JP2001036756A - Method and device for processing image - Google Patents

Abstract

PROBLEM TO BE SOLVED: To obtain image processing method and device capable of realizing color reproducing space mapping without deteriorating the quality of images. SOLUTION: Only a false contour is extracted by detecting (S103 to S107) an edge part from an image obtained by applying color reproducing space mapping processing (S102) to a target image and comparing (S108) the detected edge part with an edge part directly detected (S121 to S125) from the target image and the mapping processing is corrected (S110) in accordance with the degree of false contour.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

The present invention relates to an image processing method and apparatus, and more particularly, to an image processing method and apparatus for mapping a color reproduction space.

[0002]

2. Description of the Related Art In an image processing apparatus such as an ink jet printer, when a color image is output from an image output unit based on a three-dimensional color signal input from an image input unit, good color reproducibility is maintained. Need to be. As a technique for this, a so-called color reproduction space mapping technique is known in which a range in which color cannot be reproduced by an image output unit is compressed and mapped to a range in which color can be reproduced.

[0003] As a conventional color reproduction space mapping technique, for example, a method disclosed in Japanese Patent Application Laid-Open No. 5-284347 is known. That is, when performing color conversion using a three-dimensional lookup table (LUT) to perform color reproduction space mapping, the output color space (the space in which color signals are formed between the color reproduction conversion system and the output system) is changed. An area where the color reproduction space mapping processing is performed (mapping area) and an area where the processing is not performed (non-mapping area) are divided, and a color signal is formed between the input color space (input system and color reproduction conversion system). Mapping) to determine the clipping area.

[0004] A conventional mapping technique will be specifically described with reference to the drawings. FIG. 15 is a diagram showing an example in which the input color space of a monitor or the like is wider than the output color space of a printer or the like. It is necessary to compress a range that cannot be output in the input color space into a compressed color reproduction area in the output color space. You can see that there is. Therefore, according to the conventional mapping technique, the output color space of the printer is separated at an arbitrary boundary into a mapping area that is a compressed color reproduction area and a non-mapping area that is a faithful color reproduction area.

Next, an actual mapping example is shown in FIG. In the figure, the vertical axis indicates the printer saturation, and the horizontal axis indicates the monitor saturation. So2 indicates the maximum printer saturation and Si2 indicates the maximum monitor saturation. In printer saturation, 0 to So1 correspond to the faithful color reproduction area in FIG. 15, and So1 to So2 correspond to the compressed color reproduction area. FIG. 16 shows that 0 to Si1 in the monitor saturation are mapped in the faithful color reproduction area on the printer side, and Si1 to Si2 are mapped in the compressed color reproduction area. As described above, in the conventional color space mapping processing, the mapping method to the first space and the mapping method to the second space are changed.

[0006]

However, according to the above-mentioned conventional color reproduction space mapping method, the mapping method is different in two regions of the color reproduction space of the output device.
Depending on how to select the boundary between the two regions that is arbitrarily set,
In some cases, a false contour or a tone (color tone) jump is generated, thereby deteriorating the image quality.

SUMMARY OF THE INVENTION The present invention has been made to solve the above-mentioned problem, and has as its object to provide an image processing method and apparatus for realizing color reproduction space mapping without image quality deterioration.

It is another object of the present invention to provide an image processing method and apparatus for detecting a pseudo contour generated by a color reproduction space mapping process.

[0009]

According to one aspect of the present invention, an image processing method includes the following steps.

That is, a first edge detecting step of detecting an edge portion from image data, a second edge detecting step of detecting an edge portion after performing color signal conversion on the image data, and the first detecting step. A comparison step of comparing a detection result in the step with a detection result in the second detection step; and a pseudo contour detection step of detecting a pseudo contour based on the comparison result.

Further, the method further comprises a correction step of correcting the color signal conversion method based on the pseudo contour detected in the pseudo contour detection step.

[0012] The method may further include a setting step of setting a parameter indicating the degree of the pseudo contour, wherein the correcting step corrects the color signal conversion method based on the parameter.

