JP6350473B2 - Calibration system, calibration method, image forming apparatus, and calibration program - Google Patents

Description

  The present invention relates to a calibration system, a calibration method, an image forming apparatus, and a calibration program for correcting gradation characteristics.

  2. Description of the Related Art Conventionally, as a calibration method for correcting input / output characteristics due to aging of image forming apparatuses such as a printer dedicated machine and an MFP (Multifunction Peripheral), so-called gamma correction, a scanner or an image forming apparatus attached to the MFP A method is known in which the current output color is measured using the density sensor in the image sensor, and the input / output characteristics are corrected so that the color value of the output color becomes the target color value.

  However, an expensive dedicated colorimeter such as a spectrocolorimeter is required in order to align the color values of output colors between image forming apparatuses of different models. However, preparing and using a dedicated colorimeter is a hurdle for general users.

  Therefore, a calibration system has been proposed that realizes simple and low-cost calibration by using a photographing device such as a digital camera or a camera-equipped mobile phone as an alternative device for a dedicated colorimeter (for example, patents). References 1 and 2).

  In the calibration system described in Patent Document 1, first, a reference chart including a plurality of color patches and a test chart printed by an image forming apparatus corresponding to the reference chart are simultaneously photographed by a photographing device. Then, the gradation characteristics of the image forming apparatus are corrected based on the RGB values of the respective patches of the reference chart and the test chart in the captured image generated by being captured by the capturing device.

  In the calibration system described in Patent Document 2, first, a reference chart including a plurality of color patches and a test chart corresponding to the reference chart and printed by the image forming apparatus are separately photographed by the photographing device. . Next, the color value of the patch of the reference chart in the first captured image that is captured and generated by the imaging device, and the color value of the patch of the test chart in the second captured image that is captured and generated by the imaging device Based on the above, the first correction value is calculated. The second correction value is calculated based on the color value of the reference chart patch in the first captured image and the reference color value stored in advance in the image forming apparatus. Then, the gradation characteristics of the image forming apparatus are corrected based on the first correction value and the second correction value.

JP 2006-222552 A JP 2010-226562 A

  However, in the calibration system described in Patent Document 1 or Patent Document 2, halation occurs depending on the illumination conditions depending on the shooting environment of the reference chart and the test chart, and the brightness becomes higher than the original brightness. If the shadow of an object such as the image appears in the captured image and becomes lower than the original brightness, resulting in brightness spots in the captured image, the result of correction of the gradation characteristics due to the effect of brightness spots in the captured image As a result, there is a problem that the accuracy of correction of gradation characteristics is lowered.

  Therefore, an object of the present invention is to provide a calibration system, a calibration method, an image forming apparatus, and a calibration program that can improve the accuracy of correction of gradation characteristics.

  The calibration system of the present invention includes an imaging device, an image forming apparatus that generates a sheet by printing a test chart including a plurality of color patches, and a reference chart that corresponds to the test chart with gradation characteristics of the image forming apparatus Table generating means for generating a gamma correction table for correcting the gradation characteristics along the line, wherein the table generating means is configured such that the test chart of the sheet and the reference chart are simultaneously photographed by the photographing device. After calculating a weighting coefficient based on a variance of color values of pixels in the patch in the generated captured image for each patch, based on a weighted average of color values of the plurality of patches based on the weighting coefficient Each patch of the test chart and the reference chart Calculating the gray level of the representative value of the color value, and generates the gamma correction table based on each of the representative value of the test chart and the reference chart.

  With this configuration, the calibration system of the present invention performs weighting for each patch based on the dispersion of the color values of the pixels in the patch in the captured image even when lightness spots occur in the captured image. Because the representative value of each color value of each patch of the test chart and reference chart can be calculated with high accuracy, it is confirmed that the brightness variation in the photographed image affects the correction result of the gradation characteristics. Can be suppressed. Therefore, the calibration system of the present invention can improve the accuracy of correction of gradation characteristics.

  In the calibration system of the present invention, the table generation unit may calculate the representative value based on the photographed image subjected to the shading correction after performing the shading correction on the photographed image.

  With this configuration, the calibration system of the present invention calculates the representative value after suppressing the lightness unevenness generated in the captured image due to the characteristics of the image capturing device by the shading correction, so the lightness unevenness in the captured image has the gradation characteristic. The influence on the correction result can be further suppressed. Therefore, the calibration system of the present invention can further improve the accuracy of gradation characteristic correction.

  In the calibration system of the present invention, the image forming apparatus may include the table generating unit.

  With this configuration, in the calibration system of the present invention, since the photographing device does not need to include a table generating unit, a general photographing device can be used, and convenience can be improved.

  In the calibration system of the present invention, the imaging device may include the table generating unit.

  With this configuration, the calibration system of the present invention does not need to include a table generating unit in the image forming apparatus, so that the processing load on the image forming apparatus when updating the gamma correction table can be suppressed.

  The calibration method of the present invention includes a sheet generation step of generating a sheet by printing by a test chart image forming apparatus including a plurality of color patches, the test chart of the sheet generated by the sheet generation step, A captured image generation step in which a reference chart corresponding to a test chart is simultaneously captured by an imaging device to generate a captured image, and the gradation characteristics of the image forming apparatus are corrected to the gradation characteristics according to the reference chart A table generation step for generating a gamma correction table of the image, wherein the table generation step uses a weighting coefficient based on a variance of color values of pixels in the patch in the captured image generated by the captured image generation step. After calculating for each patch, the weight Calculating a representative value for each gradation of the color values of the patches of the test chart and the reference chart based on a weighted average of the color values of the plurality of patches based on a shift coefficient, and the test chart and the reference It is a step of generating the gamma correction table based on the representative value of each of the charts.

