JP2012142897A - Image processing device, image processing method, and image processing program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an image processing device, an image processing method, and an image processing program that are capable of highly accurately performing correction processing in the case of using an unknown paper sheet as an output medium for an image.SOLUTION: An image processing device comprises: a data storage unit 30 that holds characteristic data pieces of standard paper sheets; a characteristic data acquisition unit 22 that acquires a characteristic data piece of a user paper sheet; a similarity calculation unit 23 that calculates similarities between the user paper sheet and the standard paper sheets based on differences between the characteristic data pieces of the user paper sheet and the standard paper sheets; a similar paper sheet specification unit 24 that specifies a standard paper sheet having the largest similarity to the user paper sheet as a similar paper sheet; a similarity evaluation unit 25 that evaluates the magnitude of the similarity between the user paper sheet and the similar paper sheet; a correction method selection unit 26 that selects a correction method in a paper sheet-corresponding correction unit 15 from plural methods based on the evaluation result; and the paper sheet-corresponding correction unit 15 that performs correction processing according to the method selected by the correction method selection unit 26 in the case of using the user paper sheet as an output medium.

Description

  The present invention relates to an image processing apparatus, an image processing method, and an image processing program that perform correction processing according to the type of paper used as an image output medium.

  In color printers, color copiers, and the like, image processing called color management is performed in order to unify reproduced colors for input image data. In such color management, ideally, the same color is always required to be reproduced regardless of the paper used as an image output medium. For this reason, for a plurality of standard papers having known color reproduction characteristics, a correction profile used for correction processing for reproducing colors on each standard paper is created and held in advance, and the plurality of correction profiles are stored. It is known that a correction profile corresponding to a sheet actually used as an image output medium is selected from the above, and correction processing such as color conversion is performed using the selected correction profile.

  In addition, a sheet actually used as an image output medium is a sheet different from any of a plurality of standard sheets having a correction profile prepared in advance, that is, a sheet with unknown color reproduction characteristics (hereinafter referred to as an unknown sheet). In this case, correction processing such as color conversion is performed using a correction profile corresponding to a standard paper having color reproduction characteristics close to unknown paper used as an image output medium among a plurality of standard papers. Is also known (for example, see Patent Document 1).

  In Patent Document 1, a white paper portion of an unknown sheet used as an image output medium is measured with a spectrophotometer to obtain spectral characteristic data. Compared with the spectral data of the white area of the standard paper that has been prepared, the standard paper with color reproduction characteristics close to that of the unknown paper used as the image output medium is specified, and the correction corresponding to the specified standard paper A configuration that uses a profile is disclosed.

  However, the technique described in Patent Document 1 can ensure a certain degree of correction accuracy when the color reproduction characteristics of the unknown paper and the standard paper are very similar, but there is a difference in the color reproduction characteristics of the unknown paper and the standard paper. If it is large, there is a problem that sufficient correction accuracy cannot be obtained.

  The present invention has been made in view of the above, and provides an image processing apparatus, an image processing method, and an image processing program capable of performing correction processing with high accuracy when an unknown sheet is used as an image output medium. The purpose is to do.

  In order to solve the above-described problems and achieve the object, an image processing apparatus according to the present invention is an image processing apparatus that performs correction processing according to the type of paper used as an image output medium, and has color reproduction characteristics. For each of a plurality of standard papers that are known papers, a characteristic data holding unit that holds a plurality of characteristic data representing the color reproduction characteristics of each standard paper, and a color reproduction characteristic of an unknown paper that is a paper whose color reproduction characteristics are unknown Between the unknown sheet and each of the plurality of standard sheets based on the difference between the characteristic data acquisition means for acquiring characteristic data representing the characteristic data, the characteristic data of the unknown sheet, and the characteristic data of each of the plurality of standard sheets A similarity calculation means for calculating the similarity of the sheet, and a similar sheet feature that specifies a standard sheet having the highest similarity to the unknown sheet as the similar sheet among the plurality of standard sheets. And selecting a correction method for using the unknown sheet as the output medium from a plurality of predetermined correction methods based on the degree of similarity between the means and the unknown sheet and the similar sheet And correction means for correcting image data in accordance with a method selected by the selection means when the unknown sheet is used as the output medium.

  The image processing method according to the present invention is an image processing apparatus that performs correction processing according to the type of paper used as an image output medium, and each of a plurality of standard papers that have known color reproduction characteristics. An image processing method executed by an image processing apparatus having characteristic data holding means for holding a plurality of characteristic data representing the color reproduction characteristics of each standard paper, wherein an unknown paper whose color reproduction characteristics are unknown Obtaining the characteristic data representing the color reproduction characteristics; based on the difference between the characteristic data of the unknown sheet and the characteristic data of each of the plurality of standard sheets held by the characteristic data holding unit; Calculating a similarity between each of the plurality of standard papers; and, out of the plurality of standard papers, a standard paper having the largest similarity to the unknown paper Correction when using the unknown sheet as the output medium from a plurality of predetermined correction methods based on the step of specifying the similar sheet and the degree of similarity between the unknown sheet and the similar sheet Selecting a method and correcting the image data according to the selected method when the unknown sheet is used as the output medium.

  The image processing program according to the present invention is an image processing apparatus that performs correction processing according to the type of paper used as an image output medium, and each of a plurality of standard papers that have known color reproduction characteristics. Acquire characteristic data representing the color reproduction characteristics of an unknown paper whose color reproduction characteristics are unknown in an image processing apparatus having characteristic data holding means for holding a plurality of characteristic data representing the color reproduction characteristics of each standard paper Between the unknown sheet and each of the plurality of standard sheets based on the difference between the characteristic data of the unknown sheet and the characteristic data of each of the plurality of standard sheets held by the characteristic data holding unit. A function for calculating a similarity, a function for specifying a standard paper having the largest similarity to the unknown paper among the plurality of standard papers as a similar paper, A function of selecting a correction method when the unknown sheet is used as the output medium from a plurality of predetermined correction methods based on the degree of similarity between the sheet and the similar sheet; And a function of correcting image data in accordance with the selected method when the image is used as the output medium.

  According to the present invention, based on the degree of similarity between an unknown sheet and a similar sheet, a correction processing method when using the unknown sheet as an image output medium is selected. When the similarity is small, it is possible to perform finer correction processing than when the similarity is large, and it is possible to perform correction processing when an unknown sheet is used as an image output medium with high accuracy. .

FIG. 1 is a configuration diagram of an image forming system including an image processing apparatus. FIG. 2 is a functional block diagram showing a functional configuration for realizing an image processing process in the image processing apparatus. FIG. 3 is a diagram showing the spectral reflectance data of three types of standard papers A to C and nine types of user papers U1 to U9 in comparison. FIG. 4 is a diagram summarizing the results of calculating the similarity between nine types of standard papers A to C and nine types of user papers U1 to U9. FIG. 5 is a flowchart showing a series of processes executed by the paper difference correction curve generation unit. FIG. 6 is a flowchart showing details of the correction curve optimization process for each color of CMYK in step S104, step S105, step S106, and step S108 of FIG. FIG. 7 is a diagram illustrating an example of updating the output value corresponding to the input value in the correction curve optimization. FIG. 8 is a diagram showing the result of calculating the average color difference for each of the three output value candidates a, b, and c. FIG. 9 is a flowchart illustrating processing in which the color conversion parameter generation unit generates color conversion parameters corresponding to user paper using color conversion parameters corresponding to similar paper. FIG. 10 is a diagram for explaining the “interpolation by difference prediction” process in step S302 of FIG.

