JP2006025217A - Image processing apparatus, image processing method, image processing program, and recording medium recorded with the program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an image output apparatus such as a printer printing out dots with a plurality of densities that applies gradation conversion to gradation data of an input image by a prescribed method so as to produce dots wherein occurrence or the like processes of a characteristic pattern liable to be caused in an output image of the image output apparatus is suppressed. <P>SOLUTION: An image processing section 30 is composed of: a color correction/color conversion section 31 for acquiring input gradation data comprising CMYK data; a gradation value division section 33 for dividing the input gradation data into a plurality of gradation data; a dot generating section 343 for generating large dense dots, small dense dots, and large light dots corresponding to the divided gradation data; a negative value correction section 35 for correcting data of a negative value from differential gradation data denoting errors in produced dots; and a small light dot generating section 36. The small light dot generating section 36 selects new pixels one after another until the total sum of gradation values of the differential gradation data to which the negative value correction processing is applied reaches a prescribed threshold value or over to produce cells and to generate a small light dot to the gravity center of the cells. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  In an image output apparatus such as a printer capable of ejecting dots having a plurality of densities, the present invention converts the gradation data of an input image into a output image of the image output apparatus by performing gradation conversion by a predetermined method. The present invention relates to an image processing technique that suppresses occurrence of a peculiar pattern and generates an image with excellent image quality.

  Conventionally, in an image output device such as a laser printer or an ink jet printer, gradation data of an input image is binarized with a predetermined threshold value for each pixel. An output image is output by forming dots of a predetermined size at the position of the pixel to which the gradation value is given by binarization and printing on the printing paper. Here, the process of converting multi-value gradation data into binarized gradation data is generally referred to as halftone processing.

  In recent years, ink jet printers that can form dots having a plurality of densities by changing the density of ink and the size of dots have appeared. Therefore, in Patent Document 1, in order to appropriately perform halftone processing and generate dots of a plurality of densities, halftone processing by systematic dither is performed for dark density dots, and gradation data of an input image is obtained. A technique for hitting the error data with dots having the lightest density is disclosed. Further, halftone processing by an error diffusion method is used for processing of dots having a light density at this time.

  In the halftone processing by the error diffusion method, a specific pattern that does not exist in the input image may occur in the output image, for example, the dots of the output image are connected in a chain depending on the positions of the dots to be formed. In order to suppress the occurrence of such a unique pattern, for example, Patent Document 2 proposes a technique for reducing dot connections. In Patent Document 2, for pixels of an input image, a cell is generated by searching and selecting pixels one after another according to a predetermined table until the total gradation value of gradation data reaches a threshold value, and the vicinity of the center of the cell is generated. A technique for forming dots at the positions is disclosed.

Japanese Patent Laid-Open No. 2000-6445 JP 11-27528 A

  In Patent Document 1, error data generated by systematic dither is diffused by using an error diffusion method for an ink jet printer capable of hitting dots having a plurality of densities (shades). However, according to the error diffusion method, for example, an error is propagated with a weight as shown in FIG. 17 of Patent Document 1, so that the propagation direction is biased in a specific direction. In addition, since the process of changing the weight distribution is not performed, it always propagates in a certain direction. Therefore, the technique disclosed in Patent Document 1 has a problem in that a quantization pattern is amplified and a singular pattern is generated by diffusing a difference between pixel values generated by quantization by the error diffusion method in the same direction. It was.

  In addition, in the halftone processing in Patent Document 2, a method is proposed in which a search for pixels constituting a cell is performed using a table in which a predetermined search order is recorded in order to suppress the occurrence of singular patterns in the error diffusion method. However, for example, in the pixel search order shown in FIG. 4 of Patent Document 2, the pixel on the left side in the table is selected from the pixels on the left side of the pixel. In many cases, it has already been processed when it is generated, and as a result, the generated cell is distorted (for example, a crescent moon). In a cell having such a shape, the vicinity of the center of the cell may be located outside the cell. For such a cell, if a dot is formed near the center of the cell, it overlaps with the dot of the adjacent cell, resulting in biased output dots, resulting in the generation of a unique pattern. There was a problem.

  Further, in Patent Document 2, in order to prevent a regular pattern generated in an output image due to a series of cells having the same shape, a plurality of tables in which pixel search orders are recorded are prepared, and the table to be used is defined as a cell. A process of switching the cell shape irregularly by switching sequentially or randomly every time has also been proposed. However, such processing has a problem that a processing load is large because a plurality of tables are prepared, and the processing speed is slow. For example, if the input image is gradation data having approximately the same density, cells having substantially the same shape may be generated even if the search order is random, resulting in a regular unique pattern. There was also a problem of generating.

  The present invention solves at least a part of such problems, and an image processing apparatus, a printing apparatus, an image processing method, an image processing program for acquiring an output image in which generation of a specific pattern is suppressed from dots having a plurality of densities, Another object is to provide a recording medium on which the program is recorded.

  In order to solve the above-described problem, the present invention provides an image processing apparatus that performs gradation conversion on input data having any one of P (P is an integer of 2 or more) kinds of gradation values for each pixel. The first gradation conversion for converting the gradation value of the input data into gradation data consisting of Q (Q <P, Q is an integer of 2 or more) types of gradation values for each pixel. M, which is an error between the first gradation conversion means for obtaining gradation data and the gradation data obtained by the first gradation conversion means and the input data for each pixel (M is 2 With respect to error data consisting of the above (integer) types of gradation numbers, new pixels are selected one after another until the sum of gradation values becomes equal to or greater than a threshold value, and a pixel group is generated. And second gradation conversion means for providing N (N is an integer of 2 or more) kinds of gradation values. And effect.

  According to this configuration, gradation data composed of Q (Q <P) types of gradation values is acquired from input data composed of P types of gradations, and gradations expressed with a smaller number of gradations are obtained. By converting to a value (first gradation conversion), the gradation data of the input image is varied. Then, by performing the second gradation conversion process for each pixel on the error data that is an error between the gradation data to which the variation is given and the input data, the sum of the gradation values of the gradation data is obtained. Until the threshold value is exceeded, new pixels of the gradation data are selected one after another to generate a pixel group (hereinafter referred to as “cell”). Then, N types of gradation values are assigned to predetermined pixels of the cell in which the total gradation value of the pixels in the generated cell is equal to or greater than the threshold value. In this way, when selecting the pixels to be incorporated into the cell, the pixel selection order varies even if the selection order is not changed for each cell. Therefore, the shape of the generated cell becomes irregular and the error data varies. Can be given. In other words, the output image acquired based on the pixel values of the gradation data and the error data gives a variation in the gradation data by the first gradation conversion, and performs the second gradation conversion on the error data that is the error. By performing, it is acquired by combining the gradation data after the first gradation conversion and the error data after the second gradation conversion. Therefore, an error with respect to input data is reduced, and a visually comfortable output image in which the occurrence of a peculiar pattern is suppressed can be obtained by performing processing in which error data is appropriately varied. In this manner, visually comfortable input data having P types of gradation values is reproduced from gradation data having Q types of gradation numbers and error data having N types of gradation values. An output image can be obtained.

  Here, the first gradation is further provided with gradation value dividing means for dividing the gradation value of the input data into a plurality of gradation values and acquiring a plurality of gradation data having the respective gradation values. The conversion means performs first gradation conversion on the plurality of divided gradation data, and the second gradation conversion means applies error data which is an error between the plurality of gradation data and the input data. It is preferable to perform the second gradation conversion.

  In this way, an output image can be obtained based on the pixel values of the plurality of gradation data and error data. It is more desirable to acquire a plurality of gradation values so as to have different gradation values. In this way, it is possible to express more gradation values and obtain a visually comfortable output image by combining gradation data having different gradation values and error data. For example, if a plurality of gradation data are associated with dots having different densities, the combination of a plurality of dots makes it possible to increase the number of gradation values that can be expressed compared to the case where a single dot is used. A comfortable image can be obtained.

  Here, the second gradation converting unit generates a pixel group by newly selecting a pixel closest to the centroid position of the generated pixel group, and determines the pixel determined based on the centroid position of the pixel group. It is preferable to provide N kinds of gradation values.

  In this way, in the cell generation process, the pixel closest to the center of gravity of the cell is always selected and incorporated into the cell, so the shape of the generated cell is approximately circular with the center of gravity of the cell as the center. Then, a gradation value is given to the approximate center position of the circle. Therefore, for example, when the second gradation conversion is performed on the data having a uniform gradation value for each pixel and the gradation value is given, the distance between the pixels to which the gradation value is given becomes almost uniform. . As described above, since the dispersibility of gradation values is improved, a comfortable output image can be obtained.

  Here, the sign of the gradation value of the error data is determined for each pixel, and the negative sign of the pixel is determined by moving the positive gradation value from the pixel having the positive sign to the pixel having the negative sign. Gradation value moving means for acquiring error data that cancels out the gradation value of the second gradation conversion means, and the second gradation conversion means applies the second gradation to the error data that cancels out the negative gradation value. Conversion is preferably performed.

  In this way, with respect to the error data having a difference value between the gradation component data processed by the first conversion means and the input data, the pixel having a positive sign with respect to the pixel having a negative sign is divided from the pixel having the positive sign. By moving the tone value and canceling the negative tone value, the second tone conversion can be performed on the error data having the positive tone value. Here, when there is a negative gradation value in the cell at the time of the second gradation conversion, the center of gravity of the pixel in the cell is located outside the cell due to the influence of the pixel having the negative gradation value. There is a possibility that a gradation value is given outside. In such a case, dots are formed inside other cells, which increases the possibility of overlapping with dots generated by other cells, resulting in biased output dots, resulting in unique patterns. Will be generated. Therefore, it is possible to prevent the gradation value from being given to the pixel outside the cell by the gradation value moving means canceling out the negative value of the error data. Therefore, the second gradation conversion with improved reproducibility of dot distance and dot dispersibility is performed, so that a comfortable output image can be obtained. Note that when selecting a pixel having a positive gradation value, it is preferable to perform processing by selecting a pixel having a positive gradation value closest to a pixel having a negative gradation value.

