JP6351368B2 - Image processing apparatus, image processing method, and program - Google Patents

Description

  The present invention relates to an image processing apparatus, an image processing method, and a program for forming an image on a recording medium by a recording head that ejects ink.

  In an inkjet printer, various image quality deterioration factors such as twist, discharge failure, satellite, and ink mist are known. The twist means that the dot does not land at the intended position on the paper (there is a landing error). Undischarge means that dots should be discharged but not discharged. Satellites are additional dots that are generated in addition to the main drop. Ink mist refers to minute ink droplets flying in the air and adhering to the paper surface or the head surface. These image quality degradation factors include a phenomenon in which the same degradation is likely to occur repeatedly (easy to predict), such as when depending on the surface condition of the head, and a phenomenon that occurs randomly (difficult to predict), such as when depending on airflow. The former is called the fixed deterioration factor and the latter is called the indefinite deterioration factor.

  Satellites will be described in detail as a representative example of fixed deterioration factors. FIG. 16 shows a paper surface when satellites are generated. In FIG. 16, main drops 1610, 1620, 1630, 1640 and satellites 1615, 1625, 1635, 1645 associated with these main drops are shown. The landing position, size, and number of satellites vary depending on the state of the head surface (ink mist adhesion, etc.), the moving speed of the head, and clogging of the nozzles. If the satellite lands on the same spot as the main droplet, the satellite has little effect on the image quality. However, if the satellite lands on a location away from the main droplet, the coverage of the paper changes, resulting in a density that is different from the intended density. cause. For example, when there are many cyan satellites, color shift may occur, such as bluish overall. In the vicinity of the edge, the sharpness may deteriorate due to the satellite landing outside the edge area and landing on a blank area.

  In Patent Document 1, in a multi-pass recording type printer, density unevenness on the paper surface due to low density is detected using a sensor mounted on a head unit, and the output density in subsequent passes is corrected using the detected density unevenness. The method is disclosed. The multi-pass recording method is a method in which the recording data in the same recording area is divided into a plurality of passes, and the same recording area is divided into a plurality of times by a plurality of recording scans sandwiching the conveyance operation of the recording medium. In Patent Document 1, the recording density ratio of a pass whose density can be corrected after the density is detected by a sensor is set higher than the recording density ratio of a pass where density correction is not performed. It is a technique to do.

JP 2009-262456 A

  If the method of Patent Document 1 is used, density unevenness including density unevenness caused by satellites can be partially reduced. However, the method of Patent Document 1 cannot correct density unevenness that occurs in a pass without density correction. Further, if the output density exceeds the density when the density unevenness is detected, it cannot be corrected. For this reason, it cannot be corrected when a satellite hits a blank area near the edge.

An image processing apparatus according to the present invention includes: a recording unit that records dots based on a dot pattern; an acquisition unit that acquires image data indicating dots recorded by the recording unit; and the dot pattern and the acquisition unit Predicting image quality deterioration due to dots recorded by the recording means from the next time on the basis of the image data, and correcting the dot pattern from the next time onward based on the image quality deterioration predicted by the prediction means Yes, and the prediction unit and a correcting means for, for each nozzle, calculated the probability of occurrence of degradation factors of the image quality, when the occurrence probability is a predetermined value or more, when the recording by said recording means after the next The nozzle predicts that the deterioration factor of the image quality is generated in the nozzle, and the correction unit prints dots recorded by the nozzle predicted to generate the deterioration factor of the image quality. The dot pattern of dots within a predetermined range including, characterized in that and correcting on the basis of the deterioration factor of the predicted image quality.

  According to the present invention, the influence of deterioration factors such as satellites on a printed output image can be reduced, and a high-quality image can be formed.