Further, the image processing apparatus further includes a conversion step of performing a color signal conversion process on the image data by the color signal conversion method corrected in the correction step.

[0014]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment according to the present invention will be described below in detail with reference to the drawings.

First Embodiment System Configuration An example of a system according to the present embodiment is shown in FIG. A printer 105 such as an ink jet printer and a monitor 106 are connected to the host computer 100.

The host computer 100 includes application software 101 such as a word processor, a spreadsheet, and an Internet browser, and an OS (Operati).
ngSystem) 102, a printer driver 103 that processes various drawing commands (image drawing command, text drawing command, graphics drawing command) indicating an output image issued by the application 101 to the OS 102, and creates print data, and the application 101
The software has a monitor driver 104 that processes various drawing command groups issued by the PC and displays the result on the monitor 106.

The host computer 100 includes a central processing unit CPU 108, a hard disk HD 107, a random access memory RAM 109, a read only memory ROM 110, and the like as various hardware on which the software can operate.

As an example of the image processing system shown in FIG. 1, for example, PC-A manufactured by IBM, which is widely spread, is used.
Microsoft compatible with T compatible personal computer
It is possible to use Windows 98 of the company as an OS, install a desired application capable of printing, and connect a monitor and a printer.

In the host computer 100, based on the display image displayed on the monitor, the application 10
In step 1, output image data is created using text data classified as text such as characters, graphics data classified as graphics such as figures, image image data classified as natural images, and the like. When printing out the output image data, the application 101 issues a print output request to the OS 102, and the graphics data portion is a graphics drawing command, and the image image data portion is a drawing command indicating an output image composed of an image drawing command. The group is issued to the OS 102. OS
102 receives an output request from an application and issues a drawing command group to a printer driver 103 corresponding to the output printer. The printer driver 103 processes a print request and a drawing command group input from the OS 102, creates print data printable by the printer 105, and transfers the print data to the printer 105. When the printer 105 is a raster printer, the printer driver 103 sequentially performs image correction processing in response to a drawing command from the OS, and sequentially executes RGB2
After rasterizing into a 4-bit page memory and rasterizing all drawing commands, the contents of the RGB 24-bit page memory are converted into a data format printable by the printer 105, for example, CMYK data, and transferred to the printer 105.

Next, processing performed by the printer driver 103 will be described with reference to FIG.

The image correction processing unit 120 performs an image correction process on the color information included in the drawing command group input from the OS 102. As this image correction processing, for example, RG
The B color information is converted into a luminance / color difference signal, an exposure correction process is performed on the luminance signal, and the corrected luminance / color difference signal is converted into an RG signal.
Inverse conversion to B color information.

The printer correction processing unit 121 first rasterizes a drawing command based on the RGB color information subjected to the image correction processing, generates a raster image on an RGB 24-bit page memory, performs color reproduction space mapping processing, and color separation into CMYK. Processing and gradation correction processing are performed to generate CMYK data for each pixel depending on the color reproducibility of the printer, and the printable image data is transferred to the printer 105.

Printer Correction Processing The processing in the printer correction processing unit 121 shown in FIG. 2 will be described in detail below. FIG. 3 is a diagram illustrating a detailed block configuration of the printer correction processing unit 121. In the following, for the sake of simplicity, it is assumed that a color space normally represented in three dimensions is schematically represented in two dimensions (FIG. 4 and the like).

First, 8-bit RGB image data, whose brightness, contrast, and color tone have been adjusted by the image correction processing unit 120, are input to an image signal input unit 131 in the printer correction processing unit 121. Here, the input R
The 8-bit image data of each GB corresponds to a color reproduced on a monitor. For example, CIEL which is a uniform color space
In the coordinate values of * a * b * color system, (L_Monitor, a_Mo
nitor, b_Monitor).