  Here, the second gradation conversion means sets a first reference point for pixel selection, and M based on the first reference point until the sum of gradation values for each pixel becomes equal to or greater than a threshold value. A pixel group is generated by selecting pixels having different gradation values, a second reference point is determined based on the generated pixel group, and N types of pixels are determined based on the second reference point. It is preferable to give the gradation value of.

  In this way, a reference point for selecting a pixel to be incorporated into the cell when generating the cell is distinguished from a reference point for determining the position of the dot formed in the generated cell. For example, a pixel to be incorporated can be selected using a reference point specified by the shape of the cell, and a cell can be generated regardless of the gradation value of each pixel in the cell. Therefore, it is possible to select a pixel to be incorporated by paying attention only to the shape of the cell. In addition, since the process is performed regardless of the gradation value of the pixel, even if the error data has a negative value, the gradation value is given in the cell without canceling the negative value. Processing can be performed.

  Here, the second gradation conversion means may update the first reference point every time a pixel is selected a predetermined number of times.

  In this way, for example, the generated cells are more likely to be formed in a circular shape than when the first reference point is fixed, and a comfortable output image with a uniform inter-dot distance is maintained. Can be obtained.

  Here, the first reference point is a center position calculated from the position of the selected pixel, and the second reference point is a center of gravity position calculated from the pixel position of the pixel group and the gradation value. preferable.

  In this way, selection of pixels to be incorporated into the pixel group may be calculated based on the position of each pixel, and the processing load related to calculation processing can be reduced as compared with the case where the center of gravity is calculated. Further, since the position of the dot to be formed is the center of gravity of the generated cell, it is possible to form a dot faithful to the gradation data of the input image in the cell.

  Here, it is preferable that the first reference point is a center position calculated from the position of the selected pixel, and the second reference point is a center position calculated from the position of the pixel of the pixel group.

  In this way, for example, in the process of determining the reference point used for pixel selection and the reference point for forming the dot, it is not necessary to perform an operation including the gradation value of the pixel. The processing efficiency of the entire apparatus can be increased by reducing the number.

  Here, the second gradation converting means continues to select new pixels, and when the sum of the gradation values in the generated pixel group exceeds the threshold value, the gradation value obtained by subtracting the gradation value from the threshold value is obtained. It is preferable to return to the last selected pixel.

  In this way, since the gradation value exceeding the threshold value is returned to the last pixel incorporated in the cell exceeding the threshold value, the gradation value of the error data is converted into the second gradation conversion process. Since the gradation value remains at almost the same position as the original image without being lost in the middle, dots that are faithful to the error data and the input data can be formed.

  Here, it is preferable that the first gradation conversion means performs gradation conversion by any one of a systematic dither method, an error diffusion method, and an average error minimum method.

  In this way, for example, when the input image is input data composed of gradation values representing approximately the same density, the gradation value of the input data of the input image is increased or decreased by converting it to a predetermined gradation value. can do. As a result, gradation data and error data vary, and in the second gradation conversion performed thereafter, the probability that cells having substantially the same shape are generated without changing the pixel selection order for each cell. Decrease. Therefore, a singular pattern is unlikely to occur in the error data subjected to the second gradation conversion due to the variation in the cell shape, and a visually comfortable output image can be obtained.

  Here, the present invention provides the above-described image processing apparatus, and in a printing apparatus that prints an image by forming a plurality of dots on a print medium, density and size corresponding to a plurality of gradation data. It is good also as a printing apparatus characterized by printing by forming at least one different dot.

  In this way, in a so-called ink jet printer, it is possible to form dots having at least one different density and size corresponding to a plurality of gradation data. Therefore, considering that printing is performed by generating a plurality of different dots corresponding to the gradation values of the plurality of gradation data, the dots corresponding to the gradation values subjected to the first gradation conversion are generated. The error with respect to the input data can be reduced by generating a new dot. Further, in this error correction, it is possible to generate dots corresponding to error data in which the generation of unique patterns is suppressed. From the above, it is possible to print an output image that is excellent in gradation value reproducibility and visually comfortable.

  The image processing method of the present invention that achieves the above object is an image that performs gradation conversion on input data having any one of P (P is an integer of 2 or more) kinds of gradation values for each pixel. A first method for converting gradation values of input data into gradation data composed of gradation values of Q types (Q <P, Q is an integer of 2 or more) for each pixel. A first gradation conversion step that performs gradation conversion and acquires gradation data, and M (M), which is an error between the gradation data acquired by the first gradation conversion means and the input data, for each pixel. M is an integer greater than or equal to 2) For error data consisting of the number of types of gradations, new pixels are selected one after another until the sum of gradation values is equal to or greater than a threshold value, and a pixel group is generated. A second gradation conversion step of assigning N (N is an integer of 2 or more) kinds of gradation values to the pixels of , Characterized by comprising a.

  According to this configuration, the input data is varied by the first gradation conversion, and the second conversion process is performed on the error data that is an error component of the first conversion process. In the second conversion process, even if the selection order is not changed for each cell when selecting a pixel, the probability that cells having the same shape are continuously generated decreases, so that an error that suppresses the occurrence of a singular pattern is suppressed. Data can be obtained. Then, it is possible to acquire gradation data composed of gradation data after the first gradation conversion composed of Q kinds of gradation values and error data. Therefore, a visually comfortable output image can be obtained from the gradation data thus obtained.

  Further, the present invention may be a computer program or a recording medium recording the program. That is, an image processing program that performs gradation conversion on input data having any one of P (P is an integer of 2 or more) kinds of gradation values for each pixel, the computer having the input data Gradation data is obtained by performing first gradation conversion for converting gradation values into gradation data consisting of Q (Q <P, Q is an integer of 2 or more) types of gradation values for each pixel. The first gradation converting means for each pixel, M (M is an integer of 2 or more) types of gradations, which are errors between the gradation data acquired by the first gradation converting means and the input data. With respect to error data consisting of numbers, new pixels are selected one after another until the sum of gradation values is equal to or greater than a threshold value, and a pixel group is generated. Among the pixel groups, N (N is 2 or more) for a predetermined pixel (Integer) of the second gradation conversion means for assigning various gradation values. To. As a recording medium on which the program is recorded, various computer-readable media such as a flexible disk, a CD-ROM, an IC card, and a punch card can be used.

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

  FIG. 1 is a configuration diagram showing the entire system of this embodiment. As shown in FIG. 1, this system is roughly composed of a host computer 10 and an image output device 20.

  The host computer 10 includes an application unit 11 and a rasterizing unit 12. The application unit 11 generates output image data to be printed, such as character data, graphic data, and bitmap data. For example, the host computer 10 generates character data, graphic data, and the like by operating a keyboard or the like using an application program such as a word processor or a graphic tool. The generated data is output to the rasterizing unit 12. In this way, the processing described below is performed as a target for printing the above-described data. However, the host computer 10 may receive data output from an image input device (not shown) such as a scanner and output this data to the rasterizing unit 12 as data generated by the application unit 11.

  The rasterizing unit 12 converts the print target data output from the application unit 11 into 8-bit gradation data for each pixel (or for each dot). In the present embodiment, 8-bit gradation data is output for each color and pixel of R (red), G (green), and B (blue). Therefore, the output gradation data has 256 values (gradation values) from 0 to 255 for each pixel. Actually, the gradation data generating process in the rasterizing unit 12 is performed by a driver installed in the host computer 10. The gradation data generated by the rasterizing unit 12 is output to the image output device 20.

  The image output device (printing device) 20 includes an image processing unit 30 and a print engine 40 as a whole. In the image processing unit 30, the gradation data output from the host computer 10 is processed, and the processed data is output to the print engine 40, and the print engine 40 actually outputs it to a recording medium such as printing paper. Is printed.

  Here, the print engine performs printing by ejecting dot-like ink onto a recording medium. However, the print engine 40 according to the present embodiment can select light / dark / large / small for the ejected dots. . That is, the print engine 40 can eject four types of dots, which are large dots, dark dots, light large dots, and light small dots (details will be described later).

  The image processing unit 30 includes a color correction / color conversion unit 31 that acquires CMYK data from RGB data from the rasterization unit, a gradation value dividing unit 33 that divides gradation data of each color into a plurality of gradation data, and a division A dot generation unit 34 that generates dark, dark, and light large dots corresponding to each gradation data, a negative value correction unit 35 that corrects a negative value for the generated dot error, and light and small dots. And a small light dot generation unit 36. As described above, the image processing unit 30 according to the present embodiment generates four types of dots, dark, dark, light, and light dots, for an input composed of RGB data. The generation in the image processing unit 30 indicates that the dot is turned on / off for each dot. In practice, each dot is generated by being ejected onto a recording medium such as printing paper by the print head 42 in accordance with the dot on / off information determined here.

  The color correction / color conversion unit 31 receives gradation data of 8 bits for each of RGB (256 bits in total) having 256 kinds of gradation values input from the host computer 10 and is actually printed by the print engine 40. Color correction processing of RGB gradation data is performed using the color correction table 32 so that the printed material can express good colors. Thereafter, color correction processing is performed on the corrected RGB gradation data from RGB to CMYK. Here, C represents cyan, M represents magenta, Y represents yellow, and K represents black. However, since the CMYK gradation data is data input to the gradation value dividing unit 33, it is hereinafter referred to as input gradation data (input data) Di.

  A gradation value dividing unit (gradation value dividing means) 33 divides each gradation value of the input gradation data Di output from the color correction and color conversion unit 31, and each of the large, dark, and light large dots. Determine the level value given to. Then, level data Dl having a level value corresponding to the gradation value of the input gradation data Di is acquired. Here, the level value is a value obtained by using a table shown in FIG. 7 to be described later because it is used in the next generation processing of dark, dark, and light large dots. This process is called a gradation value division process.

  The dot generation unit (first gradation conversion means) 34 performs gradation conversion by a systematic dither method for each level value of the large, dark, and light large dots output from the gradation value dividing unit 33. Processing is performed to generate binarized quantized data Do. This binarized quantized data Do is data corresponding to whether each dot of dark / dark / light large dots is generated. That is, the dot generation unit 34 performs determination using a dither matrix as a threshold value for the gradation value for each pixel, and determines on / off of each dot.

