JP2016052063A - Image forming device, image forming method, and image forming program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To improve accuracy of calibration correction using a graduation pattern on an image carrier.SOLUTION: An image forming device includes: first calibration means which generates correction parameters set to graduation conversion used for image processing, on the basis of a reading value of a first graduation pattern formed on a transfer material; second calibration means which generates correction parameters setting to the graduation conversion, on the basis of a reading value of a second graduation pattern formed on an image carrier; and correction means which estimates fluctuation amount between the first graduation pattern and the second graduation pattern, on the basis of graduation characteristics obtained by the first calibration means and graduation characteristics obtained by the second calibration means, and corrects the correction parameters generated by the second calibration means.SELECTED DRAWING: Figure 8

Description

  The present application relates to an image forming apparatus, an image forming method, and an image forming program.

  In a color copying machine, a method of adjusting image density by using both a first calibration and a second calibration is known. In the first calibration, the first gradation pattern output on the transfer paper is read by a scanner and the γ conversion table is corrected according to an instruction from a user or a service engineer. In the second calibration, the second gradation pattern formed on the image carrier (intermediate transfer belt) is read by an optical sensor facing the image carrier, and a γ conversion table is read according to the reading value of the optical sensor. It correct | amends (for example, refer patent document 1).

  However, in the method of Patent Document 1 described above, a plotter variation or the like occurs, a deviation occurs in the relationship between the first gradation pattern and the second gradation pattern, and the correction accuracy of the second calibration is It does not take into consideration that the correction accuracy of the first calibration is low and the stability of the image density is lowered.

  In one aspect, an object of the present invention is to improve the accuracy of calibration correction using a gradation pattern on an image carrier.

  In one aspect, a first calibration unit that generates a correction parameter to be set for gradation conversion used in image processing based on a read value of a first gradation pattern formed on a transfer material, and an image carrier Second calibration means for generating a correction parameter to be set for the gradation conversion based on the read value of the formed second gradation pattern; and gradation characteristics obtained by the first calibration means; Based on the gradation characteristics obtained by the second calibration means, a variation amount between the first gradation pattern and the second gradation pattern is predicted and generated by the second calibration means. Correction means for correcting the corrected parameters.

  It is possible to improve the accuracy of calibration correction using the gradation pattern on the image carrier.

1 is a diagram illustrating an example of a configuration of an entire image forming apparatus. It is a figure explaining the control system incorporated in an image forming apparatus. It is a figure which shows an example of the functional block of an image processing control part. It is a figure explaining a YMCK gradation conversion table. It is a flowchart which shows the flow which acquires IBACCγ correction characteristics. It is a flowchart which acquires the correction characteristic of an IBACC pattern write value. FIG. 5 is a diagram illustrating a correspondence relationship between the characteristics illustrated in FIG. 4 and different characteristics. It is a figure explaining the variation | change_quantity with a 1st gradation pattern and a 2nd gradation pattern. It is a figure which shows the example which acquires the IBACC correction result from the prediction of the fluctuation amount. It is a flowchart which shows the flow of the process which acquires a YMCK conversion table. It is a figure explaining the adjustment example of a 2nd gradation pattern. It is a figure explaining the variation | change_quantity of the image density by a plotter fluctuation | variation. It is a figure explaining automatic gradation correction. It is a flowchart which performs automatic gradation correction. FIG. 6 is a diagram illustrating an example of a second gradation pattern formed on an intermediate transfer belt. It is a figure explaining the method of determining the necessity of ACC execution. It is a figure which shows an example of the screen which notifies the necessity of ACC execution. It is a flowchart which shows the flow of the process which updates an IBACC reference value. It is a figure explaining the update procedure of IBACC reference value. It is a figure explaining operation | movement of 1st calibration (1st time). It is a figure explaining the production | generation operation | movement of (gamma) conversion table. It is a figure explaining the operation | movement of a 2nd calibration. It is a figure explaining operation | movement of 1st calibration (after 2nd time).

  Next, embodiments of the present invention will be described in detail.

<Configuration of entire image forming apparatus>
FIG. 1 is a diagram illustrating an example of a configuration of the entire image forming apparatus. As shown in FIG. 1, in the central portion of a copying machine 10 as an example of an image forming apparatus, four organic photoreceptor (OPC) drums 11A to 11D of φ30 [mm] as image carriers are arranged side by side. ing.

  Around the photosensitive drums 11A to 11D, the charging devices 12A to 12D for charging the surfaces of the photosensitive drums 11A to 11D and the surface of the uniformly charged photosensitive drums 11A to 11D are irradiated with semiconductor laser light. A laser optical system 13 for forming an electrostatic latent image, a black developing device 14 for supplying each color toner to the electrostatic latent image and developing the toner image, and a yellow (Y) developing as a color developing device. An apparatus 15, a magenta (M) developing device 16, and a cyan (C) developing device 17 are arranged.

  Further, an intermediate transfer belt 18 that sequentially transfers toner images of respective colors formed on the photosensitive drums 11A to 11D, bias rollers 19A to 19D that apply a transfer voltage to the intermediate transfer belt 18, and a photosensitive drum after transfer. 11. Cleaning devices 20A to 20D for removing the toner remaining on the surface of 11 and a neutralizing unit for removing charges remaining on the surfaces of the photosensitive drums 11A to 11D after transfer are arranged.

  Further, the intermediate transfer belt 18 is provided with a transfer bias roller for applying a voltage for transferring the transferred toner image to the transfer material, and a belt cleaning device for cleaning the toner image remaining on the transfer material after transfer. It is installed.

  A fixing device 22 for fixing the toner image by heating and pressurizing is disposed at the exit end of the conveying belt 21 that conveys the transfer material peeled off from the intermediate transfer belt 18. A paper discharge tray 23 is attached to the exit of the fixing device 22.

  An upper part of the laser optical system 13 is provided with a contact glass serving as a document placement table disposed above the copying machine 10 and an exposure lamp for irradiating the document on the contact glass with scanning light. Reflected light from the document is guided to an imaging lens by a reflecting mirror, and enters an image sensor array 24 of a CCD (Charge Coupled Device) which is a photoelectric conversion element. The image signal converted into an electrical signal by the CCD image sensor array 24 controls the laser oscillation of the semiconductor laser of the laser optical system 13 through an image processing unit (IPU) for processing the image.

<Control system of image forming apparatus>
FIG. 2 is a diagram illustrating a control system built in the image forming apparatus. The copying machine 10 shown in FIG. 2 includes a main control unit (CPU: Central Processing Unit) 30, and a ROM 31 and a RAM 32 are attached to the main control unit 30.

  The main control unit 30 is installed in a laser optical system drive unit 34, a power supply circuit 35, an optical sensor 36 installed in each YMCK image forming unit, and each YMCK developer via an interface I / O 33. A toner density sensor 37, an environmental sensor 38, a photoreceptor surface potential sensor 39, a toner replenishing circuit 40, an intermediate transfer belt drive unit 41, and an operation unit 42 are connected.

  The laser optical system drive unit 34 adjusts the laser output of the laser optical system 13. The power supply circuit 35 gives a predetermined charging discharge voltage to the charging device 12. Further, the power supply circuit 35 gives a developing bias of a predetermined voltage to the developing devices 14 to 17 and gives a predetermined transfer voltage to the bias roller 19 and the transfer bias roller.

  The optical sensor 36 faces the photoconductor drums 11A to 11D and opposes the optical sensor 36A for detecting the toner adhesion amount on the photoconductor drums 11A to 11D, and the intermediate transfer belt 18 so as to face the photoconductor drums 11A to 11D. An optical sensor 36 </ b> B for detecting the toner adhesion amount on the upper side and an optical sensor 36 </ b> C for detecting the toner adhesion amount on the conveyance belt 21 are provided opposite to the conveyance belt 21. In practice, detection may be performed at any one of the optical sensors 36A to 36C.

  The optical sensor 36 includes a light-emitting element such as a light-emitting diode and a light-receiving element such as a photosensor that are disposed in the vicinity of the areas after transfer of the photosensitive drums 11A to 11D. The optical sensor 36 detects the toner adhesion amount in the toner image of the detection pattern latent image formed on the photoconductor drum 11 and the toner adhesion amount in the background portion for each color, and detects the residual potential after static elimination on the photoconductor. To do.

  The detection output signal from the optical sensor 36 is applied to the photoelectric sensor control unit. The photoelectric sensor control unit obtains a ratio between the toner adhesion amount in the detection pattern toner image and the toner adhesion amount in the background portion, compares the ratio value with a reference value, detects a change in image density, and detects each color of YMCK. The control value of the toner density sensor 37 is corrected.

  The toner concentration sensor 37 detects the toner concentration based on a change in magnetic permeability of the developer present in the developing devices 14-17. The toner density sensor 37 compares the detected toner density value with a reference value, and if the toner density falls below a certain value and becomes a toner shortage state, a toner replenishment signal having a magnitude corresponding to the shortage is supplied. Applied to circuit 40.

  The photoconductor surface potential sensor 39 detects the surface potential of each of the photoconductor drums 11 </ b> A to 11 </ b> D, and the intermediate transfer belt drive unit 41 controls driving of the intermediate transfer belt 18.

  The main control unit 30 controls driving of a sensor control unit 50 that performs various sensor controls, a power source control unit 51 that controls a power source and a bias, an intermediate transfer belt driving unit 41, and a laser optical system driving unit 34. A drive control unit 52 is connected.

  The main control unit 30 is connected to an image processing control unit 53 that controls a scanner and an image processing unit (IPU) that processes images, an image processing control unit 53, the Internet, and the like via an interface I / O 33. A communication control unit 54 that controls communication, a storage device 55 that stores various data acquired via the communication control unit 54, and a storage device control unit 56 that controls the storage device 55 are connected.