  With this configuration, the calibration method of the present invention performs weighting for each patch based on the dispersion of color values of pixels in the patch in the captured image even when lightness spots occur in the captured image. Because the representative value of each color value of each patch of the test chart and reference chart can be calculated with high accuracy, it is confirmed that the brightness variation in the photographed image affects the correction result of the gradation characteristics. Can be suppressed. Therefore, the calibration method of the present invention can improve the accuracy of correction of gradation characteristics.

  The image forming apparatus of the present invention is an image forming apparatus that generates a sheet by printing a test chart including a plurality of color patches, and the gradation characteristics of the image forming apparatus are in accordance with a reference chart corresponding to the test chart. Table generating means for generating a gamma correction table for correcting to the gradation characteristics, wherein the table generating means is generated by photographing the test chart of the sheet and the reference chart simultaneously by a photographing device. After calculating a weighting coefficient based on dispersion of color values of pixels in the patch in the captured image for each patch, the test chart and the test chart based on a weighted average of color values of the plurality of patches based on the weighting coefficient Calculates the representative value for each gradation of the color value of each patch in the reference chart , And generates the gamma correction table based on each of the representative value of the test chart and the reference chart.

  With this configuration, the image forming apparatus of the present invention performs weighting for each patch based on the dispersion of the color values of pixels in the patch in the captured image, even when lightness spots occur in the captured image. Because the representative value of each color value of each patch of the test chart and reference chart can be calculated with high accuracy, it is confirmed that the brightness variation in the photographed image affects the correction result of the gradation characteristics. Can be suppressed. Therefore, the image forming apparatus of the present invention can improve the accuracy of correction of gradation characteristics.

  A calibration program according to the present invention is a calibration program executed by an image forming apparatus that generates a sheet by printing a test chart including a plurality of color patches, and the gradation characteristics of the image forming apparatus are measured with the test chart. The image forming apparatus is caused to function as a table generating unit that generates a gamma correction table for correcting gradation characteristics along a reference chart corresponding to the table, the table generating unit including the test chart of the sheet, and the reference After calculating a weighting coefficient for each patch based on the dispersion of color values of pixels in the patch in a captured image that is generated by capturing the chart at the same time with the imaging device, the plurality of patches based on the weighting coefficient The test based on the weighted average of color values of A representative value for each gradation of the color value of the patch of each of the chart and the reference chart is calculated, and the gamma correction table is generated based on the representative value of each of the test chart and the reference chart. And

  With this configuration, the image forming apparatus that executes the calibration program of the present invention enables each patch based on the dispersion of the color values of the pixels in the patch in the captured image, even when lightness spots occur in the captured image. By weighting, the representative value for each gradation of the color value of each patch of the test chart and the reference chart can be calculated with high accuracy. It can suppress affecting a result. Therefore, the image forming apparatus that executes the calibration program of the present invention can improve the accuracy of correction of gradation characteristics.

  The calibration system, calibration method, image forming apparatus, and calibration program of the present invention can improve the accuracy of gradation characteristic correction.

1 is a block diagram of a calibration system according to a first embodiment of the present invention. It is a block diagram of the smart phone shown in FIG. FIG. 2 is a block diagram of the MFP shown in FIG. 1. It is a figure which shows the principle of the gamma correction table shown in FIG. (A) It is a figure which shows an example of the test sheet printed based on the test chart image data shown in FIG. (B) It is a figure which shows an example of the reference sheet used with the test sheet shown to Fig.5 (a). It is a flowchart of the method of the calibration in the calibration system shown in FIG. FIG. 6 is a diagram illustrating an example of a test sheet and a reference sheet in a state where the reference sheet illustrated in FIG. 5 is disposed within a frame line of the test sheet. It is a figure which shows an example of the picked-up image produced | generated by the smart phone shown in FIG. 7 is a flowchart of a gamma correction table generation process shown in FIG. 6. It is a figure which shows an example of the area | region near the center of the patch in the test chart shown in FIG. 10 is a flowchart of a weighting coefficient calculation process shown in FIG. It is a figure which shows an example of the relational expression produced | generated in the gamma correction table production | generation process shown in FIG. FIG. 4 is a diagram illustrating an example of gradation characteristics of the MFP illustrated in FIG. 3. It is a block diagram of the smart phone shown in FIG. 1, Comprising: It is a figure which shows the example different from the example shown in FIG. It is a flowchart of the gamma correction table production | generation process in the calibration system which concerns on the 2nd Embodiment of this invention. It is a flowchart of the weighting coefficient calculation process shown in FIG. FIG. 17 is a flowchart of a predicted brightness reference weighting coefficient calculation process shown in FIG. 16. It is a figure which shows an example of the lattice point part in the test chart shown in FIG. It is a figure which shows an example of the part between the lattice points in the test chart shown in FIG. FIG. 6 is a diagram illustrating an example of a region near the center of a patch, a lattice point portion, and a portion between lattice points in the test chart illustrated in FIG. 5.

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

(First embodiment)
First, the configuration of the calibration system according to the first embodiment will be described.