  The negative value correction unit (tone value moving unit) 35 is a density expressed by the large, small, and light large dots based on the quantization data Do output from the dot generation unit 34 and the density of each dot. And the difference value between the input gradation data Di and the gradation value of the input gradation data Di is calculated to obtain difference gradation data (error data) Dd. Then, for the negative gradation value of the difference gradation data Dd, a negative value correction process is performed to cancel the negative gradation value by moving the gradation value from a pixel having a positive gradation value.

The light small dot generation unit (second gradation value conversion means) 36 quantizes the difference gradation data Dd subjected to the negative value correction processing, with the difference value being obtained with a predetermined gradation value as a threshold value. It converts to data Do and outputs the quantized data Do. Here, the binarized quantized data Do corresponds to whether or not to generate light small dots. In the present embodiment, as the light small dot generation process, a process of forming a cell CL based on the center of gravity GC of a plurality of pixels and generating a dot on the center of gravity GC of the pixel inside the cell (Circular Cell method, hereinafter referred to as CC method) is performed. Use. Specific contents of the CC method will be described later.
Then, drive data is generated from the quantized data Do corresponding to ON / OFF of the large / dark / light large / light small dots generated as described above, and is output to the print engine 40.

  The print engine 40 includes a head drive unit 41 and a print head 42. The head drive unit 41 generates control data for driving the print head 42 from the drive data input from the image processing unit 30 and outputs the control data to the print head 42. The print head 42 is driven based on the control data output from the head drive unit 41, and further, a print head scanning mechanism and a paper transport mechanism (not shown) are driven, so that the print medium 42 is actually transferred from the host computer 10 to a recording medium such as print paper. Will be printed.

Next, the print head 42 will be described with reference to FIGS. 2 and 3.
FIG. 2 is a view showing the nozzle surface of the print head 42. As described above, the print head 42 is composed of K (black), C1 (cyan), C2 (light cyan), M1 (magenta), M2 (light magenta), and Y (yellow) heads. The heads are arranged so as to form one head unit. The nozzle surface of each print head 42 has a rectangular shape, and 32 nozzles Nz are arranged in a staggered manner in the long side direction. Although not shown, each nozzle Nz group is provided with an ink cartridge corresponding to each nozzle, and when the ink cartridge is attached, the color corresponding to the ink supply port of each color ink cartridge. The nozzles Nz communicate with each other. Thus, the nozzle Nz can receive ink supplied from the ink cartridge. The print head 42 has two different shades of nozzles Nz for each of cyan and magenta colors. Accordingly, when ejecting cyan or magenta ink, it is possible to eject two types of light and dark ink (dots) by using different nozzles Nz.

  FIG. 3 is an explanatory diagram showing a schematic configuration of the inside of the print head 42 shown in FIG. 2 and a state in which ink is ejected in a dot shape. As can be seen in FIG. 3A, each print head 42 includes a nozzle Nz, an ink chamber Ti, and a piezo element PE. As described above, the ink chamber Ti communicates with an ink supply port (not shown) of the ink cartridge, and is supplied with ink from the ink cartridge and is filled with ink. In addition, a piezo element PE which is a kind of electrostrictive element and has excellent responsiveness is disposed at a position outside the one side surface of the ink chamber Ti. The piezo element PE has a crystal structure distorted by applying a voltage as is well known, and can perform electro-mechanical energy conversion at high speed. When a voltage is applied to the piezo element PE, the piezo element PE itself expands and presses the ink chamber Ti of the print head 42 as shown in FIG. Inside the pressed ink chamber Ti, the wall surface in contact with the piezo element PE is deformed, and the volume of the ink chamber Ti is changed, whereby the ink existing in the ink chamber Ti is pushed out of the ink chamber Ti. In this way, the ink that has reached the nozzle Nz is discharged from the tip of the nozzle to the outside, and dot-like ink is ejected. As described above, the print head 42 can perform printing by ejecting dot-like ink by driving the piezo element PE to expand and contract.

  Here, the amount of displacement of the piezo element PE can be freely adjusted by increasing or decreasing the applied voltage. Therefore, by adjusting the voltage applied to the piezo element PE, it is possible to change the volume change amount of the ink chamber Ti and change the ink amount ejected from the nozzle Nz. Thus, by adjusting the ink amount of dots, the size (dot diameter) of the dots of ink ejected from the nozzles Nz can be controlled. In the present embodiment, two types of large and small dots are created by adjusting the voltage applied to the piezo element PE. However, by controlling the voltage applied to the piezo element PE, large, medium, small, or a larger number of different dots can be created.

  As described above, the print head 42 according to the present embodiment can create dots having two types of sizes for cyan and magenta inks while having two types of ink densities. Considering that these combinations are performed, the print head 42 can produce a total of four types of dots. Therefore, by combining the density for density and the size for size, each dot will be called “dark dot”, “dark dot”, “light large dot” and “light small dot” below. To do. In addition, when dark dots are formed on a printed material, the density expressed on the printed material is the highest, and when light dots are formed, the density expressed on the printed material is the lowest. Thus, when the printed dots are viewed macroscopically, it can be said that dots having four types of density can be generated on the printed matter. Accordingly, the dark, dark, light, and light dots are expressed as dots having different densities. Further, since yellow and black inks do not have low density ink, it is possible to generate dots that differ only in the size of the dots. In the following description, the case of generating large, small, small, light large, and light small dots is described. However, for yellow and black, large and small dots are generated by the same method. Processing should be done. Of course, yellow and black may also be configured to use two types of light and dark inks.

  Next, a specific configuration of the image output apparatus 20 will be described with reference to FIG. Here, in the image output apparatus 20 of FIG. 1, the color correction and color conversion unit 31, the gradation value division unit 33, the dot generation unit 34, the negative value correction unit 35, and the light small dot generation unit 36 are the same as those in FIG. This corresponds to the CPU 22, the hard disk 23, the ROM 24, and the RAM 25.

  As a whole, the image output apparatus 20 includes an input interface (I / F) 21, a CPU 22, a hard disk 23, a ROM 24, a RAM 25, and a print engine 40, which are connected to each other via a bus. The input I / F 21 serves as an interface between the host computer 10 and the image output apparatus 20. The input I / F 21 receives RGB gradation data from the host computer 10 transmitted by a predetermined transmission method, and is converted into data that can be processed by the image output device 20. The RGB gradation data is temporarily stored in the RAM 25.

  The CPU 22 is connected to each of the input I / F 21, the hard disk 23, the ROM 24, the RAM 25, and the print engine 40 via the bus, reads out a program stored in the hard disk 23 or the ROM 24, and performs gradation value division processing, dot described later. Various processes such as a generation process and a light small dot generation process are performed. The program in which these processes are recorded may be stored in advance in the hard disk 23 or the ROM 24, or may be supplied from the outside by a computer-readable recording medium such as a CD-ROM, and is not shown in the figure. The image may be stored by being stored in the hard disk 23 provided in the image output device 20 via the RW drive. However, the program may be stored by accessing a server or the like that supplies a program via a network means such as the Internet and downloading data.

  The RAM 25 serves as a working memory for each process executed under the control of the CPU 22 and stores various data after the process. And the drive data for driving the head drive part 41 of the print engine 40 are also stored. In a predetermined area of the storage area of the RAM 25, a buffer area for storing various data is provided.

  The print engine 40 has the same configuration as the print engine of FIG. 1, and receives the drive data stored in the RAM 25 under the control of the CPU 22 and performs the above-described print processing.

  Next, before describing specific processing performed by the image output apparatus 20 configured as described above, how the gradation data output from the host computer 10 is converted by each processing in the image processing unit 30. This will be described with reference to FIG. In FIG. 5, first, R, G, B gradation data Ri, Gi, Bi output from the host computer 10 are input to the color correction / color conversion unit 31. The gradation data of Ri, Gi, Bi is 256 gradations, that is, gradation data having 256 kinds of gradation values from 0 to 255. The color correction / color conversion unit 31 performs color correction processing of Ri, Gi, Bi data using the color correction table 32, and also performs C (cyan), M (magenta), Y (yellow) from R, G, B. ), Color conversion processing to K (black). The color conversion process is usually performed using a predetermined color conversion table (not shown). In this embodiment, the CMYK gradation data of the color conversion table used for color conversion is gradation data of 8 bits for each color, and gradation data Cc, Mc, Yc and Kc are gradation data having 256 kinds of gradation values from 0 to 255.

  Next, a flowchart of a program showing the entire processing performed by the image output apparatus 20 described above is shown in FIG. This program is stored in the RAM 25 provided in the image output apparatus 20 and is executed by the CPU 22 under a predetermined operation system. When the program is executed, each component of the image output apparatus 20 operates and can perform each function. In the program of FIG. 6, step S10 is the color correction / color conversion unit 31, step S20 is the gradation value division unit 33, step S30 is the dot generation unit 34, and step S40 is the negative value correction unit. S50 corresponds to the processing performed by the light small dot generator 35.

  When the image processing routine shown in FIG. 6 is started, first, in step S 10, the color correction / color conversion unit 31 acquires image data (RGB data) output from the host computer 10, and based on the color correction table 32. Thus, color correction corresponding to the dot output characteristics of the individual print engine 40 is performed (color correction processing). At this time, the image data is actually read from the host computer 10 through the input I / F 21 into the RAM 25. In this step, color coordinate conversion processing (color conversion processing) from RGB data to CMYK data is also performed on the image data that has undergone color correction in accordance with the characteristics of each CMYK ink of the inkjet printer. The color conversion process is usually performed using a predetermined color conversion table (not shown). Accordingly, here, the input image data is represented by 8 bits (256 gradations) and pixels for each color of C (cyan), M (magenta), Y (yellow), and K (black). Input gradation data Di having an input gradation value is acquired for each time and stored in a predetermined buffer area of the RAM 25.

