JP6384268B2 - Image processing apparatus, image processing method, and image processing system - Google Patents

Description

  The present invention relates to an image processing device, an image processing method, and an image processing system.

  Conventionally, a technique for performing color matching between image output devices by measuring the color chart output from the image output device using a colorimeter or the like and updating the color profile of each image output device is known. ing.

  Further, a technique that can perform color matching between image output devices without using a color chart has been conventionally known (see, for example, Patent Document 1).

  However, in the above prior art, for example, when color matching is performed between two image output devices, the result of color matching may not be what the user intended. In such a case, the user has to perform color matching manually. On the other hand, manual color matching requires specialized knowledge about image processing, and it is difficult to perform color matching as intended by the user.

  The embodiment of the present invention has been made in view of the above points, and aims to support color matching intended by a user.

  In order to achieve the above object, according to an embodiment of the present invention, the first image output unit outputs the color of the first output result obtained by outputting the document image data, and the second image output unit stores the document image data. An image processing apparatus for reproducing the output second output result, wherein the reading device estimates the color of the first output image data obtained by reading the first output result from the color of the original image data. A pixel value of the original image data based on the mapping and a second mapping that estimates the color of the original image data from the color of the second output image data read by the reading device by the reading device; Color conversion means for converting the image data to generate intermediate image data, difference processing means for calculating difference between pixel values of corresponding pixels of the original image data and the intermediate image data, and generating difference image data, Manuscript Based on the conversion intensity parameter acquisition means for acquiring the conversion intensity parameter for controlling the color conversion intensity of the pixel value of the data, and the difference image data and the conversion intensity parameter, the pixel value of the difference image data is converted. Correction processing means for generating the corrected difference image data, a predetermined calculation is performed on pixel values of corresponding pixels of the document image data and the correction difference image data, and the document image data and the correction difference image data And image synthesizing means for synthesizing.

  According to the embodiment of the present invention, color matching intended by the user can be supported.

It is a figure of an example explaining the relation between pixel values a and b typically. It is a block diagram of an example of the image processing system which concerns on this embodiment. It is a hardware block diagram of an example of the image processing system which concerns on this embodiment. It is a hardware block diagram of an example of the computer which concerns on this embodiment. FIG. 2 is a hardware configuration diagram of an example of an MFP according to the present embodiment. 1 is a functional block diagram of an example of an image processing system or MFP according to an embodiment (Example 1). 6 is a flowchart of an example of color conversion processing (Example 1). FIG. 11 is a functional block diagram of an example of an image processing system or MFP according to the present embodiment (Example 2). 10 is a flowchart of an example of color conversion processing (second embodiment). FIG. 10 is a functional block diagram of an example of an image processing system or MFP according to the present embodiment (Example 3). 10 is a flowchart of an example of color conversion processing (Example 3). FIG. 10 is a functional block diagram of an example of an image processing system or MFP according to the present embodiment (Example 4). 10 is a flowchart of an example of color conversion processing (Example 4). 6 is a flowchart illustrating an example of a reference color reproduction characteristic estimation process. It is a figure of an example of the data recorded in list format. It is a flowchart of an example of the estimation process of the reverse characteristic of a user color reproduction characteristic. It is a figure of the other example of the data recorded by the list format.

  DESCRIPTION OF EMBODIMENTS Hereinafter, embodiments for carrying out the present invention will be described with reference to the drawings.

First,
P ₁ (x), the color reproduction characteristics of the first image output device
P ₂ (x), the color reproduction characteristics of the second image output device
The color reproduction characteristics of the image reading device are S (x),
It is defined as Here, “x” is a color or a pixel value. In consideration of the existence of various color spaces, the pixel value is not equal to the color, but in the same color space, the pixel value is equal to the color. For example, in the case of the RGB color space, it is three-dimensional data of x = [R, G, B] ^t . For example, in the case of the CMYK color space, it is four-dimensional data of x = [C, M, Y, K] ^t .

At this time,
The first color reproduction characteristic is S (P ₁ (x)),
The second color reproduction characteristic is S (P ₂ (x)),
It can be expressed as.

P ₁ (x) is the color when the first image output device prints the pixel value x, and S (P ₁ (x)) is the color when the image reader reads the color P ₁ (x). Color. P ₂ (x) is the color when the second image output device prints the pixel value x, and S (P ₂ (x)) is the color when the image reading device reads the color P ₂ (x). Color.

When the pixel value x printed by the first image output device and the pixel value x printed by the second image output device are the same, the color reproduction characteristics P ₁ (x) and P ₂ (x) are different from each other, S (P ₁ (x)) = S (P ₂ (x)) is not satisfied. On the other hand, even when the pixel value printed by the first image output device and the pixel value printed by the second image output device are not the same, the image data to be printed (original image data described later) includes S ( It can be expected that there is a combination of colors (a, b) such that P ₁ (a)) = S (P ₂ (b)) (S (P ₁ (a)) and S (P ₂ (b)) May not match exactly).

Here, if one or more combinations (a, b) of S (P ₁ (a)) = S (P ₂ (b)) can be obtained, the second image output device prints the pixel value b. Thus, S (P ₂ (b)) is obtained, so when the second image output device prints the pixel value a by converting a to be regarded as b, the pixel value b is actually set to Since printing is performed, the second image output device can print in the same color as the first image output device.

  FIG. 1 is a diagram schematically illustrating the relationship between pixel values a and b. Both the first image output device and the second image output device print the same image data. This image data is called original image data.

  When the first image output device prints the pixel value “a”, the first image output device prints a first output product having the color s when read by the scanner. When the second image output device prints the pixel value b, it prints a second output product having the color s when read by the scanner. When the color reproduction characteristics of the second image output device are matched with those of the first image output device, the second image output device prints so that the pixel value a becomes s (when read, it becomes s). I know that it should be. Therefore, the second image output device replaces the pixel value a of the document image data with b. By performing this color conversion, the second image output device can print in the same color as the first image output device.

In the present embodiment, processing for performing color conversion on document image data in order to match colors between the first image output device and the second image output device will be described by taking the following combination of devices as an example.
First image output device: Printer (referred to as “reference printer”)
Second image output device: Printer (referred to as “user printer”)
-Image reading device: scanner The terms used in the following are defined as follows.
-Reference printer: a printer that corresponds to the first image output device and is the target of color matching-User printer: a printer that corresponds to the second image output device and wants to match the color to the reference printer-Scanner: Image reading device・ Document image data: Image data used when the printer outputs a printed matter ・ Reference printed matter: Printed original image data from the reference printer, a target for color matching / reference image data: Image reading of the reference printed matter Image data obtained by reading by the apparatus / user printed matter: original image data output by the user printer, printed matter to be matched with the reference printed matter, user image data: image data obtained by reading the user printed matter by the image reading apparatus In this mode, the standard print product and the user print product are used, and the color change is applied to the document image data to be given to the user printer. By performing the conversion, a user print having a color equivalent to the color of the reference print is obtained. In addition, when the user printed material obtained by outputting the original image data after color conversion by the user printer is not the color intended by the user, a desired user printed material can be obtained by adjusting predetermined parameters.

In the following Examples 1 to 3, S (P ₁ (x)) (referred to as “reference color reproduction characteristic”) and the inverse characteristic P ₂ ^{−1 of} S (P ₂ (x)) described above are used. In the following description, it is assumed that (S ⁻¹ (x)) (which is called “inverse characteristics of user color reproduction characteristics”) is known. On the other hand, in the fourth embodiment, processing for estimating the reference color reproduction characteristic S (P ₁ (x)) and the inverse characteristic P ₂ ^-1 (S ^-1 (x)) of the user color reproduction characteristic will also be described.

Note that S ⁻¹ (x) is a value (color) read as the pixel value x when read by the image reading apparatus. P ₂ ⁻¹ (S ⁻¹ (x)) is a pixel value output as the color S ⁻¹ (x) when output from the second image output device.

Example 1
First, Example 1 will be described. In the first embodiment, when the reverse characteristics of the reference color reproduction characteristics and the user color reproduction characteristics are known, the original image data is subjected to color conversion in order to obtain a user print having a color equivalent to the color of the reference print. If the user print obtained using the color-converted document image data is not the color intended by the user, a desired user print can be obtained by adjusting predetermined parameters. .

<System configuration>
FIG. 2 is a configuration diagram of an example of an image processing system according to the present embodiment. An image processing system 600 illustrated in FIG. 2 includes a computer 100, a user printer 200, and a scanner 300 connected via a network 500. An offset printer, a gravure printer, or the like may be used instead of the user printer 200, and a spectrocolorimeter, a camera, or the like may be used instead of the scanner 300. Since it is assumed that the reference printer 400 does not exist on the user side of the image processing system 600, the reference printer 400 is not connected to the network 500, but may be connected. It is assumed that the user of the image processing system 600 has already acquired or can acquire the reference printed matter from which the reference printer 400 has output the reference image data.

  The network 500 is an in-house LAN (Local Area Network), a wide area LAN (WAN: Wide Area Network), an IP-VNP (Virtual Private Network), the Internet VPN, the Internet, or the like. It is sufficient that the computer 100, the user printer 200, and the scanner 300 can communicate with each other such as a network in which these are combined. A part of the telephone line may be included, and a wired connection or a wireless connection may be used.

  Note that the reference printer 400 and the user printer 200 do not have to be different devices, for example, when the same color is used to match the past and present colors. Further, the reference printer 400 and the user printer 200 may have one or more of a scanner function, a FAX function, and a copy function in addition to the printer function. Similarly, the scanner 300 may have one or more of a printer function, a FAX function, a copy function, and the like in addition to the scanner function. An apparatus having a plurality of functions is called an MFP (Multifunction Peripheral).