However, as can be seen from FIG. 4, the color reproduction space of the monitor 106 and the color reproduction space of the printer 105 are different in size on a uniform color space such as an L * a * b * space. . Therefore, the image data expressed on the monitor 106 is processed by the post-stage color conversion unit 1 described later.
33, the RGB-CMYK conversion is performed by the gradation correction unit 134, and then the image is output to the image output unit 135. The upper area occurs.

Therefore, on the printer 105 side, a pseudo color (L * a * b) in an area in a color space that can be reproduced by the monitor 106 but cannot be reproduced by the printer 105 (the shaded area in FIG. 4). * Value), that is, a pseudo color different from the color of the monitor 106 must be generated and printed.

Hereinafter, the L * a * b * space is defined as a uniform color space in cylindrical coordinates H (= atan (b / a)), S (= sqrt (a * ² + b *).
² )), a case where an HSV space copied to V (= L *) is considered, and color space compression is performed for the entire space using the compression formula for one S shown in FIG.

The first-stage color conversion unit 132 in the first-stage color conversion unit 132 in the printer correction processing unit 121 compresses an area that cannot be reproduced by the printer 105 in the color reproduction space (monitor gamut) of the monitor 106. And a point in the color reproduction space (printer gamut) of the printer 105. That is, the RGB 8-bit data processed by the pre-stage color conversion unit 132 outputs R ′ corresponding to a point in the printer color reproduction space.
G'B 'is converted to 8-bit data.

More specifically, the pre-stage color conversion section 132 converts the RGB color signal space of the monitor 106 into the R'G'B 'color signal space of the printer 105 according to the following procedure.

The RGB color signal space has three axes of R, G, B
(Red, green, blue), and the value that each color signal can take is “0”, “1”, “2”, …, 25 of “255”
There are six types. Therefore, the number of colors that can be represented by the RGB color signals is 16777216 colors, but it is not realistic to obtain the L * a * b * values calorimetrically for all the colors.
Therefore, the present embodiment is characterized in that the R, G, and B color signal intensities are divided into nine and the RGB values located between the lattices are interpolated as described below.
Note that in the present embodiment, the corresponding L *
As a method of obtaining the a * b * values, a cubic interpolation method is used.

For example, image data shown in FIG. 6 is output from two types of devices, and a color reproduction space (gamut) of each device is converted into a three-dimensional image in an RGB space, which is associated with a coordinate point in a uniform color space. Let us consider the case of expressing by the lattice of

In this case, as the coordinates of the grid points, the signal value of each color is “0”, “16”, “32”,.
And “255” are considered as nine combinations. As a result, the number of grid points is 9 ³ = 729.
At this time, it is possible to compare the absolute colors of the input and output color signals by measuring the color signal intensity with a general colorimetric device and obtaining, for example, L * a * b * values. This means that R
The GB color signal intensity is associated with the L * a * b * value.

Incidentally, the comparison of the color arrangement in the uniform color space is performed by, for example, Japanese Patent Laid-Open No. 5-28, instead of the method using the colorimetric values.
No. 7347 may be used. According to this method, an approximate expression applying a neural network is used for prediction, so that C * can be directly obtained from L * a * b * values.
The MYK value can be obtained.

When an arbitrary RGB value is obtained from CMYK values at 729 grid points as in the present embodiment, interpolation must be performed. However, the interpolation processing is omitted by using the above approximate expression. Efficient processing becomes possible.
Various methods are known as a prediction method, but an appropriate method may be used in consideration of required element accuracy and the like.

The pre-stage color converter 132 shown in FIG. 3 holds, for example, the lightness L * so that the gamut by the monitor RGB enters the printer R'G'B 'gamut in the L * a * b * space. Saturation S (= sqrt (a * × a * +
b * × b *)) to compress the image. FIG. 7 shows an example of this compression processing. FIG. 7 conceptually shows gamut compression in the saturation (S) direction on the SV plane. That is, the monitor RGB
A set of L * a * b * values in the printer gamut corresponding to the values is obtained.