  Exemplary embodiments of an image processing apparatus, an image processing method, and an image processing program according to the present invention are explained in detail below with reference to the accompanying drawings.

(System configuration)
FIG. 1 is a configuration diagram of an image forming system including an image processing apparatus according to the present embodiment. The image processing apparatus 1 according to the present embodiment is realized by, for example, a control PC and expansion hardware and control software installed in the control PC.

  The image processing apparatus 1 is connected to an image forming apparatus 2 that forms an image on a sheet that is a physical image output medium based on the image data processed by the image processing apparatus 1. The image forming apparatus 2 uses, for example, four colors of cyan (C), magenta (M), yellow (Y), and black (K) as basic color materials, and forms a full-color image on a sheet by using these mixed colors.

  On the other hand, the image processing apparatus 1 is connected to the network 3, receives image data sent from the user PC 4 connected to the same network 3, performs an image processing process to be described later, and then performs the image forming apparatus 2. Forward to.

  Further, the image processing apparatus 1 is connected with a color measuring means 5 for measuring the color of the correction patch image formed on the sheet by the image forming apparatus 2. As the color measuring means 5, for example, a spectrocolorimeter is used. However, any means capable of obtaining spectral reflectance data of a sheet may be used, and a color scanner can be used instead.

(Functional configuration)
FIG. 2 is a functional block diagram showing a functional configuration for realizing an image processing process in the image processing apparatus 1. As shown in FIG. 2, the image processing apparatus 1 has a functional configuration for realizing an image processing process. As illustrated in FIG. 2, the image data acquisition unit 11, the first color conversion unit 12, a paper correspondence correction unit 15, and a correction control unit. 20 and a data storage unit 30.

  The image data acquisition unit 11 acquires image data sent from the user PC 4 via the network 3. The image data acquired by the image data acquisition unit 11 is not limited to the image data sent from the user PC 4 via the network 3. For example, when the image forming apparatus 2 includes a scanner, the image data acquisition unit 11 may acquire image data read by the scanner.

  The first color conversion unit 12 applies a PCS (Profile Connection Space), which is a device-independent color space, from the color values of device-dependent color spaces such as RGB and CMYK to the image data acquired by the image data acquisition unit 11. ) Is converted to color values.

  The paper correspondence correction unit 15 is a functional block that performs corrections corresponding to paper used as an image output medium in the image forming apparatus 2, and includes a second color conversion unit 13 and a gradation correction unit 14.

  The second color conversion unit 13 uses, for example, a color conversion parameter constituted by a three-dimensional LUT (Look Up Table), and converts the PCS color value obtained by the color conversion in the first color conversion unit 12 into an image. Color conversion is performed for conversion to color values in the CMYK color space handled by the forming apparatus 2.

  The gradation correction unit 14 uses, for example, a sheet difference correction curve that is a one-dimensional LUT set for each color of CMYK, and represents the color of the CMYK color space obtained by color conversion in the second color conversion unit 13. The gradation correction for each color of CMYK is performed on the value.

  The correction control unit 20 is a functional block that controls correction by the paper correspondence correction unit 15, and includes a correction patch image data transfer unit 21, a characteristic data acquisition unit 22, a similarity calculation unit 23, a similar paper specification unit 24, and a similarity. An evaluation unit 25, a correction method selection unit 26, a paper difference correction curve generation unit 27, and a color conversion parameter generation unit 28 are included.

  The data storage unit 30 is a data holding unit that holds various data necessary for the image processing process in the image processing apparatus 1, and includes correction patch image data 31 and a plurality of color conversion parameters corresponding to a plurality of standard sheets. A data set 32 and a data set 33 of a plurality of characteristic data corresponding to a plurality of standard sheets are held. The correction patch image data 31 is data for forming a plurality of single-color patch images and mixed-color patch images on a sheet. The standard sheet is a sheet having a known color reproduction characteristic, and a sheet in which a corresponding color conversion parameter is generated in advance and held in the data storage unit 30. Further, the characteristic data is data representing the color reproduction characteristics of the paper, for example, spectral reflectance data.

  For example, the correction patch image data transfer unit 21 reads the correction patch image data 31 stored in the data storage unit 30 in response to a user operation using an operation panel provided in the image forming apparatus 2. The image is transferred to the image forming apparatus 2. In response to the transfer of the correction patch image data 31, the image forming apparatus 2 forms and outputs a correction patch image on a sheet set by the user. Here, the sheet set on the image forming apparatus 2 by the user has an unknown color reproduction characteristic and the corresponding color conversion parameter is not held in the data storage unit 30 (hereinafter referred to as user sheet). Suppose that

  The characteristic data acquisition unit 22 receives characteristic data (for example, a paper white portion and a paper white portion) representing the color reproduction characteristics of the user paper from the color measurement unit 5 that measures the user paper X on which the correction patch image is formed by the image forming apparatus 2. A colorimetric value including the spectral reflectance data of the patch image is acquired. Here, the correction patch image is formed on the user paper by the image forming apparatus 2, and the user paper X on which the correction patch image is formed is measured by the color measuring unit 5. The configuration in which the characteristic data acquisition unit 22 acquires the characteristic data of the user paper will be described. However, the method for the characteristic data acquisition unit 22 to acquire the characteristic data of the user paper is not limited to this. The configuration may be such that the user paper characteristic data held by another connected terminal is acquired via the network 3.

  The similarity calculation unit 23 calculates the difference between the characteristic data of the user paper acquired by the characteristic data acquisition unit 22 and the plurality of characteristic data corresponding to the plurality of standard papers included in the data set 33 held by the data storage unit 30. Based on the above, the similarity between the user paper and each of the plurality of standard papers is calculated. When spectral reflectance data is used as the characteristic data, the similarity calculation unit 23 determines the user paper based on the difference between the spectral reflectance data of the user paper and the spectral reflectance data of each of the plurality of standard papers. The similarity between each of the plurality of standard sheets is calculated. A specific example of the similarity calculation method by the similarity calculation unit 23 will be described later in detail.

  Based on the similarity calculated by the similarity calculation unit 23, the similar sheet specifying unit 24 has the highest characteristic for the standard sheet having the highest similarity to the user sheet among the plurality of standard sheets, that is, the user sheet. The near standard paper is specified as a similar paper similar to the user paper.

  The similarity evaluation unit 25 evaluates the degree of similarity between the user paper and the standard paper identified as the similar paper by the similar paper identification unit 24. For example, the similarity evaluation unit 25 compares the similarity T_x between the user sheet and the similar sheet with the first threshold value Sh1 and the second threshold value Sh2 (Sh1> Sh2), and has a relationship of Sh1 ≦ T_x. Or by determining whether Sh2 ≦ T_x <Sh1, or T_x <Sh2, the similarity T_x between the user paper and the similar paper is high: Evaluation is made in three stages: Sh1 ≦ T_x), similarity: moderate (Sh2 ≦ T_x <Sh1), and similarity: small (T_x <Sh2).