  The computer 100 also accepts an input of a conversion strength parameter for controlling the strength of color conversion when the user print output from the user printer 200 with the original image data after color conversion is not the color intended by the user. Further, in the fourth embodiment, the computer 100 outputs the document image data used by the reference printer 400 for outputting the reference print product, the reference image data obtained by reading the reference print product by the scanner 300, and the user printer 200 outputs the document image data. A reference color reproduction characteristic and an inverse characteristic of the user color reproduction characteristic are estimated from three pieces of image data of user image data obtained by reading a user print by the scanner 300.

  The document image data may be stored in advance by the user printer 200 or may be acquired from the reference printer 400. Further, the computer 100, the user printer 200, and the scanner 300 can be mounted on one MFP.

<Hardware configuration>
FIG. 3 is a hardware configuration diagram of an example of the image processing system according to the present embodiment. The image processing system 600 includes an image input device 601, an image output device 602, an image storage device 603, an image analysis device 604, a color reproduction characteristic storage device 605, an image processing device 606, and a parameter input device 607.

  The image input device 601 inputs an image output by an image output device, and corresponds to the scanner 300 in FIG. The image storage device 603 stores the image data received by the image input device 601 and corresponds to the computer 100 in FIG. The image analysis device 604 analyzes the reference image data, the user image data, and the document image data to estimate the reverse characteristics of the reference color reproduction characteristics and the user color reproduction characteristics, and corresponds to the computer 100 in FIG. The color reproduction characteristic storage device 605 stores the reverse characteristic of the reference color reproduction characteristic and the user color reproduction characteristic, and corresponds to the computer 100 in FIG. The parameter input device 607 inputs a conversion intensity parameter for controlling the intensity of color conversion, and corresponds to the computer 100 in FIG. The image processing apparatus 606 performs color conversion of image data based on the inverse characteristics of the reference color reproduction characteristics and the user color reproduction characteristics, and image data correction processing based on the conversion intensity parameter. To do. The image output device 602 outputs a color-converted image, and corresponds to the user printer 200 in FIG.

  FIG. 4 shows an example of a hardware configuration diagram of the computer 100. The computer 100 includes a CPU (Central Processing Unit) 101, a RAM (Random Access Memory) 102, a ROM (Read Only Memory) 103, an external storage I / F 104, a communication device 105, an input device 106, A drawing control device 107 and an HDD (Hard Disk Drive) 108 are included.

  The CPU 101 is an arithmetic device that implements control and functions of the entire computer 100 by reading a program and data from a storage device such as the ROM 103 and the HDD 108 onto the RAM 102 and executing processing.

  The RAM 102 is a working memory (main memory) that temporarily stores data necessary when the CPU 101 executes a program. The ROM 103 stores a program and data for starting up a basic input output system (BIOS) and an operating system (OS).

  The external storage I / F 104 is detachable from the storage medium 110, reads data recorded on the storage medium 110, and stores it in the HDD 108. The external storage I / F 104 can also write data stored in the HDD 108 into the storage medium 110. The storage medium 110 is, for example, a USB memory or an SD card. The program 111 is distributed, for example, by being downloaded from a state stored in the storage medium 110 or a server (not shown).

  The input device 106 is a keyboard, a mouse, a touch panel, or the like, and accepts various operation instructions from the user to the computer 100.

  The HDD 108 is a non-volatile storage device that stores programs and data. The HDD 108 may be a nonvolatile memory such as an SSD (Solid State Drive) and stores various data such as an OS, a program, and image data. The HDD 108 stores a program 111 for realizing the present embodiment.

  The communication device 105 is a NIC (Network Interface Card) for connecting to a network such as the Internet, and is, for example, an Ethernet (registered trademark) card.

  The drawing control device 107 is, for example, a graphic controller, and the CPU 101 executes the program 111 to interpret a drawing command written in the graphic memory, generates a screen, and draws it on the display 109.

  FIG. 5 shows an example of a hardware configuration diagram of the MFP 700 when the image processing system 600 is realized by a single MFP. The MFP 700 includes a controller 30, an operation panel 31, a fax control unit 32, a plotter 33, a scanner 34, and other hardware resources 35. The controller 30 includes a CPU 11, a MEM-P 12, an NB (North Bridge) 13, an ASIC 16, a MEM-C 14, an HDD 15, and a peripheral device 17 connected to the NB 13 via a PCI bus.

  In the controller 30, the ASIC 16 is connected to the MEM-C 14, the HDD 15, and the NB 13, and the NB 13 is connected to the CPU 11 and the MEM-P 12. The NB 13 is one of CPU chip sets, and is a bridge for connecting the CPU 11, the MEM-P 12, the ASIC 16, and the peripheral device 17.

  The ASIC 16 is an IC for image processing applications and performs various image processing. The ASIC 16 also serves as a bridge for connecting the AGP, HDD 15 and MEM-C 14 respectively. The CPU 11 performs overall control of the MFP 700 and activates and executes various applications installed in the MFP 700.

  The MEM-P 12 is a system memory used by the MFP 700 system, and the MEM-C 14 is a local memory used as a buffer for image data during image processing.

  The HDD 15 is a large-capacity storage, and an SSD or the like may be used. The HDD 15 stores an OS, various applications, font data, and the like. The HDD 15 stores a program 23 for realizing the present embodiment. The program 23 is distributed via a state stored in the storage medium 18 or a server (not shown).

  The peripheral device 17 is a serial bus, NIC, USB host, IEEE802.11a / b / g / n, IEEE1394, memory card I / F, or the like. For example, a Centronics cable is connected to the serial bus. The NIC controls communication via the network. A device is connected to the USB host via a USB cable. IEEE802.11a / b / g / n is an interface for a wireless LAN according to these standards, and controls communication by the wireless LAN. IEEE 1394 is an interface that controls high-speed serial communication. Various memory cards are mounted on the memory card I / F, and data is read and written. The memory card is, for example, an SD card, a multimedia card, an xD card, or the like.

  The operation panel 31 has a hardware keyboard and display means such as a liquid crystal. The operation panel 31 accepts input operations from the user and displays various screens for the user. The operation panel 31 is equipped with a touch panel and can accept user operations from displayed soft keys.

  The fax control unit 32 is connected to a public communication network via an NCU (Network Control Unit), and performs facsimile transmission / reception according to a communication procedure (communication protocol) compatible with, for example, a G3 or G4 standard facsimile. The fax control unit 32 performs signal processing such as data compression and modulation on the image data and transmits the image data, and decompresses the image data received from the other party and corrects the error to restore the image data.

  The plotter 33 is, for example, a black-and-white plotter or a color plotter using an electrophotographic method, and forms an image for each page based on print target data or image data read by the scanner 34, and transfers the image to a sheet. For example, a toner image formed on a photosensitive drum or the like is transferred onto a sheet using an electrophotographic process using a laser beam, and is fixed by a fixing device with heat and pressure and output. Moreover, you may print in the form which apply | coats an ink droplet.

  The scanner 34 optically scans the document placed on the contact glass, A / D converts the reflected light, performs known image processing, converts it into digital data of a predetermined resolution, and generates image data. .

  In the MFP of FIG. 5, the image input device 601 of FIG. 2 corresponds to the scanner 34, the image output device 602 corresponds to the plotter 33, the image storage device 603 corresponds to the HDD 15, and the image analysis device 604 corresponds to the CPU 11. . The color reproduction characteristic storage device 605 corresponds to the HDD 15, the image processing device 606 corresponds to the ASIC 16, and the parameter input device 607 corresponds to the operation panel 31.

<Functional configuration>
FIG. 6 is a functional block diagram of an example of an image processing system or MFP according to the present embodiment (Example 1). The image processing system 600 or the MFP 700 includes a color conversion unit 301, a difference processing unit 302, a conversion intensity parameter acquisition unit 303, a correction processing unit 304, and an image composition unit 305.

  The color conversion unit 301 is realized by, for example, the image processing device 606 and the like, and performs color conversion on the document image data by using a reverse characteristic of the reference color reproduction characteristic and the user color reproduction characteristic to generate intermediate image data. As described above, in this embodiment, it is assumed that the reverse characteristics of the reference color reproduction characteristics and the user color reproduction characteristics are known and stored in advance in the color reproduction characteristic storage device 605.

  The difference processing unit 302 is realized by, for example, the image processing device 606 and the like, calculates a difference between the document image data and the intermediate image data, and generates difference image data.

  The conversion intensity parameter acquisition unit 303 is realized by, for example, the parameter input device 607 and the like, and acquires a conversion intensity parameter for controlling the intensity (degree) of color conversion for document image data.

  The correction processing unit 304 is realized by, for example, the image processing device 606 and the like, performs correction processing on the difference image data using the conversion intensity parameter, and generates correction difference image data.

  The image composition unit 305 is realized by, for example, the image processing device 606 and the like, and synthesizes the document image data and the corrected difference image data to generate color-converted document image data.

<Details of processing>
Next, details of the processing of the image processing system 600 or the MFP 700 according to the present embodiment will be described. FIG. 7 is a flowchart of an example of color conversion processing (first embodiment). FIG. 7 evaluates whether or not the user print obtained from the document image data subjected to color conversion based on the reverse characteristic of the reference color reproduction characteristic and the user color reproduction characteristic is intended by the user. When the user printed material is not intended by the user, the desired user printed material can be obtained by adjusting the conversion intensity parameter.