  FIG. 1 is a block diagram of a calibration system 10 according to the present embodiment.

  As shown in FIG. 1, the calibration system 10 includes a smartphone 20 as an imaging device and an MFP (Multifunction Peripheral) 30 as an image forming apparatus. The smartphone 20 and the MFP 30 are configured to be able to communicate with each other. Here, the smartphone 20 and the MFP 30 may be able to communicate with each other via a network 11 such as a LAN (Local Area Network) or the Internet, or a USB (Universal Serial Bus) cable or the like without going through the network 11. It may be possible to communicate directly with each other via a communication cable.

  FIG. 2 is a block diagram of the smartphone 20.

  As shown in FIG. 2, the smartphone 20 includes an operation unit 21 that is an input device such as buttons for inputting various operations, and a display unit that is a display device such as an LCD (Liquid Crystal Display) that displays various information. 22, a camera 23, a communication unit 24 that is a communication device that communicates with an external device via a network 11 (see FIG. 1) or a communication cable, and a nonvolatile memory such as a semiconductor memory that stores various data. A storage unit 25 that is a storage device and a control unit 26 that controls the entire smartphone 20.

  The control unit 26 includes, for example, a CPU (Central Processing Unit), a ROM (Read Only Memory) storing various data, and a RAM (Random Access Memory) used as a work area of the control unit 26 CPU. I have. The CPU of the control unit 26 executes a program stored in the ROM of the control unit 26 or the storage unit 25.

  FIG. 3 is a block diagram of the MFP 30.

  As shown in FIG. 3, the MFP 30 includes an operation unit 31 that is an input device such as buttons for inputting various operations, a display unit 32 that is a display device such as an LCD that displays various information, and an image from a document. Fax communication is performed via a communication line such as a public telephone line such as a scanner 33 which is a reading device, a printer 34 which is a printing device which performs printing on a recording medium such as paper, and an external facsimile apparatus (not shown). A fax communication unit 35 that is a fax device, a communication unit 36 that is a communication device that communicates with an external device via the network 11 (see FIG. 1) or a communication cable, a semiconductor memory that stores various data, A storage unit 37 that is a nonvolatile storage device such as an HDD (Hard Disk Drive), and an MFP And a control unit 38 for controlling the whole 30.

  The storage unit 37 stores a calibration program 37 a for correcting the gradation characteristics of the printer 34. The calibration program 37a may be installed in the MFP 30 at the manufacturing stage of the MFP 30, or may be additionally installed in the MFP 30 from a storage medium such as an SD card or a USB (Universal Serial Bus) memory, or the network 11 ( (See FIG. 1.) It may be additionally installed in the MFP 30 from above.

  In addition, the storage unit 37 stores a gamma correction table (LUT: Lookup Table) 37 b for correcting the gradation characteristics of the MFP 30.

  FIG. 4 is a diagram showing the principle of the gamma correction table 37b.

  As shown in FIG. 4, even if the actual gradation characteristic 41 of the printer 34 deviates from the expected gradation characteristic 42, by applying the gamma correction table 37b to the actual gradation characteristic 41 of the printer 34, The expected gradation characteristic 42 can be realized.

  As illustrated in FIG. 3, the storage unit 37 includes test chart image data 37 c as image data of a test chart including a plurality of color patches, and a reference in which a device-independent chromaticity value is known for the patch. Reference chromaticity value information 37d indicating the chromaticity value of each patch of the chart is stored.

  In the following description, the chromaticity value independent of the device is described as an XYZ value.

  FIG. 5A is a diagram illustrating an example of a test sheet 50 as a sheet printed based on the test chart image data 37c. FIG. 5B is a diagram illustrating an example of a reference sheet 60 that is used together with the test sheet 50.

  As shown in FIG. 5, the test sheet 50 includes a test chart 51 as a chart including a plurality of color patches 51 a, a frame line 52 indicating a position where the reference sheet 60 is disposed, and a frame line surrounding the test chart 51. 53 and a frame line 52 and a frame line 54 surrounding the frame line 53 are printed by the printer 34. Further, the test sheet 50 is printed with a triangle 55 indicating the direction of the test chart 51 and a triangle 56 indicating the direction in which the reference sheet 60 should be arranged.

  The reference sheet 60 includes a reference chart 61 including a plurality of color patches 61 a and a triangle 62 indicating the orientation of the reference chart 61. The reference chart 61 is the same as the test chart 51 that is horizontally inverted, and is a chart corresponding to the test chart 51. Since the reference chart 61 is not printed by the printer 34, the color value is superior to the test chart 51 in terms of accuracy.

  The control unit 38 illustrated in FIG. 3 includes, for example, a CPU, a ROM that stores programs and various data, and a RAM that is used as a work area for the CPU of the control unit 38. The CPU of the control unit 38 executes a program stored in the ROM of the control unit 38 or the storage unit 37.

  The control unit 38 executes a calibration program 37a stored in the storage unit 37, thereby correcting the tone characteristics of the MFP 30 to the tone characteristics according to the reference chart 61 (see FIG. 5). It functions as table generating means 38a for generating the correction table 37b.

  Next, a calibration method in the calibration system 10 will be described.

  FIG. 6 is a flowchart of a calibration method in the calibration system 10.