<Functional block of image processing control unit>
FIG. 3 is a diagram illustrating an example of functional blocks of the image processing control unit. The image processing control unit 53 shown in FIG. 3 includes a scanner 60A that uses a CCD (Charge Coupled Device) as a reading device and a scanner 60B that uses a CIS (Contact Image Sensor) as a reading device.

  The image processing control unit 53 also includes a shading correction circuit 61A for the scanner 60A, a shading correction circuit 61B for the scanner 60B, an FL correction processing circuit 62 for the scanner 60A, and an inter-chip pixel interpolation circuit 63 for the scanner 60B. And a memory controller 64.

  The image processing control unit 53 includes an image memory 65, a scanner γ conversion circuit 66, an image area separation / ACS determination circuit 67, a spatial filter 68, an automatic density adjustment level (ADS) detection / removal circuit 69, A hue determination circuit 70, a color correction / UCR processing circuit 71, and a scaling processing circuit 72 are provided.

  The image processing control unit 53 includes a printer γ conversion circuit 73, a binary gradation processing circuit 74, an editing processing circuit 75, a Multilayer BUS 76, a pattern generation circuit 77, a printer γ conversion circuit 78, and a printer 79. And have.

  The image processing control unit 53 includes a compression / expansion circuit 80, an image memory 81, an HDD I / F 82, an HDD 83, a rotation processing circuit 84, an external I / F 85, a feature amount extraction processing circuit 86, A printer γ conversion circuit 87 and a gradation processing circuit 88 are provided.

  Here, when the user designates double-sided simultaneous reading, the original to be copied is color-separated into R, G, and B by the scanner 60A with one side of the original as the front, and is read as a 10-bit signal as an example. . Further, both sides of the document are simultaneously read by one transport of the scanner 60B with the opposite side of the front side of the document as the back side.

  The image signal read from the scanner 60A is output as an 8-bit signal after the shading correction circuit 61A corrects the unevenness in the main scanning direction. The image signal read from the scanner 60B is corrected for unevenness in the main scanning direction by the shading correction circuit 61B and output as an 8-bit signal.

  The FL correction processing circuit 62 corrects the sensitivity difference (tone difference) between the two sets of CCDs arranged in the main scanning direction. The inter-chip pixel interpolation circuit 63 interpolates the image data of the gap between the chips of the CIS device arranged in the main scanning direction from the adjacent pixels.

  The memory controller 64 is read by the scanner 60 </ b> A and read by the shading correction circuit 61 </ b> A and the FL correction processing circuit 62, the image data 1 after being processed by the scanner 60 </ b> B, and the shading correction circuit 61 </ b> B and the inter-chip pixel interpolation circuit 63. The processed image data 2 is temporarily stored in the image memory 65 using a DDR memory.

  The image memory 65 stores the image signal after the scanner γ conversion. The scanner γ conversion circuit 66 converts the read signal from the scanner from reflectance data to brightness data.

  The image area separation / ACS determination circuit 67 performs an image area separation determination result (signal X) such as a character area and a photographic area for each of the image data 1 and the image data 2, and performs color determination as to whether a color document or a monochrome document.

  The spatial filter 68 performs edge enhancement according to the edge degree of the image signal in addition to processing for changing the frequency characteristics of the image signal, such as edge enhancement and smoothing according to the user's preference, such as a sharp image or a soft image. Processing (adaptive edge enhancement processing) is performed. For example, edge enhancement is performed on a character edge, and adaptive edge enhancement without edge enhancement is performed on each of the R, G, and B signals on a halftone image.

  The hue determination circuit 70 determines to which hue the read image data is determined. Based on the determined result, a color correction coefficient for each hue is selected.

  The color correction / UCR processing circuit 71 performs color correction processing. The color correction / UCR processing circuit 71 corrects the difference between the color separation characteristics of the input system and the spectral characteristics of the output system color material, and calculates the amount of color material YMC necessary for faithful color reproduction; , And a UCR processing unit for replacing a portion where the three colors of YMC overlap with Bk (black).

  The UCR process can be performed by calculating using the following equation.

Y ′ = Y−α · min (Y, M, C)
M ′ = M−α · min (Y, M, C)
C ′ = C−α · min (Y, M, C)
Bk = α · min (Y, M, C)
In the above equation, α is a coefficient that determines the amount of UCR. When α = 1, 100% UCR processing is performed. α may be a constant value, for example. In the high density portion, α is close to “1”, for example, and in the highlight portion (low image density portion), for example, is close to “0”. This makes it possible to smooth the image in the highlight portion.

  The above-described color correction coefficient is different for each of the 14 hues of black and white, with 12 hues obtained by further dividing the 6 hues of RGBYMC into two.

  The scaling processing circuit 72 performs main scanning and sub-scanning scaling. The printer γ conversion circuit 73 performs printer γ conversion for characters and photographs according to the image area separation signal. The binary gradation processing circuit 74 performs printer γ conversion before performing binarization processing. The binary gradation processing circuit 74 performs binarization processing such as simple binarization processing, binary dither processing, binary error diffusion processing, binary fluctuation threshold error diffusion processing, etc. when performing FAX transmission or scanner distribution. Is performed according to an instruction from a personal computer (PC) or the like via a LAN connected to the operation unit and the external I / F 85, such as a character mode, a photo mode, and a character / photo mode.

  The editing processing circuit 75 performs editing processing such as edge mask processing and logical inversion. At the time of image data storage, compression processing is performed by the compression / decompression circuit 80 via the Multilayer Bus 76, and the compressed image data is stored in the HDD 83 via the HDD I / F 82.

  The stored image data is stored as an RGB signal, a K (Gray) signal, a CMYK signal, and an RGBX signal (X signal is an image area separation result) according to the purpose of use. RGB signals are for distribution, K signals are for distribution and FAX transmission, CMYK signals are for printing on paper, RGBX signals are for CMYK data generation, sRGB signals are for color space conversion and distribution, etc. Store for processing.

  When the image data read by the scanner 60 is used for FAX transmission and scanner transmission, the color correction / UCR processing circuit 71 converts the image data into s-RGB and K signals, and then the external I / F 85 is used. Delivered through. When printing on transfer paper, the color correction / UCR processing circuit 71 converts the data into CMYK data, which is output to the feature amount extraction processing circuit 86 via the Multilayer Bus 76.

  The feature amount extraction processing circuit 86 performs determination processing such as an edge of an image, a non-edge, and a weak edge between the edges. The printer γ conversion circuit 87 performs printer γ conversion processing in accordance with the determination results such as edge, non-edge, and weak edge. The gradation processing circuit 88 performs gradation processing such as binary or multi-value dither processing, binary or multi-value error diffusion processing, and binary or multi-value variation threshold error diffusion processing.

  The dither process selects a dither process having an arbitrary size from a 1 × 1 no dither process to a dither process of m × n pixels (m and n are positive integers). Here, as an example, dither processing using up to 36 pixels is performed. As an example, the dither size using all 36 pixels is 36 pixels in total in the main scanning direction 6 pixels × sub scanning direction 6 pixels, 36 pixels in the main scanning direction 18 pixels × 2 pixels in the sub scanning direction, and the like. .

<YMCK gradation conversion table>
Next, the correction relationship between the first calibration and the second calibration will be described using the YMCK gradation (γ) conversion table used in the image processing described above. FIG. 4 is a diagram for explaining the YMCK gradation conversion table. FIG. 4A shows a four-quadrant graph of the first calibration (ACC). FIG. 4B shows a four-quadrant graph of the second calibration (IBACC).

  In the example of FIG. 4A, when the ACC pattern (first gradation pattern) is formed, “(a0) ACCγ adjustment reference data (target value)” shown in the first quadrant and the second quadrant. “(B1) ACC pattern read value” is acquired. Based on (b1) and (a0), “(d1) ACC execution result (1)” shown in the fourth quadrant of FIG. 4A is acquired. “(D1) ACC execution result (1)” shown in the fourth quadrant of FIG. 4A is a correction parameter for correcting the image density, and is a YMCK gradation conversion table.

  In the example of FIG. 4B, after an ACC pattern is formed, for example, after a predetermined number (for example, 100) of images is formed, an IBACC pattern (second gradation pattern) is formed. (E0) IBACC pattern reading reference value ”and“ (f1) IBACC pattern reading current value shown in the second quadrant ”are acquired. Further, based on (e0) and (f1), “(c1) IBACCγ correction (1)” shown in the third quadrant of FIG. 4B is acquired. “(C1) IBACCγ correction (1)” shown in the third quadrant of FIG. 4B is a correction parameter for correcting the image density.

  Further, “(b2) IBACC correction value (1)” shown in the second quadrant of FIG. 4A is acquired as the read predicted value of the ACC pattern predicted from (c1). Further, “(d2) IBACC correction result (1)” shown in the fourth quadrant of FIG. 4A is acquired from (b2). “(D2) IBACC correction result (1)” shown in the fourth quadrant of FIG. 4A is a YMCK gradation conversion table for correcting the image density based on the IBACC pattern.

  On the other hand, at the timing when the “YMCK gradation conversion table of (d2)” shown in the fourth quadrant of FIG. 4 is obtained, the ACC pattern is formed again, and “(b3) shown in the second quadrant of FIG. ) ACC pattern read value (second time) ”and“ (d3) ACC execution result (2) ”shown in the fourth quadrant. This (d3) is a YMCK gradation conversion table for correcting the image density obtained based on “(b3) ACC pattern read value (second time)”.

  Since (d3) is a YMCK conversion table obtained at the same timing as (d2) described above, the third quadrant of FIG. 4 (A) is used as a calibration value for matching (d3) and (d2). “(C2) Calibration value of IBACCγ correction (1)” is acquired. Based on (c2), “(g1) IBACC pattern write value correction characteristic 1” shown in the fourth quadrant of FIG. 4B is acquired.