  First, the user prints document image data with the user printer 200 (step S701). That is, the user print is obtained by printing the document image data using the user printer 200.

  The user evaluates the obtained user print (step S702). That is, the user compares the user printed material with the reference printed material and evaluates the quality of the user printed material. If the quality of the user printed material is sufficient (Yes in step S703), the process ends. If not (No in step S703), the process proceeds to the next step S704.

  As a method for evaluating the quality of a user print, for example, there is a method using a color difference from a reference print. Other examples include a method using a hue difference and a method using an absolute value of a difference between each color component. Moreover, you may perform quality evaluation visually. The image processing system 600 or the MFP 700 according to the present embodiment may include color evaluation means (not shown) that evaluates the quality of a user print using the following evaluation method.

a) Evaluation Method Using Color Difference Color difference is the distance between two colors in the L * a * b * color space or L * u * v * color space. Since this embodiment uses a printer as an image output device, an explanation will be given using the L * a * b * color space. The color difference ΔE * _{ab in the} L * a * b * color space is defined by the following equation.

ここで、(ΔL*, Δa* ,Δb*)はL*a*b*色空間における２色の色度差である。

Here, (ΔL *, Δa *, Δb *) is a chromaticity difference between two colors in the L * a * b * color space.

At this time, an example of a procedure for obtaining the color difference between the reference print and the user print is shown below.
(1) A reference print is read by the scanner 300 to obtain reference image data.
(2) A user print is read by the same scanner 300 as in (1) to obtain user image data.
(3) The reference image data and user image data are converted into a device-independent color space (for example, XYZ color space) using the color profile of the scanner 300.
(4) The reference image data and user image data converted into the device-independent color space are converted into the L * a * b * color space.
(5) The color difference for each pixel is obtained by the above equation.

  The reference print product and the user print product are read by the same scanner 300. However, even if the two print products are read by separate scanners 300 under the condition that the color profile of the scanner 300 can be used to convert the color print into a device-independent color space. Good.

  When only one scanner 300 is used, it is not essential to convert to a device-independent color space using a color profile. In cases where color difference values are evaluated quantitatively, absolute values are important, so conversion to a device-independent color space is necessary.However, in cases where color difference values are evaluated qualitatively, they are relative. Since it is only necessary to grasp the tendency, conversion to a device-independent color space may be omitted.

  Once the color difference for each pixel is found, this information can be statistically analyzed to quantitatively evaluate the quality of the user print. Examples of the analysis method include an average value, maximum value, value distribution, and variance of color differences.

The judgment of whether the quality is sufficient is
-Whether the average color difference is within a predetermined value,
-Whether the maximum color difference is within a predetermined value,
・ Whether the variance is within the prescribed value,
It can be judged by such criteria. When evaluating the quality of user prints, it is desirable to remove the outline portion of the content of the image data. this is,
・ It is difficult to perfectly align the contours in the alignment required for later processing.
・ The reproducibility of the contour varies depending on the printer (color, sharpness, etc.)
This is because there is a possibility that a large color difference appears in the contour portion.

  Since the area of the contour portion is a small part of the area of the entire printed matter, the influence on the overall color evaluation by visual inspection is limited. On the other hand, in quantitative evaluation, there is a concern that the large color difference of the contour portion described above may be an outlier and reduce the reliability of the evaluation result. it can.

  Examples of the method for detecting the contour portion include a method using binarization and a method using edge detection. As an example of a method using binarization, there is a method in which image data is binarized into black and white with a predetermined threshold, and a portion where a white region and a black region are adjacent is determined as a contour portion. As an example of a method using edge detection, there is a method in which an edge image is created from image data using a Sobel method or the like, and binarized with a predetermined threshold value, and a pixel equal to or higher than the threshold value is determined as a contour portion.

  There is also a method for alleviating the above problem without removing the contour portion. For example, the image data is smoothed to smooth the contour portion, and the color difference appearing at the contour portion is reduced. Conventional techniques such as an averaging filter and a low-pass filter may be used for smoothing.

  In the above example, the CIE 1976 color difference formula is described as the color difference formula. However, the present invention is not limited to this color difference formula. Also good.

b) Evaluation method using hue difference
The hue difference ΔH * _{ab in the} L * a * b * color space is defined by the following equation.

ここで、ΔE*_abは色差、(ΔL*, Δa*, Δb*)は２色の色度差、ΔC*_abはクロマの差ある。クロマC*_abは次式で定義される。

Here, ΔE * _ab is a color difference, (ΔL *, Δa *, Δb *) is a chromaticity difference between two colors, and ΔC * _ab is a chroma difference. Chroma C * _ab is defined by the following equation.

基準印刷物とユーザ印刷物の色相差を求める手順は色差を求める手順と同じであるが、色差ではなく色相差を算出する。また、統計的な分析方法や品質の判定方法も同様である。

The procedure for obtaining the hue difference between the reference print and the user print is the same as the procedure for obtaining the color difference, but the hue difference is calculated instead of the color difference. The same applies to statistical analysis methods and quality determination methods.

c) Evaluation method using absolute value of difference of each color component This is a method of performing evaluation by taking the absolute value of the difference of each color component between the reference print product and the user print product in a predetermined color space. Taking the RGB color space as an example, the difference between the absolute values of the R component values, the difference between the absolute values of the G component values, and the difference between the absolute values of the B component values are used. An example of a procedure for obtaining the absolute value of the difference between the color components of the reference print and the user print is shown below.
(1) A reference print is read by the scanner 300 to obtain reference image data.
(2) A user print is read by the same scanner 300 as in (1) to obtain user image data.
(3) The reference image data and user image data are converted into a device-independent color space (such as an XYZ color space) using the color profile of the scanner 300.
(4) In the converted color space, the absolute value of the difference between the color component values is obtained for each pixel.

  The reference print product and the user print product are read by the same scanner 300. However, even if the two print products are read by separate scanners 300 under the condition that the color profile of the scanner 300 can be used to convert the color print into a device-independent color space. Good.

  As in the case of the color difference, conversion to a device-independent color space using the color profile of the scanner 300 is not essential, and the absolute value of the direct difference can be obtained directly in the device-dependent color space of the scanner 300. Good. The statistical analysis method and quality determination method are the same as in the case of color difference.

  Next, the color conversion unit 301 performs color conversion on the document image data (step S704). In other words, the color conversion unit 301 performs color conversion on the document image data using the reverse characteristics of the reference color reproduction characteristics and the user color reproduction characteristics stored in the color reproduction characteristic storage device 605, and generates intermediate image data.

Here, the color conversion unit 301 determines that the reference color reproduction characteristic S (P ₁ (x)) and the inverse characteristic P ₂ ^-1 (S ^-1 (x)) of the user color reproduction characteristic S (P ₂ (x)). A procedure for performing color conversion on document image data using the above and generating intermediate image data is shown below.
(1) From the reference color reproduction characteristics, the color s = S (P ₁ (a)) of the reference image data corresponding to the color a of the document image data is obtained.
(2) The color b = P ₂ ⁻¹ (S ⁻¹ (s)) of the original image data corresponding to the color s of the user image data is obtained from the inverse characteristic of the user color reproduction characteristic (ie, S (P ₁ ( a)) = s = S (find a combination (a, b) such that P ₂ (b)).
(3) The color a of the document image data is converted to b to generate intermediate image data.

  Next, the difference processing unit 302 generates difference image data (step S705). That is, the difference processing unit 302 calculates the difference between the pixel values of the corresponding pixels between the document image data and the intermediate image data generated in step S704 described above, and generates difference image data. To do. Note that the difference processing unit 302 calculates the pixel value difference including the sign for each color component.

  Next, the conversion intensity parameter acquisition unit 303 acquires a conversion intensity parameter for controlling the intensity of color conversion (step S706). That is, the conversion intensity parameter acquisition unit 303 acquires the value of the conversion intensity parameter input by the user via the input device 106, for example. The conversion intensity parameter may be directly input by the user, or may be selected by the user from a plurality of preset candidate values.

  Next, the correction processing unit 304 corrects the difference image data (step S707). That is, the correction processing unit 304 performs correction processing on the difference image data using the conversion intensity parameter acquired in step S706 described above, and generates correction difference image data. Examples of correction performed on the difference image data include the following.

a) Method for Correcting by Applying Uniform Conversion Intensity Parameter to Difference Image Data A case where the difference image data is in RGB color space will be described as an example. The difference image data is corrected by multiplying each pixel value of the difference image data by the conversion intensity parameter. This method is represented by the following equation.

ここで、（R_diff, G_diff, B_diff）は差分画像データのRGB値を、wは変換強度パラメータを、（R_diff', G_diff', B_diff'）は補正差分画像データのRGB値を表す。

Where (R _diff , G _diff , B _diff ) is the RGB value of the difference image data, w is the conversion intensity parameter, and (R _diff ', G _diff ', B _diff ') is the RGB value of the corrected difference image data Represents.

At this time, when the conversion intensity parameter w is 0, the RGB values (R _diff ', G _diff ', B _diff ') of the corrected difference image data are all 0. Therefore, even if the original image data and the corrected difference image data are combined in the next step S708, it means that the color of the combined original image data (represented as “color-converted original image data”) is not converted. doing.

Also, if the transformation strength parameter w is 1, the RGB values of the corrected differential image data _{(R diff ', G diff'} , B diff ') equal RGB values of the difference image data _{(R diff, G diff, B} diff) and . Therefore, when the document image data and the corrected difference image data are combined in the next step S708, it means that the color-converted document image data becomes equal to the intermediate image data.