When the monitor gamut after compression can be accommodated in the printer gamut, for example, the color difference ΔE (= sqrt
The L * a * b * value is used as a key so that ((L * ′ − L *) ² + (a * ′ − a *) ² + (b * ′ − b *) ² )) is minimized. By determining a set of the monitor (R, G, B) and the printer (R ', G', B '), a printer R'G'B' value corresponding to the monitor RGB value can be obtained.

However, according to the above-described pre-stage color conversion processing, in a region common to both the monitor gamut and the printer gamut, the monitor (R, G, B) and the printer ( R ', G',
Although the set B ′) can be determined, color collapse is likely to occur in an area in a color space that cannot be reproduced by the printer. That is, in a region where the color tone sharply changes in a spatially short section, sufficient gradation is not maintained, so that a pseudo contour is easily generated, and good color reproduction cannot be performed.

The pre-stage color conversion section 132 performs the color reproduction space mapping correction processing shown in FIG.

Hereinafter, the color reproduction space mapping correction processing in this embodiment will be described in detail with reference to the flowchart of FIG.

First, when the initial compression parameters are set in step S101, settings are made so that the compression as shown in FIG. 5 is performed. In step S102, the same color reproduction space as that of the normal former-stage color conversion process is used. A mapping process is performed, and in step S103, a natural image (sample image) is converted using the RGB-R'G'B 'conversion LUT generated by the mapping process.

Here, as described above, step S103
In the conversion result obtained in (1), there is a possibility that a contour that cannot exist in a gradation portion on the image due to lack of gradation, that is, a pseudo contour, may occur.

Therefore, in the present embodiment, the sample image after the pre-stage color processing is further differentiated to extract an edge (S104), and to perform a Fourier transform to extract a visually perceived outline (S105). The image is passed through an MTF, which is a filter representing visual characteristics in the spatial frequency domain of a human (S106), and an inverse Fourier transform is performed to return to the real domain (S107). Note that FIG.
Here is an example of the MTF used in. Thus, an edge portion in the sample image after the first-stage color processing is detected. still,
This edge detection can be performed only by differential processing or only by a combination of Fourier transform, MTF, and inverse Fourier transform.

Since the edge group extracted by the above processing may exist in the original sample image (original sample image) as well, in order to detect only the pseudo contour candidate, the edge group similar to the above is used. Edge extraction processing is also performed in parallel on the original sample image, that is, an image on which the color reproduction space mapping processing has not been performed (S121 to S125). Then, by taking the difference between the two images in step S108, it is possible to detect only the true pseudo contour.

Thereafter, although details will be described later, step S1
The compression parameter γ indicating the strength of the pseudo contour detected in step 08 is calculated (S109), and the color reproduction space mapping process is corrected based on the parameter (S11).
0).

At the time of pseudo contour extraction, an image obtained by subjecting the original sample image to filter processing using MTF or the like,
A shift may occur between the extracted contour line and the image obtained by performing the filter process on the sample image after the first-stage color processing. In such a case, these 2
For example, before taking the difference between the two images, the contour line generated in the original image is subjected to dilation processing, and then the difference is taken, so that the error due to the deviation can be suppressed.

The pixel value of the corresponding point in the differential image is different between the sample image after the color processing and the original sample image. However, in order to absorb the difference, each differential image is compared with the original sample image. It is also effective to set a low gradation number. Alternatively, it is also effective to normalize the pixel value in the differential image to, for example, 0 to 100. When a plurality of detected pseudo contour candidates are included in the same hue region, it is also effective to use the average or weighted sum of the two.

Instead of subjecting the sample image to Fourier transform and then performing MTF correction, a set of original sample images (coordinate values on the image, RGB values) and a Fourier transform, MTF correction, Fourier inverse If a set of transformed images (coordinate values and RGB values on the image) is used as an LUT, it is not necessary to perform a Fourier transform every time the gamut is compressed, so that the processing can be sped up.

