JP2005235238A - Method and apparatus of image processing and storage medium - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a method and apparatus of image processing to realize high level color noise reduction without deteriorating apparent color resolution, and a storage medium therefore. <P>SOLUTION: The method of image processing comprises an input stage for entering a draw command to show an output color image, a detecting stage for detecting the image data based on the draw command, and a color noise reduction stage for carrying out color noise reduction of the image data. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

The present invention relates to an apparatus for correcting an image signal, and more particularly to an image processing method, an apparatus thereof, and a recording medium for reducing color noise.

  In recent years, along with the development of hard copy technology, particularly full-color hard copy technology, high-fidelity images can be reproduced using printing technology such as an ink jet recording method and a high-pixel digital camera. In color reproduction, it has evolved to the extent that it is said to have the same reproducibility as silver halide photography by recording materials and image processing.

  However, there is a drawback in that the image itself to be printed may contain color noise due to the characteristics of the imaging device or the like in the input device. For example, noise due to dark current of the CCD element, fixed pattern noise, noise generated in a signal line for color signal transmission, and the like may be mixed in the input image.

  In order to overcome this drawback, the smoothing filter in the vertical direction is applied to the dye data without degrading the brightness of the image by taking the arithmetic average of the upper and lower dye data adjacent to the target pixel in the vertical direction. Is known (see, for example, Patent Document 1).

Further, by detecting the correlation between the luminances of three consecutive pixels in the vertical direction, for the edge portion where the luminance change in the vertical direction is drastically dependent on the luminance correlation, the dye data is also a portion with a large change. Assuming that the smoothing filter is lightly applied, and if there is little change in brightness, the smoothing filter is strengthened. Further, Patent Document 1 discloses a method of extending the smoothing of the dye data in the vertical direction with a simple configuration by repeating the arithmetic operation for performing the arithmetic averaging for a target pixel a plurality of times. Yes.
JP 05-153608 A

  However, when the above conventional color noise removal is applied to an image signal, the apparent resolution of the texture on the image that is difficult to detect only by the edge from the luminance signal may be deteriorated. In addition, when the chromaticity of the color noise of interest is significantly different from the surrounding chromaticity, there is a problem in that it is necessary to repeat the color noise removal processing a plurality of times for the pixel of interest.

An object of the present invention is to provide an image processing method, apparatus, and storage medium capable of realizing a high level of color noise reduction without deteriorating the apparent resolution related to a color signal.

The present invention inputs color image data composed of a signal indicating brightness and a signal indicating color, and performs a smoothing process on the signal indicating color while holding the signal indicating brightness. Is what you do.

According to the present invention, it is possible to achieve a high level of color noise reduction without deteriorating the apparent resolution related to the color signal.

  The best mode for carrying out the invention is the following embodiment.

  FIG. 1 schematically shows an image processing apparatus 100 according to the first embodiment of the present invention.

  The image processing apparatus 100 includes a host computer, and a printer 106 such as an ink jet printer and a monitor 105 are connected to the image processing apparatus 100.

  The image processing apparatus 100 includes application software 101 such as a word processor, spreadsheet, and Internet browser, an OS (Operating System) 102, and various drawing command groups (image drawing commands, The printer driver 103 that generates print data by processing text drawing commands and graphics drawing commands) and the monitor driver 104 that processes various drawing commands issued by the application and displays them on the monitor 106 are provided as software. .

  The image processing apparatus 100 includes a central processing unit CPU 108, a hard disk driver HD 107, a random access memory 109, a read-only memory ROM 110, and the like as various hardware capable of operating the software.

  Specifically, the image processing apparatus 100 uses, for example, a Windows (registered trademark) 95 of Microsoft (registered trademark) as an OS in a personal computer of an AT compatible machine manufactured by IBM, which is widely used. An application in which a printable application is installed is conceivable, and a monitor 105 and a printer 106 are connected thereto.

  In the image processing apparatus 100, text data classified as text such as characters created by the application 101 based on a display image displayed on the monitor, graphics data classified as graphics such as graphics, natural images, and the like. The image data to be classified is converted into output image data.

  When printing the output image data, the application 101 issues a print output request to the OS 102. The application 101 issues a graphics drawing command to the OS 102 for the graphics data portion, and issues a drawing command group indicating an output image composed of the image drawing commands to the OS 102 for the image image data portion.