  If the conversion intensity parameter w is 0 <w <1, then when the original image data and the corrected difference image data are combined in the next step S708, the color-converted original image data is more color-converted than the intermediate original image data. It means that the degree becomes weaker.

  When the conversion intensity parameter w is 1 <w, when the original image data and the corrected difference image data are combined in the next step S708, the color converted original image data has a degree of color conversion higher than that of the intermediate original image data. It means to become stronger.

b) Correction Method by Applying Different Conversion Intensity Parameters to Each Axis of the Color Space of the Difference Image Data A case where the difference image data is in the RGB color space will be described as an example. The difference image data is corrected by multiplying each pixel value of the difference image data by a different conversion intensity parameter for each of the R component value, the G component value, and the B component value. This method is represented by the following equation.

ここで、（R_diff, G_diff, B_diff）は差分画像データのRGB値を、（w_R, w_G, w_B）はR成分値、G成分値、B成分値毎に異なる変換強度パラメータを、（R_diff', G_diff', B_diff'）は補正差分画像データのRGB値を表す。

Where (R _diff , G _diff , B _diff ) is the RGB value of the difference image data, and (w _R , w _G , w _B ) is the conversion intensity parameter that differs for each R component value, G component value, and B component value (R _diff ', G _diff ', B _diff ') represents the RGB value of the corrected difference image data.

c) Correction Method Using Different Conversion Intensity Parameters for Different Tone Values of Difference Image Data A case where the difference image data is in RGB color space will be described as an example. The difference image data is corrected by multiplying each pixel value of the difference image data by the conversion intensity parameter. The difference from the method a) is that the value of the conversion intensity parameter differs for each gradation value. Conversion intensity parameters that differ for each gradation value may be managed by a look-up table (LUT). This method is represented by the following equation.

ここで、（R_diff, G_diff, B_diff）は差分画像データのRGB値を、w[・]は階調値毎の変換強度パラメータを格納したルックアップテーブルを、（R_diff', G_diff', B_diff'）は補正差分画像データのRGB値を表す。

Here, (R _diff , G _diff , B _diff ) is the RGB value of the difference image data, w [•] is a lookup table that stores the conversion intensity parameter for each gradation value, and (R _diff ', G _diff ', B _diff ') represents the RGB value of the corrected difference image data.

d) Correction method using different conversion intensity parameters for each tone value of each axis in the color space of the difference image data As an example, a case where the difference image data is in the RGB color space will be described. The difference image data is corrected by multiplying each pixel value of the difference image data by a different conversion intensity parameter for each of the R component value, the G component value, and the B component value. A difference from the above method b) is that the value of the conversion intensity parameter differs for each gradation value. Conversion intensity parameters that differ for each gradation value may be managed using a lookup table. This method is represented by the following equation.

ここで、（R_diff, G_diff, B_diff）は差分画像データのRGB値を、（w_R[・], w_G[・], w_B[・]）はR成分値、G成分値、B成分値の階調値毎の変換強度パラメータを格納したルックアップテーブルを、（R_diff', G_diff', B_diff'）は補正差分画像データのRGB値を表す。

Where (R _diff , G _diff , B _diff ) is the RGB value of the difference image data, (w _R [•], w _G [•], w _B [•]) is the R component value, G component value, In the lookup table storing the conversion intensity parameter for each gradation value of the B component value, (R _diff ', G _diff ', B _diff ') represents the RGB value of the corrected difference image data.

Subsequently, the image composition unit 305 synthesizes the document image data and the corrected difference image data (step S708). That is, the image composition unit 305 synthesizes the document image data and the corrected difference image data generated in step S707, and updates the document image data. This updated document image data is color-converted document image data. An example of a procedure for combining the document image data and the corrected difference image data is shown below.
(1) A pixel value at a certain coordinate in the document image data is acquired.
(2) The pixel value of the corresponding coordinate of the corrected difference image data is acquired.
(3) The average of the two pixel values obtained in the above (1) and (2) is calculated, and this is used as the combined pixel value.
(4) Update the pixel value of the corresponding pixel of the document image data using the combined pixel value.
(5) Perform (1) to (4) above for all coordinates.

  In the above procedure, the original image data is updated with the combined pixel value. However, new image data may be generated using the combined pixel value. Moreover, in (3) of said procedure, an average is not restricted to an arithmetic mean, For example, you may use a geometric average, a weighted average, etc.

  Next, the user prints the color-converted document image data using the user printer 200 (step S709). That is, by printing the color-converted document image data using the user printer 200, a color-converted user print is obtained.

  The user evaluates the obtained color-converted user print (step S710). That is, the user evaluates the quality of the user print by comparing the color-converted user print and the reference print. If the quality of the color-converted user printed material is sufficient (Yes in step S711), the process ends. If not (No in step S711), the process proceeds to the next step S712. Note that the method for evaluating the quality of the color-converted user printed material is the same as the method described in step S702.

  Thereafter, the conversion intensity parameter acquisition unit 303 determines whether or not adjustment of the conversion intensity parameter is instructed by a user or the like (step S712). When the conversion strength parameter adjustment is instructed (Yes in step S712), the process returns to step S706, and the conversion strength parameter acquisition unit 303 acquires the conversion strength parameter again. Otherwise (No in step S712), the process ends.

  This completes a series of flows. In the color conversion process shown in FIG. 7, three determinations of the process end condition are set, but it is not necessary to set all of them. Although it may be omitted as appropriate, any one is desirably set at least.

(Example 2)
Next, Example 2 will be described. In the second embodiment, when the correction process of the difference image data is performed, the correction is not performed based on the value of the difference image data, but based on the pixel value of the pixel of the document image data corresponding to the pixel of the difference image data. The correction is different from the first embodiment. Hereinafter, the same reference numerals as those in the first embodiment are used for the portions having substantially the same functions as those in the first embodiment and the portions for performing the same processing, and the description thereof is omitted.

<Functional configuration>
FIG. 8 is a functional block diagram of an example of an image processing system or MFP according to the present embodiment (Example 2). The image processing system 600 or the MFP 700 according to the second embodiment is different from the first embodiment in the function of the correction processing unit 304A.

  The correction processing unit 304A is realized by, for example, the image processing device 606 and the like, and performs correction processing on the difference image data using the document image data and the conversion intensity parameter to generate correction difference image data.

<Details of processing>
FIG. 9 is a flowchart of an example of color conversion processing (second embodiment). The color conversion process according to the second embodiment is different from the first embodiment in the process of step S901. Therefore, only the process of step S901 will be described.

  The correction processing unit 304A corrects the difference image data (step S901). That is, the correction processing unit 304A performs correction processing on the difference image data using the document image data and the conversion intensity parameter acquired in step S706, and generates correction difference image data. Examples of correction performed on the difference image data include the following.

a) Method of performing correction using different conversion intensity parameters for each gradation value of a pixel of original image data corresponding to a pixel of difference image data When original image data and difference image data are in an RGB color space Will be described. The difference image data is corrected by multiplying each pixel value of the difference image data by the conversion intensity parameter. The conversion intensity parameter can be controlled for each gradation value of the pixel of the document image data corresponding to the pixel of the difference image data. Conversion intensity parameters that differ for each pixel gradation value of the document image data may be managed by a lookup table. This method is represented by the following equation.

ここで、（R_diff, G_diff, B_diff）は差分画像データのRGB値を、（R_in, G_in, B_in）は原稿画像データのRGB値を、w[・]は階調値毎の変換強度パラメータを格納したルックアップテーブルを、（R_diff', G_diff', B_diff'）は補正差分画像データのRGB値を表す。

Where (R _diff , G _diff , B _diff ) is the RGB value of the difference image data, (R _in , G _in , B _in ) is the RGB value of the original image data, and w [ (R _diff ', G _diff ', B _diff ') represents the RGB values of the corrected difference image data.

b) Correction method using different conversion intensity parameters for each tone value of each axis of the color space of the pixel of the original image data corresponding to the pixel of the difference image data As an example, the original image data and the difference image data are The case of the RGB color space will be described. The difference image data is corrected by multiplying each pixel value of the difference image data by a different conversion intensity parameter for each of the R component value, the G component value, and the B component value. The conversion intensity parameter can be controlled for each gradation value of each axis of the color space of the pixel of the original image data corresponding to the pixel of the difference image data. Conversion intensity parameters that differ for each tone value of each axis may be managed by a look-up table. This method is represented by the following equation.

ここで、（R_diff, G_diff, B_diff）は差分画像データのRGB値を、（R_in, G_in, B_in）は原稿画像データのRGB値を、（w_R[・], w_G[・], w_B[・]）はR成分値、G成分値、B成分値の階調値毎の変換強度パラメータを格納したルックアップテーブルを、（R_diff', G_diff', B_diff'）は補正差分画像データのRGB値を表す。

Where (R _diff , G _diff , B _diff ) is the RGB value of the difference image data, (R _in , G _in , B _in ) is the RGB value of the original image data, and (w _R [•], w _G [•], w _B [•]) is a lookup table that stores the conversion intensity parameter for each gradation value of R component value, G component value, and B component value, (R _diff ', G _diff ', B _diff ') Represents the RGB value of the corrected difference image data.