However, when the evaluation in the spatial frequency domain is performed by performing the MTF correction, the image size must be defined in advance, so the original sample image size is given as a parameter.

Instead of performing the MTF correction after the differentiation process in the frequency domain, the same effect can be obtained by a single filtering process using a Laplacian filter, for example. The same effect can be obtained by using a plurality or one of a high-pass filter, a low-pass filter, a band-pass filter, or a combination thereof that passes through different frequency bands. In this case, for example, the band-pass filter can be configured to ignore pseudo contours that are unavoidable in terms of the performance of the printer to be used and to detect only pseudo contours that can be serious defects.

As described above, in the former-stage color conversion section 132, a target image (sample image) can be set when detecting a false contour, and the hue at which a pseudo-transformation occurs can be checked for the target image. However, a pseudo contour may also occur in a region in a color space that does not appear in the target image.

Therefore, instead of a natural image as a target image, a sample image composed of gradations for each hue of RGBCMY and each hue of an achromatic color shown in FIG. Detection can be performed. In the gradation image shown in FIG. 10, the correspondence between spatial coordinates and chromaticity is one-to-one.

When the target image is a natural image, it is necessary to divide and analyze a plurality of generated pseudo contours and to detect the hue and saturation at which the pseudo contour appears. By using an image as shown in FIG. 10 in which the correspondence between the coordinate values on the image and the chromaticity coordinates in the L * a * b * space is known in advance, when a plurality of pseudo contour lines are generated, Also, the hue to be adjusted can be easily checked, and the speed of the pseudo contour detection processing can be increased.

It is also possible to perform pseudo contour detection and pre-stage color signal processing correction directly from the L * a * b * space without using a sample image, and this example is shown in the flowchart of FIG.

When a printer to which gradation is added by area modulation, for example, a printer having a substantial resolution of, for example, 360 dpi, is used as an output device, it is assumed that the image size after printing and output is 5 inches both vertically and horizontally. In this case, instead of the gradation image for each hue as shown in FIG. 10, the monitor gamut after compression (L * _mon
Image data sampled at a sampling interval of _max-L * _mon_min) / (5 × 360) may be used. In this case, since it is not necessary to store all data of the target (sample) image in the apparatus, for example, the RAM
109, etc., the memory in the device can be saved.

As shown in the flowchart of FIG. 12, it is also effective to repeatedly apply feedback until the pseudo contour becomes equal to or less than the target value (0 in the figure) during the color conversion correction processing in the preceding-stage color conversion unit 132. It is.

Hereinafter, the color reproduction space mapping process correction (step S110 in FIG. 8) based on the compression parameter γ in the former-stage color converter 132 will be described in detail.

In the present embodiment, the compression parameter γ representing the strength of the pseudo contour is defined by the following equation.

[0058]

(Equation 1)

In the above equation, f (x, y) is a function representing the saturation at each point (x, y) of the image of the real area that has passed through the MTF after the image is differentiated. Also, (x, y)
Indicates the coordinate value on the image, and the double integration means taking the right integration over the entire screen. Here, the hue,
Since gamut compression was performed under constant conditions for both lightness and lightness, f (x, y) was assumed to be a function for saturation, but if this condition was not used, a function with appropriate considerations for hue and lightness was reapplied. It is conceivable to define and use f (x, y) in the above equation.

Here, the f _{original image} (x, y) indicates a function for performing the above-described processing on the original image data, and the f- _{compressed image}
(x, y) indicates a function for performing the above-described processing after performing color gamut conversion on the original image.

This embodiment is characterized in that the gamut compression method is changed using the compression parameter γ representing the strength of the pseudo contour obtained according to the above equation.

Here, the saturation corresponding to the monitor RGB values and the saturation corresponding to the printer R'G'B 'values are associated by the curve shown in FIG. In the example shown in FIG. 5, the solid curve tends to crush the high-saturation region more than the broken curve, so that a pseudo contour is more likely to occur.