  The user instructs the MFP 30 to generate the test sheet 50 via the operation unit 31 of the MFP 30 or the like. Therefore, the control unit 38 of the MFP 30 executes the calibration program 37a stored in the storage unit 37, thereby printing the test chart 51 by the printer 34 and generating the test sheet 50 as shown in FIG. (S101).

  After the step S101, the user places the reference sheet 60 within the frame 52 of the test sheet 50 as shown in FIG. 7 and uses the camera 23 of the smartphone 20 as shown in FIG. Is photographed (S102). Therefore, the smartphone 20 simultaneously captures the test chart 51 and the reference chart 61 to generate an image (hereinafter referred to as “captured image”), and transmits the generated captured image to the MFP 30.

  FIG. 8 is a diagram illustrating an example of the captured image 70 generated in S102.

  As illustrated in FIG. 8, the captured image 70 includes the test sheet 50 and the reference sheet 60 in a state where the reference sheet 60 is disposed within the frame line 52 of the test sheet 50.

  The photographed image 70 shown in FIG. 8 has lightness spots. For example, the photographed image 70 includes a region 71 in which the shadow of a certain object such as a photographer is reflected and the brightness is low, and a region 72 in which halation occurs and the brightness is high.

  As shown in FIG. 6, the table generating unit 38a of the MFP 30 executes the gamma correction table generating process shown in FIG. 9 based on the photographed image transmitted from the smartphone 20 after the process of S102 (S103).

  FIG. 9 is a flowchart of the gamma correction table generation process shown in FIG.

  As shown in FIG. 9, the table generating unit 38a specifies the position of each patch of the chart in the captured image by image processing (S131).

  Next, the table generating unit 38a performs shading correction on the captured image (S132).

  Next, the table generating unit 38a acquires a color value for each of the patches whose positions are specified in S131 (S133).

  Here, when acquiring the color value from the patch in the entire image of the patch, the table generating unit 38a acquires the color value of the pixel in the area near the outline of the patch. In addition, there is a possibility that the color value of the pixel outside the patch is erroneously acquired. Therefore, when the table generation unit 38a acquires color values from the patch in the captured image, the table generation unit 38a avoids an area near the outline of the patch and avoids a specific area near the center of the patch (hereinafter referred to as “near-center area”). Get only the color value of the pixel.

  Specifically, as illustrated in FIG. 10, the table generation unit 38 a acquires and acquires only the color value of the pixel in the region 51 b near the center of the patch 51 a when acquiring the color value from the patch 51 a in the captured image. The average value of the color values of the pixels is acquired as the color value of the patch 51a. The center vicinity region 51b is, for example, a region whose vertical and horizontal lengths are each half of the entire region of the patch 51a. Although the patch 51a has been described, the same applies to the patch 61a.

  In the following description, the color values in the captured image will be described as RGB values.

  The table generation unit 38a calculates a weighting coefficient for calculating a representative value for each gradation of the color values of the patches of the test chart 51 and the reference chart 61 in the photographed image after the process of S133. Is executed (S134).

  FIG. 11 is a flowchart of the weighting coefficient calculation process shown in FIG.

As shown in FIG. 11, the table generating unit 38a calculates the variance of the color values of the pixels in the patch in the captured image for each patch as shown in Equation 1 (S161). In Equation 1, S ′ is the variance of the RGB values of the target patch. rgb _n is the RGB value of each pixel in the region near the center of the target patch. subscript n of rgb _n is an integer such as one or more N or less of N kinds for rgb _n to identify whether the value of any pixel in the center near the area of the patch. N is the number of pixels in the region near the center of the target patch. rgb _ave is the average value of the RGB values of each pixel in the region near the center of the target patch.

Next, the table generation unit 38a uses the variance calculated in S161 as a weighting factor based on the variance of the color values of the pixels in the patch in the captured image, as shown in Equation 2. Calculation is performed for each patch (S162). In Equation 2, ScatterWeight is a color value dispersion reference weighting coefficient of the target patch. S ′ is the variance of the RGB values of the target patch. S _ave is the average value of the variance S ′ of the RGB values of all patches in the captured image. abs () is a function for obtaining the absolute value of the numerical value in ().

In Equation 2, ScatterWeight becomes extremely large when abs (S′−S _ave ) is close to 0. Accordingly, an upper limit value may be set for ScatterWeight.

  When the process of S162 ends, the table generation unit 38a ends the weighting coefficient calculation process shown in FIG.

  As shown in FIG. 9, the table generation unit 38 a performs the test chart 51 and the reference chart 61 based on the color value dispersion reference weighting coefficient calculated by the weighting coefficient calculation process of S134 after the weighting coefficient calculation process of S134. The representative value for each gradation of the color value of the patch is calculated (S135).

Specifically, the table generating unit 38 a calculates the weighted average RGB _s of the color values of a plurality of patches based on the color value dispersion reference weighting coefficient, as shown in Equation 3, for each of the test chart 51 and the reference chart 61. The color value of the patch is calculated as a representative value for each gradation. That is, the table generating unit 38a is configured to calculate a weighted average RGB _s as a representative value for each tone color values of the patch 51a of the test chart 51, the weighted average RGB _s to each gray level of the color value of the patch 61a of the reference chart 61 As a representative value. In Equation 3, ScatterWeight _m is the color value dispersion reference weighting coefficient for each patch calculated in S162. RGB _m is the brightness for each patch acquired in S133. Subscript m of ScatterWeight _m and RGB _m are, ScatterWeight _m and RGB _m multiple of the same tone in each of the test chart 51 and reference chart 61 for identifying whether the value of any of the patches of the patch For example, M is an integer of 1 or more and M or less. M is the number of a plurality of patches of the same gradation in each of the test chart 51 and the reference chart 61. For example, in the case where the table generation unit 38a calculates the weighted average RGB _s of a specific gradation of the color value of the patch 51a of the test chart 51, the number of patches 51a of this gradation in the test chart 51 is four. At some point, it is 4.