  The correction method selection unit 26 corresponds to a sheet when the user sheet is used as an image output medium in the image forming apparatus 2 based on the similarity evaluation result between the user sheet and the similar sheet by the similarity evaluation unit 25. A correction method in the correction unit 15 is selected from a plurality of predetermined methods.

  Specifically, for example, when the similarity evaluation unit 25 evaluates the similarity between the user paper and the similar paper in three stages as described above, the correction method selection unit 26 determines whether the user paper and the similar paper are If the similarity between them is evaluated to be large (Sh1 ≦ T_x), among the plurality of color conversion parameters included in the data set 32 held by the data storage unit 30, a color conversion parameter corresponding to a similar sheet is used. Color conversion by the two-color conversion unit 13 is selected as a correction method in the paper correspondence correction unit 15. In this case, a paper difference correction curve in which the input value = the output value is applied to the gradation correction unit 14 to substantially invalidate the correction by the gradation correction unit 14.

  Further, when the similarity between the user paper and the similar paper is evaluated as medium (Sh2 ≦ T_x <Sh1), the correction method selection unit 26 includes a plurality of data included in the data set 32 held by the data storage unit 30. Color conversion by the second color conversion unit 13 using color conversion parameters corresponding to similar paper, and tone correction using the paper difference correction curve generated by the paper difference correction curve generation unit 27. A combination with gradation correction by the unit 14 is selected as a correction method in the paper correspondence correction unit 15.

  Further, when the similarity between the user paper and the similar paper is evaluated to be small (T_x <Sh2), the correction method selection unit 26 performs color conversion corresponding to the user paper generated by the color conversion parameter generation unit 28. Color conversion by the second color conversion unit 13 using parameters is selected as a correction method in the paper correspondence correction unit 15. In this case, a paper difference correction curve in which the input value = the output value is applied to the gradation correction unit 14 to substantially invalidate the correction by the gradation correction unit 14.

  The paper difference correction curve generation unit 27 generates a paper difference correction curve used for gradation correction in the gradation correction unit 14 for correcting the difference between the user paper and the similar paper. Note that a specific example of how the paper difference correction curve generation unit 27 generates the paper difference correction curve will be described later in detail.

  The color conversion parameter generation unit 28 uses the color conversion parameters corresponding to similar sheets included in the data set 32 held by the data storage unit 30 to generate color conversion parameters corresponding to user sheets. Note that a specific example of a method by which the color conversion parameter generation unit 28 generates color conversion parameters corresponding to user paper will be described later in detail.

(Overview of operation)
Next, an outline of the operation of the image processing apparatus 1 according to the present embodiment will be described.

  When the user sets user paper in the paper cassette of the image forming apparatus 2 and presses the “Paper Corresponding Correction” button displayed on the operation panel of the image forming apparatus 2, for example, image processing is performed from the image forming apparatus 2. A request signal for requesting transfer of the patch image data for correction is sent to the apparatus 1.

  When the request signal is sent from the image forming apparatus 2 to the image processing apparatus 1, the correction patch image data transfer unit 21 of the correction control unit 20 reads the correction patch image data 31 from the data storage unit 30. The correction patch image data 31 is transferred to the image forming apparatus 2. In response to the transfer of the correction patch image data 31, the image forming apparatus 2 forms and outputs a correction patch image on the user paper.

  When the user paper X on which the correction patch image is formed is output from the image forming apparatus 2 and the user sets the user paper X on which the correction patch image is formed on the color measurement unit 5, the color measurement unit 5 performs correction. Color measurement is performed on the user paper X on which the patch image is formed, and a color measurement value including the characteristic data of the user paper is sent to the image processing apparatus 1 and acquired by the characteristic data acquisition unit 22 of the correction control unit 20. .

  When the colorimetric value including the user paper characteristic data is acquired by the characteristic data acquisition unit 22 of the correction control unit 20, the similarity calculation unit 23 of the correction control unit 20 uses the user paper characteristic data and the data storage unit 30. The similarity between the user paper and each of the plurality of standard papers is calculated based on the difference from the plurality of characteristic data corresponding to the plurality of standard papers included in the data set 33 held by the user.

  When the similarity calculation unit 23 of the correction control unit 20 calculates the similarity between the user paper and each of the plurality of standard papers, the similar paper specifying unit 24 of the correction control unit 20 determines the similarity to the user paper. The standard paper with the largest is specified as a similar paper similar to the user paper.

  When the similar paper is specified by the similar paper specifying unit 24 of the correction control unit 20, the similarity evaluation unit 25 of the correction control unit 20 determines the similarity between the user paper and the similar paper, for example, three levels of large, medium, and small. Evaluate with. Then, the correction method selection unit 26 of the correction control unit 20 selects a correction method in the paper correspondence correction unit 15 from a plurality of predetermined methods based on the evaluation result of the similarity evaluation unit 25.

  For example, when it is evaluated that the degree of similarity between the user paper and the similar paper is large, the correction method selection unit 26 performs color conversion by the second color conversion unit 13 using the color conversion parameter corresponding to the similar paper. This is selected as a correction method in the paper correspondence correction unit 15. Then, the correction method selection unit 26 reads out the color conversion parameter corresponding to the similar paper from the data storage unit 30 and sets it in the second color conversion unit 13 of the paper correspondence correction unit 15, and the level of the paper correspondence correction unit 15. For the tone correction unit 14, a paper difference correction curve in which input value = output value is set.

  When the similarity between the user paper and the similar paper is evaluated as medium, the correction method selection unit 26 performs color conversion by the second color conversion unit 13 using the color conversion parameter corresponding to the similar paper. Then, a combination with gradation correction by the gradation correction unit 14 using the sheet difference correction curve generated by the sheet difference correction curve generation unit 27 of the correction control unit 20 is selected as a correction method in the sheet correspondence correction unit 15. Then, the correction method selection unit 26 reads out the color conversion parameter corresponding to the similar sheet from the data storage unit 30 and sets it in the second color conversion unit 13 of the sheet correspondence correction unit 15, and the sheet difference correction curve generation unit 27. Send an active signal to.

  The paper difference correction curve generation unit 27 receives the active signal from the correction method selection unit 26 and receives the active signal from the correction method selection unit 26 to correct the difference between the user paper and the similar paper. Generate a correction curve. Then, the paper difference correction curve generation unit 27 sets the generated paper difference correction curve in the gradation correction unit 14 of the paper correspondence correction unit 15.