  “(G1) IBACC pattern write value correction characteristic 1” shown in the fourth quadrant of FIG. 4B is, for example, “(c1) IBACCγ correction (1)” shown in the third quadrant of FIG. 4B. This is the correction amount to be corrected. Therefore, by using (g1), the YMCK conversion table obtained based on the IBACC pattern can be matched with the YMCK conversion table obtained based on the ACC pattern. That is, by using (g1), it is possible to improve the correction accuracy by the second calibration with respect to the correction accuracy by the first calibration.

<About the four-quadrant graph of FIGS. 4A and 4B>
Next, the 4-quadrant graph of FIG. 4A and the 4-quadrant graph of FIG. The first quadrant (a) shown in FIG. 4A indicates “reference data (ACC adjustment target value)”. The horizontal axis represents the input value n to the YMCK gradation conversion table. The vertical axis represents the reading value (after processing) of the scanner.

  Note that the reading value (after processing) of the scanner is the value after averaging and adding the reading data at several locations in the gradation pattern with respect to the value obtained by scanning the gradation pattern formed on the transfer material with the scanner. It is. Here, for example, a 12-bit data signal is processed to improve calculation accuracy.

  The second quadrant (b) shown in FIG. 4A indicates “read value of ACC pattern”. The horizontal axis indicates the written value of laser light (LD) for convenience in forming the gradation pattern. The write value a [LD] of the ACC pattern is convenient because it indicates an input value before gradation processing by the quantization threshold matrix used in the gradation processing. In addition, the value after gradation processing shows various values shown by a predetermined pattern. The second quadrant (b) shown in FIG. 4A shows, for example, the characteristics of the printer unit.

  Moreover, the convenient write value a [LD] of the pattern actually formed is, for example, 16 points of 00h (background), 11h, 22h,... EEh, FFh, and the like. As described above, the write value a [LD] indicates a skip value, but here, the detected values are interpolated and handled as a continuous graph.

  (B1) shown in the second quadrant of FIG. 4A shows a reference “ACC pattern read value (first time)”. (B2) shown in the second quadrant of FIG. 4A shows an “ACC pattern read predicted value (IBACC correction value (1))” predicted from a γ correction value (IBACC γ correction characteristic) using the IBACC pattern. ing. For example, “(b2) IBACC correction value (1)” is obtained using (a0) in the first quadrant, (c1-1) in the third quadrant, (d1) in the fourth quadrant, and the like.

  Also, (b3) shown in the second quadrant of FIG. 4A shows the “ACC pattern read value (second time)” when executing the ACCγ adjustment after the reference ACCγ adjustment.

  The third quadrant (c) shown in FIG. 4A indicates “IBACCγ correction characteristics”. The vertical axis indicates the written value of the LD as with the horizontal axis. (C0) shown in the third quadrant of FIG. 4A indicates linear γ and is also used as “IBACC reference γ characteristic 1” used when executing ACCγ adjustment.

  (C1-1) shown in the third quadrant of FIG. 4A is an example of “IBACCγ correction characteristics (IBACCγ correction (1) (after processing))”. (C2) shown in the third quadrant of FIG. 4A shows the “calibration value of IBACCγ correction (1)” acquired by executing the second ACC (first calibration).

  The fourth quadrant (d) shown in FIG. 4A indicates “YMCK gradation conversion table (γ conversion table (copy application)) LD (i)”. In this embodiment, this YMCK gradation conversion table is obtained.

  The first quadrant (e) shown in FIG. 4B indicates “IBACC pattern reading reference value”. The horizontal axis represents a value obtained by converting a writing value when forming an IBACC pattern formed by attaching toner onto an image carrier (for example, the intermediate transfer belt 18) into a writing value of the ACC pattern. .

  This conversion can be performed based on the area ratio as an initial value. For example, in the case of a gradation pattern with an area ratio of 20%, it is treated as 20% of the FFh (= 255) value, the write value 33H (= 51) value, and the like. The vertical axis indicates the read value (after processing) by the reflectance sensor for reading the IBACC pattern.

  The second quadrant (f) shown in FIG. 4B indicates “the current reading value of the IBACC pattern”. The horizontal axis is the same as the horizontal axis in the first quadrant shown in FIG.

  The third quadrant (c) shown in FIG. 4B indicates “IBACCγ correction characteristics”. The horizontal axis is the same as the horizontal axis of the first quadrant. The vertical axis represents the output value. (C1) shown in the third quadrant of FIG. 4 (B) corresponds to “(e0) IBACC pattern reading reference value” shown in the fourth quadrant of FIG. 4 (B) and the second quadrant of FIG. 4 (B). "(F1) IBACC pattern read current value (1)". When obtaining (c1), "(g0) No correction" shown in the fourth quadrant of FIG. (G1) IBACC pattern write value correction characteristic 1 ”is also used.

  The fourth quadrant (g) shown in FIG. 4B shows an “IBACC pattern and ACC pattern conversion table”. (G) shown in the fourth quadrant of FIG. 4B shows characteristics when, for example, “IBACC pattern write value” is converted to “ACC pattern write value”.

<IBACCγ correction characteristics>
Next, an example of acquiring the IBACC γ correction characteristic shown in FIG. 4 will be described. FIG. 5 is a flowchart for acquiring the IBACC γ correction characteristic. The flowchart shown in FIG. 5 is executed by an image processing unit (IPU) controlled by the image processing control unit 53, for example.

  In FIG. 5, the image processing unit (IPU) forms an IBACC pattern (reference pattern) on the intermediate transfer belt 18 (S10). The optical sensor 36B detects the IBACC pattern and acquires IBACC pattern detection data (for example, “(f1) IBACC pattern read current value (1)” shown in the second quadrant of FIG. 4B) (S11). ).

  Next, the image processing unit (IPU) acquires IBACCγ correction characteristics (for example, “(c1) IBACCγ correction (1)” shown in the third quadrant of FIG. 4B) from the IBACC pattern detection data (S12). ,finish.

  The above-described processing is preferably performed every time image formation is performed on a transfer material, for example, a predetermined number (for example, 10 to 100 sheets). For example, when a temperature / humidity sensor capable of detecting the humidity, temperature, and the like in the apparatus is provided, the above-described processing is preferably performed when a change larger than a preset change amount is obtained.

<Correction characteristics of IBACC pattern write value>
Next, a method for obtaining the correction characteristic 1 of the (g1) IBACC pattern write value shown in the fourth quadrant of FIG. 4B described above will be described. FIG. 6 is a flowchart for acquiring the correction characteristic of the IBACC pattern write value.

  The flowchart shown in FIG. 6 is executed by, for example, an image processing unit (IPU). In FIG. 6, the image processing unit (IPU) executes reference IBACCγ correction (second calibration) (S20). The process of S20 is executed based on the flowchart shown in FIG.

  Next, the image processing unit (IPU) executes reference ACCγ correction (first calibration) (S21). In the process of S21, the process is executed based on the flowchart shown in FIG. For example, the characteristic of (a0) is used as an example of “reference data for ACCγ adjustment (target value)” in the first quadrant of FIG.

  As the read value of the ACC pattern, for example, “(b1) ACC pattern read value (first time)” shown in the second quadrant of FIG. 4A is used. In the third quadrant of FIG. 4A, for example, “linear γ of (c0)” is used. For example, “(d1) ACC execution result (1)” shown in the fourth quadrant of FIG. 4A is acquired by (a0), (b1), (c0), and the like described above.

  Next, the image processing unit (IPU) sets a reference value for the IBACC pattern (S22). In the process of S22, the IBACC pattern execution result acquired in the process of S20 is stored in a nonvolatile memory or the like as a reference for the IBACC pattern read value. In this way, for example, “(e0) IBACC pattern reading reference value” shown in the first quadrant of FIG. 4B is determined.

  Next, the image processing unit (IPU) executes normal copying, printing, and the like (S23). In the process of S23, “(d1) ACC execution result (1)” obtained by the process of S21, or “(d2) IBACCγ correction result (shown in the fourth quadrant of FIG. 4A) after the process of S24 ( 1) ”is used to form an image on the image carrier (intermediate transfer belt 18).

  Next, the image processing unit (IPU) executes IBACCγ correction (second calibration) (S24). The process of S24 is executed based on the flowchart shown in FIG. The process of S24 is performed in order to correct plotter fluctuations due to, for example, plotter fluctuations due to wear due to the process of S23, seasonal fluctuations, daytime and nighttime room temperature of the installation location, and the like.

  For example, after a predetermined number of images are formed from the process of S23, or a change in the internal environment such as temperature and humidity during the execution of the process of S21, the difference from the execution result of the IBACCγ correction of the above-described process of S20 or S24 is a predetermined value. Execute when it becomes larger.

  Next, the image processing unit (IPU) uses, for example, “(c1-1) IBACCγ correction (1) (after processing)” shown in the third quadrant of FIG. “(D2) IBACC γ correction result (1)” shown in the fourth quadrant is acquired (S25). The process of S25 is the process content shown in the procedure 1) of FIG. 4 and is a process included in the process of S24, and will be described again based on FIG. 4 described above.

  First, as the “(e0) IBACC pattern reading reference value” shown in the first quadrant of FIG. 4B, the reading value of the IBACC pattern stored in the nonvolatile memory or the like in the process of S22 is used. As the “(f1) current reading value of IBACC pattern” shown in the second quadrant of FIG. 4B, the reading value of the IBACC pattern acquired in the process of S24 is used.

  The fourth quadrant of FIG. 4B uses, for example, “(g0) No correction” or “IBACC pattern write value correction characteristics” in which the correction amount is calculated by the method shown in FIG. Thereby, “(c1-0) IBACCγ correction (1) (before processing)” shown in the third quadrant of FIG. 4B is acquired.

  Note that “(c1-0) IBACCγ correction (1) (before processing)” is an example in which “(g0) no correction” in the fourth quadrant of FIG. 4B is used. In the third quadrant of FIG. 4B, “(h1) high density region (each color plate)” shown as an example of the ACC pattern writing value (converted value) on the horizontal axis is a color plate (for example, K plate). Is a region where sufficient sensitivity to the reflectance of the IBACC pattern cannot be obtained. Based on this, “(c1-0) IBACCγ correction (1) (before processing)” is processed so that “(h1) high density region” is not corrected or the absolute value of the correction amount is reduced. The processed result is defined as “(c1-1) IBACC γ correction (1) (after processing)”.