  The table generation unit 38a, after the process of S135, shows the correspondence between the representative value for each gradation of the RGB value of the patch 61a of the reference chart 61 calculated in S135 and the known XYZ value as shown in FIG. A relational expression is acquired (S136). Here, the table generating unit 38a can acquire the known XYZ values of the patches 61a of the reference chart 61 based on the reference chromaticity value information 37d. Therefore, the table generation unit 38a determines what XYZ value each representative value of the RGB values in the captured image corresponds to based on the positional relationship of the plurality of patches 61a of the reference chart 61. Can be recognized.

  After the process of S136, the table generating unit 38a substitutes the representative value for each gradation of the RGB value of the patch 51a of the test chart 51 calculated in S135 into the relational expression acquired in S136, thereby the test chart 51. The XYZ value of the output color by the MFP 30 of the patch 51a is acquired (S137). Therefore, the table generating unit 38a can acquire the gradation characteristic 81 of the MFP 30 as shown in FIG. The gradation characteristic 81 is the relationship between the color value in the test chart image data 37c for the patch 51a of the test chart 51, that is, the input color value, and the XYZ value acquired in S137 for the gradation of the patch 51a. is there. Further, as shown in FIG. 13, the table generating unit 38 a displays the color value in the test chart image data 37 c for the patch 51 a of the test chart 51, that is, the input color value, and the patch 61 a of the reference chart 61 corresponding to the patch 51 a. On the other hand, the relationship 82 with the XYZ values set in the reference chromaticity value information 37d can be acquired.

  After the process of S137, the table generating unit 38a generates a gamma correction table for correcting the gradation characteristic 81 of the MFP 30 to the relationship 82 in the reference chart 61 as shown by the arrow in FIG. 13 (S138). That is, the table generating unit 38a references the gradation characteristic 81 of the MFP 30 based on the difference between the XYZ value of the output color of the patch 51a of the test chart 51 from the MFP 30 and the known XYZ value of the patch 61a of the reference chart 61. A gamma correction table for correcting the gradation characteristics along the chart 61 is generated.

  After completing the processing of S138, the table generating unit 38a ends the gamma correction table generating process shown in FIG.

  As illustrated in FIG. 6, the control unit 38 executes the calibration program 37a after the gamma correction table generation process in S103, thereby changing the gamma correction table 37b on the storage unit 37 into the gamma correction table generation process in S103. Calibration is executed by updating the gamma correction table generated in step S104.

  As described above, the calibration system 10 performs weighting for each patch based on the dispersion of the color values of the pixels in the patch in the captured image even when lightness spots occur in the captured image ( S135), the representative values of the color values of the patches of the test chart 51 and the reference chart 61 for each gradation can be calculated with high accuracy. It can suppress affecting a result. Therefore, the calibration system 10 can improve the accuracy of correction of gradation characteristics.

  When the lightness of a portion corresponding to a specific patch becomes higher than the original lightness due to halation occurring according to the illumination conditions depending on the shooting environment of the test chart 51 and the reference chart 61, the calibration system 10 The portion where the lightness is higher than the original lightness is easily affected by noise generated in the photographed image at the time of photographing with the smartphone 20 by the amount of lightness. That is, the dispersion (variation) in the patch whose lightness is higher than the original lightness is increased. Therefore, the calibration system 10 reduces the influence of the color value of the patch in which the color value dispersion of the pixels is too large, that is, the patch whose lightness is too high than the original lightness, and represents the representative value for each gradation of the patch color value. By calculating this, it is possible to improve the accuracy of the representative value for each gradation of the color value of the patch.

  In the calibration system 10, the brightness of a part corresponding to a specific patch is higher than the original brightness due to the shadow of some object such as a photographer appearing in the captured image depending on the shooting environment of the test chart 51 and the reference chart 61. When the brightness is lowered, the portion where the brightness is lower than the original brightness is less affected by noise generated in the captured image when the smartphone 20 captures the image, because the brightness is lowered. That is, the dispersion (variation) in the patch whose brightness is lower than the original brightness is reduced. Therefore, the calibration system 10 reduces the influence of the color value of the patch in which the color value dispersion of the pixel is too small, that is, the patch whose lightness is too low than the original lightness, and represents the representative value for each gradation of the color value of the patch. By calculating this, it is possible to improve the accuracy of the representative value for each gradation of the color value of the patch.

  Since the calibration system 10 calculates the representative value after suppressing the brightness spots generated in the captured image due to the characteristics of the smartphone 20 by the shading correction (S132) (S134), the brightness spots in the captured image have the gradation characteristics. The influence on the correction result can be further suppressed. Therefore, the calibration system 10 can further improve the accuracy of correction of the gradation characteristics.

  In the calibration system 10, since the MFP 30 includes the table generating unit 38a, the photographing device does not need to include the table generating unit. Therefore, the calibration system 10 does not need to use a highly functional device such as the smartphone 20 as an imaging device, and can use a general imaging device, thereby improving convenience. .