(Example 3)
Next, Example 3 will be described. In the third embodiment, when the correction process of the difference image data is performed, the color space of the document image data is not used as it is, but is converted into a color space that can express hue, lightness, or saturation. The difference from the second embodiment is that the difference image data is corrected based on at least one of the hue, brightness, and saturation of the pixel of the original image data after color space conversion corresponding to the pixel of the difference image data. . As a result, the intensity (degree) of color conversion can be controlled in accordance with the hue, brightness, or saturation of the document image data.

  That is, for example, when it is desired to reduce the degree of color conversion for yellow in the original image data, or when it is desired to reduce the degree of color conversion for the high brightness area of the original image data, the degree of color conversion for the high saturation area of the original image data. Even when it is desired to weaken the color, the intensity of color conversion can be controlled. In the following description, the same reference numerals as those in the first and second embodiments are used for the portions having substantially the same functions as those in the first and second embodiments and the portions for performing the same processing, and the description thereof is omitted.

<Functional configuration>
FIG. 10 is a functional block diagram of an example of an image processing system or MFP according to the present embodiment (Example 3). The image processing system 600 or the MFP 700 according to the third embodiment is different from the second embodiment in that the function of the correction processing unit 304B and the color space conversion unit 306 are included.

  The correction processing unit 304B is realized by, for example, the image processing device 606, performs correction processing on the difference image data by using the original image data whose color space has been converted by the color space conversion unit 306, and the conversion intensity parameter. Difference image data is generated.

  The color space conversion unit 306 is realized by the image processing device 606, for example, and converts the color space of the document image data into a color space that can express hue, lightness, or saturation.

<Details of processing>
FIG. 11 is a flowchart of an example of color conversion processing (third embodiment). The color conversion process according to the third embodiment is different from the second embodiment in the processes of step S1101 and step S1102. Therefore, only the processing in steps S1101 and S1102 will be described.

  The color space conversion unit 306 converts the color space of the document image data into a color space that can express hue, lightness, or saturation (step S1101). Here, the color space capable of expressing the hue, lightness, or saturation is, for example, the HSV color space, the HLS color space, the YCbCr color space, the L * a * b * color space, or the L * u * v * Color space, etc. Note that the document image data subjected to color conversion in this step S1101 is used only in the processing of step S1101.

  The correction processing unit 304B corrects the difference image data (step S1102). That is, the correction processing unit 304B performs correction processing on the difference image data using the document image data obtained by converting the color space in step S1101 and the conversion intensity parameter acquired in step S706, and the corrected difference image. Generate data. Examples of correction performed on the difference image data include the following. In the following description, it is assumed that the color space of the document image data is converted to the L * a * b * color space. In the L * a * b * color space, the hue h and saturation C * in the color space are defined by the following equations.

ａ) 差分画像データの画素と対応する原稿画像データの画素の色相毎に異なる変換強度パラメータを用いて補正する方法
一例として、色空間変換後の原稿画像データがL*a*b*色空間、差分画像データがRGB色空間である場合について説明する。差分画像データの各画素値に、変換強度パラメータを掛けることで、差分画像データを補正する。変換強度パラメータは、差分画像データの画素と対応する、色空間変換後の原稿画像データの画素の色相毎に制御可能である。色相毎に異なる変換強度パラメータは、ルックアップテーブルで管理すればよい。この方法は、以下の式で表される。

a) Correction method using different conversion intensity parameters for each hue of pixels of original image data corresponding to pixels of difference image data As an example, original image data after color space conversion is L * a * b * color space, A case where the difference image data is in the RGB color space will be described. The difference image data is corrected by multiplying each pixel value of the difference image data by the conversion intensity parameter. The conversion intensity parameter can be controlled for each hue of the pixels of the original image data after color space conversion corresponding to the pixels of the difference image data. What is necessary is just to manage the conversion strength parameter which changes for every hue with a look-up table. This method is represented by the following equation.

ここで、（R_diff, G_diff, B_diff）は差分画像データのRGB値を、h_inは色空間変換後の原稿画像データの色相hを、w[・]は色相毎の変換強度パラメータを格納したルックアップテーブルを、（R_diff', G_diff', B_diff'）は補正差分画像データのRGB値を表す。

Where (R _diff , G _diff , B _diff ) is the RGB value of the difference image data, h _in is the hue h of the original image data after color space conversion, and w [•] is the conversion intensity parameter for each hue. In the stored lookup table, (R _diff ', G _diff ', B _diff ') represents the RGB value of the corrected difference image data.

b) Method of correcting using different conversion intensity parameters for each pixel value of original image data corresponding to pixels of difference image data As an example, original image data after color space conversion is L * a * b * color space, A case where the difference image data is in the RGB color space will be described. The difference image data is corrected by multiplying each pixel value of the difference image data by the conversion intensity parameter. The conversion intensity parameter can be controlled for each lightness of the pixel of the original image data after color space conversion corresponding to the pixel of the difference image data. What is necessary is just to manage the conversion strength parameter which changes for every brightness with a look-up table. This method is represented by the following equation.

ここで、（R_diff, G_diff, B_diff）は差分画像データのRGB値を、L*_inは色空間変換後の原稿画像データの明度L*を、w[・]は色相毎の変換強度パラメータを格納したルックアップテーブルを、（R_diff', G_diff', B_diff'）は補正差分画像データのRGB値を表す。

Where (R _diff , G _diff , B _diff ) is the RGB value of the difference image data, L * _in is the lightness L * of the original image data after color space conversion, and w [•] is the conversion intensity for each hue In the lookup table storing the parameters, (R _diff ', G _diff ', B _diff ') represents the RGB value of the corrected difference image data.

c) Method of correcting using different conversion intensity parameters for each saturation of the original image data pixels corresponding to the pixels of the difference image data. As an example, the original image data after color space conversion is L * a * b * color space The case where the difference image data is in the RGB color space will be described. The difference image data is corrected by multiplying each pixel value of the difference image data by the conversion intensity parameter. The conversion intensity parameter can be controlled for each saturation of the pixels of the original image data after color space conversion corresponding to the pixels of the difference image data. What is necessary is just to manage the conversion strength parameter which changes for every saturation with a look-up table. This method is represented by the following equation.

ここで、（R_diff, G_diff, B_diff）は差分画像データのRGB値を、C*_inは色空間変換後の原稿画像データの彩度C*を、w[・]は色相毎の変換強度パラメータを格納したルックアップテーブルを、（R_diff', G_diff', B_diff'）は補正差分画像データのRGB値を表す。

Where (R _diff , G _diff , B _diff ) is the RGB value of the difference image data, C * _in is the saturation C * of the original image data after color space conversion, and w [•] is the conversion for each hue. In the lookup table storing the intensity parameter, (R _diff ', G _diff ', B _diff ') represents the RGB value of the corrected difference image data.

  The method of correcting the difference image data is not limited to the above methods a) to c), and the difference image data may be corrected by a method combining two or more of the methods a) to c). For example, after the difference image data is corrected by the method a), the correction may be further performed by the method b) or c).

Example 4
Next, Example 4 will be described. The fourth embodiment is different from the first to third embodiments in that the reverse characteristics of the reference color reproduction characteristics and the user color reproduction characteristics are unknown. Therefore, in the fourth embodiment, the inverse characteristics of the reference color reproduction characteristics and the user color reproduction characteristics are estimated. Hereinafter, the same reference numerals as those in the first embodiment are used for the portions having substantially the same functions as those in the first embodiment and the portions for performing the same processing, and the description thereof is omitted.

<Functional configuration>
FIG. 12 is a functional block diagram of an example of an image processing system or MFP according to the present embodiment (Example 4). The image processing system 600 or MFP 700 according to the fourth embodiment is different from the first embodiment in that the image processing system 600 or the MFP 700 includes an image reading unit 307, a geometric transformation parameter estimation unit 308, a pixel value association unit 309, and a color reproduction characteristic estimation unit 310. In FIG. 12, each configuration other than these is the same as that of the first embodiment, but may be the same as that of the second or third embodiment. In other words, the correction processing unit 304 may be configured instead of the correction processing unit 304, or the correction processing unit 304B may be included instead of the correction processing unit 304, and the color space conversion unit 306 may be included.

  The image reading unit 307 is realized by, for example, the image input device 601 and the like, and reads a reference print product and a user print product, which are output results of document image data, and generates reference image data and user image data.

  The geometric conversion parameter estimation unit 308 is realized by, for example, the image analysis device 604 and the like, and estimates the respective geometric conversion parameters of document image data and reference image data, document image data, and user image data.

  The pixel value associating unit 309 is realized by, for example, the image analysis device 604 and the like, and detects the pixels of the reference image data at the position corresponding to the pixels of the document image data using the geometric conversion parameters, and sets the pixel values. Pixel value association data is created in association with each combination of color components. Similarly, the pixel value associating unit 309 detects the user image data pixels at positions corresponding to the pixels of the document image data using the geometric conversion parameters, and associates the pixel values with each color component combination. In addition, pixel value association data is created.

  The color reproduction characteristic estimation unit 310 is realized by, for example, the image analysis device 604 and the like, and estimates (determines) an inverse characteristic of the reference color reproduction characteristic and the user color reproduction characteristic using the pixel value association data.

<Details of processing>
≪Color conversion process≫
FIG. 13 is a flowchart illustrating an example of color conversion processing (fourth embodiment). The color conversion process according to the third embodiment is different from the first embodiment in the processes of steps S1301 to S1305. Therefore, only the processing in steps S1301 to S1305 will be described.

  The color reproduction characteristic estimation unit 310 estimates the reference color reproduction characteristic based on the document image data and the reference image data (step S1301). Details of the reference color reproduction characteristic estimation processing by the color reproduction characteristic estimation unit 310 will be described later. Note that this step need only be executed once. If it is executed a plurality of times, it should be noted that the original image data used in this step is the original one, and the color-converted one is not used.