FIG. 3 is a diagram illustrating a configuration example of a head unit in the first embodiment. FIG. 3 is a diagram illustrating a configuration example of Embodiment 1. It is a figure explaining the structural example of the deterioration measurement part in Embodiment 1. FIG. It is a figure explaining the process example of the degradation measurement part in Embodiment 1. FIG. It is a figure explaining the classification example of the degradation factor of the degradation measurement part in Embodiment 1. FIG. It is a figure explaining the structural example of the deterioration estimation part in Embodiment 1. FIG. FIG. 5 is a diagram illustrating an example of halftone processing in the first embodiment. It is a figure explaining the structural example of the deterioration estimation part in the modification 1 of Embodiment 1. FIG. It is a figure explaining the error diffusion coefficient in the modification 2 of Embodiment 1. FIG. 6 is a diagram illustrating a configuration example of Embodiment 2. FIG. FIG. 6 is a diagram illustrating a configuration example of a head unit according to a third embodiment. 10 is a diagram illustrating a configuration example of a third embodiment. FIG. FIG. 10 is a diagram illustrating a configuration example of a fourth embodiment. It is a figure explaining the structural example of the conventional head unit. It is a figure explaining the flow of multipass printing. It is a figure explaining the generation example of a satellite.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings. The following embodiments do not limit the present invention related to the scope of claims, and all combinations of features described in the present embodiments are essential for the solution means of the present invention. Is not limited.

<Multipass recording method>
The multi-pass recording method will be briefly described below. FIG. 14 is a diagram showing a recording head portion of a conventional ink jet printer. The recording head 1400 is an ink jet recording head that includes nozzle arrays that discharge C (cyan), M (magenta), Y (yellow), and K (black) inks, respectively. As shown in the figure, each color is composed of two banks on the upstream side and the downstream side in the main scanning direction.

  FIG. 15 is a diagram for explaining multi-pass printing (when the number of passes is 4) by a printer having the print head shown in FIG. FIGS. 15A and 15B show a recording head 1500, a formed image 1510, and a recording medium (paper) 1520. If the recording head 1500 reaches the end of the paper 1520 by the recording operation in the main scanning direction, the paper is fed in the sub-scanning direction by 1/4 of the head length. The next recording operation is as shown in FIG. In general, when an image is formed with N passes, the paper feed width is 1 / N of the head length. By repeating such a recording operation and paper feeding, a final print image is formed.

<Embodiment 1>
A configuration example of a head unit (carriage) of an ink jet printer used in the present embodiment is shown in FIG. As shown in the figure, the head unit 100 according to this embodiment includes nozzle rows 110, 120, 130, and 140 and sensors 150 for each color. While the head unit 100 forms an image on the paper surface by the multi-pass method, the sensor 150 captures the paper surface as needed to acquire a series of captured image data. Note that because of the multi-pass method, a captured image including an intermediate state of printing is acquired. As a result, the satellite position and the like can be dynamically measured during printing, and the print image can be corrected based on the result. The sensor 150 may acquire a two-dimensional image at a time using an area sensor, or may form a two-dimensional image from a one-dimensional image using a line sensor in the sub-scanning direction.

  The flow of processing in this embodiment will be described with reference to the block diagram of FIG. The image processing unit 200 includes a halftone unit 210, an image quality deterioration measurement unit 250, and an image quality deterioration prediction unit 260. The image processing unit 200 receives multi-valued original image data as an input and outputs a binarized dot pattern to the head 220. Further, the halftone unit 210 outputs a dot pattern obtained as a result of the halftone process to the image quality deterioration measurement unit 250. The head 220 ejects ink onto the paper surface 230. At this time, image quality deterioration factors such as satellites may be formed on the paper simultaneously with the main droplets. The sensor 240 captures the paper surface 230 and inputs the captured image to the image processing unit 200. The image quality deterioration measurement unit 250 measures image quality deterioration on the paper surface based on the dot pattern output from the halftone unit 210 and the captured image, and acquires deterioration measurement information. The image quality degradation prediction unit 260 predicts the subsequent occurrence of image quality degradation from the degradation measurement information measured by the image quality degradation measurement unit 250 and sends the degradation prediction information to the halftone unit 210. Prediction is performed for each scan (while the head moves from end to end in the main scanning direction). That is, during printing of the Nth scan, correction based on the deterioration measurement information obtained from the (N−1) th scan and collection of deterioration measurement information for the (N + 1) th and subsequent scans are performed. Yes.

