JP2007335982A - Image processing apparatus and method, and program and storage medium - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an image processing apparatus and method, and a program and a storage medium capable of preventing deterioration in the image quality and decreasing a vector code amount in the case of applying vector processing to a scan image. <P>SOLUTION: A clip art conversion section 203 includes: an input section 301; a noise elimination section 302; a region division section 303 for defining a region to a received image on the basis of pixel values to divide the image into a plurality of regions; a region integration section 304 for integrating the same regions among the division regions; a noise region integration section 305 applying discrimination and elimination of noise regions to data as a result of the region integration; a vector processing section 306 for applying vector processing to data after elimination of the noise regions; a halftone region elimination section 307 for eliminating halftone regions from the data after the vector processing; and an output section 308. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to an image processing apparatus and method, a program, and a storage medium, and more particularly, to an image processing apparatus and method, a program, and a storage medium that are characteristic of image vectorization technology.

In copiers and multi-function peripherals (equipment that has copier function, printer function, facsimile function, etc.), there is a vectorization technique as a technique for realizing the reuse of scanned images. is there. As an image to be subjected to this vectorization processing, there is an image clip art created with a computer and having a relatively small number of colors as shown in FIG.
Japanese Patent Laid-Open No. 2004-246577 JP 2004-265384 A

  Since scan noise generated during scanning occurs in the scan image, a smoothing filter is applied to remove the scan noise. However, even if a smoothing filter is used, as shown in FIG. 15, noise spreads and a region (halftone region) A that appears as if it exudes from the edge may appear. This halftone area A is a problem in image quality, and the presence of this halftone area A also causes an extra vector code amount.

  An object of the present invention is to provide an image processing apparatus and method, a program, and a storage medium that can prevent deterioration in image quality and reduce the vector code amount when vectorizing a scan image.

  In order to achieve the above object, an image processing apparatus according to claim 1, an area dividing unit that divides an input image into a plurality of areas by defining an area based on a pixel value; Among them, a region integration unit that integrates the same region, a noise region determination / removal unit that determines and removes a noise region from the region integration result data, and vectorization of the data after the removal of the noise region It is characterized by comprising vectorization means and halftone area removal means for removing halftone areas from the vectorized data.

  The image processing method according to claim 6, wherein an input image is defined based on pixel values and divided into a plurality of areas, and the same area among the divided areas is integrated. A region integration step, a noise region determination / removal step for determining and removing a noise region from the region integration result data, a vectorization step for vectorizing the data after the noise region removal, and a post-vectorization A halftone area removing step for removing the halftone area from the data.

  11. An image processing program comprising: an area dividing module for defining an area based on pixel values and dividing the input image into a plurality of areas; and area integration for integrating the same area among the divided areas. A module, a noise area determination / removal module for determining and removing a noise area from the data of the area integration result, a vectorization module for vectorizing the data after the noise area removal, and data after vectorization On the other hand, the computer is caused to execute a halftone area removing module for removing a halftone area.

  A computer-readable storage medium according to a twelfth aspect stores the image processing program according to the eleventh aspect.

  According to the present invention, an input image is divided into a plurality of regions by defining regions based on pixel values, and the same region is integrated among the divided regions. Then, the determination and removal of the noise region is performed on the data of the region integration result, and vectorization is performed on the data after the noise region removal, and the halftone region is removed from the data after the vectorization. Therefore, there is no halftone region that causes a problem in image quality, and when the scan image is vectorized, image quality deterioration can be prevented and the vector code amount can be reduced.

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

  FIG. 1 is a diagram schematically showing a configuration of a multifunction peripheral as an image processing apparatus according to an embodiment of the present invention.

  In FIG. 1, the multifunction peripheral 100 includes a scan unit 101, an area determination unit 102, an image conversion unit 103, an HDD 104, and a print unit 105.

  Next, the operation of the multifunction device 100 will be described. The scan unit 101 scans an image designated by the user and outputs scan data to the region determination unit 102. The area determination unit 102 determines an attribute for each area in the image, and then outputs a determination result and image data to the image conversion unit 103.

  Here, it is assumed that the attribute of the region is determined by a clip art made of CG or the like as shown in FIG. 14 and other two types. In the present embodiment, an image (illustration image or the like) having fewer colors than a predetermined threshold (for example, 32 colors) is determined as a clip art. The image conversion unit 103 performs processing such as image compression and vectorization according to the characteristics of the region in the image, and outputs the processing result to the HDD 104. When there is a print instruction from the user, the print unit 105 reads the image data converted from the HDD 105 and prints it.