  In the calibration system 10 described above, the process of S103 is executed by the MFP 30, but at least a part of the process of S103 may be executed by the smartphone 20. For example, when all of the processing of S103 is executed in the smartphone 20, the control unit 26 of the smartphone 20 generates a gamma correction table for correcting the gradation characteristics of the MFP 30, as illustrated in FIG. Function as. Then, the control unit 26 of the smartphone 20 transmits the gamma correction table generated by the table generating unit 26 a to the MFP 30. When all the processing of S103 is executed in the smartphone 20, it is not necessary to provide the table generation means in the MFP 30, so that the processing load on the MFP 30 when the gamma correction table 37b is updated can be suppressed.

(Second Embodiment)
Since the configuration of the calibration system according to the second embodiment is the same as the configuration of the calibration system according to the first embodiment, detailed description thereof is omitted.

  The operation of the calibration system according to the second embodiment is the same as the operation of the calibration system according to the first embodiment, except for the operations described below.

  The calibration system according to the second embodiment executes the operation shown in FIG. 15 instead of the operation shown in FIG.

  FIG. 15 is a flowchart of a gamma correction table generation process in the calibration system according to the second embodiment.

  As illustrated in FIG. 15, the table generation unit 38 a performs the processes of S <b> 131 to S <b> 133 similarly to the gamma correction table generation process illustrated in FIG. 9.

  Then, the table generation unit 38a calculates the weighting coefficient for calculating the representative value for each gradation of the color values of the patches of the test chart 51 and the reference chart 61 in the photographed image after the process of S133. Calculation processing is executed (S234).

  FIG. 16 is a flowchart of the weighting coefficient calculation process shown in FIG.

  As shown in FIG. 16, the table generating unit 38a performs a color value dispersion reference weighting coefficient calculation process for calculating a color value dispersion reference weighting coefficient as a weighting coefficient based on the dispersion of color values of pixels in a patch in a captured image. Execute (S261). The color value dispersion reference weighting coefficient calculation process is the same as the weighting coefficient calculation process shown in FIG.

  After the color value dispersion reference weighting coefficient calculation processing in S261, the table generating unit 38a calculates a predicted lightness reference weighting coefficient based on the predicted lightness of this patch in the photographed image when it is assumed that the patch in the photographed image is a blank portion. A predicted brightness reference weighting coefficient calculation process to be calculated is executed (S262).

  FIG. 17 is a flowchart of the predicted brightness reference weighting coefficient calculation process shown in FIG.

  As shown in FIG. 17, the table generating means 38a is a lattice portion formed between patches in a captured image, that is, a portion of a lattice point (hereinafter referred to as a “lattice point portion”) in a blank portion. The brightness is acquired (S291). For example, as shown in FIG. 18, the table generation unit 38a acquires the brightness of the lattice point portion 51d among the lattice portions 51c formed between the patches 51a in the captured image. Although the patch 51a has been described, the same applies to the patch 61a. The test sheet 50 is generated by printing a test chart 51 on a white recording medium. Therefore, the lattice portion 51c of the test chart 51 in the test sheet 50 is white. The lattice portion of the reference chart 61 in the reference sheet 60 is also white.

  After the process of S291, the table generating unit 38a calculates the average value of the brightness of the captured image, that is, the average brightness, based on the brightness of the lattice point portion acquired in S291 (S292). That is, the table generation unit 38a calculates the average brightness of the captured image by averaging the brightness of the plurality of grid point portions acquired in S291.

  After the processing of S292, the table generating unit 38a sets the brightness of the portion between the lattice points of the chart in the captured image (hereinafter referred to as “inter-grid point portion”) to the brightness of the lattice point portion acquired in S291. It calculates by the linear interpolation based on (S293). For example, as shown in FIG. 19, the table generating unit 38 a has a brightness of 80 and 100, respectively, of adjacent lattice point portions 51 d among the lattice portions 51 c formed between the patches 51 a in the captured image. The lightness of the inter-grid point portion 51e located between these lattice point portions 51d is calculated as 90 which is the average of the lightness of these lattice point portions 51d. Although the patch 51a has been described, the same applies to the patch 61a.

  The table generation unit 38a, after the process of S293, calculates the predicted brightness of the patch in the photographed image when the patch in the photographed image is a blank part, that is, a white part, of the lattice point part acquired in S291. Calculation is performed for each patch based on the lightness and the lightness of the portion between the lattice points calculated in S293 (S294). That is, as shown in FIG. 20, the table generating unit 38a includes a patch 51a surrounded by four closest lattice point portions 51d and four closest lattice point portions 51e in the captured image, as a blank portion, that is, The brightness of the patch 51a in the captured image when it is assumed that the portion is white is predicted.

Specifically, the table generating unit 38a firstly calculates the reciprocal number W _{n, k} of the distance from any of the four lattice point portions 51d and the nearest four inter-grid point portions 51e, as shown in Equation 4. Is calculated for each pixel in the region 51b near the center of the patch 51a. In Equation 4, X _k and Y _k are the X coordinate and Y coordinate of the four lattice point portions 51d and the four inter-grid point portions 51e, respectively. x _n and y _n are the X coordinate and Y coordinate of the target pixel, respectively. W _n, k, subscript k of _{X k} and _{Y k is, W n,} k, or a _{X k} and _{Y k} are four lattice points part 51d, which is one of the values of the partial 51e between the four grid points For example, eight integers of 1 to 8. W _n, k, _x subscript n of _n and _{y n are, W n, k, x n} and _{y n} example 1 for identifying whether the value of any pixel in the center near the region 51b of the patch 51a is N or less N or less integers. N is the number of pixels in the central region 51b.