Hereinafter, the operation of each element in the image processing unit 200 will be described in detail.
FIG. 3 is a block diagram of the image quality deterioration measurement unit 250. The image quality degradation measurement unit 250 includes a reference image generation unit 310, a difference calculation unit 320, a ternary unit 330, a clustering unit 340, and an analysis unit 350. The reference image generation unit 310 uses the input binary dot pattern to render a multi-value image having the same resolution as the captured image and generate reference image data indicating the reference image. The reference image represents an ideal state when there is no image quality deterioration factor. An example is shown in FIG. In FIG. 4, the reference image generation unit 310 generates a reference image 430 based on the dot pattern 410. The dotted line is for easy understanding of the boundary of the pixel and does not exist in the actual image. For example, if the printing resolution is 1200 dpi and the captured image is 4800 dpi, the resolution can be converted four times, so one dot in the dot pattern is a circle of 4 × 4 size.

  The difference calculation unit 320 calculates the difference between the captured image 420 and the reference image 430 and generates difference data indicating the difference image. The captured image 420 includes image quality deterioration factors such as main droplets and satellites, sensor noise, and the like. On the other hand, since only the main droplet is included in the reference image 430, image quality deterioration factors and sensor noise are emphasized in the difference image. Note that the difference calculation unit 320 may be configured to calculate the difference after correcting the positional deviation and rotation between the captured image and the reference image.

  When the pixel value of the target pixel of the difference image is P with respect to the predetermined threshold T, the ternary unit 330 sets the pixel value of the target pixel to “1” and “P <−T” when “P> T”. In the case of “,” the pixel value of the target pixel is set to “−1”. In other cases, the pixel value of the target pixel is set to “0”. In this way, the ternary unit 330 generates ternary image data indicating the ternary image 440. That is, a pixel having a pixel value of 1 in the ternary image is a pixel that is included in the captured image 420 but is not included in the reference image 430. A pixel having a pixel value of 0 in the ternary image is a pixel that can be regarded as having no difference between the captured image 420 and the reference image 430. A pixel having a pixel value of −1 in the ternary image is not included in the captured image 420 and is included in the reference image 430. In this embodiment, a ternary image is taken as an example. In the case of color, a plurality of threshold values are prepared and an S value image (S = “number of colors” × 2 + 1) for identifying which color is generated is generated. You may do it. In the ternary image 440 of FIG. 4, “+” represents “1”, “−” represents “−1”, and a blank area represents “0”.

  The clustering unit 340 extracts a pixel group in which “1” or “−1” is continuous from the ternary image as a connected element. The clustering result 450 in FIG. 4 shows the clustering result detected in the ternary image 440, and shows an example in which the connected elements 451, 452, 453, and 454 are detected. This processing includes removal of noise such as an isolated point 441 in FIG. 4 by morphological processing. The clustering unit 340 sends the coordinates of the extracted connected elements to the analysis unit.

  The analysis unit 350 analyzes each connected element and outputs the position of the center of gravity, the size, the sign, the contour, etc. of each connected element as deterioration measurement information. The barycentric position is obtained, for example, as an average of the positions of the respective pixels constituting the connection element. The size is obtained, for example, as the number of pixels constituting the connection element. The sign is the sign of the pixel value of the connected element. For example, as shown in FIG. 5, a contour closest to the connecting element is selected by a pattern recognition technique or the like from among predefined shapes such as a circle, a rod, a crescent, and a donut. The case of “not applicable” may be included.

  In addition, when many dots are included in the photographed image, that is, when the density is high, it is difficult to accurately detect each connected element because the dots overlap with neighboring dots. Therefore, an area with low density may be selectively photographed. Normally, printing is performed in order from the top of the paper while feeding the paper by 1 / F of the head length (F is the number of passes). However, by changing the pass order, the area where deterioration measurement information can be easily obtained is printed first. May be.

  Next, the operation of the image quality deterioration prediction unit 260 will be described with reference to FIG. The image quality deterioration prediction unit 260 includes a link unit 610, a statistics unit 620, and a prediction unit 630. The link unit 610 estimates which nozzle each connection element of the degradation measurement information sent from the image quality degradation measurement unit 250 is attributed to. That is, each connecting element is associated with a nozzle. For example, the nozzle may be the same as the closest main droplet, or may be obtained from past statistical data obtained by printing a test chart or the like. In the example of FIG. 4, the connecting element 451 is associated with the main drop 411, the connecting element 452 is associated with the main drop 412, and the connecting elements 453 and 454 are associated with the main drop 413. It is not always necessary to associate all the connected elements, and “no association” may be used when the distance to the closest main droplet is longer than a predetermined value.