  The color reproduction characteristic estimation unit 310 estimates an inverse characteristic of the user color reproduction characteristic based on the document image data and the user image data (step S1302). Details of the process of estimating the inverse characteristics of the user color reproduction characteristics by the color reproduction characteristic estimation unit 310 will be described later.

  Next, the user evaluates the reverse characteristic of the reference color reproduction characteristic estimated in the above-described step S1301 and step S1302 and the user color reproduction characteristic (step S1303). If the estimated color reproduction characteristics are appropriate (Yes in step S1304), the process proceeds to the next step S704, and if not (No in step S1304), the process ends.

Whether or not the color reproduction characteristic is appropriate is determined in order to determine whether or not it is meaningful to perform color conversion with this characteristic, and it can also be referred to as a color conversion convergence determination. For this reason, step S1303 may be omitted as necessary. Examples that do not make sense to perform color conversion include the following. Note that the image processing system 600 or the MFP 700 according to the present embodiment may include mapping determination means (not shown) that performs such determination.
a) Case where the estimation accuracy of the estimated color reproduction characteristic is extremely low In such a case, it is preferable not to convert the color because the color of the reference printed matter cannot be matched even if the color conversion is performed.
b) Case where image data hardly changes before and after conversion of color conversion In such a case, there is no point in executing color conversion.
c) Case in which image data changes extremely before and after conversion of color conversion In such a case, it is preferable not to convert color conversion because it greatly changes the color of the user print.

  An example of a criterion for determining the validity of the color reproduction characteristics described in the above a) is how far the actually measured value is from the estimated value. Taking the reference color reproduction characteristic as an example, the difference between the value of the reference image data (measured value) registered in the pixel value association data and the value estimated with the reference color reproduction characteristic (estimated value) is used.

As an example of the scale to measure the distance,
-Accumulated absolute value of difference between measured value and estimated value-Accumulated value of square of measured value and estimated value-Maximum absolute value of difference between measured value and estimated value-Phase between measured value and estimated value For example, the number of relationships.

An example of a criterion for judging the validity of the color reproduction characteristics described in the above b) and c) is how much the pixel value is converted in the color conversion. Taking the RGB color space as an example, the difference between R component values before and after conversion, the difference between G component values before and after conversion, and the difference between B component values before and after conversion are used. Examples of scales that measure this degree of conversion include:
A value obtained by accumulating the absolute value of the difference between pixel values before and after conversion, a value obtained by accumulating the square of the difference between pixel values before and after conversion, and the maximum absolute value of the difference between pixel values before and after conversion.

  Since the evaluation values obtained by measuring with these scales are within a predetermined range, it can be determined that the color reproduction characteristics are appropriate.

  When the conversion intensity parameter adjustment is not instructed in the process of step S712 (No in step S712), the color conversion unit 301 determines whether the continuation of the process is instructed by, for example, the user (step S1305). . If an instruction to continue the process is given (Yes in step S1305), the color-converted original image data generated in step S708 and the color-converted user printed matter (user image data) printed in step S700 are used again. The process of step S1302 is performed. As described above, all the document image data used in the next loop is the color-converted document image data.

  This completes a series of flows. In the color conversion process shown in FIG. 13, four determinations of process end conditions are set, but it is not necessary to set all of them. Although it may be omitted as appropriate, any one is desirably set at least.

  In FIG. 13 and FIGS. 14 and 16 to be described later, the color space used at the time of reading by the scanner 300 is used as it is. However, since this color space is a device-dependent color space, It is desirable to convert to a device-independent color space using a color profile. Examples of device-independent color spaces include device-independent RGB color space and XYZ color space. Furthermore, it is more preferable to convert to a uniform color space such as an L * a * b * color space. Therefore, the image processing system 600 or the MFP 700 according to the present embodiment may include a second color space conversion unit (not shown) that converts the reference image data and the user image data into a device-independent color space. .

  It is essential that the reference image data and user image data read by the scanner 300 are in the same color space, but the document image data and reference image data, or the document image data and user image data are in the same color space. Not required. For example, the color space of the document image data may be a CMYK color space, and the reference image data and user image data may be an RGB color space.

≪Standard color reproduction characteristics estimation process≫
Next, the reference color reproduction characteristic estimation process in step S1301 of FIG. 13 will be described. FIG. 14 is a flowchart of an example of a reference color reproduction characteristic estimation process.

  The image reading unit 307 reads the reference printed material and generates reference image data (step S1401).

  The geometric conversion parameter estimation unit 308 aligns the document image data and the reference image data (step S1402).

  Prior to aligning the two image data, the geometric conversion parameter estimation unit 308 obtains the geometric conversion parameter of the reference image data when the document image data is used as a reference. Examples of geometric transformation parameters include displacement, rotation angle, and scaling factor. A known technique may be used to estimate the geometric parameter. Examples thereof include a method using a marker, a pattern matching method not using a marker, a phase-only correlation method, and the like.

a) Method of using a marker Markers called “register marks” are arranged at the four corners and the center of each side of the document image data, and output when the reference image data is read. This is a method for obtaining the displacement amount, rotation angle, and magnification.

b) Method Using Pattern Matching Method One example of a method for estimating only the displacement amount is a template matching method. In the template matching method, one image is used as a template, the degree of coincidence with the other image is obtained while gradually shifting the position, and the position with the highest degree of coincidence is detected. When the geometric transformation cannot be limited to only displacement, it is necessary to use in combination with a method for estimating the rotation angle (such as Hough transform) or a method for estimating the amount of magnification (such as multi-scale analysis).

  In the block matching method using template matching, one image is divided into blocks, and the displacement amount can be obtained by detecting the position having the highest degree of coincidence with the other image for each block. In the block matching method, it is also possible to estimate the rotation angle and magnification from the displacement amount for each block.

c) Method using phase-only correlation method Examples of methods for obtaining displacement, rotation angle, and magnification with high accuracy include phase-only correlation (POC) and rotation-invariant phase-only correlation (RIPOC, Rotation Invariant). Phase Only Correlation). The phase only correlation method uses a phase image obtained by subjecting an image to a discrete Fourier transform, and detects a position where the correlation between two phase images obtained from two images to be compared is highest, This is a method for obtaining a displacement amount. The rotation-invariant phase-only correlation method is such that the rotation angle and the scaling factor can be detected as a displacement amount on the converted phase image by logarithmic polar coordinate conversion of the phase image.

  When the geometric transformation parameters are obtained as described above, the geometric transformation parameter estimation unit 308 performs geometric transformation on the reference image data. In the case where the pixels before and after conversion do not correspond one-to-one due to sub-pixel precision shift, some rotation, or scaling with a real value at the time of conversion, the pixel value may be derived appropriately using a pixel interpolation method. Examples of pixel interpolation methods include bilinear methods and bicubic methods.

  Note that geometric conversion is not essential, and in the next step, when obtaining the pixel at the same position in the original image data and the reference image data, coordinate conversion is performed using the geometric conversion parameter to determine whether the position is the same. It may be replaced by doing. In other words, even if the coordinate system based on the origin of each image holds different coordinate values, the pixels that have the same coordinate value as a result of geometric transformation are regarded as “pixels at the same position”. Become.

  There are cases where margins exist around the printed material obtained by outputting the document image data. In such a case, since the height and width of the margin part are included in the displacement amount of the geometric transformation, the margin part is not referred to, but a necessary area is cut out in the output image data so as to exclude the margin part, You may make the position of the origin in each image correspond.

  Next, the pixel value association unit 309 associates pixel values at the same position in the document image data and the reference image data for each combination of color components (step S1403). That is, the pixel value association unit 309 acquires the pixel values of the corresponding pixels in the two image data when the alignment of the document image data and the reference image data is completed, and associates these with each color component combination. Pixel value association data is created. Note that, when performing alignment by geometrically converting image data, the “corresponding pixels” are “pixels at the same position”. On the other hand, when the image data is not geometrically converted, the position having the same coordinate value by the coordinate conversion is regarded as “the same position”, and the pixel existing at that position is regarded as the “corresponding pixel”.

As an example of a method of recording pixel values in association with each combination of color components, there is a method of recording in a list format. Description will be made assuming that both the document image data and the reference image data are RGB images and each color component has 256 gradations. The pixel value list is recorded in the following procedure.
(1) Prepare one list.
(2) Select a coordinate with document image data.
(3) Correspondence between R component value (R _in ), G component value (G _in ), B component value (B _in ), and reference image data of the original image data pixel selected in (2) above The R component value (R _out1 ), G component value (G _out1 ), and B component value (B _out1 ) of the pixel to be added are bundled and added to the list.
(4) This is repeated for all coordinates of the document image data.

These lists may be rearranged in ascending or descending order as necessary. In order to simplify the processing, the coordinates may be limited to a specific range, or the coordinates may be moved by a predetermined step size, instead of being repeated for all the coordinates of the document image data. An example of data recorded in a list format is shown in FIG. In FIG. 15, the component values (R _in , G _in , B _in ) of the pixels of the original image data are recorded in the left half, and the corresponding pixel component values (R _out1 , G _in ) of the reference image data are recorded in the right half. _out1 , B _out1 ) is recorded.

  When generating the pixel value association data, it is desirable to remove the outline portion of the content of the image data (original image data, reference image data). This is because it is difficult to perfectly align the contour portion in alignment, and an error may occur in association of pixel values. If an error occurs in pixel value association, the estimation accuracy of the color reproduction characteristics described later is lowered.