Then, the table generating unit 38a, as shown in Equation 5, to calculate the predicted brightness l _n of the pixel when the pixel of interest is assumed to be margin, for each pixel of the center near the region 51b of the patch 51a . In Equation 5, W _{n, k} is calculated in Equation 4. L _k is the lightness of any of the four lattice point portions 51d and the four inter-grid point portions 51e, and is either the lightness acquired in S291 or the lightness calculated in S293. Subscript k of W _{n, k} and _{L k is, W n,} and _k and _{L k} is four lattice points portion 51d, for identifying which one of the values of the partial 51e between the four grid points e.g. 8 integers of 1 or more and 8 or less. W _n, the index n of _k and _{l n are, W n,} for example, 1 or more N or less of N kinds for identifying whether the value of any pixel in the center near the region 51b of _k and _{l n} patches 51a Is an integer. N is the number of pixels in the central region 51b. Equation 5 shows an interpolation method called an inverse distance weighting method, and shows an interpolation method in which the influence on the predicted brightness of the target pixel increases as the brightness becomes closer to the target pixel.

Next, as shown in Expression 6, the table generation unit 38a calculates, for each patch 51a, the predicted lightness L ′ of the patch 51a in the captured image when it is assumed that the patch 51a in the captured image is a blank portion. In Equation 6, l _n is obtained by calculating the number 5. N is the number of pixels in the central region 51b.

  Although the patch 51a has been described above, the same applies to the patch 61a.

After the process of S294, the table generating unit 38a calculates the predicted brightness reference weighting coefficient for each patch as shown in Equation 7 using the predicted brightness calculated in S294 (S295). In Equation 7, LightWeight is a predicted brightness reference weighting coefficient of the target patch. L ′ is the predicted brightness of the target patch. L _ave is an average value of predicted brightness L ′ of all patches in the captured image. abs () is a function for obtaining the absolute value of the numerical value in ().

In Equation 7, LightWeight becomes extremely large when abs (L′−L _ave ) is close to 0. Therefore, an upper limit value may be set for LightWeight.

  When the process of S295 ends, the table generation unit 38a ends the predicted brightness reference weighting coefficient calculation process shown in FIG.

  As illustrated in FIG. 16, the table generation unit 38 a ends the weighting coefficient calculation process illustrated in FIG. 16 after the predicted brightness reference weighting coefficient calculation process in S <b> 262.

  As illustrated in FIG. 15, the table generating unit 38 a performs the test chart 51 based on the color value dispersion reference weighting coefficient and the predicted brightness reference weighting coefficient calculated by the weighting coefficient calculation process of S234 after the weighting coefficient calculation process of S234. And the representative value for each gradation of the color value of each patch of the reference chart 61 is calculated (S235).

Specifically, the table generating unit 38a first calculates the weighted average RGB _s of the color values of a plurality of patches based on the color value dispersion reference weighting coefficient as shown in Equation 3, as in the first embodiment. Is calculated for each gradation of the color values of the patches of the test chart 51 and the reference chart 61.

Next, the table generating unit 38a calculates the weighted average RGB ₁ of the color values of the plurality of patches based on the predicted lightness reference weighting coefficient as shown in Equation 8, and the color values of the patches of the test chart 51 and the reference chart 61, respectively. It is calculated for each gradation. That is, the table generating unit 38a is configured to calculate a weighted average RGB _l to each gray level of the color value of the patch 51a of the test chart 51, and calculates the weighted average RGB _l to each gray level of the color value of the patch 61a of the reference chart 61 . In Equation 8, LightWeight _m is the predicted brightness reference weighting coefficient for each patch calculated in S295. RGB _m is the brightness for each patch acquired in S133. Subscript m of LightWeight _m and RGB _m are, LightWeight _m and RGB _m multiple of the same tone in each of the test chart 51 and reference chart 61 for identifying whether the value of any of the patches of the patch For example, M is an integer of 1 or more and M or less. M is the number of a plurality of patches of the same gradation in each of the test chart 51 and the reference chart 61. For example, in the case where the table generation unit 38a calculates the weighted average RGB ₁ of the specific gradation of the color value of the patch 51a of the test chart 51, the number of patches 51a of this gradation in the test chart 51 is four. At some point, it is 4.

Next, the table generating unit 38a, as shown in Equation 9, calculates the weighted average RGB _s calculated in Equation 3 for each color value of the patches of the test chart 51 and the reference chart 61, the test chart 51, and the reference chart. The average RGB with the weighted average RGB ₁ calculated in Expression 8 for each color value gradation of each patch 61 is calculated as a representative value for each color value gradation of each patch of the test chart 51 and the reference chart 61. . That is, the table generation unit 38 a calculates a representative value for each color value gradation of the patch 51 a of the test chart 51 and calculates a representative value for each color value gradation of the patch 61 a of the reference chart 61.

  After the process of S235, the table generation unit 38a executes the processes of S136 to S138, similarly to the gamma correction table generation process shown in FIG. 9, and ends the gamma correction table generation process shown in FIG.