  The OS 102 receives an application output request and issues a drawing command group to the printer driver 103 corresponding to the output printer. The printer driver 103 processes the print request and drawing command group input from the OS 102, creates output image data that can be printed by the printer 106, and transfers the output image data to the printer 106.

  When the printer 106 is a raster printer, the printer driver 103 sequentially performs image correction processing on the rendering commands from the OS 102, sequentially rasterizes them into the RGB 24-bit page memory, rasterizes all the rendering commands, and then RGB24 The contents of the bit page memory are converted into a data format that can be printed by the printer 106, for example, CMYK data, and transferred to the printer 106.

  Next, processing operations performed by the printer driver 103 in the above embodiment will be described.

  FIG. 2 is a diagram showing the printer driver 103 in the above embodiment.

  The printer driver 103 includes an image correction processing unit 120 and a printer correction processing unit 121.

  The image correction processing unit 120 performs image correction processing on the color information included in the drawing command group input from the OS 102. In this image correction processing, color noise reduction processing is performed based on the RGB color information.

  First, the printer correction processing unit 121 rasterizes the drawing command based on the color information subjected to the image correction process, and generates a raster image on the RGB 24-bit page memory. Then, CMYK data depending on the color reproducibility of the printer is generated for each pixel and transferred to the printer 106.

  An outline of the color noise reduction processing performed by the image correction processing unit 120 will be described.

(Color noise reduction processing section)
FIG. 3 is a schematic diagram for explaining the outline of the processing of the image correction processing unit 120.

  In B1, the input RGB signal is converted into a luminance signal Y and a color difference signal CrCb. Then, the signal smoothing sections B2 and B3 perform a smoothing process on the color difference signal, and in B4, inversely convert it into an RGB signal.

  In this embodiment, the color noise is reduced by smoothing the rapid change of the color difference signal while holding the luminance signal.

  Next, an operation for reducing color noise by smoothing a sudden change in the color difference signal while holding the luminance signal will be described.

  The color noise reduction processing unit uses the low-pass filter shown in FIG. 5 for the color difference signals Cr (i, j) and Cb (i, j) while maintaining the luminance signal Y (i, j). As shown in the equation (5), the rapid change of the color difference signal is smoothed.

  FIG. 5 is a diagram illustrating a weight configuration of the low-pass filter.

  As shown in FIG. 5, by configuring the filter so as to surround the target pixel (i, j), stable color noise reduction processing can be performed regardless of the orientation of the input image. Note that the target pixel is the number of pixels surrounded by a circle in the drawing.

ここで、ｍ（・，・）は、フィルタを示し、ｆ（・，・）は、信号を示している。なお、ここでは、Ｃｒ（ｉ，ｊ）、Ｃｂ（ｉ，ｊ）信号の代わりに、ｆ（・）と記述している。

Here, m (•, •) indicates a filter, and f (•, •) indicates a signal. Here, f (•) is described instead of the Cr (i, j) and Cb (i, j) signals.

  Further, when the filter process shown in Expression (5) is used as it is, the filter process cannot be performed on the peripheral edge portion of the image. At this time, as shown in Japanese Patent Application No. 11-189637, appropriate peripheral edge processing may be performed.

  In the above embodiment, the NTSC Y, Cr, and Cb signals are used, but instead of the luminance signal, for example, the G signal of the RGB signal is used, and the chromaticity signal is Cr ′ = R / (R + G + B), Cb ′. = B / (R + G + B) or the like may be used.

Even if the color noise reduction processing is performed using a coordinate system such as L ^* a ^* b ^* or Yxy, the same effect as described above can be obtained. For cylindrical coordinate systems such as L ^* c ^* h ^* and HLS, it is conceivable to perform the same processing as described above. In this case, after performing processing for converting coordinate values in the cylindrical coordinate system to orthogonal coordinates, Smoothing may be performed.

  Further, when it is sufficient to correct only the color distortion, the change of the hue angle may be smoothed. Similarly, smoothing the saturation has an effect similar to the above.