  When it is evaluated that the similarity between the user paper and the similar paper is small, the correction method selection unit 26 performs color conversion corresponding to the user paper generated by the color conversion parameter generation unit 28 of the correction control unit 20. Color conversion by the second color conversion unit 13 using parameters is selected as a correction method in the paper correspondence correction unit 15. Then, the correction method selection unit 26 transmits an active signal to the color conversion parameter generation unit 28, reads the color conversion parameter corresponding to the similar sheet from the data storage unit 30, and sends the color conversion parameter to the color conversion parameter generation unit 28. Further, the correction method selection unit 26 sets a paper difference correction curve in which the input value = the output value for the gradation correction unit 14 of the paper correspondence correction unit 15.

  The color conversion parameter generation unit 28 receives the active signal from the correction method selection unit 26 and uses the color conversion parameter corresponding to the similar paper read from the data storage unit 30 to use the color conversion parameter corresponding to the user paper. Is generated. Then, the color conversion parameter generation unit 28 sets the color conversion parameter corresponding to the generated user paper in the second color conversion unit 13 of the paper correspondence correction unit 15.

  Thereafter, when the user presses the “print execution” button displayed on the operation panel of the image forming apparatus 2, the image data acquisition unit 11 acquires the image data. Then, the first color conversion unit 12 converts the image data acquired by the image data acquisition unit 11 into a PCS color value that is a device-independent color space.

  The image data that has undergone color conversion by the first color conversion unit 12 is input to the second color conversion unit 13 of the paper correspondence correction unit 15. The second color conversion unit 13 determines the color conversion parameter corresponding to the similar sheet read from the data storage unit 30 or the color of the correction control unit 20 according to the method selected by the correction method selection unit 26 of the correction control unit 20. Using the color conversion parameters corresponding to the user paper generated by the conversion parameter generation unit 28, the CMYK color values of the PCS obtained by the color conversion in the first color conversion unit 12 are handled by the image forming apparatus 2. Convert to color space color value.

  The image data that has undergone color conversion by the second color conversion unit 13 is input to the gradation correction unit 14 of the paper correspondence correction unit 15. The gradation correction unit 14 is a paper difference correction curve generated by the paper difference correction curve generation unit 27 of the correction control unit 20 according to the method selected by the correction method selection unit 26 of the correction control unit 20, or input value = output. Using the sheet difference correction curve as a value, tone correction for each color of CMYK is performed on the color values of the CMYK color space obtained by the color conversion in the second color conversion unit 13.

  Image data that has been subjected to gradation correction for each color of CMYK by the gradation correction unit 14 is sent to the image forming apparatus 2. Then, the image forming apparatus 2 forms an image on user paper based on the image data and outputs it as a printed matter. Through the above-described series of operations, even when user paper having unknown characteristics is used as an image output medium in the image forming apparatus 2, the image processing apparatus 1 performs high-precision correction corresponding to the user paper. The reproducibility of the image can be improved.

(Specific example of similarity calculation)
Next, a specific example of similarity calculation by the similarity calculation unit 23 of the correction control unit 20 will be described by taking as an example the case where spectral reflectance data is used as the characteristic data.

If the waveform of the spectral reflectance data of the user paper is similar to the waveform of the spectral reflectance data of the standard paper, it can be said that the user paper and the standard paper are similar. Therefore, a deviation σ between the spectral reflectance data of the user paper and the spectral reflectance data of the standard paper is obtained using the following formula (1), and the reciprocal of the deviation σ is represented by the similarity as in the following formula (2). It can be obtained as T. In the following formula (1), the wavelength λ is a discrete value, and λ = 380, 390,. N is the number of data of the discrete value λ, and N = 36.

  The fact that the spectral reflectance data waveform is similar between the user paper and the standard paper means that the variation in the difference at each wavelength is small, and the deviation σ is small. On the contrary, if the variation of the difference at each wavelength is large, the deviation σ becomes a large value. Since the magnitude of the similarity and the magnitude of the deviation σ of the spectral reflectance data are opposite to each other, the reciprocal of the deviation σ is defined as the similarity T. As described above, the similarity T between the user paper and the standard paper can be defined by a value based on the difference in each wavelength of the spectral reflectance data.

(Specific example of specific paper)
Next, a specific example of specifying similar sheets by the similar sheet specifying unit 24 of the correction control unit 20 will be described. Here, for each of the three types of standard papers A to C, corresponding color conversion parameters and spectral reflectance data (characteristic data) are held in the data storage unit 30, and for the nine types of user papers U1 to U9, 3 A case where a similar sheet is specified from the types of standard sheets A to C will be described as an example.

  FIG. 3 shows the spectral reflectance data of three types of standard papers A to C and nine types of user papers U1 to U9 in comparison. 3A shows the spectral reflectance data of three types of standard papers A to C, and FIG. 3B shows the waveform of the spectral reflectance data of the standard paper A and the spectral reflectance data of the standard paper A. FIG. 3C shows spectral reflectance data of user papers U1, U2, U7, and U8 that are close to each other, and FIG. 3C shows the spectral reflectance data of standard paper B and the user paper that has a waveform close to that of standard paper B. The spectral reflectance data of U4, U5, U6, and U9 are shown, and FIG. 3D shows the spectral reflectance data of the standard paper C and the spectral reflectance of the user paper U3 whose waveform is close to the spectral reflectance data of the standard paper C. The rate data is shown.

  Since the nine types of user papers U1 to U9 are similar in spectral reflectance data waveform to the three types of standard papers A to C, as shown in FIG. A group of user papers U1, U2, U7, U8 close to the standard paper A, and a group B of user papers U4, U5, U of which the spectral reflectance data waveform is close to the standard paper B as shown in FIG. As shown in FIG. 3D, U6 and U9 and the group C user paper U3 whose spectral reflectance data waveform is close to the standard paper C can be classified.

  As described above, the similarity between the user paper and the standard paper reflects the closeness of the waveform of the spectral reflectance data. Therefore, for each of the user papers U1 to U9, specifying similar papers from the standard papers A to C is the same as classifying the user papers U1 to U9 into the A group, the B group, and the C group. That is, the standard paper A can be specified as a similar paper for the A group of user papers U1, U2, U7, U8, and the standard paper B can be similar for the B group of user papers U4, U5, U6, U9. The standard paper C can be specified as a similar paper for the group C user paper U3.

(Specific example of similarity evaluation)
Next, a specific example of similarity evaluation between a user sheet and a similar sheet by the similarity evaluation unit 25 of the correction control unit 20 will be described.

  FIG. 4 summarizes the results of calculating the similarity T between the nine types of standard papers A to C and the nine types of user papers U1 to U9. In the figure, ◯ indicates which standard paper is specified as a similar paper for each user paper. The A group, the B group, and the C group described above are classified into nine types of user papers U1 to U9 based on the results shown in FIG.