  The statistics unit 620 accumulates deterioration measurement information for the latest M scans (M is a natural number) and takes statistics for each nozzle. For example, the occurrence probability of the connection element in each nozzle is obtained for each size and outline (circle, bar, crescent moon, donut, etc.) of the associated connection element. Further, it may be divided into more detailed cases such as the first discharge within one scan or the simultaneous discharge with the adjacent nozzle. Furthermore, weighting may be performed so as to place a weight on more recent deterioration measurement information. Based on this occurrence probability, if the occurrence probability is equal to or higher than a predetermined value, the connected element is a fixed deterioration factor, and if it is equal to or lower than a predetermined value, it is an indefinite deterioration factor.

  The statistical data obtained in this way is sent to the prediction unit 630. Here, the statistical data includes deterioration measurement information, nozzle association information, and occurrence probability. By performing a statistical analysis, for example, if a connected element having a circular shape and a size within a predetermined range has an occurrence probability equal to or higher than a predetermined value, it can be estimated that the connected element is a satellite. Also, if there is a pair of positive and negative crescent-shaped connecting elements, it can be considered that the landing droplet landing error (skew) is high, but if the occurrence probability is high, it can be estimated that it is a fixed deterioration factor such as nozzle clogging, If the occurrence probability is low, it can be estimated that it is an indefinite deterioration factor such as airflow. It can be estimated that the donut-shaped connecting element having a positive sign has a dot diameter larger than expected (reference image) due to the influence of the paper absorption rate or the like. The rod shape can be estimated as paper fibers. As described above, various estimations can be made from statistical data, so that prediction with an improved probability of hitting according to each deterioration characteristic can be performed.

  The prediction unit 630 predicts image quality deterioration in the next and subsequent scans for each fixed deterioration factor based on the statistical data. That is, the center of gravity position, size, shape, etc. of the connecting element in the next and subsequent scans are predicted. For example, a nozzle in which a circular deterioration factor has occurred at a rate of 50% or more is predicted to generate a circular deterioration factor in the next scan. It is predicted that the mode value is repeated for the donut-shaped deterioration factor. For the crescent-shaped deterioration factor, it is predicted that the previous event will be repeated. Note that the prediction method is not limited to the method described above. For example, the trend or period may be analyzed by the statistical unit 620, and the fixed deterioration factor and the indefinite deterioration factor may be separated based on the analysis. Examples of the trend include a tendency that the connecting element gradually approaches the main droplet, and a tendency that the nozzle range where the satellite is generated is expanded. Furthermore, image quality deterioration in the next scan may be predicted by estimating an internal state (for example, ink mist is attached to the head surface) based on the tendency and the cycle.

  Next, the operation of the halftone unit 210 will be described with reference to FIG. Here, it is assumed that the satellite is predicted to be generated to the left of one pixel of the main droplet with respect to all the nozzles as the deterioration prediction information. As a halftone method, binarization of an input image is executed using a 4 × 4 dither matrix 710. Here, the input image indicates an image in an area corresponding to the size of the dither matrix in the multi-value image indicated by the original image data input to the halftone unit 210. Each element of the dither matrix 710 will be described using XY coordinates (0, 0) to (3, 3). Each pixel of the input image 720 will be described using XY coordinates (0, 0) to (3, 3). For example, the matrix value of the element (1, 2) of the dither matrix 710 is 60.

  When the input image 720 of FIG. 7 is input, the pixel value and the matrix value are compared for each element in the normal dither processing, and the result is output. That is, when the pixel value is equal to or greater than the corresponding matrix value, 1 representing dot ON is output, and when the pixel value is smaller than the corresponding matrix value, 0 representing dot OFF is output. The result of normal dither processing is as shown in the binarization result 730.