また、他の実施例では、式（５）を実行した後に、式（６）に示すように、色差信号についてスムージングした後の値を、原画像の信号側にフィードバックする。このようにすることによって、スムージング効果をさらに高めることができる。

In another embodiment, after executing Expression (5), as shown in Expression (6), the value after smoothing the color difference signal is fed back to the signal side of the original image. By doing so, the smoothing effect can be further enhanced.

  When the feedback processing is performed, as shown in FIG. 6, a filter that is assigned a higher weight than the unprocessed signal value may be used for the processed signal value. By doing so, the smoothing effect of the color difference signal can be further enhanced.

  Further, by configuring the filter weight used in FIG. 6 and the like so that the denominator is a power of 2, a register shift operation mechanism can be used, and it is possible to perform high-speed processing.

  The filter is not limited to a 5 × 5 size or an object in the vertical and horizontal directions, and a similar color noise reduction effect can be obtained as long as it has a low-pass characteristic. Of course, a median filter may be used, and even in this case, the same effect as described above is obtained.

  Further, the filter size may be appropriately changed according to the image resolution. By changing the filter size, a stable processing result can be obtained without depending on the image resolution.

  In the real space, the real time region, and the spatial frequency region, the same effect can be obtained by performing a process for reducing the high frequency component on the signal excluding the luminance signal Y.

  FIG. 4 is a flowchart showing the operation of the color noise reduction process.

  Color noise reduction processing is performed on the image image data portion in the same image indicated by the image drawing command. Therefore, for example, when a graphics image and an image image are included in the same output image, the image image portion is extracted according to the result of analyzing the drawing command, and the color noise reduction process is performed.

  In the case of the color noise reduction processing alone, the resolution of the appearance may be deteriorated at an edge portion with respect to luminance or a color edge portion where a sudden chromaticity change is felt. Therefore, in this embodiment, an edge determination unit, a color edge determination unit, an edge enhancement unit, and the like are added to prevent degradation of the apparent resolution at the edges and color edges.

  A color noise reduction process performed on the extracted image data will be described in detail.

(Edge calculation part)
The edge calculation unit calculates an edge of the luminance signal Y using, for example, a Laplacian filter, and holds the calculated value D_Y (S1).

  The calculated value D_Y can be used in both the edge determination unit and the edge enhancement unit, which are subsequent processes. Further, when edge enhancement is not performed or when a high-speed unit dedicated to edge enhancement is available, the obtained value D_Y may be discarded simultaneously with the end of edge determination.

  In the above embodiment, a Laplacian filter is used. However, in order to calculate an edge, a pass component of a high-pass filter in the spatial frequency domain may be used, a moving image may be assumed, and a difference value in the real-time domain may be used. Well, the same effect as above can be obtained.

(Edge judgment part)
The edge determination unit compares the value D_Y held by the edge calculation unit with a threshold TH_Edge used for edge determination, and determines an edge (S2).

  The threshold TH_Edge may be set for each processing target, for example, by analyzing a histogram of the input image. In this case, for example, a frame memory for storing the differential value for the luminance signal of the original image is prepared, a histogram is again taken for the frame memory for storing the differential value, and the obtained histogram is obtained, for example, by discriminant analysis method or the like. Analysis may be performed using a technique, pixel values may be clustered, and an appropriate threshold value may be obtained each time. A histogram calculation / analysis unit is required.

  As a result of the determination by the edge determination unit, when it is determined that the target pixel f (i, j) is an edge, the process proceeds to the chromaticity change determination unit (S3). If it is not determined to be an edge, this edge determination step may be omitted if deterioration of the apparent resolution in the edge enhancement unit (S5) is not a problem.

(Edge enhancement part)
The edge enhancement unit performs edge enhancement by adding the luminance signal value Y (i, j) to the held value D_Y (S5).

That is,
Y ′ (i, j) = Y (i, j) + D_Y (1)
Perform the process.

  Edge enhancement can be performed without enhancing color noise by performing edge enhancement after color noise reduction processing or in combination with color noise reduction processing.

  Here, the edge enhancement is performed by using the data obtained for the edge determination to speed up and simplify the process, but the edge enhancement process is performed at another stage in the flowchart shown in FIG. You may make it perform.

  If edge enhancement has already been performed on the input device side, it is not necessary to perform edge enhancement at this stage.

  Further, the edge area of one or more pixels including the target pixel may be passed without processing the original image data without performing edge enhancement. In this case, the original image can be faithfully reproduced in the image area other than the color noise area.