  For example, the similarity between the user sheet and the similar sheet is compared with the first threshold value Sh1 and the second threshold value Sh2 in the table shown in FIG. By doing so, it is evaluated in three stages of large, medium and small. Here, when the first threshold value Sh1 is 0.5 and the second threshold value Sh2 is 0.2, the similarity (0.17) between the user paper U1 and the standard paper A that is a similar paper is small. It is evaluated. Further, the similarity (0.62) between the user paper U2 and the standard paper A which is a similar paper is evaluated as being large. Further, the similarity (0.83) between the user paper U3 and the standard paper C which is a similar paper is evaluated as being large. Further, the similarity (0.36) between the user paper U4 and the standard paper B which is a similar paper is evaluated as medium. Further, the similarity (0.23) between the user paper U5 and the standard paper B which is a similar paper is evaluated as medium. Further, the similarity (0.21) between the user paper U6 and the standard paper B which is a similar paper is evaluated as medium. Further, the similarity (0.48) between the user paper U7 and the standard paper A which is a similar paper is evaluated as medium. Further, the similarity (0.62) between the user paper U8 and the standard paper A that is a similar paper is evaluated as being large. Further, the similarity (0.25) between the user paper U9 and the standard paper B which is a similar paper is evaluated as medium.

(Specific example of correction method selection)
Next, a specific example of correction method selection by the correction method selection unit 26 of the correction control unit 20 will be described.

  As described above, the standard paper specified as a similar paper similar to the user paper is the user among the plurality of standard papers in which the color conversion parameter and the spectral reflectance data (characteristic data) are held in the data storage unit 30. This is a standard sheet having the largest similarity with the sheet. However, the similar paper specified in this way has a relatively high similarity with the user paper in relation to other standard papers, and the similarity with the user paper is not necessarily sufficiently high. Not always. For example, as shown in FIG. 3B, the standard paper A is specified as a similar paper to the user paper U1, and the user paper U1 and the standard paper A have a comparatively comparative waveform in the spectral reflectance data waveform. A large difference is seen, and the similarity between them is a small value compared to other user sheets U2, U7, U8 classified into the same group A. On the other hand, as shown in FIG. 3 (d), the user paper U3 and the standard paper C specified as a similar paper to the user paper U3 have extremely close spectral reflectance data waveforms, and the similarity between them. Is a relatively large value.

  When user paper is used as an image output medium in the image forming apparatus 2, if the similarity between the user paper and the similar paper is sufficiently large, the color conversion parameter corresponding to the similar paper is diverted to the second color. By performing the color conversion by the conversion unit 13, it is considered that sufficient accuracy can be obtained for the correction processing in the paper correspondence correction unit 15. However, when the degree of similarity between the user paper and the similar paper is small, the correction corresponding to the user paper can be performed with high accuracy only by the color conversion by the second color conversion unit 13 using the color conversion parameter corresponding to the similar paper. Can not do.

  In order to perform correction corresponding to the user paper with high accuracy, a color conversion parameter corresponding to the user paper is generated, and color conversion by the second color conversion unit 13 is performed using the color conversion parameter corresponding to the user paper. It is effective. In addition, the second color conversion unit 13 performs color conversion using color conversion parameters corresponding to similar paper, and the tone correction unit 14 uses a paper difference correction curve for correcting the difference between the user paper and the similar paper. The gradation correction method is also effective as a simple method, although its accuracy is limited compared to the method of performing color conversion using color conversion parameters corresponding to user paper. However, these methods increase the processing load compared to the method of performing color conversion by diverting color conversion parameters corresponding to similar paper.

  Therefore, in the image processing apparatus 1 according to the present embodiment, the similarity between the user paper and the similar paper is evaluated, and the correction method in the paper correspondence correcting unit 15 is selected based on the evaluation result. The diversion of the color conversion parameter corresponding to the paper makes it possible to perform gradation correction using the paper difference correction curve or the color conversion parameter corresponding to the user paper only when correction corresponding to the user paper cannot be performed with high accuracy. Color conversion using is performed. Specifically, if the degree of similarity between the user paper and the similar paper is large, the second color conversion unit 13 performs color conversion using the color conversion parameters corresponding to the similar paper. If the similarity between the user paper and the similar paper is medium, the second color conversion unit 13 performs color conversion using the color conversion parameter corresponding to the similar paper, and the user paper and the similar paper. A paper difference correction curve for correcting the difference is generated, and gradation correction is performed by the gradation correction unit 14 using the paper difference correction curve. If the similarity between the user paper and the similar paper is small, a color conversion parameter corresponding to the user paper is generated using the color conversion parameter corresponding to the similar paper, and the color conversion parameter corresponding to the user paper is set. The second color conversion unit 13 performs color conversion.

  In the example shown in FIG. 4, when the user paper U1 is used as an image output medium, it is evaluated that the similarity between the user paper U1 and the standard paper A, which is a similar paper, is small. The color conversion parameter corresponding to the user paper U1 is generated by correcting the color conversion parameter corresponding to the paper A, and the color conversion in the second color conversion unit 13 is performed using the color conversion parameter corresponding to the user paper U1. Is done.

  Further, when the user paper U2 is used as an image output medium, it is evaluated that the similarity between the user paper U2 and the standard paper A that is a similar paper is large, so that the color conversion parameter corresponding to the standard paper A is obtained. The color conversion in the second color conversion unit 13 is performed using the above.

  Further, when the user paper U3 is used as an image output medium, it is evaluated that the similarity between the user paper U3 and the standard paper C which is a similar paper is large, and therefore the color conversion parameter corresponding to the standard paper C is used. The color conversion in the second color conversion unit 13 is performed using the above.

  When the user paper U4 is used as an image output medium, the similarity between the user paper U4 and the standard paper B, which is a similar paper, is evaluated as medium, so that the color conversion corresponding to the standard paper B is performed. The parameters are used to perform color conversion in the second color conversion unit 13, and a sheet difference correction curve for correcting the difference between the user sheet U4 and the standard sheet B is generated. Thus, gradation correction is performed in the gradation correction unit 14.

  Further, when the user paper U5 is used as an image output medium, the similarity between the user paper U5 and the standard paper B that is a similar paper is evaluated as medium, so that the color conversion corresponding to the standard paper B is performed. The parameters are used to perform color conversion in the second color conversion unit 13 and a sheet difference correction curve for correcting the difference between the user sheet U5 and the standard sheet B is generated. Thus, gradation correction is performed in the gradation correction unit 14.

  When the user paper U6 is used as an image output medium, the similarity between the user paper U6 and the standard paper B, which is a similar paper, is evaluated as medium, so color conversion corresponding to the standard paper B is performed. The parameters are used to perform color conversion in the second color conversion unit 13, and a sheet difference correction curve for correcting the difference between the user sheet U6 and the standard sheet B is generated. Thus, gradation correction is performed in the gradation correction unit 14.

  When the user paper U7 is used as an image output medium, the similarity between the user paper U7 and the standard paper A, which is a similar paper, is evaluated as medium, so that the color conversion corresponding to the standard paper A is performed. The parameters are used to perform color conversion in the second color conversion unit 13, and a sheet difference correction curve for correcting the difference between the user sheet U7 and the standard sheet A is generated. Thus, gradation correction is performed in the gradation correction unit 14.

  Further, when the user paper U8 is used as an image output medium, it is evaluated that the similarity between the user paper U8 and the standard paper A that is a similar paper is high, and therefore the color conversion parameter corresponding to the standard paper A is used. The color conversion in the second color conversion unit 13 is performed using the above.