  FIG. 2 is a diagram schematically showing the configuration of the image conversion unit in FIG.

  In FIG. 2, the image conversion unit 103 includes an input unit 201, a JPEG compression unit 202, a clip art conversion unit 203, an integration unit 204, and an output unit 205.

  Next, the operation of the image conversion unit 103 will be described. The image data determined by the region determination unit 102 is input to the input unit 201, and the region determined to be clip art is output to the clip art conversion unit 203. In addition, an area determined to be other than clip art is output to the JPEG compression unit 202.

  The JPEG compression unit 202 compresses the area by the international standard JPEG compression method and outputs the compressed region to the integration unit 204. The processing in the clip art conversion unit 203 will be described in detail later. The integration unit 204 integrates the data of each region and adds a header or the like so that the data processed for each region can be handled as one piece of image data. The output unit 205 outputs the integrated and generated image data to the HDD 104.

  FIG. 3 is a diagram schematically showing the configuration of the clipart conversion unit in FIG.

  In FIG. 3, the clip art conversion unit 203 includes an input unit 301, a noise removal unit 302, a region division unit 303, a region integration unit 304, a noise region integration unit 305, a vectorization unit 306, a halftone region removal unit 307, and an output. Part 308.

  Next, the operation of the clip art conversion unit 203 will be described. The input unit 301 inputs data of an area determined to be clip art (hereinafter simply referred to as “clip art image”) and outputs the data to the noise removal unit 302.

  The noise removing unit 302 performs filtering on a clipart image by using a smoothing filter such as a Gaussian filter in order to suppress scan noise generated mainly during scanning, and outputs the result to the region dividing unit 303. The area dividing unit 303 divides the clipart image according to the flowchart of FIG.

  FIG. 4 is a flowchart showing the procedure of the area dividing process executed by the area dividing unit in FIG.

  In FIG. 4, first, a first cluster is generated from the start pixel subjected to raster scanning (step S401). Then, the similarity between all the clusters is obtained for the next pixel (step S402). The higher the similarity, the closer the characteristics of the pixel and cluster. Here, RGB values are used for calculating the similarity, but information on other color spaces or information other than color can also be used as the feature amount.

  Next, the highest similarity and the cluster number corresponding to this similarity are recorded, and this similarity is compared with a preset threshold value (step S403). If the similarity is higher than the threshold, the target pixel belongs to the recorded cluster (step S404). If the similarity is lower than the threshold, a new cluster is generated by the target pixel (step S405).

  Then, it is determined whether or not the processing for all the pixels is finished (step S406). If there is an unprocessed pixel, the process returns to step S402 and the above process is repeated. If there is no unprocessed pixel, the area division process is terminated.

  With respect to the data divided in this way, the region integration unit 304 integrates regions that are considered to be the same according to the flowchart of FIG.

  FIG. 5 is a flowchart showing a procedure of region integration processing executed by the region integration unit in FIG.

  In FIG. 5, first, a target value for the number of regions to be separated is input (step S501). In the present embodiment, this is a guide for how many colors are separated. Then, the number of current clusters is counted (step S502), and the number of current clusters is compared with a target value (step S503).

  If the current number of clusters is greater than the target value, cluster integration is performed (steps S504 and S505). In step S504, the degree of similarity between the clusters is calculated, and two clusters having the highest degree of similarity are selected as the targets of the integration process. In step S505, the two clusters to be integrated are integrated into one.

  After the first region integration is completed, the process returns to step S502 again, and the number of clusters is counted. If the number of clusters is higher than the target value, the cluster integration process is repeatedly executed. If not, the area integration process is terminated.

  When the region integration processing is finished, the region integration unit 304 outputs data to the noise region integration unit 305, and the noise region integration unit 305 determines whether noise exists in the vicinity of the edge.

  This noise is mosquito noise or the like due to JPEG compression, and is not generated by compression in the JPEG compression unit 202. Since the image to be scanned is a printout of JPEG-compressed data, it exists on the image to be scanned (that is, on the hard copy).

  FIG. 6 is a diagram illustrating noise determined by the noise region integration unit in FIG. 3.

  In FIG. 6, cluster (1) 61 and cluster (2) 62 are two examples of clusters selected as representatives from the cluster after the area dividing process and the area integrating process. There are many small areas (noise areas) in these clusters, and if the cluster outlines and internal color information are converted into vector data as they are, the amount of data becomes enormous and becomes a problem.