  On the other hand, in the dither processing of the present embodiment, binarization is performed from an element having a small threshold value, and when the binarization result is 1 (when the dot is ON), satellite correction is performed on the peripheral pixels of the pixel. Assuming that the satellite size (coverage on the paper surface) is half of the main droplet, 128 is a value corresponding to half of the main droplet from the pixel value of the pixel adjacent to the left of the pixel where the dot is turned on as satellite correction. Is subtracted. For example, since the binarization result of the pixel (0, 1) of the input image is 1, it corresponds to half of the main droplet from the pixel (0, 0) of the input image which is the pixel on the left side of the pixel (0, 1). 128 is subtracted. That is, since it is predicted that half of the main droplet will adhere to the pixel adjacent to the left of the pixel where the dot is turned ON by the satellite, processing that considers the attachment due to the satellite is performed. That is, the pixel value of the pixel to which the satellite is attached is subtracted by a value corresponding to the satellite. The input image 740 is an input image in which satellite correction is performed by such processing, and the binarization result 750 indicates a comparison result of comparing the satellite-corrected input image 740 with the dither matrix 710. Comparing pixel (0, 2), in the binarization result 730 of the normal dither processing, the dot is ON because it is larger than the corresponding threshold value. On the other hand, in the dither processing of this embodiment, the pixel value of the pixel (0, 2) is subtracted as a result of the pixel (0, 3), and as a result, the dot of the pixel (0, 2) is not turned ON. Although the processing in the 4 × 4 pixel region has been described here, the entire image can be binarized by repeating this processing for each input image 4 × 4 pixel.

  The correction position and the correction value can be appropriately determined according to the predicted satellite generation position and size. For example, if the satellite generation position is predicted to be the upper left of the main droplet and the satellite coverage is predicted to be 1/4 of the main droplet, the pixel value of the pixel at the upper left of the pixel where the dot is ON is reduced to ¼ of the main droplet. The corresponding 64 may be subtracted. That is, the dot pattern of the dots within a predetermined range including the dots recorded by the nozzle predicted to generate the satellite may be corrected based on the predicted image quality degradation factor.

  When the binarization result 730 by the normal dither processing and the binarization result 750 by the dither processing of the present embodiment are replaced with dots, respectively, dot images 760 and 770 are obtained. The dot image 760 shows a state in which half of the main droplet appears as a dot when the pixel adjacent to the left of the one pixel whose dot is ON is the zero pixel whose dot is OFF. On the other hand, in the dot image 770, when the pixel adjacent to the left of the one pixel whose dot is ON is the zero pixel whose dot is OFF, half of the main droplet appears as a dot by the satellite. Yes. However, since the dot image 770 is based on the input image obtained by subtracting the pixel value based on the prediction, the number of main droplets is reduced compared to the dot image 760. For example, although the dot at the position of the pixel (0, 2) described above is not turned ON in the binarization result 750, half of the main droplet is changed to a dot by the satellite of the dot at the position of the pixel (0, 3). Appears.

  In FIG. 7, it is assumed that a satellite is generated on the left side of the main droplet. At this time, if the coverage of the paper surface by the main droplet is 100% and the coverage of the paper surface by the satellite is 50%, the dot image 760 has 10 main droplets and 4 satellites, so the density is 10/16 + 0.5. * 4/16 = 12/16. In the case of the dot image 770, since there are eight main drops and six satellites, the density is 8/16 + 0.5 * 6/16 = 11/16. Since each pixel value of the input image is 160 and the density represented by the input image is 160, the dot image 770 can achieve a density (160/255) closer to the input image by reducing the influence of the satellite.

  As described above, in the present embodiment, an example has been described in which the occurrence of image quality deterioration is predicted using a sensor mounted on the head, and the print data of the next scan is corrected to improve the image quality. At this time, the prediction accuracy can be further improved by identifying each image quality deterioration factor from the shape, size, and the like.

  In the above embodiment, prediction and correction are performed in units of scanning. However, prediction and correction may be performed in one scanning. In this case, for example, when the head is printing from the left to the right of the image, the remaining image in the same scan is corrected based on the deterioration measurement information obtained at the time of printing the left edge of the image. At this time, halftone is performed step by step as the head moves.

  The deterioration measurement information and the deterioration prediction information are not limited to those described in the above embodiment. For example, the size, shape, color and number of satellites may be measured and predicted. Further, a past captured image or the like may be used when noise is removed by the clustering unit 340. Further, although a description has been given using a 4 × 4 matrix as the dither matrix, it can be realized with an arbitrary size. For example, the present method may be applied to a 128 × 128 blue noise mask.

  In the example of FIG. 7 in the above embodiment, an example in which the same pixel value is included as an example of the input image has been described. However, the present invention is not limited to this example. For example, it is possible to predict and correct the occurrence of satellites in the input image near the edge. In particular, if the occurrence of a satellite can be predicted even in the vicinity of an edge, it can be controlled so that the satellite does not land in a blank area.