  As a method for detecting a contour portion, for example, there are a method using binarization and a method using edge detection.

  As a method using binarization, for example, there is a method in which image data is binarized into black and white with a predetermined threshold, and a portion where a white region and a black region are adjacent is determined as a contour portion.

  As a method using edge detection, for example, there is a method in which an edge image is generated from image data using a Sobel method or the like, binarized with a predetermined threshold value, and a pixel equal to or higher than the threshold value is determined as a contour portion.

  There is also a method of alleviating the above-described decrease in estimation accuracy without removing the contour portion. For example, the image data is smoothed to smooth the contour portion, and the color difference appearing at the contour portion is reduced. For smoothing, a conventional technique such as an averaging filter or a low-pass filter may be used.

  Next, the color reproduction characteristic estimation unit 310 estimates reference color reproduction characteristics (step S1404). That is, the pixel value association data generated in step S1403 is used to determine which pixel value in the reference image data corresponds to a certain pixel value in the document image data. As in step S1403, it is assumed that both the original image data and the reference image data are RGB images and each color component has 256 gradations.

The estimated reference color reproduction characteristics by using the pixel value mapping data, the color component values of the original image data _{(R in, G in, B} in) color component values of the reference image data from the (R _out1, G _{out1, Bout1} ) is to estimate a color conversion characteristic representing a correspondence relationship when color conversion is performed.

  As a method for estimating the reference color reproduction characteristics, for example, the following polynomial function approximation can be cited.

ここで、Xは原稿画像データの色成分値[R_in, G_in, B_in]^t、Yは基準画像データの色成分値[R_out1, G_out1, B_out1]^t、M_Sは補正係数行列、fは補正関数である。補正関数f(X)は、以下の式等が用いられる。

Here, X is the color component values of the original image data _{[R in, G in, B} in] t, Y color component values of the reference image data _{[R out1, G out1, B} out1] t, M S is the correction factor Matrix, f is a correction function. The following equation or the like is used for the correction function f (X).

原稿画像データと基準画像データとの間が線形歪みをもつときは、補正係数f(X)=[R_in, G_in, B_in]^tの3項に、3×3の補正係数行列M_Sを操作した線形変換による推定で十分であるが、複雑な非線形歪みをもつときは高次の関数項を用いて高精度の色再現特性の推定が必要となる。

When the original image data and the reference image data have a linear distortion, the 3 × 3 correction coefficient matrix M _{S is} ^{added to} the three terms of the correction coefficient f (X) = [R _in , G _in , B _in ] ^t. However, when there is complex nonlinear distortion, it is necessary to estimate the color reproduction characteristics with high accuracy using higher-order function terms.

The correction coefficient M _S can be obtained by, for example, the least square method. Color component value X (n) (n = 1 to N) of document image data corresponding to the color component value Y (n) (n = 1 to N) of N reference image data stored in the pixel value association data )Using,

の最小条件により、次式で計算される。

Is calculated by the following equation.

ここで、Y_N及びX_NはN個の画素値対応付けデータ行列を表し、Y_Nは基準画像データの色成分値行列であり、X_Nは原稿画像データの色成分値行列を表す。

Here, Y _N and X _N represent N pixel value association data matrices, Y _N represents a color component value matrix of reference image data, and X _N represents a color component value matrix of document image data.

ここで、R_out1(n)は画素値対応付けデータに登録されているn番目の基準画像データのR成分値を表し、R_in(n)は画素値対応付けデータに登録されているn番目の原稿画像データのR成分値を表す（G_out1(n)、B_out1(n)、G_in(n)、B_in(n)も同様である）。

Here, R _out1 (n) represents the R component value of the n-th reference image data registered in the pixel value association data, and R _in (n) is the n-th registered in the pixel value association data. Represents the R component value of the original image data (G _out1 (n), B _out1 (n), G _in (n), and B _in (n) are the same).

f (X _N ) is a function term matrix of the color component values of the original image data, and taking the case of a quadratic function as an example, it is the following 10 × N matrix.

なお、基準色再現特性の推定に用いる関数項は、複数の色成分値を基にした多次元のデータであれば、上述したものに限定されるものではない。また、基準色再現特性の推定方法の一例として多項式近似を挙げたが、その他にも画素値対応付けデータを学習データに用いたニューラルネットワークなどで特性を推定することも可能である。また、ステップＳ１４０３で生成した画素値対応付けデータにおいて原稿画像データの全ての座標について対応関係を記録してある場合には、画素値対応付けデータをそのまま基準色再現特性として用いることもできる。

Note that the function term used for estimating the reference color reproduction characteristics is not limited to the above as long as it is multidimensional data based on a plurality of color component values. In addition, polynomial approximation is given as an example of the estimation method of the reference color reproduction characteristics, but it is also possible to estimate the characteristics using a neural network or the like using pixel value association data as learning data. Further, when the correspondence relation is recorded for all the coordinates of the document image data in the pixel value association data generated in step S1403, the pixel value association data can be used as the reference color reproduction characteristic as it is.

  The reference color reproduction characteristics estimated as described above are stored in the color reproduction characteristic storage device 605, for example.

≪Process for estimating reverse characteristics of user color reproduction characteristics≫
Next, the inverse characteristic estimation process of the user color reproduction characteristic in step S1302 of FIG. 13 will be described. FIG. 16 is a flowchart of an example of a process for estimating the inverse characteristic of the user color reproduction characteristic.

  In the processes in steps S1601 to S1603, “reference image data” is read as “user image data” in the processes in steps S1401 to S1403 in FIG. Then, since the process of step S1601-step S1603 is the same as the process of step S1401-step S1403 of FIG. 14, respectively, description is abbreviate | omitted.

An example of data recorded in the list format in step S1603 is shown in FIG. In FIG. 17, each component value (R _in , G _in , B _in ) of the original image data pixel is recorded in the left half, and each corresponding pixel component value (R _out2 , G _in ) of the user image data is recorded in the right half. _out2 and _Bout2 ) are recorded.

  Next, the color reproduction characteristic estimation unit 310 estimates an inverse characteristic of the user color reproduction characteristic (step S1604). That is, the pixel value association data generated in step S1603 is used to determine which pixel value in the document image data corresponds to a certain pixel value in the user image data. As in step S1403, it is assumed that both the user image data and the document image data are RGB images and each color component has 256 gradations.

The estimation of the inverse characteristic of the user color reproduction characteristics by using the pixel value mapping data, the color component values of the user image data _{(R out2, G out2, B} out2) color component values of the original image data from the (R _in, G _in, it is to estimate the color conversion characteristic representing a relationship at the time of performing the color conversion to B _in).

  As a method for estimating the inverse characteristic of the user color reproduction characteristic, for example, the following polynomial function approximation can be cited.

ここで、Xはユーザ画像データの色成分値[R_out2, G_out2, B_out2]^t、Yは原稿画像データの色成分値[R_in, G_in, B_in]^t、M_Sは補正係数行列、fは補正関数である。補正関数f(X)は、以下の式等が用いられる。

Here, X is the color component values of the user image data _{[R out2, G out2, B} out2] t, Y color component values of the original image data _{[R in, G in, B} in] t, M S is the correction factor Matrix, f is a correction function. The following equation or the like is used for the correction function f (X).

ユーザ画像データと原稿画像データとの間が線形歪みをもつときは、補正係数f(X)=[R_out2, G_out2, B_out2]^tの3項に、3×3の補正係数行列M_Sを操作した線形変換による推定で十分であるが、複雑な非線形歪みをもつときは高次の関数項を用いて高精度の色再現特性の推定が必要となる。

When there is linear distortion between the user image data and the document image data, the 3 × 3 correction coefficient matrix M _{S is added to} the three terms of the correction coefficient f (X) = [R _out2 , G _out2 , B _out2 ] ^t. However, when there is complex nonlinear distortion, it is necessary to estimate the color reproduction characteristics with high accuracy using higher-order function terms.

The correction coefficient M _S can be obtained by, for example, the least square method. Color component value X (n) (n = 1 to N) of user image data corresponding to the color component value Y (n) (n = 1 to N) of N document image data stored in the pixel value association data )Using,

の最小条件により、次式で計算される。

Is calculated by the following equation.

ここで、Y_N及びX_NはN個の画素値対応付けデータ行列を表し、Y_Nは原稿画像データの色成分値行列であり、X_Nはユーザ画像データの色成分値行列を表す。

Here, Y _N and X _N represent N pixel value association data matrices, Y _N represents a color component value matrix of document image data, and X _N represents a color component value matrix of user image data.

ここで、R_out2(n)は画素値対応付けデータに登録されているn番目のユーザ画像データのR成分値を表し、R_in(n)は画素値対応付けデータに登録されているn番目の原稿画像データのR成分値を表す（G_out2(n)、B_out2(n)、G_in(n)、B_in(n)も同様である）。

Here, R _out2 (n) represents the R component value of the nth user image data registered in the pixel value association data, and R _in (n) represents the nth user image data registered in the pixel value association data. R component value of the original image data (G _out2 (n), B _out2 (n), G _in (n), and B _in (n) are the same).

f (X _N ) is a function term matrix of the color component values of the original image data, and taking the case of a quadratic function as an example, it is the following 10 × N matrix.