  In order to solve this problem, noise area integration (determination and removal of noise areas) is performed. This process will be described with reference to the flowchart of FIG.

  FIG. 7 is a diagram illustrating a procedure of noise region integration processing executed by the noise region integration unit in FIG.

  In FIG. 7, first, a noise region is determined (step S701). In the present embodiment, when the area (number of pixels) of the region is smaller than a predetermined value, it is determined as a noise region. Then, the similarity between the noise pixel and each adjacent cluster is calculated, and the noise pixel is assigned to the cluster having the highest similarity from the calculated similarity (step S702).

  Finally, it is determined whether or not the removal process for all the noise pixels in the noise region is finished (step S703). If there is an unprocessed pixel, the process returns to step S701, and the above process is repeated. If there is no unprocessed pixel, the noise removal processing in this noise region is terminated.

  Vectorization section 306 performs vectorization on an area other than the background area, and outputs the result to output section 308. As a vectorization method, Bezier curve approximation or the like can be considered.

  The halftone area removing unit 307 examines the ratio of the vector code amount to the area of each area (vector code amount ratio) for each area, and considers an area where the vector code quantity ratio is equal to or greater than a predetermined value as a halftone area. Of the areas to be merged into areas with similar colors. For example, in FIG. 11, if region X is a halftone region, A is the closest to the color in adjacent regions A, B, and C, so X is integrated into A (X is colored the same color as A). To do).

  FIG. 8 is a diagram schematically showing the configuration of the halftone area removing unit in FIG.

  In FIG. 8, the halftone area removal unit 307 includes a determination unit 801 and an integration unit 802.

  FIG. 9 is a flowchart showing the procedure of the halftone area determination process according to the first embodiment, which is executed by the determination unit in FIG.

  In FIG. 9, first, the whole area is searched and the number of areas is counted (step S901). In this embodiment, the number of regions is N. A counter that counts the number of processed areas is set to “0” (step S902), and it is determined whether the counter value is equal to N (step S903).

  If the counter value is not equal to N (NO in step S903), the vector amount (Amount) is measured for the region corresponding to the counter value (step S904), and the number of pixels (num) is counted (step S905).

  Subsequently, the ratio (Amount / num) of the vector amount and the number of pixels is obtained and compared with a predetermined threshold (step S906). If the ratio is equal to or greater than the threshold (YES in step S906), an integration process is performed (step S907). ), The process proceeds to step S908.

  On the other hand, if the ratio is less than the threshold value (NO in step S906), the process proceeds to step S908. In step S908, the counter is incremented, and the process returns to step S903. If the value of the counter is equal to N in step S903 (YES in step S903), the process ends.

  FIG. 10 is a flowchart showing the procedure of the halftone area integration processing according to the first embodiment, which is executed by the integration unit in FIG. 8, and is a flowchart of the integration processing executed in step S907 in FIG. .

  In FIG. 10, first, the adjacent region of the region of interest is searched and counted (step S1001). In the present embodiment, the number of adjacent regions is M. Next, a counter that counts adjacent regions that have been processed to verify whether they are valid integration destinations is set to “0”, and the variable that stores the minimum value of edge strength (Kmin) has a size that does not appear as edge strength. A number is set (step S1002).

  For example, if the bit precision of the image is 8 bits, it is sufficient to give 2 to the 32nd power as the value set for Kmin. Subsequently, it is determined whether the counter is equal to M (step S1003). If not equal (NO in step S1003), the average edge intensity (Ki) between the area corresponding to the counter value and the halftone area is measured (step S1004).

  Here, the average edge strength is obtained by performing the filtering such as the Sobel filter on each pixel constituting the boundary between the two regions, obtaining the sum of the edge strengths of the respective pixels, Is obtained by dividing by the number of pixels constituting.

  Next, the magnitudes of Kmin and Ki are compared (step S1005). If Ki is smaller than Kmin (YES in step S1005), Ki is substituted for Kmin (step S1006), and the process proceeds to step S1007. On the other hand, if Ki is larger than Kmin, the process proceeds to step S1007. In step S1007, the counter is incremented, and the process returns to S1003.

  If i = M in step S1003, the halftone area is colored with the same color as the area having the edge strength Kmin (step S1008), and the process ends.

  The output unit 308 outputs the input data to the integration unit 204.