  Next, in step S20, the gradation value dividing unit 33 centering on the CPU 22 performs gradation value dividing processing on the input gradation data Di obtained by the color correction processing and color conversion processing. Hereinafter, the gradation value dividing process will be described. In the gradation value division process, the density, density, and density are small except for the light and small dots with the lowest density among the four types of dots that can be created by the print head 42 from the given input gradation data Di. The level data D1 is acquired for the dot. FIG. 7 is a diagram showing the relationship between the level values of the large, dark, and light large dots and the printing rate corresponding to the gradation values of the input gradation data Di. Here, the recording rate is a value indicating the ratio of dots recorded on a white recording medium (for example, printing paper) by the dots ejected from the nozzles Nz with respect to the recording resolution of the printer. Therefore, when the entire surface of the recording medium is covered with dots, the recording rate is 100%. A value corresponding to the recording rate corresponding to the number of gradations from 0 to 255 is a level value. Data having this level value in a large number of pixels corresponding to the input gradation data Di is called level data Dl. Yes. Actually, this gradation value division processing is performed by reading and referring to a table showing the relationship of FIG. 7 stored in the ROM 24 or the hard disk 23. The table shown in FIG. 7 is a value obtained experimentally so that the input predetermined gradation value can be appropriately expressed by each dot in consideration of the ink density, the dot size, and the like. In the present embodiment, the level data obtained in this way is used to determine whether or not to generate dark, dark, and light large dots (ie, dot on / off).

  As described above, in the gradation value dividing process, the recording rate of the large, dark, and light large dots is obtained by referring to the table of FIG. 7 based on the input gradation value of the input gradation data Di. Yes. Here, in practice, a level value expressed in 256 gradations corresponding to the recording rate is acquired and stored in a predetermined buffer area of the RAM 25. For example, when the gradation value of a certain pixel in the input gradation data Di is “63”, the recording rate of the dark dot is 0%, and “0” is the level value corresponding to this in the RAM 25. Stored in the buffer area. When the gradation value of a pixel having the input gradation data Di is “191”, the recording rate of dark dots is 70%, and the corresponding level value “179” is stored (FIG. 7). reference). Similarly, level values for dark dots and light large dots for the gradation value “191” are stored.

Next, in step S30, the dot generation unit 34 centering on the CPU 22 performs dot generation processing.
FIG. 8 is a flowchart showing the flow of the dot generation process. The dot generation process will be described according to this flowchart. First, when the dot generation process is started, in step S100, the CPU 22 performs a process of selecting one pixel from the pixels constituting the level data Dl as a target pixel. The dot generation processing is performed for the level value of each pixel obtained by the gradation value division processing. Since the determination is performed for each pixel constituting the level value, the pixel to be processed (target pixel) is selected in this step. To do. Hereinafter, processing is performed for the target pixel.

  Next, in step S105, the CPU 22 acquires a level value corresponding to the target pixel. Here, the level value for each of the large, dark, and light large dots corresponding to the target pixel is obtained from the level value of the large, dark, and light large dots obtained by the gradation value dividing process and stored in the RAM 25. Is stored in the RAM 25.

  Next, in step S110, the CPU 22 determines whether to turn on the dark dots. Here, it is determined whether or not dark dots are turned on or off by determining whether or not the dark dot level value acquired in step S105 is greater than a predetermined threshold. In addition, as a threshold of this process, the determination by the systematic dither method is performed by using a dither matrix in which a spatial variation is given to the threshold.

  The process of step S110 using the dither matrix will be described below with reference to FIG. FIG. 9 shows an operation for determining whether or not a dark dot is on or off using a dither matrix with respect to the dark dot level data Dl. In the upper part of the figure, the input gradation data Di used in this description is shown on a 4 × 4 matrix. Assuming that the level data Dl shown on the left in the drawing is obtained by performing the above-described gradation value division processing on the input gradation data Di, the processing in step S110 will be described below. The level data D1 to be processed is shown on a 4 × 4 matrix, and a dither matrix composed of a 4 × 4 matrix is shown on the right side of the figure corresponding to the level data D1. As described above, it is assumed that the dot generation process is performed for each pixel, and the target pixel set in step S100 corresponds to one of the matrices. Here, for example, consider the pixel (n, m) at the upper left of the matrix as the target pixel. According to the middle left of FIG. 9, the level value of this pixel is “75”, and the value of the dither matrix is “1”. It is determined whether the level value is larger than the value of the dither matrix as a threshold value. When the value of the level data Dl is large, it is determined that the dark dot is turned on. When the level value is smaller than the threshold value, it is determined that the dot is turned off. Therefore, in the example of FIG. 9, the level value “75” of the pixel (n, m) is larger than the threshold value “1” of the dither matrix, so it is determined that the dark dot is on. In this way, for each pixel of interest, it is determined whether the dot is on or off, and all the pixels in the level data Dl are set as pixels of interest and determined, as shown in the lower part of FIG. , A matrix indicating dot on / off is obtained. In this way, it is possible to determine on / off of the large dots.

  Here, for convenience of explanation, the level data D1 and the dither matrix are a 4 × 4 matrix. However, in actuality, the level data D1 is calculated from the number of rows and the number of columns corresponding to the input gradation data Di. A 16 × 16 matrix is used as the dither matrix. Therefore, a dither matrix having a size smaller than that of the level data Dl is obtained by dividing the matrix of the level data Dl into 16 × 16 areas and corresponding the dither matrix to each area. To do. The size of the dither matrix is not limited to 16 × 16, and a 64 × 64 or 256 × 256 global matrix (such as a blue noise matrix) may be used. In step S110, the processing described above is performed for each pixel. When the dark dot of the target pixel is turned on (Yes), the process proceeds to step S115. When the dark dot of the target pixel is turned off (No), the process proceeds to step S120.

  In step S115, the CPU 22 obtains the result value RV corresponding to the dark dot turned on for the target pixel in step S110, and stores it in the RAM 25. Here, the result value RV is a value (density evaluation value) corresponding to the density of the target pixel. When the dot is on, that is, when a dot is formed on the target pixel, the result value RV is a value corresponding to the density of the pixel. That means. The density here means the actual density of the pixel when dots are formed on the recording medium, not the ink density. The value of the density evaluation value is an experimentally obtained value. For example, a density evaluation value of 255 is given to a large dot. Different density evaluation values are set for each of the dark, light, and light dots. When the process of step S115 is completed, the process proceeds to step S145.

  In step S120, the CPU 22 determines whether or not dark dots are turned on. Here, the dark dot level value corresponding to the pixel of interest is obtained from the dark dot level data Dl stored in the RAM 25, and it is determined whether or not the level value is greater than a predetermined threshold value. Perform dot on / off judgment. At this time, as a threshold for determining the dot, a dither matrix for dark dots (not shown) in which a threshold different from the dither matrix for dark dots is assigned to each threshold of the matrix is used. When the dark dot of the target pixel is turned on (Yes), the process proceeds to step S125. When the dark dot of the target pixel is turned off (No), the process proceeds to step S130.

  In step S125, the CPU 22 acquires a result value RV corresponding to the dark dot that was turned on for the target pixel in step S120, and stores the result value RV in the RAM 25. Next, the process proceeds to step S145.

  In step S130, the CPU 22 determines whether to turn on the light large dots. Here, the level value of the light large dot corresponding to the target pixel is acquired from the level data Dl of the light large dot stored in the RAM 25, and it is determined whether or not the level value is larger than a predetermined threshold value. Perform dot on / off determination. At this time, as a threshold value for determining the dot, a dither matrix for light large dots is used. When the light large dot of the target pixel is turned on (Yes), the process proceeds to step S135. When the light large dot of the target pixel is turned off (No), the process proceeds to step S140.

  In step S135, the CPU 22 acquires the result value RV corresponding to the light large dot turned on for the target pixel in step S130, and stores it in the RAM 25. Next, the process proceeds to step S145.

  In step S140, the CPU 22 determines in step S110, S120, and S130 that none of the large, dark, and light large dots are on, that is, no dots are formed on the recording medium. 0 is set as the value RV and stored in the RAM 25. Next, the process proceeds to step S145.

  In step S145, the CPU 22 calculates a difference between the gradation value of the target pixel of the input gradation data Di and the result value RV, and stores the difference gradation data Dd in the RAM 25. Here, in order to obtain the error that occurs when the formation of dark, dark, and light large dots having a predetermined density is performed either on or off, and to eliminate this error in the processing of light and small dots that will be described later The error is acquired as difference gradation data Dd. A process for obtaining the difference gradation data Dd will be described with reference to FIG. Here, with respect to the input gradation data Di shown on the upper right side of FIG. 10, the difference gradation data Dd is obtained when each dot is formed as shown on the left side of the upper stage of FIG. It is a figure explaining the process which calculates | requires. At this time, a density evaluation value is given as a result value RV corresponding to each of the large, dark, and light large dots. In the matrix indicating the result value RV in the middle of FIG. 10, for example, as the density evaluation value, a large dot is “255”, a dark dot is “160”, and a light small dot is “120”. Accordingly, a matrix of result values RV shown in the middle of FIG. 10 is obtained by replacing each dot with a density evaluation value corresponding to the generation state of these dots. Here, with regard to the formation of dots, discrete gradations are merely expressed by dark, dark, and light large dots. That is, the result value RV can take only four discrete values when corresponding to three dots and when no dot is generated. Therefore, there is a quantization error between the result value RV and the input gradation data Di, and this error is acquired as difference gradation data Dd (lower stage in FIG. 10). Then, the light small dot generation unit 36 performs a light small process, which will be described later, on the difference gradation data Dd, thereby eliminating this error. When the processing for obtaining the differential gradation data Dd is completed as described above, the process proceeds to step S150.

  In step S150, the CPU 22 determines whether or not dot generation processing has been performed for all the pixels of the input gradation data Di. If all pixels have already been processed (Yes), the dot generation process is terminated. If all the pixels have not been processed (No), the process returns to step S100 to set a new pixel as the target pixel.

  Next, returning to the process of FIG. 6, a negative value correction process is performed in step S40. FIG. 12 is a flowchart showing the flow of the negative value correction process. FIG. 11 is a diagram for explaining a process of performing a negative value correction process on the difference gradation data Dd obtained in the lower part of FIG. The negative value correction process will be described below according to this flowchart.

  When the negative value correction process is started, first, in step S200, the CPU 22 reads and acquires the differential gradation data Dd stored in the RAM 25 in step S145.