なお、ユーザ色再現特性の逆特性の推定に用いる関数項は、複数の色成分値を基にした値を含む多次元のデータであれば、上述したものに限定されるものではない。また、ユーザ色再現特性の逆特性の推定方法の一例として多項式近似を挙げたが、その他にも画素値対応付けデータを学習データに用いたニューラルネットワークなどで特性を推定することも可能である。ただし、ステップＳ１４０４とは異なり、ステップＳ１６０３で生成した画素値対応付けデータをそのままユーザ色再現特性の逆特性として用いることはできない。これは、基準画像データにある画素値の色成分の組合せが、ユーザ画像データ内にあるとは限らないため、画素値対応付けデータには存在しない画素値の色成分の組合せに対しても、推定する必要があるためである。

Note that the function term used to estimate the inverse characteristic of the user color reproduction characteristic is not limited to the above as long as it is multidimensional data including a value based on a plurality of color component values. In addition, polynomial approximation is given as an example of a method for estimating the inverse characteristic of the user color reproduction characteristic, but it is also possible to estimate the characteristic using a neural network or the like using pixel value association data as learning data. However, unlike step S1404, the pixel value association data generated in step S1603 cannot be directly used as the inverse characteristic of the user color reproduction characteristic. This is because the combination of the color components of the pixel values in the reference image data is not necessarily in the user image data, so even for the combination of the color components of the pixel values that do not exist in the pixel value association data, This is because it is necessary to estimate.

  The inverse characteristics of the user color reproduction characteristics estimated as described above are stored in the color reproduction characteristic storage device 605, for example.

<Summary>
As described above, the image processing system 600 or the MFP 700 according to the present embodiment can perform color matching between the reference print product and the user print product by performing color conversion on the document image data. In addition, the image processing system 600 or the MFP 700 according to the present embodiment adjusts the conversion intensity parameter when the user print output as a result of performing color conversion on the document image data is not the color intended by the user. The desired color conversion can be performed. Therefore, even if the user has no specialized knowledge regarding image processing, a desired user print can be easily obtained.

  The color conversion unit 301 is an example of a color conversion unit. The difference processing unit 302 is an example of a difference processing unit. The conversion intensity parameter acquisition unit 303 is an example of a conversion intensity parameter acquisition unit. The correction processing unit 304, the correction processing unit 304A, and the correction processing unit 304B are examples of correction processing means. The image composition unit 305 is an example of an image composition unit. The color space conversion unit 306 is an example of a first color space conversion unit. The geometric transformation parameter estimation unit 308 is an example of a geometric transformation parameter estimation unit. The pixel value association unit 309 is an example of a pixel value association unit. The color reproduction characteristic estimation unit 310 is an example of mapping estimation means.

  The present invention is not limited to the specifically disclosed embodiments, and various modifications and changes can be made without departing from the scope of the claims.

DESCRIPTION OF SYMBOLS 100 Computer 101 Color conversion part 102 Difference processing part 103 Conversion intensity parameter acquisition part 104 Correction processing part 105 Image composition part 106 Color space conversion part 107 Image reading part 108 Geometric conversion parameter estimation part 109 Pixel value matching part 110 Color reproduction characteristic Estimator 200 User printer 300 Scanner 400 Reference printer 500 Network 600 Image processing system 601 Image input device 602 Image output device 603 Image storage device 604 Image analysis device 605 Color reproduction characteristic storage device 606 Image processing device 607 Parameter input device 700 MFP

JP2013-30996A

Claims (12)

An image processing apparatus for reproducing a color of a first output result obtained by outputting original image data by a first image output means in a second output result obtained by outputting the original image data by a second image output means. ,
A first mapping for estimating the color of the first output image data obtained by the reading device reading the first output result from the color of the original image data, and the first image obtained by the reading device reading the second output result. Color conversion means for converting the pixel values of the document image data to generate intermediate image data based on a second mapping for estimating the color of the document image data from the color of the second output image data;
Difference processing means for calculating difference between pixel values of corresponding pixels of the original image data and the intermediate image data to generate difference image data;
Conversion intensity parameter acquisition means for acquiring a conversion intensity parameter for controlling the intensity of color conversion of pixel values of the document image data;
Correction processing means for generating corrected difference image data in which pixel values of the difference image data are converted based on the difference image data and the conversion intensity parameter;
Image combining means for performing a predetermined calculation on pixel values of corresponding pixels of the document image data and the corrected difference image data, and combining the document image data and the corrected difference image data;
An image processing apparatus. The correction processing means includes
The image processing apparatus according to claim 1, wherein corrected difference image data obtained by converting a pixel value of the difference image data is generated based on a pixel value of the difference image data and the conversion intensity parameter. The correction processing means includes
The corrected difference image data obtained by converting the pixel value of the difference image data is generated based on the pixel value of the pixel of the original image data corresponding to the pixel of the difference image data and the conversion intensity parameter. The image processing apparatus according to 1. First color space conversion means for converting the color space of the document image data into a color space capable of expressing hue, brightness, or saturation;
The correction processing means includes
At least one value of hue, brightness, or saturation of the pixel of the original image data converted by the first color space conversion unit corresponding to the pixel of the difference image data, and the conversion intensity parameter The image processing apparatus according to claim 1, wherein based on the difference image data, corrected difference image data obtained by converting pixel values is generated. A first geometric conversion parameter for aligning the positions of the first output image data and the document image data; and a second geometric conversion parameter for aligning the positions of the second output image data and the document image data. A geometric transformation parameter estimating means for estimating;
First pixel value association data in which combinations of color components of corresponding pixels of the first output image data and the document image data are associated using the first geometric transformation parameter; and the second Pixel value correspondence that generates second pixel value correspondence data in which a combination of color components of corresponding pixels of the original image data is associated with the second output image data using the geometric conversion parameters Attaching means;
Have
The first map is
Estimating the color of the first output image data from the document image data based on the first pixel value association data;
The second map is
5. The image processing apparatus according to claim 1, wherein a color of the document image data is estimated from a color of the second output image data based on the second pixel value association data. 6. Mapping estimation means for determining the first mapping and the second mapping;
The mapping estimation means includes
The first mapping for estimating the color of the first output image data is determined using multidimensional data based on a plurality of color component values of the document image data, and the second output image data 6. The image processing apparatus according to claim 5, wherein the second mapping for estimating the color of the document image data is determined using multidimensional data based on a plurality of color component values. A second color space conversion means for converting image data into a device-independent color space;
The mapping estimation means includes
After the first output image data and the second output image data are converted into a device-independent color space by the second color space conversion means, the converted first output image data and second The image processing apparatus according to claim 6, wherein the first mapping and the second mapping are determined using output image data. Mapping determination means for determining whether or not the first mapping and the second mapping satisfy a predetermined criterion;
The color conversion means includes
When the mapping determination unit determines that the first mapping and the second mapping satisfy the predetermined criterion, pixels of the document image data based on the first mapping and the second mapping The image processing apparatus according to claim 6, wherein the intermediate image data is generated by converting a value. The pixel value association unit includes:
After excluding contour portions of the contents of the original image data, the first output image data, and the second output image data, the colors of the corresponding pixels of the first output image data and the original image data The image according to any one of claims 5 to 8, wherein the association of the combination of components and the combination of the color components of the corresponding pixels of the second output image data and the document image data are associated. Processing equipment. Color evaluation means for evaluating the first output image data and the second output image data by a predetermined evaluation method ;
The color conversion means includes
If it that evaluation result to the color evaluation means does not meet a predetermined standard, the converted pixel values of the original image data to generate the intermediate image data, according to any one of claims 1 to 9 Image processing apparatus. Used in an image processing apparatus for reproducing the color of the first output result obtained by outputting the original image data by the first image output means in the second output result obtained by outputting the original image data by the second image output means. An image processing method comprising:
A first mapping for estimating the color of the first output image data obtained by the reading device reading the first output result from the color of the original image data, and the first image obtained by the reading device reading the second output result. A color conversion procedure for generating intermediate image data by converting pixel values of the document image data based on a second mapping for estimating the color of the document image data from the color of the second output image data;
A difference processing procedure for generating difference image data by calculating a difference between corresponding pixel values of the original image data and the intermediate image data;
A conversion intensity parameter acquisition procedure for acquiring a conversion intensity parameter for controlling the intensity of color conversion of the pixel value of the document image data;
Based on the difference image data and the conversion intensity parameter, a correction processing procedure for generating corrected difference image data in which pixel values of the difference image data are converted;
An image composition procedure for performing a predetermined calculation on pixel values of corresponding pixels of the document image data and the corrected difference image data, and combining the document image data and the corrected difference image data;
An image processing method. A first image output device that outputs a first output result from the document image data; a second image output device that outputs a second output result from the document image data; the first output result and the first output A reading device that reads the second output result and generates first output image data and second output image data, respectively, and a process for reproducing the color of the second output result in the first output result. An image processing system having an image processing device to perform,
A first mapping for estimating the color of the first output image data from the color of the original image data, and a second mapping for estimating the color of the original image data from the color of the second output image data. Based on color conversion means for converting the pixel values of the document image data to generate intermediate image data;
Difference processing means for calculating difference between pixel values of corresponding pixels of the original image data and the intermediate image data to generate difference image data;
Conversion intensity parameter acquisition means for acquiring a conversion intensity parameter for controlling the intensity of color conversion of pixel values of the document image data;
Correction processing means for generating corrected difference image data in which pixel values of the difference image data are converted based on the difference image data and the conversion intensity parameter;
Image combining means for performing a predetermined calculation on pixel values of corresponding pixels of the document image data and the corrected difference image data, and combining the document image data and the corrected difference image data;
An image processing system.

Images