(Chromaticity change judgment part)
The chromaticity change determination unit detects a “color edge” (a rapidly changing portion of chromaticity) that cannot be detected from a change in the luminance signal Y (S3).

D_col_CrL (i, j) = | {Cr (i−2, j−1) + Cr (i−1, j−1) + Cr (i, j−1)} − {Cr (i + 2, j + 1) + Cr (i + 1, j + 1) + Cr (i, j + 1)} |
D_col_CrR (i, j) = | {Cr (i + 2, j + 1) + Cr (i + 1, j + 1) + Cr (i, j + 1)}-{Cr (i-2, j-1) + Cr (i-1, j-1) + Cr (i, j-1)} |... (3)
And Similar to the above, the Cb component is also obtained.
D_col (i, j) = D_col_CrL (i, j) + D_col_CrR (i, j) + D_col_CbL (i, j) + D_col_CbR (i, j) (4)
Here, the calculated value D_col (i, j) is compared with a threshold TH_col for determining chromaticity change, and chromaticity change determination is performed.

  In the above-described embodiment, the threshold value TH_col is a fixed value, but a feature amount such as a histogram of the input image may be calculated, and chromaticity change determination may be performed based on the calculated feature amount. In addition, a similar effect can be obtained by configuring the chromaticity change value D_col (i, j) using a differential value for the color difference signal. Here, as the method for automatically setting the threshold TH_col, a method such as the discriminant analysis method described in the “edge determination unit” may be appropriately used.

  In the chromaticity change determination process in the above-described embodiment, the color noise portion may be determined as a color edge depending on the value of the threshold value TH_col. In such a case, considering the frequency characteristics of the color edge, a bandpass filter configured to pass only the color edge may be used instead of the value D_col (i, j) used for the determination. .

  It should be noted that other methods may be used in which a sudden change in chromaticity is detected only from the color difference signal regardless of the change in luminance Y. For the edge portion and the color edge portion, the color noise reduction processing is not performed in order to prevent deterioration of the apparent resolution, and the processing proceeds to the next pixel processing (S6). Here, when it is determined as the edge portion or the color edge portion, several pixels may be skipped in order to maintain the consistency of the edge peripheral portion on the image. For pixels that are not edge portions or color edge portions, color noise reduction processing is performed in S4. These processes are sequentially performed on the part extracted as an image.

  Here, in order to avoid the complexity of the description, only the parts different from the first embodiment in the description of the second embodiment will be partially described.

(Edge determination unit / chromaticity change determination unit)
In the first embodiment, input image data is not subjected to processing for those determined to be edges and color edges by the edge determination unit or the chromaticity change determination unit, respectively.

  However, in the processing after the edge determination and the processing after the chromaticity change determination, the connecting portion between the edge portion and the non-edge portion may be too conspicuous.

In such a case, the chromaticity data Col (i, j) (= Cr (i, j), Cb (i, j)) to be processed is
E (i, j) = D_Y (i, j) * H (D_Y (i, j) −TH_Edge) / TH_Edge (7)
Thus, the edge degree E (i, j) is defined. Using this,
Col (i, j) = E (i, j) * Col (i, j) + (1-E (i, j)) * Low (Col (i, j)) Expression (8)
As described above, by obtaining a weighted average of the target chromaticity data and the chromaticity data after passing through the low-pass filter, the connecting portion between the edge portion and the non-edge portion can be made inconspicuous.

  Here, H (•) is a snake side step function, and Low (•) is a low-pass filter.

(Other determination unit, high saturation determination unit)
Color noise is often noticeable in low chroma areas. Accordingly, when the saturation S (i, j) (= sqrt (Cr ^ 2 + Cb ^ 2)) of the pixel of interest is calculated and this saturation S (i, j) exceeds, for example, TH_Saturation, the color noise is reduced. Do not perform processing. By doing so, it is possible to realize faithful reproduction of the high saturation portion and speeding up of the processing.

  Color noise may appear in the achromatic region due to color noise in the achromatic region. Therefore, when the target pixel is an achromatic color, the color noise reduction process is not performed. By doing so, it is possible to achieve faithful reproduction of the achromatic color portion and to increase the processing speed.