When a pseudo contour is generated by the gamut compression using the solid line, a compression parameter γ is calculated in accordance with the degree of the pseudo contour, and is calculated as D = S_ptr × (S_mon−S_ptr) / sqrt (S_mon ² + S_ptr).
The distance d between the point P and the gamut compression curve in FIG. 5 is changed by d = γ / D standardized by ² ). The gamut compression curve always has the points (0, 0), (S_mon, S_
Since it is necessary to pass through (prt), a curve may be defined by these three points, and may be smoothly connected by a curve such as a spline.

It should be noted that the use of a hyperbola with d as the focal point has the same effect. Further, by using the hyperbola, it is possible to more easily form the compression curve while reliably satisfying the asymptote indicated by the broken line in FIG.

Here, an example has been described in which all hues are compressed by a compression curve generated based on one compression parameter γ, but compression by another method is also possible.
For example, as shown in FIG.
A compression curve is independently defined for each hue of the CBM. For example, when the compression curve of the R hue is changed, the I and IV regions adjacent to the R axis are also changed in consideration of the distance between the R axis and the point of interest. The printer gamut having different saturation can be used more effectively. In this case, it is needless to say that it is necessary to detect the hue area where the pseudo contour occurs and specify the compression curve to be changed.

In such a case, there is a possibility that a plurality of pseudo contours may occur adjacent to each other, and it is necessary to divide the image into regions in order to divide each pseudo contour.
By using a sample image as shown in (1), it is possible to specify the coordinate values in the color space where a true pseudo contour occurs without performing area division.

Also, an example has been described in which a compression curve is set for only the S component of the HSV space and monitor gamut compression is performed. However, similar compression is possible for the H and V components. However, the H component needs to be configured in consideration of the connection between 0 ° and 360 °, and the V component needs to have an S-shaped configuration as shown in FIG.

Also, an example in which gamut compression is performed in the first-stage color conversion unit 132 has been described. However, the balance with the second-stage color conversion unit 133 to be described later, or a pure color of a device such as CMY is expressed by the single ink. If the result is not obtained, the gamut compression curve automatically generated as described above is presented on the GUI,
On the other hand, by performing a process such as adding a change by a manual operation, fine adjustment can be performed.

As described above, the former-stage color converter 132
In, by appropriately detecting the pseudo contour and compressing all the points in the input color space into the output color reproduction area by a compression method according to the degree of occurrence, an appropriate color space free of pseudo contour is generated. Compression can be performed. Therefore, all color signals in the input color space can be associated with the output color space without significantly impairing the gradation.

Next-stage color conversion unit Next, the second-stage color conversion unit 1 in the printer correction processing unit 121
33 will be described.

The second-stage color conversion section 133 converts the 8-bit signal of each color converted into R'G'B 'for the printer by the first-stage color conversion section 132 into a C-color signal corresponding to the ink color of the printer.
MYK is converted into an 8-bit signal value. This R'G '
As a method of converting a color signal from B ′ to CMYK, for example, a method using color masking is known. The following equation shows a color conversion method using this color masking.

[0072]

(Equation 2)

[0073]

(Equation 3)

As a result, a CMY value is obtained. In addition, K
There are various methods for determining the (black) signal value. For example, the rightmost side of the above determinant is calculated by subtracting the K value from each of the vector elements [Dr-K
Dg-K Db-K] ^t , and the masking matrix is erroneously obtained in a trial chain while restricting the K value using the condition that each color signal value CMY corresponding to the ink amount is always positive or 0. Thus, the K value can be determined.

Incidentally, it is of course possible to realize the latter-stage color conversion section 132 using an LUT.

Tone Correction Unit Next, the tone correction unit 13 in the printer correction processing unit 121
4 will be described.

The tone correcting section 134 converts the color signals of the input image sequentially by the preceding-stage color converting section 132 and the succeeding-stage color converting section 133, and converts the CMYK 8-bit data into CMYK 2-bit data printable by a printer. Convert.