  For example, “(c1-1) IBACCγ correction (1) (after processing)” shown in the third quadrant of FIG. 4B is applied to the third quadrant of FIG. In addition, in the first and second quadrants shown in FIG. 4A, “(a0) ACCγ adjustment reference data (target value)” used in the above-described processing of S21 and “(b2) IBACC correction value ( 1) ”applies. As a result, in the fourth quadrant shown in FIG. 4A, “(d2) IBACCγ correction result (1)” is acquired using (a0), (b2), (c1-1), and the like.

  Next, it is determined whether the difference between the “(d2) IBACCγ correction result (1)” acquired in the process of S25 and the reference ACC result is within the adjustment tolerance (S26). In the process of S26, the deviation of the color and gradation from the execution result of the reference ACCγ correction (for example, “(d1) ACC execution result (1)” acquired in the process of S21) is adjusted in the IBACCγ correction. It is determined whether it is within the tolerance or within the adjustment tolerance by the ACCγ correction.

  Note that the determination in S26 may be, for example, a visual determination by a user or a service engineer or a determination in the apparatus. When judging in the apparatus, it is shown in the second quadrant of FIG. 4B acquired by the process of S25 (“f1) Current reading value of IBACC pattern” and shown in the first quadrant of FIG. 4B. The determination may be made based on a case where the difference from “(e0) IBACC pattern reading reference value” is larger than a predetermined value.

  In the process of S26, for example, the deviation in color and gradation between “(d2) IBACCγ correction result (1)” and “(d1) ACC execution result (1)” acquired in S21 is IBACCγ correction. If it is within the adjustment tolerance of (second calibration), the process returns to S23. If the adjustment tolerance is within the ACCγ correction, the image processing unit (IPU) executes ACCγ correction (first calibration) (S27).

  In the process of S27, the process is executed based on the flowchart shown in FIG. For example, the case where the characteristic of “(b3) ACC pattern read value (second time)” shown in the second quadrant of FIG. 4A is acquired as the read value of the ACC pattern used in the process of S27 will be described. Similar to the processing of S21 described above, “(a0) ACCγ adjustment reference data” shown in the first quadrant of FIG. 4A and (c0) linear γ shown in the third quadrant of FIG. 4A. "(D3) ACC execution result (2)" is acquired by, for example, (a0), (b3), (c0), etc. in the fourth quadrant of FIG.

  Here, in the fourth quadrant shown in FIG. 4A, it is desirable that “(d2) IBACCγ correction result (1)” and “(d3) ACC execution result (2)” match. If they do not match, the difference is reflected in “(g1) IBACC pattern write value correction characteristic 1” shown in the fourth quadrant of FIG. 4B by the process of S28.

  First, the image processing unit (IPU) uses the ACC pattern read value (second time) shown in the second quadrant of FIG. 4A to show the third quadrant of FIG. 4A (c2). A calibration value for IBACC correction (1) is acquired (S28). In the process of S28, the processing content shown in the procedure 2) of FIG. 4 is executed.

  Specifically, in the processing of S28, (a0) ACCγ adjustment reference data (target value) shown in the first quadrant of FIG. 4A or “(b3) shown in the second quadrant of FIG. 4A. ) ACC pattern read value (second time) ”and“ (d1) ACC execution result (1) ”shown in the fourth quadrant of FIG. As a result, “(c2) IBACCγ correction (1)” for matching “(d2) IBACCγ correction result (1)” shown in the fourth quadrant of FIG. 4A with “(d3) ACC execution result (2)”. 1) Calibration value ”is acquired.

  Next, the image processing unit (IPU) corrects the writing value (converted value) of the ACC pattern corresponding to the IBACC pattern by using “(c2) calibration value of IBACC γ correction (1)” acquired in the process of S28. “(G1) IBACC pattern write value correction characteristic 1” is acquired (S29).

  In the process of S29, the processing content shown in the procedure 3) of FIG. 4 is executed. In the process of S29, the “(c2) calibration value of IBACCγ correction (1)” acquired in the process of S28 is applied to the third quadrant of FIG. 4B. Also, “(e0) IBACC pattern reading reference value” shown in the first quadrant of FIG. 4B and “(f1) IBACC pattern of the second quadrant of FIG. Read current value ". As a result, in the fourth quadrant of FIG. 4B, “(g1) IBACC pattern write value correction characteristic 1” is acquired by, for example, (e0), (f1), (c2), or the like.

  Further, in the process of S26, when the adjustment tolerance of IBACCγ correction is not within the adjustment tolerance of ACCγ correction, a service call is executed (S30), and the process is terminated.

  When IBACCγ correction (second calibration) is executed in the process of S24 after the process of S29 described above, “(g0) no correction” shown in the fourth quadrant of FIG. Instead of “(g1) IBACC pattern write value correction characteristic 1” acquired in the process of S29, it is possible to apply.

  Further, the above-described flowchart for obtaining the correction characteristic of the IBACC pattern writing value shown in FIG. 6 may be executed for each of the ACC patterns to which different gradation processes are applied. For example, the ACC pattern for the copy application and the ACC pattern for the printer application may be executed as necessary. Also, the ACC pattern for the printer application uses different multilevel levels, such as when the resolution is different from 600 dpi, 1200 dpi, 2400 dpi, etc., or binary dither, 4-value dither, 16-value dither, 32-value dither, etc. It is better to execute as necessary.

<Example having characteristics different from those in FIG. 4>
Next, an example in which the read value of the ACC pattern is different from the read value of the IBACC pattern as compared with FIG. 4 described above. FIG. 7 is a diagram for explaining the correspondence between the characteristics shown in FIG. 4 and different characteristics. FIG. 4 is an example having the characteristic that the image density is increased due to the influence of the fluctuation of the plotter, for example. FIG. 7 is an example having the characteristic that the image density is decreased, for example.

  “(G2) IBACC pattern write value correction characteristic 2” shown in the fourth quadrant of FIG. 7B and “(g1) IBACC pattern write value correction characteristic 1” shown in the fourth quadrant of FIG. 4B. And preferably match.

  However, for example, when the quantization threshold matrix that forms the ACC pattern is different from the quantization threshold matrix that forms the IBACC pattern, a difference occurs. Differences in the quantization threshold matrix include, for example, the pattern shape (number of lines, screen angle, resolution), how to use low-value pixels, and the presence / absence of low-value pixels.

  Specifically, the correspondence between the characteristics shown in FIG. 4 and the characteristics shown in FIG. 7 will be described. As a read value different from “(f1) IBACC pattern read current value (1)” shown in the second quadrant of FIG. 4B, “(f2) IBACC pattern read shown in the second quadrant of FIG. 7B. The current value (2) "is obtained. Further, as a read value different from “(b3) ACC pattern read value (second time)” shown in the second quadrant of FIG. 4A, “(b5) ACC pattern read shown in the second quadrant of FIG. Value (third time) "is obtained.

  Accordingly, “(c3-1) IBACCγ correction (1) (after processing)” shown in the third quadrant of FIG. 4A is “(c3-1) shown in the third quadrant of FIG. 7A. ) IBACCγ correction) (2) (after processing) ”. Further, “(d4) IBACCγ correction result (2)” shown in the fourth quadrant of FIG. 7A is different from “(d2) IBACCγ correction result (1)” shown in the fourth quadrant of FIG. 4A. Is obtained. Further, “(d5) ACC execution result (3)” shown in the fourth quadrant of FIG. 7A is different from “(d3) ACC execution result (2)” shown in the fourth quadrant of FIG. 4A. Is obtained.

  Therefore, “(c4) IBACCγ correction” shown in the third quadrant of FIG. 7A (“c2) IBACCγ correction (1) (calibration value)” shown in the third quadrant of FIG. 2) (calibration value) "is obtained. In addition, “(g2) IBACC pattern write value correction characteristic 1” shown in the fourth quadrant of FIG. 4B corresponds to “(g2) IBACC pattern write value of the fourth quadrant of FIG. 7B. Correction characteristic 2 "is obtained.

  Note that “(a0) ACCγ adjustment reference data (target value)” shown in the first quadrant of FIG. 4A is “(a0) ACCγ adjustment reference shown in the first quadrant of FIG. 7A. Data (target value) ”. Also, “(b1) ACC pattern read value (first time)” shown in the second quadrant of FIG. 4A is “(b1) ACC pattern read value (first time) shown in the second quadrant of FIG. 7A. ) ”. Further, “(c0) linear γ” shown in the third quadrant of FIG. 4A is common to “(c0) linear γ” shown in the third quadrant of FIG. 7A. Also, “(d1) ACC execution result (1)” shown in the fourth quadrant of FIG. 4A is common to “(d1) ACC execution result (1)” shown in the fourth quadrant of FIG. 7A. It is.

  Further, “(e0) IBACC pattern reading reference value” shown in the first quadrant of FIG. 4B is the same as “(e0) IBACC pattern reading reference value” shown in the first quadrant of FIG. 7B. . “(G0) No correction” shown in the fourth quadrant of FIG. 4B is the same as “(g0) No correction” shown in the fourth quadrant of FIG. 7B.

  In the present embodiment, as described above, the gradation characteristics (ACC adjustment result) obtained by the first calibration in FIGS. 4 and 7 and the gradation characteristics (IBACC adjustment result) obtained by the second calibration are described. Get the difference. In addition, the variation amount between the first gradation pattern (ACC pattern) and the second gradation pattern (IBACC pattern) due to the plotter variation is predicted from the plurality of combinations of the gradation characteristics described above.