  Next, in step S210, the CPU 22 searches for a negative pixel from each pixel of the difference gradation data Dd, and determines whether there is a negative pixel. In the present embodiment, the search is performed from the upper left pixel of the differential gradation data Dd along the upper right line (main scanning), and when the search for one line is completed, the line located below by one pixel is then left. Search from to the right. In this way, the negative line of the differential gradation data Dd is searched by successively searching for the downward line (sub-scanning). For example, when the differential gradation data Dd in FIG. 11A is searched, first, the search is performed in order from the pixel (n, m) located at the upper left. When the search is performed, since the pixel (n + 1, m) has a negative gradation value, the negative value correcting means 35 selects the pixel (n + 1, m) and sets it as the target pixel (FIG. 11B). ) Shaded pixels). If there is a negative pixel, it is set as the pixel of interest, and the process proceeds to step S220. If there is no negative pixel for all the pixels of the differential gradation data Dd, the negative value correction process is terminated.

  In step S220, the CPU 22 searches for a pixel having a positive gradation value located closest to the target pixel selected in step S210. If there is a positive pixel after searching, it is selected as the target pixel. If there are a plurality of positive pixels equidistant from the target pixel, here, the plurality of pixels are simultaneously selected as the target pixel to perform the processing. Therefore, for the pixel (n + 1, m) in FIG. 11A, the pixel (n, m) and the pixel (n + 2, m) are selected as the pixels having the closest positive gradation value (FIG. 11). 11 (b) pixels in the area covered by the thick line).

  Next, in step S230, the CPU 22 determines whether or not there is a pixel having a positive gradation value in the search in step 220. If there is a positive pixel (Yes), the process proceeds to step S240. If there is no positive pixel (No), the process returns to step S210 to newly set a target pixel.

Next, in step S240, the CPU 22 corrects the gradation value indicating how much gradation value should be moved from the target pixel having the positive gradation value to the target pixel having the negative gradation value. The amount s and the gradation value movement amount t are calculated. Here, when the gradation value is moved, the target pixel having a positive gradation value by moving too much does not turn to a negative gradation value, and the negative gradation of the target pixel is reversed. The gradation value is moved so as to eliminate the value. That is, when the gradation value of the target pixel has a gradation value of −a (a is a positive integer) and the target pixel has a gradation value of b (b is a positive integer), the gradation value correction amount s and gradation The value movement amount t is given by the following equation.
s = a (1)
t = a (a ≦ b) (2)
t = b (a> b) (3)

If a plurality of target pixels are selected at the same distance from the target pixel in step S220, the gradation value is moved so that the plurality of pixels are selected in step S240 and an equal amount of gradation value is moved from each pixel. What is necessary is just to set quantity. For example, taking FIG. 11B as an example, for the pixel value “−30” of the target pixel (m, n), “15” from the target pixels (n, m) and (n + 2, m) respectively. Since the gradation value has only to be moved, the gradation value movement amount t is set to “15” for each pixel. At this time, the pixel values of the pixel (n + 1, m), the pixel (n, m), and the pixel (n + 2, m) after moving the gradation value are “0”, “35”, and “35”, respectively. The negative gradation value of the pixel is eliminated (FIG. 11 (c)). In addition, when the gradation value is moved from a plurality of target pixels, the target pixel is not changed to a negative gradation value. That is, assuming that the number of target pixels is k and their tone values are b (i) (i is an integer equal to or less than k indicating the target pixel), the tone value movement amount t is given by the following equation.
t = a / k (a / k ≦ Min (b (i))) (4)
t = Min (b (i)) (a / k> Min (b (i))) (5)

  Next, in step S250, the CPU 22 moves the gradation value corresponding to the gradation value movement amount t from the target pixel to the target pixel. That is, the gradation value of the target pixel is subtracted by the gradation value movement amount t, and the gradation value of the target pixel is added by the gradation value movement amount t. This is performed for each target pixel. Actually, at this time, in the differential gradation data Dd stored in the buffer area of the RAM 25, the value after the gradation value movement is stored as a value corresponding to the target pixel and the target pixel. If there are a plurality of target pixels, the process of moving the gradation value is performed in the same manner for each target pixel.

  Next, in step S260, the gradation value movement amount t is subtracted from the gradation value correction amount s by the number k of the target pixels, and the gradation value of the target pixel after the gradation value movement is calculated.

  Next, in step S270, the CPU 22 determines whether or not the gradation value correction amount s obtained in step S260 is zero. When the gradation value correction amount s is not 0 (No), it is necessary to move the gradation value further, so that the process returns to step S220 to search for another target pixel having a positive gradation value. When the gradation value correction amount s becomes 0 (Yes), the gradation value of the target pixel is corrected to a negative value and is “0”, so the process returns to step S210, and a new target pixel is selected. Set.

  As described above, a pixel having a negative gradation value is selected as a target pixel, and the process of moving the positive gradation value of the nearest pixel is repeatedly performed so as to eliminate the negative gradation value. Pixels having gradation values are eliminated. For example, in FIG. 11, as shown in FIGS. 11B to 11E, the target pixel is a pixel (n + 1, m), a pixel (n + 1, m + 1), a pixel (n + 3, m + 2), and a negative floor. Pixels having tone values are successively selected as the target pixel, and the above-described operation is repeated to eliminate the negative tone value of the target pixel. Therefore, when this process is finished, finally, as shown in FIG. 11F, the negative gradation value is corrected, and differential gradation data Dd having a gradation value of 0 or more is obtained.

  Here, depending on the magnitude relationship between the value of the input tone data Di and the threshold value of each of the dither matrixes of dark, dark, and light large dots, the overall negative tone value is larger than the absolute value of the positive tone value. There is a possibility that differential gradation data Dd having a large absolute value may be obtained. In such a case, if a negative value correction process is performed, there is a possibility that a pixel having a positive gradation value disappears in the middle of the process and a pixel value of a pixel having a negative gradation value cannot be eliminated. is there. In such a case, the process is terminated while the differential gradation data Dd has no positive gradation value and a negative gradation value remains. The fact that the difference gradation data Dd is negatively biased as a whole indicates that the gradation values resulting from the generation of dark, dark, and light large dots are already larger than the gradation values of the input gradation data Di. This means that it is not necessary to newly generate a light small dot by a light small dot generation process described later. Therefore, it is preferable to prevent generation of small light dots by leaving only negative gradation values in the difference gradation data Dd.

Next, returning to the process of FIG. 6, a light small dot generation process is performed in step S50.
In the light small dot generation process, the light small dot generation unit 36 performs a process of generating a light small dot using the CC method based on the difference gradation data Dd having the positive gradation value obtained in step S40. . FIG. 14 is a flowchart showing the flow of processing in the light small dot generation processing. FIG. 13 is a diagram showing an example of processing for performing light small dot generation processing. Hereinafter, according to this flowchart, the light small dot generation process will be described with the example of FIG.

  In the light small dot generation process, a buffer area is provided on the storage area on the RAM 25 to store whether or not the process has been performed for every pixel of the differential gradation data Dd. For example, a bit indicating “0” is assigned to a buffer area corresponding to a pixel that has not been subjected to light small dot generation processing (unprocessed pixel), and a pixel that has been subjected to light small dot generation processing (processed pixel) A bit indicating “1” is assigned to the buffer area corresponding to. In this way, the determination as to which pixel has been subjected to the light processing has been made each time by referring to the buffer area of the RAM 25, and no further processing is performed on the processed pixel. .

  First, when the light small dot generation process is started, in step S300, the CPU 22 reads the differential gradation data Dd after the negative value correction process from the buffer area of the RAM 25. Here, FIG. 13A shows differential gradation data Dd obtained by performing a negative value correction process on the differential gradation data Dd obtained in the lower part of FIG. Hereinafter, a case where a light small dot generation process is performed on the difference gradation data Dd will be described.

  Next, in step S310, the CPU 22 searches for a pixel to be the first unprocessed pixel in cell generation and determines it as an initial pixel. At this time, in the dot generation process, similarly to the search order, after searching in the main scanning direction (X direction) from the upper left of the differential gradation data Dd, the search in the main scanning direction is performed again by shifting one line in the sub scanning direction. Do this and repeat the search. Actually, referring to the buffer area of the RAM 25, a pixel in which “0” indicating an unprocessed pixel is stored among the pixels of the difference gradation data Dd is searched. At this time, since the pixel whose gradation value is “0” does not contribute to the calculation of the center of gravity, it is assumed that the pixel has been processed without performing the processing described below, and the processed pixel is stored in the buffer area. A bit of “1” indicating that is stored. Therefore, in the example of FIG. 13, since the pixel (n, m) has the gradation value “0” in FIG. 13A, the pixel (n + 1, m) to be searched next is set as the initial pixel. (The shaded pixel in FIG. 13B). When there is an unprocessed pixel (Yes), this unprocessed pixel is set as an initial pixel, and the gradation value and “1” indicating the processed pixel are stored in the buffer area of the RAM 25, and step S320 is performed. Proceed to If there is no unprocessed pixel (No), the process proceeds to step S390.

  Next, in step S320, the CPU 22 determines whether or not the sum of the gradation values of the pixels in the cell CL is smaller than the threshold value. However, when the process proceeds from step S310, only the initial pixel is determined as a pixel in the cell CL. Specifically, the threshold value for the light small dot stored in the ROM 24 is read out and compared with the gradation value of the pixel in the cell CL. In the example of FIG. 13, the gradation value “75”, which is the density evaluation value of the light small dots, is used as the threshold for generating the light small dots. In step S320, when the sum of the gradation values of the pixels in the cell CL is smaller than the threshold value (No), the process proceeds to step S330, and when it is equal to or larger than the threshold value (Yes), the process proceeds to step S340.

  In step S330, if it is determined in step S320 that the threshold value is smaller than the threshold value (YES), the CPU 22 performs processing for enlarging the cell CL by incorporating a new unprocessed pixel into the cell CL. Here, in selecting the unprocessed pixel in the present embodiment, the pixel nearest to the center of gravity GC is selected using the center of gravity GC of the cell CL. By selecting a pixel using the center of gravity GC, the shape of the finally generated cell CL becomes a circle, and by forming dots on the center of gravity GC, the inter-dot distance becomes an appropriate value, and visual Comfortable print output can be obtained.