As this gradation correction method, for example, C, M,
This is realized by assigning 1 to each of the Y and K images and setting the value to 1 when the pixel value on the corresponding image is larger than the matrix element and setting it to 0 when the pixel value is equal to or less than the matrix element. Also, an error diffusion method or the like can be used as the halftoning method.

For example, instead of the interpolation processing used in the present embodiment, a tetrahedral volume described in JP-A-3-13066 can be applied.

Although a plurality of methods are known as a color reproduction space mapping technique, this method is not particularly limited in the present embodiment.

As described above, according to the present embodiment,
By detecting only the true pseudo contour and appropriately changing the color signal conversion method using a parameter indicating the degree, it is possible to perform appropriate color signal conversion without generating a pseudo contour.

<Other Embodiments> The present invention is for implementing the functions of the embodiments in an apparatus or system connected to the various devices so as to operate various devices so as to realize the functions of the above-described embodiments. The program code itself and means for supplying the program code to a computer, for example, a storage medium storing the program code constitute the present invention.

As a storage medium for storing such a program code, for example, a floppy disk, hard disk, optical disk, magneto-optical disk, CD-ROM, magnetic tape, nonvolatile memory card, ROM and the like can be used.

When the computer executes the supplied program code, not only the functions of the above-described embodiment are realized, but also the OS (Operating System) running on the computer, or another program. Needless to say, the program code is also included in the embodiment of the present invention when the functions of the above-described embodiment are realized in cooperation with application software or the like.

Further, the supplied program code is stored in a memory provided on a function expansion board of the computer or a function expansion unit connected to the computer, and then stored in the function expansion board or the function storage unit based on the instruction of the program code. It is needless to say that the present invention includes a case where a provided CPU or the like performs part or all of actual processing, and the processing realizes the functions of the above-described embodiments.

Further, the above embodiments may be combined.

[0087]

As described above, according to the present invention, it is possible to perform color reproduction space mapping without image quality deterioration.

Further, it is possible to detect a pseudo contour generated by the color reproduction space mapping process.

[Brief description of the drawings]

FIG. 1 is a block diagram showing a system configuration according to an embodiment of the present invention;

FIG. 2 is a block diagram illustrating a functional configuration of a printer driver according to the embodiment.

FIG. 3 is a block diagram showing a detailed configuration for performing color processing in a printer driver.

FIG. 4 is a diagram schematically showing a two-dimensional comparison between a monitor color reproduction range and a printer color reproduction range.

FIG. 5 is a diagram showing a curve for setting a gamut compression method based on a degree of a pseudo contour;

FIG. 6 is a diagram showing an example of a chart used in determining a gamut of a device.

FIG. 7 is a diagram showing the concept of gamut compression in the saturation direction;

FIG. 8 is a flowchart illustrating pseudo contour detection using a sample image and pre-stage color signal processing according to the present embodiment;

FIG. 9 is a diagram showing an MTF representing human visual characteristics in a frequency space;

FIG. 10 is a diagram showing an example of a gradation image used as a sample image in the embodiment;

FIG. 11 is a flowchart illustrating pseudo contour detection and pre-stage color signal processing performed directly from L * a * b * space without using a sample image;

FIG. 12 is a flowchart illustrating an example of performing pseudo contour detection using a sample image and performing feedback at the time of pre-stage color signal processing;

FIG. 13 is a diagram showing an example of dividing a hue area;

FIG. 14 is a diagram showing a curve defining a compression method for brightness.

FIG. 15 is a diagram schematically showing a two-dimensional comparison between a conventional monitor color reproduction range and a printer color reproduction range.

FIG. 16 is a diagram showing the concept of a conventional gamut compression method.