<Modification 1>
In the above embodiment, only the fixed deterioration factor is corrected based on the prediction, but the indefinite deterioration factor may be corrected. FIG. 8 shows a configuration example of the image quality deterioration prediction unit 260 in this modification. The processes of the link unit 810, the statistics unit 820, and the prediction unit 830 are the same as those in FIG. In this modification, information on indefinite deterioration factors is sent from the statistical unit 820 to the mask unit 840, and processing for generating compensation image data in the mask unit 840 is added.

  The position of the fixed deterioration factor can be specified by the processing in the statistics unit 820. Therefore, by masking and excluding the position of the indefinite deterioration factor from the difference image output from the difference calculation unit in FIG. 3, only indefinite deterioration factors such as ink mist and temporary non-discharge are included. Compensation image data can be generated. By subtracting the compensated image data from the image of the next scan, a dot pattern for the next scan can be generated so as to cancel the deviation from the ideal due to the indefinite deterioration factor.

<Modification 2>
In the above embodiment, the case of halftone by dither has been described, but error diffusion may be used. In this case, only the processing inside the halftone unit 210 in FIG. 2 is different, and the others are the same.

  In the error diffusion processing, the density is preserved by adding a value obtained by multiplying, for example, the diffusion coefficient 910 of FIG. 9 to the difference between the pixel values before and after quantization (quantization error) to the surrounding pixels of the target pixel. For example, in the example of FIG. 9, a value obtained by multiplying the quantization error value of the target pixel indicated by “*” by 7/16 is added to the pixel on the right side of the target pixel. Similarly, a value multiplied by another diffusion coefficient is added to pixels around the target pixel. In general, if the input pixel value of the target pixel is X, the quantization error is the difference between the input pixel value and the representative quantization value, so that when the target pixel dot is ON, (X-255), X when the dot is OFF.

  In this modification, when a satellite is predicted to occur, the quantization representative value used to calculate the quantization error of the pixel of interest when the dot is ON is changed. For example, the satellite generation position is left by one pixel, and the satellite coverage is 50%. At this time, the quantization representative value is 255 * 1.5 = 382.5. That is, the quantization error when the dot is ON is (X−382.5). That is, when a dot is turned on, a satellite associated with the dot is generated. Therefore, the increase in density due to the satellite is predicted, and the value of the quantization representative value is changed according to the coverage of the satellite so that the density of the increase in density due to the satellite is subtracted from the pixel value of the surrounding pixels. However, the quantization representative value is not changed for the target pixel when the left adjacent pixel is ON. That is, when the dot of the pixel in the satellite generation direction is turned ON, the correction for the pixel in the generation direction is not performed. As a result, when two pixels are continuously ON in the main scanning direction, the main droplet on the left and the satellite overlap each other. In this case, the density can be stored correctly without correction.

  As a result, the increase in density due to satellites can be diffused around as an error, so printing with the intended density can be performed more accurately. Although an example in which the quantized representative value is changed has been described here, the diffusion coefficient may be controlled instead of the quantized representative value.

<Embodiment 2>
In the above embodiment, the image quality degradation is measured for each scan using the sensor mounted on the head. However, in this embodiment, the sensor is not mounted on the head, and the image quality degradation is caused by reading the print image with the scanner after printing one page. measure. A scanner mounted on an ink jet printer may be used, or a dedicated flatbed scanner or a digital camera may be used.

  A block diagram of the present embodiment is shown in FIG. Other than the scanner 1040 is the same as FIG. 2 described in the first embodiment, but satellite measurement and correction are performed for each page. That is, in the first embodiment, the next scan is corrected using the image quality degradation measured in the previous scan, but in this embodiment, the image quality degradation in the next page is predicted using the image quality degradation measured in the previous page, and the next scan is performed. Correct the page image. The page that measures image quality degradation is a test chart that is designed so that dots are sparsely arranged and all nozzles are used multiple times so that image quality degradation is easy to measure.

  For example, since the characteristics of the satellite can change from page to page, the satellite prediction accuracy may decrease. However, image quality degradation can be prevented without adding sensors.