The CPU 22 reads the gradation value and coordinates of the pixel where the initial pixel is located from the buffer area of the RAM 25, and calculates the center of gravity GC of the pixel in the cell when a new unprocessed pixel is incorporated. Specifically, the CPU 22 obtains the center of gravity GC of the cell CL by the following arithmetic expression. Here, x _GC and y _GC are the x and y coordinates of the center of gravity GC, respectively.

x _GC = {{(x coordinate of the center of gravity GC of the cell CL) × (total gradation value of pixels in the cell)}
+ {(X coordinate of selected unprocessed pixel) × (tone value of selected unprocessed pixel)}}
/ {(Total gradation value of pixels in cell) + (gradation value of selected unprocessed pixel)} (6)
y _GC = {{(y coordinate of center of gravity GC of cell CL) × (total gradation value of pixels in cell)}
+ {(Y coordinate of selected unprocessed pixel) × (tone value of selected unprocessed pixel)}}
/ {(Total gradation value of pixels in cell) + (gradation value of selected unprocessed pixel)} (7)
This arithmetic expression is stored in the ROM 24, and in step S330, the CPU 22 reads and performs the arithmetic operation. However, in the case of the initial pixel, the xy coordinates of the center of gravity GC of the cell CL are both calculated as 0.

  Further, the center of gravity GC of the cell CL and the sum of the gradation values of the pixels in the cell CL obtained by the equations (6) and (7) are calculated each time a new unprocessed pixel is incorporated, and the latest value is stored in the RAM 25. . By doing so, it is not necessary to calculate the center of gravity GC and the total gradation value in the equations (6) and (7) when a new unprocessed pixel is incorporated, and the processing is speeded up because it is obtained directly from the RAM 25. be able to. When the process of enlarging the cell is completed, “1” indicating that it is a processed pixel is stored in the buffer area of the RAM 25 corresponding to the pixel in the cell CL.

  As described above, the process of selecting an unprocessed pixel and newly incorporating it into the cell CL (step S330) is performed by selecting a pixel closest to the center of gravity GC of the pixel in the cell CL. . However, there may be a plurality of pixels equidistant from the center of gravity GC. In such a case, it is necessary to select one of the pixels, but in this embodiment, the CPU 22 selects the pixel in accordance with the scanning direction of the initial pixel and enlarges the cell CL.

  Next, when the process reaches step S320 via step S330, the CPU 22 determines again whether or not the sum of the gradation values of the pixels in the cell CL exceeds the threshold value. That is, it is determined whether or not the total gradation value of the pixels in the cell CL including the gradation value of the newly selected unprocessed pixel is less than a predetermined threshold value. When the threshold value is not exceeded, the process proceeds again to step S330, and a process of incorporating a new unprocessed pixel into the cell CL is performed. If it exceeds the threshold, the process proceeds to step S340.

  In this way, each time the processes in steps S320 and S330 are repeated, the CPU 22 determines whether the sum of the gradation values of the pixels in the cell CL calculated in step S330 reaches the threshold while determining whether or not it is equal to the threshold. The cell CL will be expanded.

  In step S340, the CPU 22 determines whether or not the sum of the gradation values of the pixels in the cell is equal to the threshold value. As described above, the sum of the gradation values is acquired by referring to the values stored in the RAM 25. If the sum is equal to the threshold (Yes), the process proceeds to step S350. When the total of the gradation values is not equal to the threshold value (No), the process proceeds to step S360.

  In step S350, the position of the center of gravity GC is calculated using Expression (6) and Expression (7). That is, the CPU 22 calculates the center of gravity GC for the pixels in the cell CL including the unprocessed pixels selected again. The specific calculation of the CPU 22 is performed by reading out the arithmetic expressions (6) and (7) from the ROM 24 described above. The position of the center of gravity GC calculated in step S350 is stored together with the sum of the gradation values of the cells CL stored in the buffer area of the RAM 25 under the control of the CPU 22. Then, a process for forming a dot of a predetermined size on a pixel located at the center of gravity GC is performed. At this time, the dot to be generated is assigned a value equivalent to the threshold used in steps S320 and S330. For example, in the example of FIG. 13, a gradation value of “75” (density evaluation value of light small dots) is given. As the threshold value, a value “75” equivalent to the threshold value is given. When the process of step S350 is completed, the process proceeds to step S380.

  In step S360, a process of calculating the center of gravity GC of the cell CL is performed ignoring the gradation value exceeding the threshold value. Therefore, first, the CPU 22 performs an operation of subtracting a threshold value from the total gradation value in the cell CL obtained so far in step S320. This calculated value is called a “return value”.

  Specifically, the calculation of the center of gravity GC at this time is performed by the following arithmetic expression.

x _GC = {{(x coordinate of the center of gravity GC of the cell CL) × (total gradation value of pixels in the cell)}
-{(X coordinate of the last selected unprocessed pixel) × (calculated value of step S360)}}
/ {(Total gradation value of pixels in cell) − (calculated value of step S360)} (8)
y _GC = {{(y coordinate of center of gravity GC of cell CL) × (total gradation value of pixels in cell)}
-{(Y coordinate of the last selected unprocessed pixel) × (calculated value of step S360)}}
/ {(Total gradation value of pixels in cell) − (calculated value of step S360)} (9)
Unlike the equations (6) and (7), the gradation value in the cell CL is not obtained by using the gradation value of the pixel in the cell as it is, but by subtracting the return value that is an excess of the gradation value. So that the total is equal to the threshold.

  This arithmetic expression is stored in advance in the ROM 24 in the same manner as in the expressions (6) and (7), and is executed by the CPU 22 reading it from the ROM 24 when executing this step. Here, the gradation value of the pixel finally selected in step S330 is calculated to be equal to the threshold by taking the sum of the gradation values calculated so far, and the center of gravity is calculated based on the calculation result. The position will be determined. Then, the CPU 22 writes the calculated position data of the center of gravity GC in the buffer area of the RAM 25 again.

  Next, in step S370, the CPU 22 writes the return value calculated in step S360 again into the buffer area indicating the gradation value of the pixel selected last in the cell CL. Thereby, the return value is stored in the gradation value of the pixel. In the example shown in FIG. 13B, the threshold value is “75”, and the gradation value stored so far in step S320 is “51 + 62 = 113” when viewed from the coordinates (n + 1, m). However, the threshold value is subtracted from this, and the value “113−75 = 38” becomes the return value. The gradation value corresponding to this value is returned to the pixel (n + 1, m + 1) at the lower coordinate without contributing to dot generation with the coordinate (n + 1, m) as the initial pixel. At this time, the pixel (n + 1, m + 1) to which the gradation value is returned is given “0” in the buffer area of the RAM 25 and is treated as an unprocessed pixel having the gradation of the return value. That is, since the pixel whose gradation value has been returned has an effective gradation value, it is again subjected to the processing in steps S310 to S330. In this way, the return value is stored again in the original pixel that generated the return value, so that a dot distribution faithful to the gradation value of the input gradation data Di can be formed.

  In step S380, the CPU 22 determines the dot output position of light small dots. Since the position of the light small dot determined by repeating the above-mentioned process is the barycenter GC of the pixel in the cell CL, the coordinate has a value having a fraction. However, the pixel referred to in the above processing indicates a minimum unit in which the print engine 40 generates a dot, and therefore the coordinates of the dot cannot be controlled in a unit smaller than the minimum unit. Therefore, in order to determine the coordinates for actually generating the light small dots, the coordinates having the fractions are converted to the nearest integer value, and the dot output position of the light small dots is determined. For example, in FIG. 13 (e), the coordinates of the light small dots are shifted from the center of the pixel, whereas the light small dots actually generated are as shown in FIG. 13 (f). Convert to center. Specifically, the CPU 22 performs this operation by storing “1” indicating that the light small dot is on in the buffer area of the RAM 25 corresponding to the pixel where the barycentric position exists. As a result, the CPU 22 refers to the buffer area of the RAM 25, acquires on / off information of light small dots for each pixel, and controls the operation of the print engine 40, so that the dots on the light small dots are turned on. Can be depressed. In step S380, the CPU 22 refers to the generation state of the dark, dark, and light large dots stored in the RAM 25, and when the light and small dots overlap, of the pixels around the pixel to be originally generated, the darkness is dark. Light small dots are generated for pixels for which large, dark, light, and large dots are not generated.

  When the dot generation in step S380 is completed, the process returns to step S310 to search for an unprocessed pixel again. In step S310, there is no unprocessed pixel in the difference gradation data Dd, and the process is completed for all pixels. If yes (No), the process proceeds to step S390.

  In step S390, the CPU 22 combines the light small dots generated in the light small dot generation processing and the data of the large / dark / light large dots generated by the dot generation processing to quantize the data representing dot on / off. Output to the print engine 40 as Do. When step 390 is completed, the light small dot generation process ends.

  Next, with reference to FIG. 15, a printed matter finally obtained when printing is performed by superimposing dark / dark / light large dots and light small dots will be described. In FIG. 15, the distribution of large / dark / light large dots generated by the dot generation process is shown on the upper left side. On the upper right side, the distribution of light small dots generated by the light small dot generation process is shown. Note that the difference gradation data Dd obtained in FIG. 15 is obtained by performing a negative value correction process and a light small dot generation process. When such a dot array is generated, a dot array as shown in the middle of FIG. 15 is obtained by superimposing these two dot arrays. As described above, it is possible to obtain a dot arrangement in which gradation is reproduced by appropriately arranging four types of dots for the input gradation data Di (see FIG. 9). Therefore, the printed matter obtained by printing the output data having such a dot arrangement by the print engine 40 has excellent gradation reproducibility.

  Note that the light large dots and the light small dots are overlapped at the coordinates (n + 2, m + 1) in the middle of FIG. In such a case, since the dots overlap, it is considered that the density that is estimated to be exhibited by each dot is not exhibited. That is, the value of the density evaluation value set for each dot indicates the density when the dots are generated independently. Therefore, when such dots overlap, the density does not become as estimated. For example, when a light small dot is generated by being superimposed on a dark dot, the light small dot is swallowed by the dark dot density and hardly contributes to the reproduced density. Therefore, in such a case, in step S380 of the light small dot generation process, the CPU 22 refers to the generation state of the large / small / light / large dots stored in the RAM 25 so that the dots do not overlap each other. In this way, small light dots are generated (see the lower part of FIG. 15). By doing this, the total amount of density with respect to the input gradation data Di is maintained, so that the original data can be faithfully reproduced.