<Variation between first gradation pattern and second gradation pattern>
FIG. 8 is a diagram for explaining a variation amount of the first gradation pattern and the second gradation pattern. FIG. 8 is generated from a plurality of combinations of the gradation characteristic (ACC adjustment result) obtained by the first calibration described above and the gradation characteristic (IBACC adjustment result) obtained by the second calibration. The

  The horizontal axis shown in FIG. 8 indicates, for example, “IBACCγ correction amount Δc (t) with respect to output value t”. The vertical axis shown in FIG. 8 indicates, for example, “correction amount Δg (t) for IBACC pattern write value with respect to output value t”.

  Here, the output value t is one of “output values: 256 values from 00H to 0FFH” which is the vertical axis of the third quadrant and the fourth quadrant shown in FIGS. 4B and 7B. The value is shown. Further, the values on the horizontal axis and the vertical axis shown in FIG. 8 can be obtained from the following characteristics.

  The horizontal axis (IBACC γ correction amount Δc (t) with respect to the output value t) shown in FIG. 8 is, for example, “(c 1−) in the vertical axis (output value t) in the third and fourth quadrants of FIG. 1) IBACC γ correction (1) (after processing) ”and“ (c0) linear γ ”. Further, for example, “(c3-1) IBACCγ correction (2) (after processing)” and “(c0) linear γ” in the vertical axis (output value t) in the third and fourth quadrants of FIG. Obtained from the difference.

  The vertical axis shown in FIG. 8 (correction amount Δg (t) for the IBACC pattern write value with respect to the output value t) is, for example, in the vertical axis (output value t) in the third and fourth quadrants of FIG. It is obtained from the difference between “(g1) IBACC pattern write value correction characteristic 1” and “(g0) No correction”. Further, for example, the difference between “(g2) IBACC pattern write value correction characteristic 2” and “(g0) no correction” in the vertical axis (output value t) in the third and fourth quadrants of FIG. Obtained from.

  Similarly, by interpolating and extrapolating characteristics obtained from other conditions, “(j1) correction characteristics for ACC pattern 1” shown in FIG. 8 can be acquired.

  Further, gradation processing different from “(j1) ACC pattern 1” shown in FIG. 8, for example, binary error diffusion, multi-level error diffusion, or processing that distinguishes binary dither from multi-level dither, etc. By performing the same processing on the ACC pattern 2 using the quantization threshold matrix (for example, resolution, screen angle, number of lines, etc.), “(j2) correction characteristic for ACC pattern 2” shown in FIG. 8 is acquired. To do.

  Further, the correction characteristics (j3),..., (Jn) are acquired in the same manner for the ACC pattern using n types of gradation processes using different gradation processes. The correction characteristics of each pattern thus obtained are stored in a nonvolatile memory.

  For example, “(j1) correction characteristic for ACC pattern 1” shown in FIG. 8 has a constant value on the vertical axis on the positive side of the horizontal axis shown in FIG. 8 (when the image density is higher than the reference). On the negative side of the horizontal axis (when the image density is lower than the reference), the value on the vertical axis has a negative slope. This occurs, for example, when multi-level gradation processing is used for the ACC pattern and binary gradation processing is used for the IBACC pattern.

  In addition, “(j2) correction characteristic for ACC pattern 2” shown in FIG. 8 has a constant value on the vertical axis with respect to the horizontal axis shown in FIG. 8 (that is, when the slope is “0”). This occurs when the ACC pattern and the IBACC pattern are both binary patterns or when the same multi-value pattern is used.

  Further, in “(j3) correction characteristic for ACC pattern 3” shown in FIG. 8, the value of the vertical axis is constant with respect to the positive side of the horizontal axis shown in FIG. On the negative side of the horizontal axis, the value on the vertical axis has a positive slope. This occurs, for example, when the ACC pattern is a binary gradation process and the IBACC pattern is a multi-value pattern, contrary to the correction characteristic for (j1) ACC pattern 1 shown in FIG.

  Further, “(j4) correction characteristic for ACC pattern 4” illustrated in FIG. 8 is a case where the value on the vertical axis has a negative slope with respect to the value on the horizontal axis illustrated in FIG. 8, for example. This is the gradation processing of the ACC pattern. For example, the exposure amount and area ratio of 16 steps (0 to 15 values) can be changed for one pixel on the photoconductor, and the ratio of small to medium pixels in the ACC pattern. This occurs, for example, when the IBACC pattern is a large pixel gradation pattern, in contrast to the case where there are many large pixels.

  For example, “0 value” is a white part (a pixel to which toner or coloring material is not attached), and 15 value is a black part (a pixel to which toner or coloring material is attached and the reflectance is saturated). Are small pixels (low image density pixels), 5 to 10 values are medium pixels (medium image density pixels), and 11 to 15 values are large pixels (high image density pixels).

  Further, “(j5) correction characteristic for ACC pattern 5” shown in FIG. 8 is a case where the value of the vertical axis has a positive inclination with respect to the body of the horizontal axis shown in FIG. This is a case where the ACC pattern is a gradation process of a large pixel and the IBACC pattern is a small pixel, contrary to “(j4) Correction characteristics for ACC pattern 4” shown in FIG.

<Prediction of fluctuation amount of first gradation pattern and second gradation pattern>
FIG. 9 is a diagram illustrating an example of acquiring the IBACC correction result from the prediction of the fluctuation amount. In the example of FIG. 9, an example will be described in which the fluctuation amount of the ACC pattern and the IBACC pattern is predicted from the gradation characteristics shown in FIG. 8, and the IBACC correction result is acquired.

  In the example of FIG. 9, when performing the IBACC γ correction shown in FIG. 5, “(f3) IBACC pattern read current value (3)” is acquired as an example in the second quadrant of FIG. 9B. Will be described.

  From “(f3) IBACC pattern read current value (3)” shown in the second quadrant of FIG. 9B, for example, “(c5-1) IBACCγ correction (3 ) (After processing) ”(correction parameter obtained by the second calibration).

  Here, using “(c5-1) IBACC γ correction (3) (after processing)” shown in the third quadrant of FIG. 9B, the fluctuation amount of the ACC pattern and the IBACC pattern is predicted. That is, “(c5-1) IBACCγ correction (3) (after processing)” shown in the third quadrant of FIG. 9B and “(c0) linear γ” shown in the third quadrant of FIG. Is the horizontal axis value of “(j1) correction characteristics for ACC pattern 1” shown in FIG. 8, the vertical axis value Δg (t) can be obtained.

  Δg (t) is the difference between “(g3) IBACC pattern write value correction characteristic (3)” and “(g0) No correction” in the output value t. Since (g0) is a linear characteristic, for example, “(g3) IBACC pattern write value correction characteristic (3)” shown in the fourth quadrant of FIG. 9B can be acquired.

  Next, using “(g3) IBACC pattern write value correction characteristic (3) (correction amount)” shown in the fourth quadrant of FIG. 9B, “(g3) shown in the third quadrant of FIG. c5-1) IBACCγ correction (3) (after processing) (correction parameter) ”is corrected, and“ (c5-2) IBACC correction (3) (after processing) ”shown in the third quadrant of FIG. Get the correction result.

  Further, from the correction result of “(c5-2) IBACC correction (3) (after processing)” shown in the third quadrant of FIG. 9B, “(d6) IBACC shown in the fourth quadrant of FIG. 9A. When “correction result (3)” is acquired, “(d6) IBACC correction result (3)” shown in the fourth quadrant of FIG. 9A is used as, for example, an image processing parameter (YMCK gradation conversion table).

<Process for obtaining YMCK conversion table>
FIG. 10 is a flowchart for acquiring the YMCK gradation conversion table. In the example of FIG. 10, the flow of processing for acquiring an IBACC correction result from the prediction of the fluctuation amount of the ACC pattern and the IBACC pattern shown in FIG.

  As shown in FIG. 10, the image processing unit (IPU) executes IBACCγ correction (second calibration), and “(f3) IBACC pattern reading current shown in the second quadrant of FIG. 9B as an example. Value (3) "is acquired (S40).

  Next, the image processing unit (IPU) performs the procedure similar to that for obtaining “(c1-1) IBACCγ correction (1) (after processing)” shown in the third quadrant of FIG. ) From “(f3) current reading value of IBACC pattern (3)” shown in the second quadrant, “(c5-1) IBACCγ correction (3) (after processing) shown in the third quadrant of FIG. Obtain (S41).

  Next, the image processing unit (IPU) performs “(c5-1) IBACCγ correction (3) (after processing)” shown in the third quadrant of FIG. 9B and the third quadrant of FIG. 9B. The difference Δc (t) from “(c0) linear γ” shown in FIG. 8 is taken as the value of the horizontal axis of “(j1) correction characteristics for ACC pattern 1” shown in FIG. get. Further, the image processing unit (IPU) uses the value Δg (t) on the vertical axis and “(g0) no correction” shown in the fourth quadrant of FIG. "(G3) IBACC pattern write value correction characteristic (3)" shown in the fourth quadrant is acquired (S42).

  Next, the image processing unit (IPU) displays “(g3) IBACC pattern write value correction characteristic (3)” shown in the fourth quadrant of FIG. 9B or the first quadrant of FIG. 9B. According to “(e0) IBACC pattern reading reference value”, “(f3) IBACC pattern reading current value” shown in the second quadrant of FIG. 9B, etc., “( The correction result of “c5-2) IBACC correction (3) (after processing)” is acquired (S43).

  Next, the image processing unit (IPU) performs the correction result of “(c5-2) IBACC correction (3) (after processing)” shown in the third quadrant of FIG. According to “(d1) ACC execution result (1)” shown in the four quadrants, “(a0) ACCγ adjustment reference data (target value)” shown in the first quadrant of FIG. “(B6) IBACC correction result (3)” shown in the second quadrant is acquired (S44).

  Next, the image processing unit (IPU) performs “(b6) IBACC correction result (3)” shown in the second quadrant of FIG. 9A and “(a0) ACCγ of the first quadrant of FIG. 9A”. “(D6) IBACC correction shown in the fourth quadrant of FIG. 9A is based on“ reference data for adjustment (target value) ”and“ (c0) linear γ ”shown in the third quadrant of FIG. 9A. Result (3) ”is acquired. This is used as a YMCK gradation conversion table (S45), and the process is terminated.