(Modification)
In the above embodiment, the color noise reduction process is uniformly performed regardless of whether or not the target pixel is color noise.

  However, the effect can be further enhanced by performing color noise detection or color noise level detection processing, and performing color noise reduction processing according to the detected color noise portion or color noise level.

  As a method for detecting color noise, for example, there is a method using a differential value on a chromaticity plane. A mode for reducing color noise may be provided based on the specification of the color noise region by the user's manual.

  If the ratio that the processing target area is color noise is defined as the color noise level, the color noise level Cn (i, j) can be obtained by the following equation.

Cn (i, j) = | (Col (i, j) −med (Col (i, j))) | / (| med (Col (i, j)) |) ............ Formula (9)
Here, med (•) is an intermediate value of the chromaticity of the target pixel and eight pixels adjacent to the target pixel.

  For data with a high degree of color noise Cn (i, j), a higher weight can be obtained by performing the above color noise reduction processing using a filter having a high weight on the pixels away from the target pixel as shown in FIG. A noise reduction effect can be obtained.

  Here, the shape and size of the target region for calculating the intermediate value med (•) are not limited as long as it is a region including the target pixel. Furthermore, instead of using the intermediate value med (•), an average value may be used. Even in this case, the same effect as described above can be obtained.

  Here, in order to avoid complication of description, the same reference numerals are assigned to the same processing units as those in the first embodiment, and description thereof is omitted.

  FIG. 8 is a flowchart showing the operation of the color noise reduction process of this embodiment.

(Highlight judgment part)
Hard copy apparatuses such as ink jet printers use a system that substitutes paper white as white. For this reason, the white portion (R = G = B = 255) is paper white, but in the non-white portion, for example, an ink jet printer has dots, which are similar in color, but the presence or absence of dots May feel discontinuous.

  FIG. 14 is a schematic diagram illustrating image data that is subjected to filter processing with different colors for each divided area and a method for dividing the image during the image data dividing process.

  By the way, as shown in FIG. 14, when there are a white block (FIGS. 14-11) and a block (FIG. 14-12) including a white block but partially non-white texture (FIG. 14-12), the above color noise reduction is performed. When the target pixel and surrounding pixels are smoothed by the processing, the color may be slightly different between the white block and the non-white block. The white block is paper white, and dots may be hit on the non-white block.

  Therefore, the highlight determination unit determines a point that should become paper white (S11), and does not perform color noise reduction processing on the determined point that should become paper white (S6). As a highlight determination method here, a method of determining a highlight such as (R ≧ 250) AND (G ≧ 250) AND (B ≧ 250) or Y ≧ 250 may be appropriately used.

  In addition, after performing the color noise reduction processing, it may be desired to perform image processing such as brightness correction processing using the brightness correction curve shown in FIG. Also in this case, similarly to the above, the pixel having the pixel value of 235 is changed to the pixel value of 234 or 236 by the smoothing process in the color noise reduction process. And an uneven band region where dots are struck.

  In such a case, the highlight point HL is obtained from the determined brightness correction curve (S11), and the color noise reduction processing is not performed on the pixels having pixel values near the obtained highlight point HL ( S6)

  Further, in a printer capable of obtaining sufficient gradation, such as a sublimation printer, the band unevenness is often not noticeable. In such a case, the highlight determination process may not be performed.

  In the case of the color noise reduction processing alone, the apparent resolution of the edge portion with respect to the luminance or the color edge portion where a sudden chromaticity change is felt although the luminance change is small may be deteriorated. Therefore, in this embodiment, an edge determination unit, a color edge determination unit, an edge enhancement unit, and the like are added to prevent the resolution of the appearance of edges and color edges from being deteriorated.

(Scene change judgment part: Color difference judgment part between lines)
In the above embodiment, the color noise reduction processing is performed using the 5 × 5 filter shown in FIG.

  In this configuration, the image data sent as numbers 3 and 8 in FIG. 12 are also subjected to filter processing in the same manner as other image processing unit blocks. For example, image data such as landscape photographs is processed. In this case, smoothing is performed using discontinuous ground brown and sky blue in the original image data (the thick broken line portion in FIG. 12), and the color of the image after filtering is changed. There is room for improvement.