<Embodiment 3>
In the first and second embodiments, the ink jet printer using the multi-pass method has been described. However, in the present embodiment, the case of using the ink jet printer using the full multi method will be described. The full multi method is a method of completing printing by scanning a head having a head length equal to or larger than the page width once in the page height direction. A configuration example of the head unit in this embodiment is shown in FIG. The head unit 1100 is fixed, and the length of each nozzle row 1110, 1130, 1150, 1170 corresponds to a size including the paper surface width (full size). Printing is performed by continuously feeding paper 1190 (roll paper or the like) upward in the drawing. During printing, each sensor 1120, 1140, 1160, and 1180 shoots the paper surface 1190 intermittently.

  The flow of processing in this embodiment is shown in FIG. Similar to FIG. 2 of the first embodiment, the image quality degradation is measured by measuring the image quality degradation from the photographed image on the paper and feeding back the image quality degradation predicted based on the image quality degradation to the halftone portion. However, in the full multi method, printing is completed in one scan, so measurement and correction are performed as needed within the scan. That is, the subsequent correction is performed using the deterioration measurement information obtained during printing near the top of the paper. Note that a pattern for the purpose of collecting deterioration measurement information may be printed in the area at the top of the paper, and the area may be cut out by post-processing.

  The role of each sensor will be described with reference to FIGS. The image quality deterioration measurement unit 1250-1 measures the image quality deterioration related to the nozzle array 1110 using the captured image of the sensor 1120. The image quality deterioration measurement unit 1250-2 measures the image quality deterioration related to the nozzle row 1130 using the captured image of the sensor 1140. However, the image captured by the sensor 1140 includes dots output from the nozzle array 1110 and dots output from the nozzle array 1130. Therefore, by subtracting the image of the sensor 1120 from the image of the sensor 1140, the image quality deterioration measurement unit 1250-2 measures only the image quality deterioration related to the nozzle row 1130. Similarly, the image quality deterioration measurement unit 1250-3 measures the image quality deterioration related to the nozzle array 1150 from the image captured by the sensor 1160, and the image quality deterioration measurement unit 1250-4 determines the image quality deterioration related to the nozzle array 1170 from the image captured by the sensor 1180. measure. As described above, image quality degradation such as the satellite position of each nozzle row is measured with high accuracy. The operations of the image quality degradation prediction unit 1260 and the halftone unit 1210 are the same as those in the first embodiment.

  As described above, it has been shown that the same effect can be obtained in the embodiment using the full multi-type inkjet printer. In the above embodiment, the head unit is provided with the same number of sensors as the number of nozzle rows, but a smaller number of sensors can be realized. For example, in the case of the configuration of only the sensor 1180, the captured image includes dots for all the nozzle rows, but it may be identified which nozzle is based on the position of the dots. For example, when only the sensor 1120 is configured, only information on the nozzle row 1110 is obtained, but the other nozzle rows may be predicted to be the same as the nozzle row 1110.

<Embodiment 4>
In the present embodiment, as illustrated in FIG. 13, the printer 1320 and the scanner 1340 are connected to the cloud 1300 (server group), and calculate a dot pattern in consideration of satellites in the cloud 1300.

  The original image data is sent to the cloud 1300 from a PC (not shown), for example. The cloud 1300 obtains an optimal dot pattern by analyzing printing defects such as satellites based on the print image (past print image) scanned by the scanner 1340. For example, by collecting data of the same model in the cloud 1300 or repeatedly simulating printing including satellites, an optimal dot arrangement that does not deteriorate image quality even when the above printing defects occur is calculated. . As a result, even for processing with a heavy calculation load such as repetitive simulation, high-speed and high-quality processing is possible by utilizing the abundant calculation resources and data of the cloud 1300.

<Other embodiments>
The present invention also provides a storage medium storing a program code of software for realizing the functions of the above-described embodiments (for example, processing shown by a flowchart in which the processing of each unit described above is associated with each step), a system or apparatus It can also be realized by supplying to. In this case, the function of the above-described embodiment is realized by the computer (or CPU or MPU) of the system or apparatus reading and executing the program code stored in the storage medium so as to be readable by the computer.