  As described above, when executing the second calibration, the difference in the influence of the plotter variation between the first gradation pattern (ACC pattern) and the second gradation pattern (IBACC pattern) is predicted, The adjustment result of the second calibration is corrected. Thereby, it is possible to improve the correction accuracy of the second calibration, and it is also possible to reduce the trouble of executing the first calibration.

  The first gradation pattern uses a pattern processed by binary or multi-value gradation processing used to process document image data for a copy application or a printer application. The gradation pattern uses a gradation process different from the first gradation pattern, or uses a gradation pattern generated without performing the gradation process. Therefore, even if the second gradation pattern is, for example, one set, the adjustment results of the second calibration described above are reflected in the results of the plurality of sets of first calibrations having different gradation processes. Is possible.

<Example of adjustment of second gradation pattern>
FIG. 11 is a diagram illustrating an example of adjusting the second gradation pattern. In the example of FIG. 11, a second gradation pattern (IBACC pattern) is shown. An A pattern shown in FIG. 11 shows an example of a pattern having high image density pixels. Further, the B pattern and the C pattern shown in FIG. 11 are examples of patterns having “high image density pixels”, “medium image density pixels”, and “low image density pixels”.

  In the example of FIG. 11, the high image density pixel (●) is “15 values”, the medium image density pixel (hatched to ○) is “10 values”, and the low image density pixel (◯) is “5 values”. The copying machine 10 has the following condition: “(15-value high-density pixel (●)) = (10-value medium-density pixel (hatched to ○)) + (5-value low-image density pixel ◯)”. If the equation holds, it is possible to obtain the same image density for all of the A to C patterns.

  However, when a change that reduces all the pixels uniformly by “5 values” occurs due to a change in image forming conditions (when the value is negative by reducing “5 values”), A Compared to the pattern, each of the B and C patterns has a larger change amount of the total value (that is, used as an alternative to the image density).

<Change in image density due to plotter fluctuation>
FIG. 12 is a diagram illustrating the amount of change in image density due to plotter fluctuation. FIG. 12A shows “total value of main scanning 4 × sub-scanning 5 pixels (A pattern shown in FIG. 11)”. FIG. 12B shows “total value of main scanning 4 × sub-scanning 5 pixels (B pattern shown in FIG. 11)”. FIG. 12C shows “the total value of main scanning 4 × sub-scanning 5 pixels (C pattern shown in FIG. 11)”.

  As shown in FIGS. 12A to 12C, when the total values before and after the change are compared, the B pattern and the C pattern shown in FIG. The amount of change in the total value is large.

  When “(j1) correction characteristics with respect to ACC pattern 1” shown in FIG. 8 described above and the IBACC pattern is composed of high image density pixels such as A pattern, A pattern → B pattern → C pattern In addition, the ratio of pixels with high image density is gradually reduced, and the ratio of medium image density pixels and low image density pixels is gradually increased accordingly.

  As a result, from the correction characteristic having the inclination of the minus part of the horizontal axis as shown in “(j1) correction characteristic for ACC pattern 1” shown in FIG. 8 as described above, the characteristic of (j2) shown in FIG. It is possible to reduce the difference between the adjustment accuracy of the IBACCγ correction and the adjustment accuracy of the ACCγ correction so that the correction characteristic is such that the value on the vertical axis is constant relative to the value on the horizontal axis. In addition, it is possible to improve the adjustment accuracy of IBACCγ correction.

<First Gradation Pattern (ACC Pattern) for Automatic Gradation Correction>
FIG. 13 is a diagram for explaining automatic gradation correction. FIG. 13A shows an operation screen for selecting a function of automatic gradation correction (ACC) of image density (gradation property). When “print start” in the screen shown in FIG. 13A is selected, a plurality of density gradation patterns (first gradation patterns) are formed on a transfer material (for example, transfer paper).

  FIG. 13B shows an example of the plurality of density gradation patterns (first gradation patterns) described above. The plurality of density gradation patterns shown in FIG. 13B correspond to, for example, each image quality mode of each color of YMCK, character mode, and photo mode.

  This density gradation pattern is stored in advance in the ROM of the image processing unit (IPU). The written values of the density gradation pattern are, for example, 16 patterns of 00h, 11h, 22h... EEh, FFh in hexadecimal notation. In the example of FIG. 13B, for example, patches of 5 gradations are displayed except for the background portion, but any value can be selected from the 8-bit signals of 00h-FFh. . In the character mode, a dither process such as a pattern process is not performed and a pattern is formed with 256 tones per dot. In the photo mode, dithering is performed.

  FIG. 13C is an example of a screen displayed on the operation screen after the density gradation pattern is output on the transfer material. In the example of FIG. 13C, the transfer material on which the density gradation pattern as shown in FIG. 13B is formed is placed on the document placement table, and “read start” or “cancel” is selected. Is instructed.

<Flowchart for Executing Automatic Gradation Correction (ACC)>
FIG. 14 is a flowchart for executing automatic gradation correction. In the flowchart shown in FIG. 14, the image processing unit (IPU) forms an IBACC pattern and reads it with the optical sensor 36B (S50). Next, the image processing unit (IPU) outputs the ACC pattern to the transfer material (S51). The ACC pattern formed by the process of S51 is a density gradation pattern stored in advance in the ROM of the IPU.

  Next, when the transfer material on which the ACC pattern is formed in the process of S51 is placed on the document placing table (S52), the image processing unit (IPU) determines, for example, whether cancellation is selected on the operation screen. (S53). When the image processing unit (IPU) determines that cancel is not selected (reading is selected) (NO in S53), the transfer material is read by the scanner, and the RGB data of the YMCK density pattern is read (S54). In the process of S54, it is preferable to read the data of the YMCK density pattern portion and the data of the background portion of the transfer material.

  Next, the image processing unit (IPU) determines whether the data of the YMCK density pattern portion has been read (read error?) By the process of S54 (S55). If the image processing unit (IPU) determines that the image has not been read normally (YES in S55), for example, it is determined whether the image has not been read normally (S56). If the image processing unit (IPU) determines that it is not the second time that the image has not been read normally (NO in S56), the above-described screen shown in FIG. 13C is displayed, and the process returns to S52.

  If the image processing unit (IPU) determines that the image has been read normally (no reading error) (NO in S55), each YMCK color plate has a character area based on the value read in S54. A gradation conversion table for image or photo area is created (S57). Next, the image processing unit (IPU) stores a gradation conversion table (S58). In the process of S58, the read value of the YMCK density pattern read in the process of S54 may be stored.

  Next, the image processing unit (IPU) newly stores the read value of the IBACC pattern read in the process of S50 (or the read value of the IBACC pattern formed on the image carrier most recently) as a reference value (S59). ), The process is terminated.

<Second gradation pattern (IBACC pattern)>
FIG. 15 is a diagram illustrating an example of a second gradation pattern formed on the intermediate transfer belt. The IBACC pattern (second gradation pattern) shown in FIG. 15 is formed, for example, as np patterns having different gradation densities on the intermediate transfer belt 18 as the image carrier. This IBACC pattern is detected by the optical sensor 36B and is treated as IBACC pattern detection data.

<Method for determining whether or not ACC execution is necessary>
FIG. 16 is a diagram illustrating a method for determining whether or not ACC execution is necessary. A method for determining whether or not ACC execution is necessary will be described using the four-quadrant graph shown in FIGS.

  The second quadrant shown in FIG. 16A indicates “IBACC pattern detection data (reference value)”. The horizontal axis indicates “detection output [V] of the optical sensor”, and the vertical axis indicates “IBACC pattern write value (FFh)”. “(A1) Detection result 1” shown in FIG. 16A indicates IBACC pattern detection data formed at a predetermined timing such as when ACC is executed.

  The first quadrant shown in FIG. 16C indicates “IBACC toner adhesion amount γ characteristics (reference value)”. The horizontal axis indicates the toner adhesion amount [mg / cm 2] on the image carrier. “(C1) Adhesion amount γ characteristic 1” shown in FIG. 16C is related to the toner adhesion amount on the intermediate transfer belt 18 corresponding to “(a1) detection result 1” shown in FIG. It is an example to show. Note that “(a1) detection result 1” shown in FIG. 16A and “c1) adhesion amount γ characteristic 1” shown in FIG. 16C are separately obtained at the time of design or the like.

  “(A1-1) High sensitivity” shown in FIG. 16A indicates a range in which the sensitivity to the toner adhesion amount is larger than “(a1) detection result 1” shown in FIG. Further, “(a1-2) low sensitivity” shown in FIG. 16A indicates a range in which the sensitivity to the toner adhesion amount is smaller than “(a1) detection result 1” shown in FIG. Yes. In the range of “(a1-2) low sensitivity” shown in FIG. 16A, the toner adhesion amount changes, whereas the IBACC pattern detection data shown in FIG. This shows a range in which a large toner adhesion amount cannot be acquired from the detection result of the optical sensor.

  The third quadrant shown in FIG. 16B indicates “IBACC pattern detection data (latest value)”. The vertical axis represents the write value of the IBACC pattern (latest). In the example of FIG. 16B, IBACC pattern detection data “(b1) detection result 2” and IBACC pattern detection data “(b2) detection result 3” are illustrated. The detection timing is different from “(a1) detection result 1” shown in FIG.

  “(A1) Detection result 1” illustrated in FIG. 16A indicates a result detected at a predetermined timing such as when ACC is executed. On the other hand, “(b1) detection result 2” and “(b2) detection result 3” shown in FIG. 16 (b) indicate that after the lapse of a predetermined time from the ACC execution, or after the predetermined number of images are formed from the ACC execution, It shows the detection results after the environment such as temperature and humidity is different from the execution.

