image data 17 for each object, the sort processing may be performed using the tag information TG as the sort key before the binary image generation unit 15 performs the sort processing using the color information CL as the sort key.

FIG. 13F shows a case where the edge sort unit 151 of the binary image generation unit 15 performs such sort processing on the image data 13 shown in FIG. When this figure is compared with FIG. 13B, it can be seen that the arrangement of the edge structures is different. In FIG. 13F, for example, as indicated by the scan line of y = 2, the edge structure that is the text object (T) is output before the edge structure that is the graphics object (G). It is sorted. For such edge data, the data output unit 152 outputs an edge structure in which the color information CL and the tag information TG match as one binary image data 17. Thereby, binary image data for each object can be obtained.
Also, when performing the sorting process using the tag information TG as a sort key, the tag information TG is appropriately set in advance, so that, for example, the binary image data of the text object is always preceded by the binary image data of the graphics object. An operation such as generation can also be performed. This is realized by setting the tag information TG corresponding to the text object to a smaller value than the tag information TG corresponding to the graphics object when the sorting process is performed in ascending order with respect to the tag information TG.

  By doing in this way, the image processing apparatus 10 of this embodiment can also generate binary image data 17 for each object. Therefore, if the objects are different even if they are the same color, each binary image data 17 is different from each other. Data 17 can be obtained. Thereby, the binary image data desired by the user can be obtained more reliably, and the binary image data reflecting the user's intention faithfully can be obtained.

(6) Modifications The present invention is not limited to the above-described embodiments, and various modifications can be made. Some examples are shown below.
In the above-described embodiment, the case where the image processing apparatus 10 is realized by a personal computer has been described. However, the present invention is not limited to this. For example, an image output apparatus such as a printer or an image forming apparatus such as a so-called multifunction machine 10 may be provided. Further, a function equivalent to that of the image processing apparatus 10 may be realized by cooperation between the computer and the image forming apparatus. The control program PRG1 for operating the image processing apparatus 10 can be provided by being recorded on a recording medium such as a magnetic recording medium, an optical recording medium, or a ROM that can be read by an arithmetic device such as a CPU. Of course, downloading via a network such as the Internet is also possible.

  Various modifications can be made to the various data described in the above embodiment. For example, the types of objects are not limited to the above-mentioned “text”, “graphics”, “image”, and “background”, and other objects can be added. Further, the attribute of “background” is not assigned as an object, but may be expressed as a flag indicating whether or not the background area is inside the edge structure. In addition, the edge structure may include magnification information corresponding to the enlargement / reduction of the image object.

  In the binary image generation unit 15, the edge structure from which the data output unit 152 has output binary image data has been described as being deleted from the memory, but has already been output as binary image data inside the edge structure. If an edge structure having this flag set to “ON” is not output again, it is not necessary to delete the edge structure from the memory.

  In addition, it is possible to arbitrarily set whether to assign each object to multi-value image data or binary image data. This may be set by the user via the operation unit 5, or a definition file describing this assignment may be stored in advance in the storage unit 2 or the like.

It is a block diagram showing the hardware constitutions of the image processing apparatus which concerns on one Embodiment of this invention. FIG. 2 is a functional block diagram illustrating a functional configuration of the image processing apparatus according to the embodiment. FIG. 3 is a diagram conceptually showing document data, multi-value image data and binary image data generated from the document data in the embodiment. It is the figure which showed the format of the edge structure in the same embodiment. It is the figure which showed notionally the data structure of the image data in the embodiment. FIG. 4 is a diagram conceptually showing circumscribed rectangle information generated for the document data shown in FIG. 3. It is a functional block diagram showing the functional composition of the analysis processing part of the embodiment. It is the figure which illustrated the process which produces | generates the circumscribed rectangle information of star-shaped graphics. It is the figure which illustrated the process which produces | generates the circumscribed rectangle information of star-shaped graphics. In the embodiment, it is the figure which showed notionally the process in which a multi-value image generation part calculates a positional information and size information. It is the functional block diagram which showed the function structure of the binary image generation part of the embodiment. In the embodiment, it is the figure which showed notionally the image data by which binary image data is produced | generated by the binary image production | generation part. FIG. 6 is a diagram for describing a process in which a binary image generation unit generates binary image data from image data in the embodiment. It is the figure which showed the format of the edge structure of FIG.

Explanation of symbols

DESCRIPTION OF SYMBOLS 10 ... Image processing apparatus, 1 ... Control part, 2 ... Memory | storage part, 3 ... Input-output I / F, 4 ... Display part, 5 ... Operation part, 11 ... Document data, 12 ... Analysis processing part, 13 ... Image data, DESCRIPTION OF SYMBOLS 14 ... Multi-value image generation part, 15 ... Binary image generation part, 16 ... Multi-value image data, 17 ... Binary image data, 120 ... Input data analysis part, 121 ... Text processing part, 122 ... Graphics processing part, 123 DESCRIPTION OF SYMBOLS Image processing part 124 ... Intermediate code generation part 125 ... circumscribed rectangle generation part 126 ... rendering processing part 150 ... edge preservation part 151 ... edge sort part 152 ... data output part

Claims (2)

A storage unit for storing document data composed of a plurality of types of objects;
An analysis processing unit that converts the stored document data into image data configured by an edge structure representing a color and a position of an object existing on each scan line of the document data;
An edge storage unit for storing image data obtained by the analysis processing unit;
Among the edge structures constituting the image data stored in the edge storage unit, an edge sort unit that rearranges the edge structures representing objects of the same color so as to be adjacent to each other;
An image processing apparatus comprising: a data output unit configured to generate and output binary image data representing a binary image of an image represented by an edge structure representing an object of the same color among the sorted edge structures. The edge structure represents the type of the object in addition to the color and position of the object,
The edge sorting unit is arranged so that edge structures representing objects of the same type and the same color are adjacent to each other among the edge structures constituting the image data stored in the edge storage unit,
The data output unit is a binary image data representing a binary image of an image represented by an edge structure representing an object of the same type and the same color among the edge structures rearranged by the edge sort unit. The image processing apparatus according to claim 1, wherein the image processing apparatus generates and outputs.

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