Claims (19)

Recording means for recording dots based on a dot pattern;
Obtaining means for obtaining image data indicating dots recorded by the recording means;
Predicting means for predicting deterioration in image quality due to dots recorded by the recording means from the next time on the basis of the dot pattern and the image data acquired by the acquiring means;
Based on the degradation of the predicted image quality by the prediction means, have a correction means for correcting the subsequent dot pattern next,
The predicting means obtains the occurrence probability of an image quality deterioration factor for each nozzle, and when the occurrence probability is equal to or higher than a predetermined value, the image quality deterioration factor is applied to the nozzle at the time of the next recording by the recording means. Is expected to occur,
The correction means corrects a dot pattern of dots within a predetermined range including dots recorded by a nozzle predicted to cause the image quality deterioration factor based on the predicted image quality deterioration factor. An image processing apparatus.   The image processing according to claim 1, wherein the predicting unit predicts the deterioration of the image quality based on difference data between reference image data indicating the dot pattern and the image data acquired by the acquiring unit. apparatus.   3. The image according to claim 2, wherein the prediction unit associates a nozzle group including at least one pixel and a nozzle caused by image quality degradation from the difference data and the reference image data. Processing equipment.   The predicting means statistically analyzes at least one of the shape, size, or center of gravity of the pixel group for each associated nozzle, and determines a deterioration factor of image quality for each nozzle. The image processing apparatus according to claim 3. The deterioration factors include satellites,
The image processing apparatus according to claim 4 , wherein the correction unit lowers the density of pixels in the satellite generation direction of the nozzle that generates the satellite. The image processing apparatus according to claim 5 , wherein when the dot of the pixel in the satellite generation direction of the nozzle that generates the satellite is ON, the correction unit does not perform correction on the pixel in the generation direction. The prediction means generates compensated image data in which dots of image quality degradation factors when the occurrence probability is equal to or higher than the predetermined value are excluded from the difference data,
It said correction means, the image processing apparatus according to any one of claims 4 6, characterized in that the correction further using the compensated image data. The dot pattern, the image processing apparatus according to any one of claims 1 to 7, characterized in that it is produced by halftoning the original image data. The image processing apparatus according to claim 8 , wherein the correction unit performs the correction by changing a pixel value of the original image data. The image processing apparatus according to claim 8 , wherein the correction unit performs the correction by changing a quantization representative value used for the halftone process. The image processing apparatus according to claim 8 , wherein the correction unit performs the correction by changing a diffusion coefficient of error diffusion used for the halftone process. The halftone process is a dither process,
Factors of the deterioration of the image quality include satellites,
10. The image according to claim 9 , wherein the correction unit generates a dot pattern from the next time by comparing the pixel value after the change in order from a pixel with a small threshold value included in the dither matrix. Processing equipment. The acquisition unit, an image processing apparatus according to any one of claims 1 1 2, characterized in that to obtain the captured image data obtained by photographing the dots printed as the image data by said recording means. Said acquisition means, image according to any one of claims 1 1 2, characterized in that to obtain the captured image data obtained by photographing the dot finished recording one scan by the recording means as the image data Processing equipment. Said acquisition means, image according to any one of claims 1 1 2 of the photographic image data obtained by photographing a region encompassing the nozzle array and obtains as the image data included in the recording means Processing equipment. Region encompassing the nozzle array, the image processing apparatus according to claim 1 5, characterized in that corresponding to the width of a recording medium for recording dots. The acquisition means includes
Acquire a plurality of captured image data by a plurality of imaging means,
The image processing apparatus according to claim 16 , wherein image data of each photographing unit is acquired based on a difference between the plurality of photographed image data. A recording step of recording dots by a recording means based on a dot pattern;
An acquisition step of acquiring image data indicating the dots recorded by the recording step;
A predicting step of predicting deterioration in image quality due to dots recorded by the recording means from the next time on the basis of the dot pattern and the image data acquired in the acquiring step;
On the basis of the predicted image quality degradation in the prediction step, it has a correction step of correcting a subsequent dot pattern next,
In the predicting step, an occurrence probability of an image quality deterioration factor is obtained for each nozzle, and when the occurrence probability is a predetermined value or more, the image quality deterioration factor is applied to the nozzle at the time of the next recording by the recording unit. Is expected to occur,
In the correcting step, a dot pattern of dots within a predetermined range including dots recorded by a nozzle predicted to cause the image quality deterioration factor is corrected based on the predicted image quality deterioration factor. Image processing method. Program for causing a computer to function as the image processing apparatus according to any one of claims 1 to 1 7.

Images