  As described above, according to the present embodiment, the dot generation processing by the systematic dither method is performed on the input gradation data Di, and the gradation data of each color component is given a variation, thereby increasing the density, Light dots are generated. Then, light small dot generation processing by the CC method is performed on the difference gradation data Dd that is an error between the gradation value composed of dark, dark, and light large dots and the input gradation data Di. Determines whether small dots are on or off. As described above, by performing the light small dot generation process, the error of the dot generation process is eliminated, the gradation value of the input gradation data Di is faithfully reproduced, and the gradation data in which no unique pattern is generated. Can be obtained.

  In addition, in the light small dot generation process, if the cell CL includes a pixel having a negative gradation value, the center of gravity GC may be located outside the cell due to the influence of the pixel having the negative gradation value. Get higher. In such a case, dots are formed inside other cells, which increases the possibility of overlapping with dots generated by other cells, resulting in biased output dots, resulting in unique patterns. Will be generated. For example, the difference grayscale data after the dot generation processing includes pixels (n, m) having a grayscale value of “−75” as initial pixels and pixels (n + 1, m) having a grayscale value of “150”. , The center of gravity GC is located at the center of the pixel (n + 2, m), and a gradation value is given outside the cell CL. In addition, since the negative gradation value cancels out the total gradation value, a cell having a gradation value equal to or greater than the threshold value may be unduly large. According to the present embodiment, since the light small dot generation process is performed using the difference gradation data Dd subjected to the negative value correction process, the cell CL including the negative gradation value can be generated. Decrease. That is, the center of gravity GC is located inside the cell CL, and the possibility that a small small dot is formed inside the cell CL increases. Therefore, it is possible to obtain a comfortable output image with improved dot distance reproducibility and dot dispersibility.

According to the embodiment described above, the following effects can be obtained.
(1) According to the light small dot generation process, the light small dot is generated so as to correct the quantization error (difference gradation data Dd) of the dark, dark, and light large dots that may occur in the dot generation process. Therefore, an output image faithful to the gradation of the input image can be obtained.
(2) As the light small dot generation process, there is no bias in the light small dots generated by using the CC method for generating dots at the center of gravity GC of the pixels in the cell. Therefore, it is possible to obtain a visually comfortable output image in which generation of a periodic or regular pattern that does not exist in the input image or a peculiar pattern due to the deviation of dots is suppressed.
(3) By performing a negative value correction process, the difference data used for the light small dot generation process is corrected so that there is no negative value. In this way, when a dot is generated at the barycentric position GC of the pixel in the cell in the light small dot generation process, the barycentric position may be outside the cell CL due to the influence of the pixel having a negative gradation value. Is reduced. Therefore, the possibility that a small dot is generated at an appropriate center of gravity GC inside the cell CL is increased, and a visually comfortable output image with good reproducibility of the distance between the light and small dots and good dot dispersion is obtained. Can do.
(4) By using the image output device including the image processing unit 30, it is possible to easily generate dots with excellent image quality by appropriately generating dots in response to external RGB data input such as the host computer 10. be able to.

  Although one embodiment of the present invention has been described above, the present invention is not limited to such an embodiment, and can be implemented in various forms without departing from the spirit of the present invention. Hereinafter, a modification will be described.

(Modification 1) Next, a first modification of the present invention will be described. In the above-described embodiment, the reference point for selecting the pixel constituting the cell CL is used as the center of gravity position, and is updated every time the pixel is selected. However, in the first modification, the reference point for pixel selection is Regardless of the gradation value, the center position using the coordinate position of the pixel is used. This makes it possible to increase the overall processing speed including other processes without calculating the center of gravity position every time.

  A first modification will be described with reference to FIG. Here, when the light small dot generation process is performed, when a new unsearched pixel is incorporated into the cell CL, the center position (first reference point) using the coordinate position, not the center position using the gradation value. A pixel close to is selected.

Here, assuming that the position coordinates of a pixel are represented as (x, y), the coordinates of the center position can be obtained by the following expression (where N is the number of pixels in the cell and i is the pixel in the cell). Number).
_{x = Σ i x i / N} ... (10)
y = Σ _i y _i / N (11)

  Hereinafter, a case where the differential gradation data Dd has the gradation values shown in FIG. FIG. 16A shows an example of the differential gradation data Dd when image data for 3 pixels in the vertical direction and 4 pixels in the horizontal direction is input for the sake of simplicity. The image data for one frame of the size is input. At this time, the threshold for generating dots is set to “255”.

  It is assumed that the light small dot generation process is performed on the difference gradation data Dd in FIG. In the processing, first, the CPU 22 searches for and selects an initial pixel. As in the above-described embodiment, the uppermost leftmost pixel among unprocessed pixels is searched and selected as a predetermined scanning method. This is also because the probability that the cell CL to be finally generated is arranged obliquely in one image is increased, and dots are hit in an oblique direction and are hardly perceived. In the example of FIG. 16A, the pixel at the position (n, m) is selected as the initial pixel.

  Then, when the initial pixel is incorporated in the cell CL and the center position is calculated using the equations (10) and (11), the center position SC is located at the position shown in FIG. Next, an unprocessed pixel located closest to the center position SC is selected. When there are a plurality of such pixels, they are selected by a predetermined scanning method as in the above-described embodiment. In the example shown in FIG. 16B, there are two positions (n + 1, m) and (n, m + 1) that are closest to the center position SC. Here, (n + 1, m) is selected.

  Next, as shown in FIG. 16C, the selected unprocessed pixel MG is incorporated into the cell CL, and a reference point (first reference point) for selecting the unprocessed pixel when performing the enlargement process of the cell CL. That is, when the center position is calculated, the SC position is obtained as shown in FIG. Since the total gradation value of the cell CL is “100” at this time, an unprocessed pixel is further selected. In this modification, when the threshold is “255”, unprocessed pixels are selected until this value is reached.

  The pixel closest to the reference center position SC is selected, but in the example shown in FIG. 16C, there are two pixels, pixel (n, m + 1) and pixel (n + 1, m + 1). become. In this case, the selection is made according to the scanning method, and the (n, m + 1) pixel is selected as an unprocessed pixel.

  Then, as shown in FIG. 16D, when the selected unprocessed pixel is incorporated into the cell CL and its center position is calculated using the equations (10) and (11), it is located at the position SC. At this time, the total gradation value of the cell CL is “165”, and since the threshold value is not reached, an unprocessed pixel is further selected. Since the pixel closest to the center position SC is the pixel located at (n + 1, m + 1), this pixel is selected as an unprocessed pixel.

  Next, when the selected unprocessed pixel MG is incorporated into the cell CL and the center position SC is calculated using the equations (10) and (11), the position shown in FIG. At this time, the sum of the gradation values of the cell CL is “215”, which does not reach the threshold value. Therefore, an unprocessed pixel is selected. There are four unprocessed pixels closest to the center position SC: pixel (n, m + 2), pixel (n + 1, m + 2), pixel (n + 2, m), and pixel (n + 2, m + 1). The pixel located at (n + 2, m) is selected according to the scanning method.

  Then, the selected unprocessed pixel is incorporated into the cell CL. However, the total gradation value of the cell CL generated when the unprocessed pixel is incorporated is “265”, which exceeds the threshold value. In this case as well, as in the above embodiment, the gradation value corresponding to the threshold value is incorporated into the cell CL, the remaining level value (error data) is returned to the unprocessed pixel, and the pixel is subsequently set as an unprocessed pixel. To be selected. This is to generate dots that are faithful to the gradation data of the input image, as in the above embodiment. Here, “10” is given as a return value to the pixel located at (n + 2, m) (see FIG. 16F).

  Next, since the total gradation value of the cell CL has reached the threshold value, the cell CL configured so far performs a process for generating a dot at the center of gravity GC position of the cell CL. The calculation of the center of gravity GC position is performed by calculating in the same manner as Expressions (4) and (5) of the above embodiment.

  As in the previous embodiment, when the center of gravity GC position is calculated every time, the processing calculations of Expression (4) and Expression (5) increase, but the calculation is performed with the reference point for pixel selection as the center position, and finally the lightness is small. By calculating the center of gravity GC when generating dots, the amount of calculation can be reduced, and the cell CL can grow in a circle to maintain a dot-to-dot distance and obtain a visually comfortable dot output.

(Modification 2) As a second modification, the cell CL used for selecting the unprocessed pixel in the first modification is not calculated when the light dot is generated without calculating the center of gravity position of the cell CL. Alternatively, a dot may be generated at the pixel position (second reference point) at the center position (first reference point). In this way, it is not necessary to calculate the center of gravity GC when generating light small dots, so that the amount of calculation can be reduced. In this case, the central position may be located between a plurality of pixels. In such a case, for example, any one pixel in the cell CL may be selected according to the same priority order as the scanning method. . Or you may make it select at random or select any one pixel from several pixels using a predetermined | prescribed table.

(Modification 3) Further, as a third modification, the reference point for pixel selection (first reference point) is not updated every time, but is updated every second time or every second time. Alternatively, the number of times may be determined in advance and updated when the number is reached. An example of updating every other time will be described with reference to FIG.

  As in the case of FIG. 16, the case where the differential gradation data Dd is given as shown in FIG. 17A will be described. Similarly to FIG. 16B, when the initial pixel is selected and its center position SC is calculated, the position shown in FIG. 17B is obtained. When a pixel closest to the unprocessed pixel MG from the reference point (center position SC) is selected, the pixel is located at (n + 1, m).

  Next, the selected unprocessed pixel is incorporated into the cell CL, but the next unprocessed pixel to be selected is not selected based on the center position SC calculated from the cell CL, but is selected based on the center position SC before incorporation. To do. Do not update the reference point at this point. As shown in FIG. 17C, the pixel closest to the center position SC is (n, m + 1), and this pixel is selected as the unprocessed pixel MG.

  Next, the cell CL incorporating the selected unprocessed pixel MG is as shown in FIG. Since the reference point is updated every other time, if the center position SC of the cell CL configured at this time is calculated, the center position SC moves to the position shown in FIG.

  Next, with reference to the updated center position, the unprocessed pixel MG closest to this position is selected and incorporated into the cell CL as shown in FIG. Then, without updating the reference point, the pixel closest to the previously calculated center position SC is selected as the unprocessed pixel MG (the pixel at the position (n + 2, m) is selected), and the cell CL is configured. .