  As described above, the image processing apparatus according to the present embodiment can realize a reduction in data amount by integrating halftone areas into adjacent areas.

  In the first embodiment, the halftone area is specified based on the ratio of the vector amount to the number of pixels (Amount / num), and the adjacent area to be integrated is determined based on the average value of the edge intensity. Regarding the specification of the halftone area, the area existing in the original image may be removed only by the ratio of the vector amount to the number of pixels (Amount / num).

  In the second embodiment described below, an area picked up based on the Amount / num standard is used as a halftone area candidate. If a high-frequency component that seems to be noise exists in the area on the original image corresponding to the candidate area, the candidate halftone area is considered to be generated by smoothing the noise, and is adjacent to the candidate area. Integrate into the area. Further, in the present embodiment, the integration destination selection method is based on the color difference between the representative colors of the regions.

  FIG. 12 is a flowchart illustrating a halftone region determination process according to the second embodiment, which is executed by the determination unit in FIG. 8.

  The flowchart in FIG. 12 is obtained by adding step S1201 to the flowchart (FIG. 9) in the first embodiment and replacing the integration process in step S907 with step S1202, and only these steps will be described below.

  If the ratio of the vector amount to the number of pixels (Amount / num) is larger than the threshold value in step S906, the high-frequency component in the region on the original image corresponding to the region of interest is measured to determine whether noise exists (step S906). S1201). As a determination method, a method of performing edge extraction using a Sobel filter and determining based on the number of edges having an intensity greater than or equal to a predetermined value can be considered.

  If it is determined that the high frequency component is equal to or less than the predetermined value (NO in step S1201), the region of interest is considered not to be a halftone region, and the process proceeds to S908. If it is determined that the high frequency component is greater than or equal to the predetermined value (YES in step S1501), the region of interest is considered to be a halftone region, and the process proceeds to step S1202.

  FIG. 13 is a flowchart showing the procedure of the halftone area integration processing according to the second embodiment, which is executed by the integration unit in FIG. 8, and is a flowchart of the integration processing executed in step S1202 of FIG. .

  In FIG. 13, first, the adjacent region of the region of interest is searched and counted (step S1301). In the present embodiment, the number of adjacent regions is M. Next, a counter that counts adjacent areas that have been processed to verify whether they are valid integration destinations is set to “0”, and the number of sizes that do not appear as color differences in the variable (Smin) that stores the minimum value of color differences Is set (step S1302).

  For example, as the value to be set for Smin, it is sufficient to give 2 to the 10th power if the bit accuracy of the image is 8 bits. Subsequently, it is determined whether the counter is equal to M (step S1303). If the counter is not equal to M (NO in step S1303), the color difference (Si) between the area corresponding to the counter value and the halftone area is measured (step S1304). Here, the color difference is the color difference between the representative colors of the two regions.

  Next, the sizes of Smin and Si are compared (step S1305). If Si is smaller than Smin (YES in step S1305), Si is substituted for Smin (step S1306), and the process proceeds to step S1307. On the other hand, if Si is larger than Smin, the process proceeds to step S1307.

  In step S1307, the counter is incremented, and the process returns to step S1303. If i = M in S1303, the halftone area is colored with the same color as the area having the color difference Smin (step S1308), and the process ends.

  As described above, the image processing apparatus according to the present embodiment can realize the suppression of the data amount by determining and integrating the halftone areas with higher accuracy.

  The vectorization method is not limited to Bezier, and may be spline approximation or the like.

  In addition, an object of the present invention is to supply a storage medium in which a program code of software for realizing the functions of the embodiment is recorded to a system or apparatus, and a computer (or CPU, MPU, etc.) of the system or apparatus stores the storage medium It is also achieved by reading out and executing the program code stored in.

  In this case, the program code itself read from the storage medium realizes the functions of the above-described embodiments, and the program code and the storage medium storing the program code constitute the present invention.

  Examples of the storage medium for supplying the program code include a floppy (registered trademark) disk, a hard disk, a magneto-optical disk, a CD-ROM, a CD-R, a CD-RW, a DVD-ROM, a DVD-RAM, and a DVD. -RW, DVD + RW, magnetic tape, nonvolatile memory card, ROM, etc. can be used. Alternatively, the program code may be downloaded via a network.

  Further, by executing the program code read by the computer, not only the functions of the above-described embodiments are realized, but also an OS (operating system) running on the computer based on the instruction of the program code. A case where part or all of the actual processing is performed and the functions of the above-described embodiments are realized by the processing is also included.