  Note that “(b1-1) high sensitivity” shown in FIG. 16B indicates a range in which the sensitivity to the toner adhesion amount is larger than “(b1) detection result 2” shown in FIG. Yes. “(B1-2) Low sensitivity” shown in FIG. 16B indicates a range in which the sensitivity to the toner adhesion amount is smaller than “(b1) detection result 2” shown in FIG. “(B2-1) High sensitivity” shown in FIG. 16B indicates a range in which the sensitivity to the toner adhesion amount is larger than “(b2) detection result 3” shown in FIG. “(B2-2) Low sensitivity” shown in FIG. 16B indicates a range in which the sensitivity to the toner adhesion amount is smaller than “(b2) detection result 3” shown in FIG.

  The fourth quadrant shown in FIG. 16D indicates “IBACC toner adhesion amount γ characteristics (latest value)”. “(D1) Toner adhesion amount γ characteristic 2” shown in FIG. 16D indicates the toner adhesion amount obtained from “(b1) detection result 2” shown in FIG. 16B. “(D2) Toner adhesion amount γ characteristic 3” shown in FIG. 16D indicates the toner adhesion amount obtained from “(b2) detection result 3” shown in FIG. 16B.

  Here, “(b1-2) low sensitivity” shown in FIG. 16B is a range in which the sensitivity to the toner adhesion amount is smaller than “(b1) detection result 2” shown in FIG. 16B. It is difficult to obtain an accurate toner adhesion amount from the detection data. Therefore, “(d1-1) toner adhesion amount γ characteristic 2 estimation part” shown in FIG. 16D is shown as the estimation part of “(d1) toner adhesion amount γ characteristic 2”.

  Further, “(b2-2) low sensitivity” shown in FIG. 16B is also in a range in which the sensitivity to the toner adhesion amount is smaller than “(b2) detection result 3” shown in FIG. Therefore, it is difficult to obtain an accurate toner adhesion amount from the detection data. Therefore, “(d2-1) toner adhesion amount γ characteristic 3 estimation part” shown in FIG. 16D is shown as the estimation part of “(d2) toner adhesion amount γ characteristic 3”.

  In “(d1) toner adhesion amount γ characteristic 2” shown in FIG. 16D, “(d1-2) intermediate part of toner adhesion amount γ characteristic 2” is “(b1) detection shown in FIG. 16B”. “(C1) Toner adhesion amount γ characteristics 1” shown in FIG. 16C and “(c1) Toner adhesion amount γ characteristic 1” shown in FIG. It is obtained using “(a1) detection result 1” shown.

  When the “(d1-2) intermediate portion of toner adhesion amount γ characteristic 2” is within a predetermined error range from “(c1) toner adhesion amount γ characteristic 1” shown in FIG. “(d1-1) Estimated portion of toner adhesion amount γ characteristic 2 (toner adhesion amount γ characteristic 1)” shown in d) is a toner of “(c1) Toner adhesion amount γ characteristic 1” shown in FIG. It can be assumed that it matches the amount of adhesion.

  On the other hand, among “(d2) toner adhesion amount γ characteristic 3” shown in FIG. 16D, “(b2-2) low sensitivity” of “(b2) detection result 3” shown in FIG. 16B. The portion corresponding to the region where the sensitivity to the toner adhesion amount is small may be the characteristic illustrated in “(d2-1) Estimating part of toner adhesion amount γ characteristic 3” shown in FIG. It is difficult to decide.

  Next, a four quadrant graph is used in which FIG. 16 (c) is the third quadrant, FIG. 16 (d) is the fourth quadrant, FIG. 16 (e) is the first quadrant, and FIG. 16 (f) is the second quadrant. A method for determining whether or not ACC execution is necessary will be described.

  The first quadrant shown in FIG. 16E indicates “IBACC reference γ characteristics”. The horizontal axis indicates “image input signal”, and the vertical axis indicates “image output signal (FFh)”. In FIG. 16E, (e1) indicates “IBACC reference γ characteristic 1”.

  The second quadrant shown in FIG. 16F indicates “IBACC correction γ characteristics”. “(F2) IBACC correction γ characteristic 2” shown in FIG. 16F is obtained corresponding to “(d1) toner adhesion amount γ characteristic 2” shown in FIG. “(F3) IBACC correction γ characteristic 3” shown in FIG. 16F is obtained corresponding to “(d2) toner adhesion amount γ characteristic 3” shown in FIG.

  In “(f2) IBACC correction γ characteristic 2” shown in FIG. 16F, a portion corresponding to “(d1-1) toner adhesion amount γ characteristic 2 estimation part” shown in FIG. It is possible to use “(f1) IBACC γ correction characteristic 1” that matches “(e1) IBACC reference γ characteristic 1” shown in 16 (e).

  On the other hand, in “(f3) IBACC correction γ characteristic 3” shown in FIG. 16F, a part corresponding to “(d2-1) toner adhesion amount γ characteristic 3 estimation part” shown in FIG. Is difficult to determine uniquely as shown as “(f3-1) estimator of IBACC correction γ characteristic 3” shown in FIG.

  Therefore, when the correction γ characteristic shown in “(f3) IBACC correction γ characteristic 3” shown in FIG. 16F is acquired, it is determined that “ACC (automatic gradation correction) needs to be executed” and the user is notified. It is better to inform the necessity of ACC execution.

  The “(d1-1) toner adhesion amount γ characteristic 2 estimation unit” shown in FIG. 16D matches the “(c1) toner adhesion amount γ characteristic 1” shown in FIG. Therefore, “(g1) difference Δ (3-1)” indicates “(c1) toner adhesion amount γ characteristic 1” shown in FIG. 16C and “(d2) toner adhesion amount” shown in FIG. The difference between the writing value of the IBACC pattern for obtaining a predetermined toner adhesion amount [M / A1] and the difference of the image output signal with respect to the “image input signal Nin1” in FIG. ing.

  That is, in FIG. 16D, “Δ (3-1)” = “IBACC pattern writing value of toner adhesion amount γ characteristic 1 for obtaining IBACC toner adhesion amount [M / A1]” − “IBACC toner adhesion amount [ IBACC pattern writing value of toner adhesion amount γ characteristic 3 for obtaining M / A1] ”= WL3 ([M / A1]) − WL1 ([M / A1]). At this time, if “Δ (3-1)> predetermined value ΔTh”, it is determined that “ACC needs to be executed”.

  Further, “Δ (3-1)” = “image output signal Nout of IBACC correction γ characteristic 3 for acquiring image input signal Nin1” − “image output signal Nout of IBACC correction γ characteristic 1 for acquiring image input signal Nin1”. = Nout3 (Nin1) −Nout1 (Nin1) is obtained. At this time, if “Δ (3-1)> predetermined value ΔTh”, it is determined that “ACC needs to be executed”.

<Example of a screen that informs the necessity of ACC execution>
FIG. 17 is a diagram illustrating an example of a screen informing the necessity of executing ACC. As described above with reference to FIG. 16 and the like, when it is determined that “ACC execution is necessary”, as shown in FIG. 17, the user is notified of the necessity of ACC execution on the operation screen. And good. In the example of FIG. 17, for example, in the lower area of the operation screen, “execution of automatic gradation correction is recommended on the initial setting screen” is displayed. Note that the display method (screen layout) for notifying the user of the necessity of ACC execution is not limited to this.

<Update of IBACC standard value>
A flow of processing executed when “(b2) detection result 3” shown in FIG. 16B described above is obtained will be described. FIG. 18 is a flowchart showing the flow of IBACC reference value update processing.

  As shown in FIG. 18, the image processing unit (IPU) updates the IBACC pattern detection data (reference value) (S60). In the process of S60, “(b2) detection result 3” shown in FIG. 19B is updated as “(a2) detection result 3” shown in FIG. 19A, which is a new reference value (S60). .

  Next, the image processing unit (IPU) updates the IBACC toner adhesion amount γ characteristic (S61) and ends the process. In the process of S61, “(d2) toner adhesion amount γ characteristic 3” shown in FIG. 19D is replaced with “(c2) toner adhesion amount γ characteristic 3” shown in FIG. 19C, which is a new reference value. To do.

<Procedure for updating IBACC standard value>
FIG. 19 is a diagram for explaining an IBACC reference value update procedure. Since the contents shown in FIGS. 19A to 19F are the same as those shown in FIG. 16, a detailed description thereof is omitted here.

  In the processing of S61 shown in FIG. 18 described above, by executing ACC, “IBACC pattern writing value”, which is a range in which the sensitivity to the toner adhesion amount of “(b2-2) low sensitivity” shown in FIG. The corresponding gradation is adjusted so that a predetermined gradation is obtained.

  Accordingly, as “(d2-1) toner adhesion amount γ characteristic 3 estimation part” shown in FIG. 19D, “(b2-1) high sensitivity” toner of “(d2) toner adhesion amount γ characteristic 3”. A range between a large sensitivity to the adhesion amount and a maximum adhesion amount [M / Amax] in controlling the image forming condition is arbitrarily determined by interpolation using a temporary function, spline interpolation, or the like.

<Operation of first calibration (first time)>
FIG. 20 is a diagram for explaining the operation of the first calibration (first time). In FIG. 20, data processing of the first calibration (initial) will be described using a collaboration diagram of UML (Unified Modeling Language). In the example of FIG. 20, a γ control point calculation unit is included in the image processing unit (IPU).

  In 1-1 of FIG. 20, the plotter reads the ACC pattern write value from the nonvolatile RAM (memory) (Read). In 1-2 of FIG. 20, the plotter forms an ACC pattern using the ACC pattern write value read from the memory. The nonvolatile RAM stores this image forming time as the latest value. The nonvolatile RAM stores the measurement value of the environmental sensor as the latest value of the environmental state.

  In FIG. 20A, the scanner reads the ACC pattern formed by the plotter (Read). In 2-2 of FIG. 20, the scanner generates the current value of the ACC pattern read value (Write).