  Further, when the user paper U9 is used as an image output medium, the similarity between the user paper U9 and the standard paper B which is a similar paper is evaluated as medium, so that the color conversion corresponding to the standard paper B is performed. The parameters are used to perform color conversion in the second color conversion unit 13 and a sheet difference correction curve for correcting the difference between the user sheet U9 and the standard sheet B is generated. Thus, gradation correction is performed in the gradation correction unit 14.

(Generate paper difference correction curve)
Next, a method for generating a paper difference correction curve by the paper difference correction curve generating unit 27 of the correction control unit 20 will be described in detail.

  FIG. 5 is a flowchart showing a series of processing (main routine) executed by the paper difference correction curve generation unit 27. The paper difference correction curve generation unit 27 is evaluated by the similarity evaluation unit 25 as having a medium similarity between the user paper and the similar paper, and the correction method selection unit 26 activates the paper difference correction curve generation unit 27. When the signal is sent, a series of processing shown in the flowchart of FIG. 5 is executed to generate a paper difference correction curve for correcting the difference between the user paper and the similar paper. In the present embodiment, for each of a plurality of standard papers, the colorimetric values (Lab values) obtained by measuring the standard papers on which the correction patch images are formed by the colorimetric means 5 are the color values. It is assumed that the data storage unit 30 holds the conversion parameters and characteristic data.

  When the main routine of FIG. 5 is started, the sheet difference correction curve generation unit 27 firstly, among the colorimetric values corresponding to the plurality of standard sheets stored in the data storage unit 30 in “target setting” in step S101. Then, the colorimetric value corresponding to the similar paper specified by the similar paper specifying unit 24 is read, and the colorimetric value corresponding to the similar paper is set as a target.

  Next, the paper difference correction curve generation unit 27 performs the image forming apparatus from CMYK based on the colorimetric values of the user paper acquired from the colorimetric means 5 by the characteristic data acquisition unit 22 in “printer simulator calculation” in step S102. 2 calculates a printer simulator for estimating the Lab value output from the printer. As an existing technique for calculating this printer simulator, for example, there is a method of constructing a simulator by causing a neural network to learn the data of each patch image consisting of a combination of CMYK and a Lab value which is a colorimetric value. This method can also be used in the form.

  Next, the paper difference correction curve generation unit 27 sets a paper difference correction curve in which “input value = output value” is set as the initial value of the paper difference correction curve in “initial value setting” in step S103.

  Next, the paper difference correction curve generation unit 27 optimizes the C correction curve by fixing the correction curves for colors other than C in “C correction curve optimization” in step S104. Similarly, in the “M correction curve optimization” in step S105 and the “Y correction curve optimization” in step S106, the paper difference correction curve generation unit 27 fixes the correction curves for other colors and applies them. Optimize the color correction curve.

  Next, in step S107, the paper difference correction curve generation unit 27 determines whether or not the average color difference between the Lab value estimated when the correction curve at that time is applied and the target has converged. In this convergence determination, a difference between the average color difference at the previous convergence determination and the current average color difference is determined as a threshold value. For example, if the difference is less than 0.05, it is determined that the difference has converged, and the difference is 0.05 or more. If so, it is determined that it has not converged. At the time of the first convergence determination, the difference between the average color difference from the target when the correction curve is not applied and the current average color difference is evaluated.

  If it is determined in step S107 that the paper difference correction curve generation unit 27 has not converged (step S107: No), the paper difference correction curve generation unit 27 returns to step S104 and repeats the subsequent processing. And if it determines with having converged in step S107 (step S107: Yes), it will transfer a process to the following step S108.

  Next, the paper difference correction curve generation unit 27 optimizes the K correction curve by fixing the CMY correction curve in “K correction curve optimization” in step S108. In step S109, the paper difference correction curve generation unit 27 performs the same convergence determination as in step S107. If it is determined that the paper has not converged (step S109: No), the process of step S107 is repeated to converge. If it is determined that the process has been performed (step S109: Yes), the process proceeds to the next step S110.

  Next, the paper difference correction curve generation unit 27 calculates the correction curve for each color by polynomial approximation or the like so that the shape of the correction curve for each color of CMYK optimized in step S110 “smoothing of the correction curve” becomes smooth. Arrange the shape. Thus, the generation of the paper difference correction curve by the paper difference correction curve generation unit 27 is completed.

  FIG. 6 is a flowchart showing details (subroutine) of the correction curve optimization processing for each color of CMYK in step S104, step S105, step S106, and step S108 of FIG. This correction curve optimization process is a process of optimizing the output value for each input value while sequentially changing the input gradation value I (n) for which the correction curve is optimized.

  When the subroutine of FIG. 6 is started, the paper difference correction curve generation unit 27 first sets n of I (n) to 1 and initializes the input value in step S201. Here, the input value is a gradation value used for the configuration of the patch in the correction patch image data.

  Next, the paper difference correction curve generation unit 27 determines the CMYK value of the patch whose tone value of the corresponding color is I (n) in CMYK in “Preparation of evaluation data for input value I (n)” in step S202. A set of colorimetric values (Lab values) is extracted as evaluation data.

  Next, the paper difference correction curve generation unit 27 updates the output value of the correction curve for I (n) in “output value O (I (n)) update” in step S203, and “printer output value” in step S204. In “Estimation”, the Lab value for the evaluation data (CMYK value) when the updated correction curve is applied using the printer simulator is estimated.

  Next, the paper difference correction curve generation unit 27 calculates the average color difference between the Lab value estimated in step S204 and the target in “Calculate average color difference with target” in step S205. In step S206, the sheet difference correction curve generation unit 27 determines whether or not the average color difference calculated in step S205 is a minimum value. If it is determined that the average color difference is not the minimum value (step S206). : No), returning to step S203, the output value of the correction curve is updated to another value, and the subsequent processing is repeated.

  On the other hand, when it is determined that the average color difference is the minimum value (step S206: Yes), the paper difference correction curve generation unit 27 increments (+1) the value of n in the next step S207, and in step S208. It is determined whether or not the value of n exceeds the maximum value. If the value of n does not exceed the maximum value (step S208: No), the process returns to step S202 to optimize the output value for the new input value, and when the value of n exceeds the maximum value (step S208: Yes), the correction curve optimization process is terminated.

  FIG. 7 is a diagram illustrating an example of updating the output value O (I (2)) corresponding to the input value I (2) when n = 2 is set. In the example shown in FIG. 7, the first output value candidate is c in the figure, the second output value candidate is a in the figure, and the third output value candidate is b in the figure. The second output candidate a and the third output candidate b are values obtained by increasing or decreasing the value of the first output candidate c by a minute value e. In “output value O (I (n)) update” in step S203 of FIG. 6, three output value candidates are set in this way. In step S206 in FIG. 6, an average color difference is calculated for each of these three output value candidates a, b, and c, and it is determined whether or not the value is a minimum value according to the magnitude relationship of each average color difference. If it is determined that the value is not the minimum value, the setting of a value to be the next first output value candidate in “output value O (I (n)) update” in step S203 of FIG. The minute value e used for setting the second output value candidate and the third output candidate is updated.