  Further, after the program code read from the storage medium is written in a memory provided in a function expansion board inserted into the computer or a function expansion unit connected to the computer, the function expansion is performed based on the instruction of the program code. This includes the case where the CPU or the like provided in the board or the function expansion unit performs part or all of the actual processing and the functions of the above-described embodiments are realized by the processing.

1 is a diagram schematically showing a configuration of a multifunction peripheral as an image processing apparatus according to an embodiment of the present invention. It is a figure which shows schematically the structure of the image conversion part in FIG. It is a figure which shows schematically the structure of the clip art conversion part in FIG. It is a flowchart which shows the procedure of the area division process performed by the area division part in FIG. It is a flowchart which shows the procedure of the area | region integration process performed by the area | region integration part in FIG. It is a figure which shows the noise determined by the noise area | region integration part in FIG. It is a figure which shows the procedure of the noise area integration process performed by the noise area integration part in FIG. FIG. 4 is a diagram schematically showing a configuration of a halftone area removing unit in FIG. 3. It is a flowchart which shows the procedure of the halftone area determination process which concerns on 1st Embodiment performed by the determination part in FIG. It is a flowchart which shows the procedure of the halftone area | region integration process which concerns on 1st Embodiment performed by the integration part in FIG. It is a figure explaining the halftone area removal process of the halftone area removal part in FIG. It is a flowchart which shows the procedure of the halftone area determination process based on 2nd Embodiment performed by the determination part in FIG. It is a flowchart which shows the procedure of the halftone area | region integration process which concerns on 2nd Embodiment performed by the integration part in FIG. It is a figure which shows the example of a clip art. It is a figure which shows a halftone area | region.

Explanation of symbols

101 Scan unit 102 Area determination unit 103 Image conversion unit 104 HDD
105 Print unit 203 Creep art conversion unit 303 Area division unit (area division unit)
304 Area integration unit (area integration means)
305 Noise area integration unit (noise area determination / removal means)
306 Vectorization unit (vectorization means)
307 Halftone area removing unit (halftone area removing means)
801 determination unit 802 integration unit

Claims (12)

Area dividing means for defining an area based on pixel values and dividing the input image into a plurality of areas;
Of the divided areas, area integration means for integrating the same area;
Noise region determination / removal means for determining and removing a noise region from the region integration result data;
Vectorization means for vectorizing the data after removing the noise region;
Halftone area removing means for removing the halftone area from the vectorized data;
An image processing apparatus comprising: The halftone area removing means includes
Determination means for determining a removal target region based on vector information;
An integration unit that determines an integration destination region to integrate the removal target region from adjacent regions, and colors the removal target region with the same color as the integration destination region,
The image processing apparatus according to claim 1, further comprising:   The image processing apparatus according to claim 2, wherein the determination unit determines a removal target region based on a vector code amount with respect to the area of the region.   The image processing apparatus according to claim 2, wherein the integration unit determines an integration destination region based on edge strength between regions.   The image processing apparatus according to claim 2, wherein the integration unit determines an integration destination region based on a color difference related to a representative color between the regions. An area dividing step for dividing an input image into a plurality of areas by defining areas based on pixel values;
An area integration step for integrating the same area among the divided areas;
A noise region determination / removal step for determining and removing a noise region from the region integration result data;
A vectorization step for vectorizing the data after removing the noise region;
A halftone region removal step for removing a halftone region from the vectorized data;
An image processing method comprising: The halftone area removing step includes:
A determination step of determining a removal target region based on vector information;
A step of determining an integration destination area for integrating the removal target areas from adjacent areas, and coloring the removal target area with the same color as the integration destination area;
The image processing method according to claim 6, further comprising:   8. The image processing method according to claim 7, wherein the determination step determines a removal target region based on a vector code amount with respect to the area of the region.   The image processing method according to claim 7, wherein the integration step determines an integration destination region based on an edge strength between the regions.   The image processing method according to claim 7, wherein the integration step determines an integration destination region based on a color difference related to a representative color between the regions. An area division module that divides an input image into a plurality of areas by defining areas based on pixel values;
Of the divided areas, an area integration module that integrates the same area;
A noise region determination / removal module that performs noise region determination and removal on the region integration result data;
A vectorization module that vectorizes the data after removing the noise region;
A halftone region removal module for removing halftone regions from the vectorized data;
An image processing program for causing a computer to execute.   A computer-readable storage medium storing the image processing program according to claim 11.