[Explanation of symbols]

 103 Printer Driver 120 Image Correction Processing Unit 121 Printer Correction Processing Unit 131 Image Signal Input Unit 132 Front Stage Color Conversion Unit 133 Back Stage Color Conversion Unit 134 Tone Correction Unit 135 Image Output Unit

Continued on the front page F-term (reference) 2C262 BA01 BA18 BC19 DA03 DA13 EA12 FA14 5B057 AA11 CA01 CA08 CA12 CB01 CB08 CB12 CC01 CE17 CE18 DA08 DB02 DB06 DB09 DC16 5C077 LL01 LL19 MP07 MP08 PP32 PP33 PP37 PP41 PP43 PP47 P02B01 SS06 LA14 LA15 LB02 MA17 PA03 PA05

Claims (21)

[Claims] 1. A first method for detecting an edge portion from image data.
An edge detection step, a second edge detection step of detecting an edge portion after performing color signal conversion on the image data, a detection result in the first detection step, and a detection result in the second detection step And a pseudo contour detecting step of detecting a pseudo contour based on the comparison result. 2. The image processing method according to claim 1, further comprising a correction step of correcting the color signal conversion method based on the pseudo contour detected in the pseudo contour detection step. 3. The method according to claim 2, further comprising a setting step of setting a parameter indicating a degree of the pseudo contour, wherein the correcting step corrects the color signal conversion method based on the parameter. 2. The image processing method according to 2. 4. The image processing method according to claim 2, further comprising a conversion step of performing a color signal conversion process on the image data by the color signal conversion method corrected in the correction step. 5. The image data is within a first color reproduction range, and in the conversion step, the image data is
5. The image processing method according to claim 4, wherein the image processing method is adapted to the color reproduction range. 6. The image processing method according to claim 5, wherein said second color reproduction range is narrower than said first color reproduction range. 7. The image processing method according to claim 5, wherein the first and second color reproduction ranges are color reproduction ranges corresponding to different devices. 8. The image processing method according to claim 7, wherein said first color reproduction range is a color reproduction range of a monitor, and said second color reproduction range is a color reproduction range of a printer. 9. The image processing method according to claim 2, further comprising an adjustment step of performing fine adjustment on the color signal conversion method corrected in the correction step. 10. The image processing method according to claim 9, wherein in the adjusting step, fine adjustment is performed manually on the color signal conversion method. 11. The image processing method according to claim 1, wherein in the first and second edge detection steps, a Fourier transform is performed on the image data. 12. The image processing apparatus according to claim 11, wherein, in the first and second edge detecting steps, the image data is subjected to Fourier transform, and then corrected by a predetermined band-pass filter. Image processing method. 13. The image processing method according to claim 1, wherein the image data is predetermined image data. 14. The image processing method according to claim 13, wherein the predetermined image data indicates a gradation image. 15. The image processing method according to claim 14, wherein the gradation image exhibits gradations in a plurality of hues. 16. A first color conversion means for converting a color reproduction range of image data, a second color conversion means for converting a color reproduction space of the image data converted by the first color conversion means, Wherein the first color conversion means corrects the color gamut conversion method based on a degree of a pseudo contour generated after converting the color gamut of the sample image data. Processing equipment. 17. The image processing apparatus according to claim 1, wherein the first color conversion unit compares the edge portion detected from the sample image data with an edge portion detected after converting a color reproduction range of the sample image data. 17. The image processing apparatus according to claim 16, wherein a pseudo contour is detected. 18. The apparatus according to claim 1, wherein the first color conversion means converts the image data in a color reproduction space for a first device so as to be compatible with a color reproduction space for a second device. The image processing apparatus according to claim 16, wherein: 19. The first device is a monitor,
19. The image processing apparatus according to claim 18, wherein the second device is a printer. 20. The image processing apparatus according to claim 16, further comprising a holding unit that holds the sample image data. 21. A recording medium recording a color reproduction space conversion processing program, the program comprising at least a code of a first edge detection step for detecting an edge portion from image data, and a color signal for the image data. A code of a second edge detection step of detecting an edge portion after performing the conversion, and a code of a comparison step of comparing the detection result in the first detection step with the detection result in the second detection step; A code for a pseudo contour detecting step of detecting a pseudo contour based on the comparison result.