  As described above, the calibration system according to the second embodiment performs the distribution of the color values of the pixels in the patch in the photographed image and the patch even when the brightness spot occurs in the photographed image. Is weighted for each patch on the basis of the predicted brightness of the patch in the photographed image when it is assumed that the image is a margin part (S235), so that the level of the color value of each patch of the test chart 51 and the reference chart 61 is determined. Since the representative value for tones can be calculated with high accuracy, it is possible to suppress the effect of brightness spots in the captured image from affecting the result of correction of the gradation characteristics. Therefore, the calibration system according to the second embodiment can improve the accuracy of correction of gradation characteristics.

  The calibration system according to the second embodiment has a high predicted brightness of a portion corresponding to a specific patch due to halation occurring according to the illumination condition depending on the shooting environment of the test chart 51 and the reference chart 61. If the estimated brightness of the part corresponding to a specific patch is too low due to the shadow of some object such as a photographer appearing in the captured image, the effect of the color value of these patches will be reduced. By calculating the representative value for each gradation of the color value, the accuracy of the representative value for each gradation of the color value of the patch can be improved.

  In the calibration system according to the second embodiment, the gamma correction table generation process shown in FIG. 15 is executed by the MFP 30 in the above, but it is the same as the calibration system 10 according to the first embodiment. In addition, at least a part of the gamma correction table generation process may be executed in the smartphone 20.

  The image forming apparatus of the present invention is an MFP in each of the embodiments described above, but may be an image forming apparatus other than the MFP. For example, the image forming apparatus of the present invention may be an image forming apparatus such as a printer dedicated machine, a copy dedicated machine, or a fax dedicated machine.

  The imaging device of the present invention is a smartphone in each of the above-described embodiments, but may be an imaging device other than a smartphone. For example, the photographing device of the present invention may be a photographing device such as a digital camera.

10 Calibration system 20 Smartphone (photographing device)
26a Table generation means 30 MFP (image forming apparatus)
37a calibration program 37b gamma correction table 38a table generating means 41 gradation characteristics 42 gradation characteristics 50 test sheet (sheet)
51 Test Chart 51a Patch 61 Reference Chart 61a Patch 70 Captured Image 81 Gradation Characteristics

Claims (7)

A shooting device;
An image forming apparatus that generates a sheet by printing a test chart including a plurality of color patches;
Table generating means for generating a gamma correction table for correcting the gradation characteristics of the image forming apparatus to gradation characteristics along a reference chart corresponding to the test chart;
The table generating means calculates a weighting coefficient based on a variance of color values of pixels in the patch in a captured image generated by simultaneously capturing the test chart of the sheet and the reference chart by the imaging device. After calculating for each patch, a representative value for each gradation of the color value of each patch of the test chart and the reference chart is calculated based on a weighted average of the color values of the plurality of patches based on the weighting coefficient. The calibration system is characterized in that the gamma correction table is generated based on the representative values of the test chart and the reference chart.   The calibration system according to claim 1, wherein the table generation unit calculates the representative value based on the photographed image subjected to the shading correction after performing the shading correction on the photographed image. .   The calibration system according to claim 1, wherein the image forming apparatus includes the table generating unit.   The calibration system according to claim 1, wherein the imaging device includes the table generation unit. A sheet generation step of generating a sheet by printing by an image forming apparatus of a test chart including a plurality of color patches;
A captured image generation step in which the test chart of the sheet generated by the sheet generation step and a reference chart corresponding to the test chart are simultaneously captured by a photographing device to generate a captured image;
A table generation step of generating a gamma correction table for correcting the gradation characteristics of the image forming apparatus to the gradation characteristics according to the reference chart;
The table generation step calculates a weighting coefficient based on dispersion of color values of pixels in the patch in the captured image generated by the captured image generation step for each patch, and then calculates a plurality of weights based on the weighting coefficient. Calculating a representative value for each gradation of the color values of the patches of the test chart and the reference chart based on a weighted average of the color values of the patches of the patch, and the representatives of the test chart and the reference chart, respectively. A calibration method comprising the step of generating the gamma correction table based on a value. An image forming apparatus that generates a sheet by printing a test chart including a plurality of color patches,
Table generating means for generating a gamma correction table for correcting the gradation characteristics of the image forming apparatus to gradation characteristics along a reference chart corresponding to the test chart;
The table generating means assigns a weighting coefficient based on a variance of color values of pixels in the patch in a captured image generated by simultaneously capturing the test chart of the sheet and the reference chart by a photographing device. After each calculation, based on the weighted average of the color values of the plurality of patches based on the weighting coefficient, the representative value for each gradation of the color values of the patches of the test chart and the reference chart is calculated. An image forming apparatus that generates the gamma correction table based on the representative values of the test chart and the reference chart. A calibration program executed by an image forming apparatus that generates a sheet by printing a test chart including a plurality of color patches,
Causing the image forming apparatus to function as a table generating unit that generates a gamma correction table for correcting the gradation characteristics of the image forming apparatus to the gradation characteristics according to a reference chart corresponding to the test chart;
The table generating means assigns a weighting coefficient based on a variance of color values of pixels in the patch in a captured image generated by simultaneously capturing the test chart of the sheet and the reference chart by a photographing device. After each calculation, based on the weighted average of the color values of the plurality of patches based on the weighting coefficient, the representative value for each gradation of the color values of the patches of the test chart and the reference chart is calculated. A calibration program that generates the gamma correction table based on the representative values of the test chart and the reference chart.