  Therefore, in the above embodiment, when the average color difference between the line to be processed and the line one line above the processing target line is not less than or equal to the color difference threshold Th_ColDiff_Line in the inter-row color difference determination unit, the image processing is performed. The input image data to the module is regarded as discontinuous, and the input image data is detected as a discontinuous point (S12).

  In this embodiment, the type of filter is switched based on the line detected as a discontinuous point and the positional relationship between the target image data.

  FIG. 9 is a diagram illustrating a weight configuration of the low-pass filter. FIG. 9A shows a filter for 3-line processing, FIG. 9B shows a filter for 2-line processing, and FIG. 9C shows a filter for 1-line processing.

  FIG. 10 is a state transition diagram showing a processing sequence when a scene switching determination unit is provided and a filter for processing an image is switched according to a position in the processing target image.

  FIG. 11 is a schematic diagram illustrating how to switch the filters when the filters used in the color noise reduction processing are switched according to the position in the processing target image.

  As shown in FIG. 11, in the color noise reduction process, when the first line of the input image is processed, the color difference signal is smoothed by the filter of FIG. 9C using only the first line of the image ( 11-L1 and FIG. 10-P1), when processing the second line of the input image, the filter of FIG. 9B is used by using the second line of the image and the first line above it. When the color difference signal is smoothed (FIG. 11-L2, FIG. 10-P2) and the third line of the input image is processed, the third line of the image, the second line above it, two Smoothing is performed using the upper first row and the filter of FIG. 9A (FIG. 11-L3, FIG. 10-P3) (S13).

  In other words, when the discontinuity point of the input image data is detected by the color difference determination unit between the rows, the filter processing area is set to the current three-row processing of FIG. 10-P3 as shown in FIGS. Alternatively, an operation (memory refresh operation) for switching from the two-row processing in FIG. 10-P2 to the one-row processing in FIG. 10-P1 is performed.

  By performing filter processing using such a filter configuration, for example, as shown in FIG. 12, even when one piece of image data is divided by an application and input to the image processing module, appropriate filter processing is performed. It can be performed. Here, numbers 1 to 10 in FIG. 12 indicate ranges of input image data divided into image processing modules. Moreover, the arrow of FIG. 11 has shown the range of the image data used when smoothing the attention line shown by (circle).

  Further, by performing the filtering process using such a filter configuration, an output is obtained every time image data is input, and a delay occurs in the output of the processed image data with respect to the input of the image data. There can be no filtering.

  In calculating the average color difference, the average color difference does not necessarily need to be an accurate average color difference. For example, the average color difference obtained by sampling 10 pixels of the target line on the image appropriately and calculating the average color difference is obtained. May be used. Further, the color difference may be obtained by using only the R component of RGB data and only the Y component of YCrCb data, for example.

  In order to realize the functions of the above-described embodiment, the program code itself for realizing the functions of the embodiment, and the program code for supplying the computer code to the computer are connected to various devices that operate various devices. Means, for example, a storage medium storing such program code is also an embodiment of the present invention.

  As a storage medium for storing the program code, for example, a floppy (registered trademark) disk, a hard disk, an optical disk, a magneto-optical disk, a CD-ROM, a magnetic tape, a nonvolatile memory card, a ROM, or the like can be used.

  Further, by executing the program code supplied by the computer, not only the functions of the above embodiments are realized, but also the OS (operating system) in which the program code is running on the computer, or other application software, etc. Such a program code is an embodiment of the present invention even when the functions of the above-described embodiment are realized in cooperation with the above-described embodiment.

  Further, after the supplied program code is stored in a memory provided in a function expansion board of a computer or a function expansion unit connected to the computer, the function expansion board or function storage unit is based on an instruction of the program code. The case where the functions of the above-described embodiment are realized by the CPU or the like provided in the above-described processing by performing part or all of the actual processing is also an embodiment of the present invention. Moreover, you may combine the said several Example.

  That is, in the above-described embodiment, an input procedure for inputting color image data composed of a signal indicating brightness and a signal indicating color, and a signal indicating the color while holding the signal indicating brightness 5 is an example of a computer-readable recording medium recording a program that causes a computer to execute a smoothing process procedure for performing a smoothing process on the computer.