  In FIG. 20A, for example, the γ control point calculation unit realized by a CPU or the like reads the current value of the ACC pattern read value and the ACC pattern write value (Read). In 3-2 of FIG. 20, the γ control point calculation unit checks whether or not the current value of the ACC pattern read value is abnormally read due to unevenness or the like. If there is no abnormality, the following steps are executed. 20, the γ control point calculation unit reads the γ adjustment target value held in the RAM or ROM (Read).

  20, the γ control point calculation unit calculates the current value of the γ control point (node) using the parameters acquired in 3-2 to 3-3 in FIG. 20.

  In 3-5 to 3-6 of FIG. 20, when the latest value of the current γ control point (node) is read (Read), the γ control point calculation unit stores it in the nonvolatile RAM as the previous value (Write). Further, the γ control point calculation unit stores the current value of the γ control point calculated in 3-4 of FIG. 20 in the nonvolatile RAM as the latest value of the γ control point (Write). In addition, when the latest value of the IBACC pattern read value is read, the γ control point calculation unit stores it as a reference value in the nonvolatile RAM (Write).

  Further, the γ control point calculation unit stores the current value of the ACC pattern read value in the RAM as the latest value (Write). At this timing, the γ control point calculation unit reads the latest value of the image formation time and the latest value of the measurement value of the environmental sensor, and stores them in the RAM as reference values.

<Gamma Conversion Table (LUT) Generation Operation>
Next, the flow of data for creating a γ conversion table by using, for example, spline interpolation with the γ control point stored in the above-described nonvolatile RAM as a node will be described. FIG. 21 is a diagram for explaining the generation operation of the γ conversion table. In the example of FIG. 21, the image processing unit (IPU) has a γ conversion table calculation unit.

  In FIG. 21, the γ conversion table calculation unit uses the latest value of the γ control point, the latest value of the IBACC correction γ control point, as a parameter for creating a γ conversion table (LUT) for image processing, A correction coefficient is obtained from the nonvolatile RAM (Read).

  21, the γ conversion table calculation unit calculates the γ conversion table by, for example, spline interpolation using the parameters acquired in 1-1 of FIG. 21, the γ conversion table calculation unit sets the γ conversion table calculated in 1-2 of FIG. 21 in the γ conversion circuit.

<Second calibration operation>
FIG. 22 is a diagram for explaining the second calibration operation. In the example of FIG. 22, a γ control point calculation unit is included in the image processing unit (IPU).

  In 1-1 of FIG. 22, the plotter reads the IBACC pattern write value from the nonvolatile RAM (Read). In 1-2 of FIG. 22, the plotter forms an IBACC pattern using the IBACC pattern write value. In 1-3 of FIG. 22, the optical sensor reads the IBACC pattern (Read). In 1-4 of FIG. 22, the optical sensor generates the current value of the IBACC pattern read value.

  22, the γ control point calculation unit reads the current value of the IBACC pattern read value, the reference value of the IBACC pattern read value, and the IBACC pattern write value (Read). 22, the γ control point calculation unit checks whether there is an abnormal reading or the like in the current value of the IBACC pattern reading value. If there is no abnormality, the following steps are executed.

  22, the γ control point calculation unit reads the correction coefficient, the latest value of the γ control point, the ACC pattern write value, and the latest value of the ACC pattern read value held in the RAM or ROM (Read). . In 2-4 of FIG. 22, the γ control point calculation unit calculates a virtual current value of the ACC pattern read value using the value acquired in 2-3 of FIG. 22 and stores it in the temporary storage memory.

  In 2-5 of FIG. 22, the γ control point calculation unit acquires the γ adjustment target value from the ROM or RAM (Read). 22, the γ control point calculation unit calculates the γ control point using the parameters acquired in 2-3 to 2-5 in FIG. 22. 22, the γ control point calculation unit stores the latest value of the IBACC correction γ control point in the nonvolatile RAM from the γ control point calculated in 2-6 of FIG. 22 (Write).

<Operation of first calibration (second and subsequent times)>
FIG. 23 is a diagram for explaining the operation of the first calibration (second and subsequent times). In the example of FIG. 23, a γ control point calculation unit is included in the image processing unit (IPU). In addition, since 1-1-3-4 of FIG. 23 has shown the content similar to FIG. 20, the concrete description here is abbreviate | omitted.

  In 3-5 of FIG. 23, the γ control point calculation unit reads the latest value of the work time and the reference value of the work time from the nonvolatile RAM (Read). Further, the γ control point calculation unit reads the latest value of the IBACC correction γ control point from the nonvolatile RAM (Read). Further, the γ control point calculation unit reads the latest value of the γ control point from the nonvolatile RAM (Read).

  In 3-6 of FIG. 23, the γ control point calculation unit does not change the correction when the difference between the latest value of the work time and the reference value is larger than a predetermined value. The γ control point calculator calculates the difference between the current value of the γ control point and the latest value, and compares the difference with the latest value of the IBACC correction γ control point. When there is no difference between the two, the correction coefficient is “0”.

  In 3-7 of FIG. 23, the γ control point calculation unit stores the correction coefficient acquired in 3-6 of FIG. 23 in the nonvolatile RAM (Write).

  In 3-8 of FIG. 23, the γ control point calculation unit stores the latest value of the γ control point in the RAM as the previous value (Write). Further, the γ control point calculation unit stores the current value of the γ control point acquired in 3-4 of FIG. 23 in the RAM as the latest value of the γ control point (Write). Also, the γ control point calculation unit stores the current value of the IBACC pattern read value in the nonvolatile RAM as the latest value (Write).

  Further, the γ control point calculation unit stores the current value of the ACC pattern reading value in the nonvolatile RAM as the latest value (Write). The γ control point calculation unit reads the latest value of the working time at this timing and stores it in the nonvolatile RAM as a reference value (Write). In addition, the latest value of the measured value of the environmental sensor is read and stored in the nonvolatile RAM as a reference value (Write).

  According to the above-described embodiment, in the adjustment of the gradation conversion table used in the image processing, the difference in the influence on the first gradation pattern and the second gradation pattern caused by the plotter fluctuation or the like is predicted. Further, based on this prediction, it is possible to improve the accuracy of calibration correction using the gradation pattern formed on the image carrier. As a result, it is possible to reduce time and effort for executing the first calibration.

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

DESCRIPTION OF SYMBOLS 10 Copier 11 Photosensitive drum 12 Charging device 13 Laser optical system 14 Black developing device 15 Yellow (Y) developing device 16 Magenta (M) developing device 17 Cyan (C) developing device 18 Intermediate transfer belt 19 Bias roller 20 Cleaning device 21 Conveying belt 22 Fixing device 23 Discharge tray 24 Image sensor array 30 Main control unit 31 ROM
32 RAM
33 Interface I / O
34 Laser optical system drive unit 35 Power supply circuit 36 Optical sensor 37 Toner density sensor 38 Environmental sensor 39 Photoconductor surface potential sensor 40 Toner supply circuit 41 Intermediate transfer belt drive unit 42 Operation unit 50 Sensor control unit 51 Power supply control unit 52 Drive control unit 53 Image processing control unit 54 Communication control unit 55 Storage device 56 Storage device control unit 60 Scanner 61 Shading correction circuit 62 FL correction processing circuit 63 Inter-chip pixel interpolation circuit 64 Memory controller 65, 81 Image memory 66 Scanner γ conversion circuit 67 Image area Separation / ACS determination circuit 68 Spatial filter 69 Automatic density adjustment level (ADS) detection / removal circuit 70 Hue determination circuit 71 Color correction / UCR processing circuit 72 Scaling processing circuits 73, 78, 87 Printer γ conversion circuit 74 Binary gradation Processing circuit 75 Editing processing circuit 76 Multila yer BUS
77 Pattern generation circuit 79 Printer 80 Compression / decompression circuit 82 HDD I / F
83 HDD
84 Rotation processing circuit 85 External I / F
86 Feature Extraction Processing Circuit 88 Gradation Processing Circuit

JP 2009-55606 A

Claims (7)

First calibration means for generating a correction parameter to be set for gradation conversion used in image processing based on a read value of the first gradation pattern formed on the transfer material;
Second calibration means for generating a correction parameter to be set for the gradation conversion based on the read value of the second gradation pattern formed on the image carrier;
Variation between the first gradation pattern and the second gradation pattern based on the gradation characteristic obtained by the first calibration means and the gradation characteristic obtained by the second calibration means. An image forming apparatus comprising: a correction unit that predicts an amount and corrects a correction parameter generated by the second calibration unit. The correction means includes
The image forming apparatus according to claim 1, wherein the correction amount of the correction parameter generated by the second calibration unit is changed according to the reading result of the second gradation pattern.   The image forming apparatus according to claim 1, wherein the correction parameter corrected by the correction unit is reflected for each type of gradation processing using the first gradation pattern. The type of gradation processing is
The image forming apparatus according to claim 3, wherein the image forming apparatus is at least one of a quantization threshold matrix using resolution, screen angle, number of lines, error diffusion processing, dither processing, binary, and multivalue. The correction means includes
The ratio of at least one of a high density pixel, a medium density pixel, and a low density pixel constituting the second gradation pattern is changed according to a correction amount of the correction parameter. Image forming apparatus. An image forming method executed by an image forming apparatus,
A first calibration procedure for generating a correction parameter to be set for gradation conversion used in image processing based on a read value of the first gradation pattern formed on the transfer material;
A second calibration procedure for generating a correction parameter to be set for the gradation conversion based on a reading value of the second gradation pattern formed on the image carrier;
Variation between the first gradation pattern and the second gradation pattern based on the gradation characteristics obtained by the first calibration procedure and the gradation characteristics obtained by the second calibration procedure. An image forming method comprising: a correction procedure for predicting an amount and correcting a correction parameter generated in the second calibration procedure. Computer
An image forming program for causing the image forming apparatus according to any one of claims 1 to 5 to function as each unit.

Images