  FIG. 8 is a diagram showing the result of calculating the average color difference for each of the three output value candidates a, b, and c. In the case where the result of calculating the average color difference for each of the three output value candidates a, b, and c is as shown in FIG. 8A, it is determined that the first output value candidate c is not a minimum value, and the minimum value is determined. The third output value candidate b is set as the next first output value candidate. Further, in the case where the result of calculating the average color difference for each of the three output value candidates a, b, c is as shown in FIG. 8B, it is determined that the first output value candidate c is not a minimum value, The second output value candidate a that is the minimum value is set as the next first output value candidate.

  Further, in the case where the average color difference is calculated for each of the three output value candidates a, b, and c as shown in FIG. 8C, only when the minute value e is less than the preset threshold th, It is determined that the first output value candidate c is a minimum value. If the minute value e is greater than or equal to the threshold th, it is determined that the first output value candidate c is not a minimum value, and the minute value e is updated. For example, it is assumed that the initial value of the minute value e is 2, and the value is as small as 1,0.5, 0.25,. Further, the threshold th is set to 0.01. In this case, if e <0.01 is obtained by updating the minute value e and the case shown in FIG. 8C is obtained, it is determined that the first output value candidate c is a minute value.

  As described above, the output value O (I (n)) at which the average color difference is minimized can be found by repeating the determination of the minimum value and the update of the output value candidates a, b, c and the minute value e.

(Generation of color conversion parameters)
Next, a color conversion parameter generation method by the color conversion parameter generation unit 28 of the correction control unit 20 will be described in detail.

  FIG. 9 is a flowchart illustrating a process in which the color conversion parameter generation unit 28 generates a color conversion parameter corresponding to a user sheet using a color conversion parameter corresponding to a similar sheet. The color conversion parameter generation unit 28 is evaluated by the similarity evaluation unit 25 that the similarity between the user paper and the similar paper is small, and the correction method selection unit 26 sends an active signal to the color conversion parameter generation unit 28. Then, the process shown in the flowchart of FIG. 9 is executed to generate a color conversion parameter corresponding to the user paper.

  When the processing shown in the flowchart of FIG. 9 is started, the color conversion parameter generation unit 28 first determines the three-dimensional LUT that is the base of the color conversion parameters corresponding to the user paper in “color conversion parameter generation” in step S301. Based on the colorimetric value of the user paper acquired from the colorimetric means 5 by the characteristic data acquisition unit 22, the characteristic data acquisition unit 22 creates a rough division number. Note that generation of a three-dimensional LUT that is a color conversion parameter is a well-known technique, and thus detailed description thereof is omitted. Here, it is assumed that the 3D LUT, which is a color conversion parameter corresponding to the standard paper stored in the data storage unit 30, is divided into 8 × 8 × 8, and the “color conversion parameter generation” in step S301 is more than that. It is assumed that a 4 × 4 × 4 divided three-dimensional LUT having a rough division number is generated. Since the three-dimensional LUT generated by the “color conversion parameter generation” in step S301 is rough, it should be generated with a smaller amount of calculation than generating an 8 × 8 × 8 divided three-dimensional LUT that is a color conversion parameter. Is possible.

  Next, the color conversion parameter generation unit 28 uses the three-dimensional LUT generated in step S301 and the color conversion parameters corresponding to the similar sheet read from the data storage unit 30 in “interpolation by difference prediction” in step S302. Interpolation is performed based on a difference value from a certain three-dimensional LUT, and an 8 × 8 × 8 divided three-dimensional LUT that is a color conversion parameter corresponding to the user paper is generated.

  FIG. 10 is a diagram for explaining “interpolation by difference prediction” in step S302 of FIG. FIG. 10A schematically shows an 8 × 8 × 8 divided three-dimensional LUT that is a color conversion parameter corresponding to similar paper, and FIG. 10B is a color conversion parameter corresponding to user paper. 4 is a schematic representation of a 4 × 4 × 4 divided three-dimensional LUT that is the base of the above. FIG. 10C shows a state of interpolation based on the difference prediction between these two three-dimensional LUTs. A black circle in FIG. 10C is a portion where the lattice points of two three-dimensional LUTs overlap, and a white circle in FIG. 10C is a 3 × 8 × 8 × 8 division which is a color conversion parameter corresponding to similar paper. Grid points that exist only in the dimension LUT.

  First, for the grid point position of p1 in FIG. 10C, the output value s1 of the three-dimensional LUT, which is a color conversion parameter corresponding to the similar paper set at this grid point position, and the color conversion corresponding to the user paper. A difference r1 = u1−s1 with respect to the output value u1 of the three-dimensional LUT serving as a parameter base is obtained. Similarly, differences r2, r3, and r4 of the output values of the two LUTs are obtained for the respective lattice point positions of p2, p3, and p4 in FIG.

  Next, based on the differences r1 to r4 obtained as described above, the differences r5 to r9 at the lattice point positions p5 to p9 in FIG. 10C are predicted, and the predicted differences r5 to r9 are associated with similar sheets. Are added to the output values s5 to s9 of the three-dimensional LUT, which are color conversion parameters, and are output values u5 to u9 in an 8 × 8 × 8 divided three-dimensional LUT, which is a color conversion parameter corresponding to the user paper. The prediction of the difference values r5 to r9 is, for example, r5 = (r1 + r2) / 2, r6 = (r2 + r3) / 2, r7 = (r3 + r4) / 2, r8 = (r1 + r4) / 2, r9 = (r1 + r2 + r3 + r4) / 4 It can be performed by such a linear operation. Further, the difference value may be predicted by nonlinear calculation.

  As described above, the 4 × 4 × 4 division coarse three-dimensional LUT that is the base of the color conversion parameter corresponding to the user paper is the color conversion parameter 8 × 8 × 8 division 3 corresponding to the similar paper. By performing an interpolation calculation based on a difference from the dimension LUT, an 8 × 8 × 8 divided three-dimensional LUT that is a color conversion parameter corresponding to the user sheet can be generated.

  As described above in detail with reference to specific examples, the image processing apparatus 1 according to the present embodiment evaluates the similarity between the user sheet and the similar sheet, and the sheet is based on the evaluation result. By selecting the correction method in the corresponding correction unit 15, only when the correction corresponding to the user paper cannot be performed with high accuracy by diverting the color conversion parameter corresponding to the similar paper, the paper difference correction curve is performed as necessary. Gradation correction by means of color conversion and color conversion using color conversion parameters corresponding to user paper. Therefore, even when user paper whose characteristics are unknown is used as an image output medium in the image forming apparatus 2, the paper correspondence correction unit 15 performs correction corresponding to the user paper with high accuracy to improve image reproducibility. In addition to being increased, it is possible to effectively suppress an increase in processing load caused by adding unnecessary processing.