  In the embodiment, an input procedure for inputting a drawing command indicating an output color image, a detection procedure for detecting an image image data portion based on the drawing command, and a color noise reduction process for the image image data portion. 5 is an example of a computer-readable recording medium on which a program for causing a computer to execute a color noise reduction processing procedure for performing the above is recorded.

Further, the embodiment is an image processing method for performing filter processing on color image data, and a detection procedure for detecting a scene switching unit according to the color image data and surrounding color image data, It is an example of the computer-readable recording medium which recorded the program which makes a computer perform the filter size switching procedure which switches a filter size according to the detection result.

1 is a diagram schematically showing an image processing apparatus 100 according to a first embodiment of the present invention. It is a figure which shows the printer driver 103 in the said Example. It is a block diagram which shows the image correction process part 120 in the said Example. 6 is a flowchart illustrating an operation of color noise reduction processing performed by the image correction processing unit 120 in the embodiment. It is a figure which shows the weight structure of a low-pass filter. It is a figure which shows the filter by which high weight is assigned compared with an unprocessed signal value. It is a figure which shows the filter which put the high weight in the pixel away from the attention pixel. It is a flowchart which shows operation | movement of the color noise reduction process which added edge determination etc. It is a figure which shows the weight structure of a low-pass filter. It is a state transition diagram showing a processing sequence when a scene switching determination unit is provided and a filter for processing an image is switched according to a position in a processing target image. It is a schematic diagram which shows how to switch the filter in the case of switching the filter which processes an image according to the position in the image of a process target. It is a schematic diagram which shows the division | segmentation method of the image data in the case of dividing | segmenting and processing image data. It is a figure of the brightness correction curve in the case of performing brightness correction. FIG. 5 is a schematic diagram illustrating image data that is subjected to filter processing with different colors for each divided area and a method for dividing an image during image data division processing.

Explanation of symbols

100: Image processing apparatus,
101 ... Application software,
102 ... OS,
103 ... Printer driver,
104: Monitor driver,
105. Monitor,
106: Printer,
107: Hard disk driver,
120. Image correction processing unit,
121 A printer correction processing unit.

Claims (10)

An input stage for inputting a drawing command indicating an output color image;
A detection step of detecting an image data portion based on the drawing command;
A color noise reduction processing stage for performing color noise reduction processing on the image image data portion;
An image processing method comprising: In claim 1,
The color noise reduction processing is performed on the color image data configured by the signal indicating brightness and the signal indicating color with respect to the signal indicating color while holding the signal indicating brightness. An image processing method, characterized by performing smoothing processing. In claim 1,
An image processing method, wherein when the graphic image data portion is detected based on the drawing command, the color noise reduction processing is not performed. An image processing method for performing filter processing on color image data,
A detection step of detecting a scene switching portion in accordance with the color image data and surrounding color image data;
A filter size switching step of switching the filter size according to the detection result;
An image processing method comprising: In claim 4,
The image processing method according to claim 1, wherein the filter used in the filter processing is a filter that refers to a target line to which a target pixel belongs and a line preceding the target line. In claim 4,
A drawing command group input stage for inputting a drawing command group indicating an output image;
An image data generation stage for generating output image data indicating an output image based on the drawing command group;
A division step of dividing the same image by a plurality of drawing commands;
A divided image input stage for inputting the plurality of divided images;
An image processing method comprising: Input means for inputting a drawing command indicating an output color image;
Detecting means for detecting an image data portion based on the drawing command;
Color noise reduction processing means for performing color noise reduction processing on the image image data portion;
An image forming means;
An image processing apparatus comprising: An image processing apparatus that performs filter processing on color image data,
Detecting means for detecting a scene switching portion according to the color image data and surrounding color image data;
Filter size switching means for switching the filter size according to the detection result;
An image forming means;
An image processing apparatus comprising: An input procedure for inputting a drawing command indicating an output color image;
A detection procedure for detecting an image data portion based on the drawing command;
A color noise reduction processing procedure for performing color noise reduction processing on the image data portion;
The computer-readable recording medium which recorded the program which makes a computer perform. An image processing method for performing filter processing on color image data,
A detection procedure for detecting a scene switching portion according to the color image data and the surrounding color image data;
A filter size switching procedure for switching the filter size according to the detection result;
The computer-readable recording medium which recorded the program which makes a computer perform.

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