  Note that the above-described image processing process by the image processing apparatus 1 according to the present embodiment is realized by, for example, an image processing program installed as expansion software on the control PC described above being executed by the CPU of the control PC. The The image processing program executed by the CPU of the control PC is provided by being incorporated in advance in the ROM of the control PC, for example. The image processing program executed by the CPU of the control PC is an installable or executable file, such as a CD-ROM, a flexible disk (FD), a CD-R, a DVD (Digital Versatile Disk) or the like. The information may be provided by being recorded on a recording medium that can be read by the user. Furthermore, the image processing program executed by the CPU of the control unit PC may be stored on a computer connected to a network such as the Internet and provided by being downloaded via the network. The output control program executed by the CPU of the control unit PC may be provided or distributed via a network such as the Internet.

  The image processing program executed by the CPU of the control unit PC has a module configuration including the processing functions shown in the functional block diagram of FIG. 2. As actual hardware, the CPU (processor) is an image from a ROM, for example. By reading and executing the processing program, each processing function is loaded onto the main storage device (RAM), and each processing function is generated on the main storage device.

  Although the embodiment as an application example of the present invention has been specifically described above, the present invention is not limited to the above-described embodiment as it is, and various modifications can be made without departing from the scope of the invention at the stage of implementation. It can be embodied with various modifications and changes. For example, in the above-described embodiment, the configuration in which the image processing apparatus 1 is realized as an apparatus different from the image forming apparatus 2 has been described. A built-in configuration may be adopted.

DESCRIPTION OF SYMBOLS 1 Image processing apparatus 2 Image forming apparatus 5 Color measurement means 13 2nd color conversion part 14 Tone correction part 15 Paper correction | amendment part 20 Correction control part 21 Correction patch image data transfer part 22 Characteristic data acquisition part 23 Similarity calculation part 24 Similar Paper Identification Unit 25 Similarity Evaluation Unit 26 Correction Method Selection Unit 27 Paper Difference Correction Curve Generation Unit 28 Color Conversion Parameter Generation Unit 30 Data Storage Unit 31 Correction Patch Image Data 32 Color Conversion Parameter Data Set 33 Characteristic Data Data set

JP 2008-278152 A

Claims (9)

An image processing apparatus that performs correction processing according to the type of paper used as an image output medium,
Character data holding means for holding a plurality of characteristic data representing the color reproduction characteristics of each standard paper for each of a plurality of standard papers having known color reproduction characteristics;
Characteristic data acquisition means for acquiring characteristic data representing the color reproduction characteristics of an unknown paper whose color reproduction characteristics are unknown;
Similarity calculating means for calculating the similarity between the unknown sheet and each of the plurality of standard sheets based on the difference between the characteristic data of the unknown sheet and the characteristic data of each of the plurality of standard sheets;
A similar sheet specifying means for specifying, as a similar sheet, a standard sheet having the largest similarity to the unknown sheet among the plurality of standard sheets;
Selection means for selecting a correction method when the unknown paper is used as the output medium from a plurality of predetermined correction methods based on the degree of similarity between the unknown paper and the similar paper;
An image processing apparatus comprising: a correction unit that corrects image data in accordance with a method selected by the selection unit when the unknown sheet is used as the output medium.   The image processing apparatus according to claim 1, wherein the characteristic data is spectral reflectance data.   The similarity calculation unit calculates the similarity based on a difference for each wavelength between spectral reflectance data of the unknown sheet and spectral reflectance data of each of the plurality of standard sheets. Item 3. The image processing apparatus according to Item 2.   The degree of similarity between the unknown sheet and the similar sheet is set to a value that is greater than or equal to a first threshold value or less than the first threshold value and smaller than the first threshold value. The image according to any one of claims 1 to 3, further comprising an evaluation unit that evaluates in three stages whether the threshold value is 2 or more or less than the second threshold value. Processing equipment. For each of the plurality of standard papers, further comprising color conversion parameter holding means for holding a plurality of color conversion parameters for performing color conversion of an image corresponding to the color reproduction characteristics of each standard paper,
The selection unit performs color conversion using a color conversion parameter corresponding to the similar sheet when the degree of similarity between the unknown sheet and the similar sheet is equal to or greater than the first threshold. The image processing apparatus according to claim 4, wherein the image processing apparatus is selected as a correction method when the unknown sheet is used as the output medium. For each of the plurality of standard papers, color conversion parameter holding means for holding a plurality of color conversion parameters for performing color conversion of an image corresponding to the color reproduction characteristics of each standard paper;
Correction curve generation means for generating a correction curve for correcting a difference in color reproduction characteristics between the unknown sheet and the similar sheet, and
The selection unit is configured to select a color conversion parameter corresponding to the similar sheet when the degree of similarity between the unknown sheet and the similar sheet is less than the first threshold and greater than or equal to the second threshold. 5. The image processing apparatus according to claim 4, wherein color conversion using an image and gradation correction using the correction curve are selected as correction methods when the unknown sheet is used as the output medium. For each of the plurality of standard papers, color conversion parameter holding means for holding a plurality of color conversion parameters for performing color conversion of an image corresponding to the characteristics of each standard paper;
Color conversion parameter generation means for generating a color conversion parameter corresponding to the unknown sheet by using a color conversion parameter corresponding to the similar sheet;
The selection unit performs color conversion using color conversion parameters corresponding to characteristics of the unknown sheet when the degree of similarity between the unknown sheet and the similar sheet is less than the second threshold value. The image processing apparatus according to claim 4, wherein the image processing apparatus is selected as a correction method when the unknown sheet is used as the output medium. An image processing apparatus that performs correction processing according to the type of paper used as an image output medium, and each of a plurality of standard papers that have known color reproduction characteristics represents a color reproduction characteristic of each standard paper. An image processing method executed by an image processing apparatus having characteristic data holding means for holding the characteristic data of
Obtaining characteristic data representing the color reproduction characteristics of an unknown paper whose color reproduction characteristics are unknown;
Based on the difference between the characteristic data of the unknown sheet and the characteristic data of each of the plurality of standard sheets held by the characteristic data holding unit, the similarity between the unknown sheet and each of the plurality of standard sheets is calculated. And steps to
Identifying a standard sheet having the highest similarity to the unknown sheet among the plurality of standard sheets as a similar sheet;
Selecting a correction method when using the unknown sheet as the output medium from a plurality of predetermined correction methods based on the degree of similarity between the unknown sheet and the similar sheet;
And correcting the image data in accordance with the selected method when the unknown sheet is used as the output medium. An image processing apparatus that performs correction processing according to the type of paper used as an image output medium, and each of a plurality of standard papers that have known color reproduction characteristics represents a color reproduction characteristic of each standard paper. In an image processing apparatus equipped with characteristic data holding means for holding the characteristic data of
A function for obtaining characteristic data representing the color reproduction characteristics of an unknown paper whose color reproduction characteristics are unknown;
Based on the difference between the characteristic data of the unknown sheet and the characteristic data of each of the plurality of standard sheets held by the characteristic data holding unit, the similarity between the unknown sheet and each of the plurality of standard sheets is calculated. Function to
A function of specifying a standard paper having the highest similarity to the unknown paper among the plurality of standard papers as a similar paper;
A function of selecting a correction method when using the unknown sheet as the output medium from a plurality of predetermined correction methods based on the degree of similarity between the unknown sheet and the similar sheet;
A function for correcting image data in accordance with the selected method when the unknown sheet is used as the output medium.

Images