  As in the example of FIG. 16, since the total gradation value of the pixels in the cell CL incorporating the selected pixel exceeds the threshold value, the excess is returned to the selected pixel as a return value. Then, when the center of gravity GC position for generating light small dots is calculated, the position GC shown in FIG. 17F is obtained, and dots are generated at this position. When the reference point is updated, the reference point SC moves to the position shown in FIG.

  In this way, instead of updating the reference point (first reference point) for pixel selection every time, the amount of calculation can be further reduced by updating every other time, and an image processing apparatus including other processing The overall processing speed can be improved. Furthermore, the reference point may be updated when the unprocessed pixel is selected for the fourth time from the selection of the initial pixel, and then subtracted from the third and second times, and updated when the number of times is reached.

(Modification 4) In the above embodiment, in the dot generation process, the quantized data Do is generated by determining dots using the systematic dither method. As a fourth modification, an error diffusion method or an average error minimum method may be used as the dot generation processing instead of the systematic dither method. As in the case of using the systematic dither method, it is possible to obtain the effect of giving variation to the input gradation data.

(Modification 5) In the above embodiment, when the negative value correction process is performed, the difference gradation data Dd has no positive gradation value and only the negative gradation value remains during the process. Will not perform the negative value correction process thereafter. However, as a fifth modification, in such a case, the threshold value of the dither matrix may be changed so that the positive gradation value of the difference gradation data Dd remains. In this way, for example, the number of dark dots generated can be suppressed by slightly increasing the threshold values of the dark dot dither matrix. The gradation value corresponding to the generation of dark dots is assigned to the generation of dark dots and light large dots with lower density. By doing so, many positive tone values remain in the difference tone data Dd, and by generating light small dots, the difference tone data Dd, which is an error due to dot generation, is corrected reliably and appropriately. can do. Therefore, an output image that faithfully reproduces the original input gradation data Di can be obtained.

(Modification 6) In the above embodiment, when there are a plurality of pixels closest to the center of gravity GC when performing the light small dot generation process, the selection is made according to the search order. However, the pixel selection method is not limited to this, and any one of a plurality of pixels may be selected at random, or the new center of gravity GC position when an unprocessed pixel is selected becomes the position of the initial pixel. The pixels may be selected so that they are close to each other. In this way, since the shape of the cell CL approaches a circular shape, the generated light small dots are not biased and an output image without a specific pattern can be obtained.

(Modification 7) In the above-described embodiment, dot generation by the systematic dither method is performed for dark, dark, and light large dots, and the error (difference gradation data Dd) is lightly corrected by the CC method so as to be corrected. Although the small dot quantization data Do has been created, the dots to be processed are not limited to this pattern. For example, dots are generated by a systematic dither method for dark, dark, light, and light dots, and light and light dots are further generated by the CC method so as to correct the error (difference gradation data Dd). You may do it. First, the dark / dark dots are determined by the systematic dither method, and the error is roughly corrected by generating light large dots by the CC method, and the remaining errors are further corrected by light dots. May be processed.

(Modification 8) In the above-described embodiment, gradation conversion is performed into two kinds of gradation values “0” and “255” by halftone processing. This is because it is assumed that dots having a size corresponding to the gradation value “255” are hit in the subsequent dot forming process. As an eighth modification, dots of a plurality of types of sizes may be hit, and tone conversion may be performed to a plurality of types of tone values corresponding to the plurality of types of sizes of dots. Similarly, with respect to the ink density, dots of a plurality of types of density may be applied, and gradation conversion may be performed to a plurality of types of gradation values.

(Modification 9) In the above embodiment, the image output apparatus 20 including the image processing unit 30 and the print engine 40 is used. As a ninth modification, a printing apparatus including an image processing unit that performs color correction / color conversion processing, gradation value division processing, dot generation processing, negative value correction processing, and light small dot generation processing may be used. In this way, an output image can be easily obtained by directly processing the input from the host computer 10 by the printing apparatus.

(Modification 10) As a tenth modification, the image processing apparatus in the above embodiment is configured by an ink jet printer. However, the present invention is not limited to this. The present invention can be applied to a device that can distinguish light and shade. Therefore, for example, various printers such as laser printers and thermal printers, electrophotography, and the like may be used. Alternatively, the image processing apparatus of the present invention may be configured in various apparatuses such as a copier, a computer, a word processor, and a fax machine in which such a function as a printer is incorporated.

1 is a configuration diagram showing an entire system according to an embodiment of the present invention. FIG. 3 is a schematic diagram illustrating a nozzle surface of a print head. FIG. 2 is a schematic diagram illustrating a schematic configuration inside a print head. (A) The figure which shows the inside of a printing head. (B) The figure which shows the inside of a printing head when hitting a dot. The schematic diagram shown about the structure of the image output device. FIG. 6 is a diagram for explaining gradation conversion by the image output apparatus. The flowchart which showed the flow of the whole process which an image output device performs. The figure which showed the relationship between an input gradation value, a level value, and a recording rate. The flowchart which showed the flow of the process in a dot production | generation process. The figure which showed the process which determines ON / OFF of a large dot. The figure which showed the process which acquires difference gradation data. The figure explaining the correction process of a negative value. The figure which shows progress of the process of the correction process of (a)-(f) negative value. The flowchart which showed the flow of the process in the correction process of a negative value. The figure which showed the process example which performed the light small dot production | generation process. The figure which shows progress of the process of (a)-(f) light small dot production | generation processing. The flowchart which showed the flow of the process in a light small dot production | generation process. The figure explaining the printed matter finally obtained. The figure explaining a 1st modification. (A)-(f) The figure which shows progress of the process in a 1st modification. The figure explaining the 3rd modification. (A)-(f) The figure which shows progress of the process in a 3rd modification.

Explanation of symbols

DESCRIPTION OF SYMBOLS 10 ... Host computer, 11 ... Application part, 12 ... Rasterization part, 20 ... Image output apparatus, 21 ... Input I / F, 22 ... CPU, 23 ... Hard disk, 24 ... ROM, 25 ... RAM, 30 ... As image processing apparatus Image processing unit, 31... Color correction / color conversion unit, 32... Color correction table, 33... Gradation value dividing unit, 34... Dot generation unit as first gradation conversion unit, 35. Negative value correction unit as 36, light small dot generation unit as second gradation conversion means, 40 ... print engine, 41 ... head drive unit, 42 ... print head, Nz ... nozzle, PE ... piezo element, Ti ... ink chamber, Di ... input gradation data as input data, Dd ... differential gradation data as error data, Do ... quantization data, RV ... result value, CL ... cell as pixel group, GC ... centroid SC ... center position, s ... tone value correction amount, t ... gradation value movement amount, x-coordinate of x _GC ... centroid, y coordinates of y _GC ... centroid.

Claims (14)

An image processing apparatus that performs gradation conversion on input data having any one of P (P is an integer of 2 or more) kinds of gradation values for each pixel,
First gradation conversion is performed on the gradation value of the input data, which is converted into gradation data composed of Q (Q <P, Q is an integer of 2 or more) kinds of gradation values for each pixel, First gradation converting means for acquiring gradation data;
Error data consisting of M (M is an integer of 2 or more) types of gradations, which is an error between the gradation data acquired by the first gradation conversion means and the input data for each pixel. Then, new pixels are selected one after another until the sum of the gradation values becomes equal to or greater than the threshold value, and a pixel group is generated. N (N is an integer of 2 or more) types of predetermined pixels in the pixel group An image processing apparatus comprising: a second gradation conversion unit that assigns gradation values. The image processing apparatus according to claim 1.
A gradation value dividing means for dividing the gradation value of the input data into a plurality of gradation values and obtaining a plurality of gradation data having the respective gradation values;
The first gradation converting means performs first gradation conversion on the plurality of divided gradation data,
The image processing apparatus according to claim 2, wherein the second gradation conversion unit performs second gradation conversion on error data that is an error between the plurality of gradation data and the input data. The image processing apparatus according to claim 1 or 2,
The second gradation converting unit generates the pixel group by newly selecting a pixel closest to the centroid position of the generated pixel group, and determines the pixel determined based on the centroid position of the pixel group. An image processing apparatus characterized by providing N kinds of gradation values. The image processing apparatus according to claim 3.
The sign of the gradation value possessed by the error data is judged for each pixel, and the negative gradation of the pixel is moved by moving the positive gradation value from the pixel having the sign positive to the pixel having the sign negative. A gradation value moving means for acquiring error data that cancels the tone value;
The image processing apparatus according to claim 2, wherein the second gradation conversion unit performs second gradation conversion on error data in which the negative gradation value is canceled. The image processing apparatus according to claim 1 or 2,
The second gradation converting means sets a first reference point for pixel selection;
Based on the first reference point, a pixel group is generated by selecting pixels having the M kinds of gradation values until the sum of gradation values for each pixel is equal to or greater than a threshold value,
Determining a second reference point based on the generated pixel group;
An image processing apparatus, wherein the N kinds of gradation values are assigned to a pixel determined based on the second reference point. The image processing apparatus according to claim 5.
The image processing apparatus according to claim 2, wherein the second gradation conversion unit updates the first reference point every time the pixel is selected a predetermined number of times. The image processing apparatus according to claim 5 or 6,
The first reference point is a center position calculated from the position of the selected pixel, and the second reference point is a barycentric position calculated from the pixel position and the gradation value of the pixel group. An image processing apparatus. The image processing apparatus according to claim 5 or 6,
The first reference point is a center position calculated from the position of the selected pixel, and the second reference point is a center position calculated from the pixel position of the pixel group. Processing equipment. The image processing apparatus according to any one of claims 1 to 8,
The second gradation converting means continues to select new pixels, and when the sum of the gradation values in the generated pixel group exceeds the threshold value, a gradation value obtained by subtracting the gradation value from the threshold value Is returned to the last selected pixel. The image processing apparatus according to any one of claims 1 to 9,
The image processing apparatus according to claim 1, wherein the first gradation converting means performs gradation conversion by any one of a systematic dither method, an error diffusion method, and an average error minimum method. A printing apparatus comprising the image processing apparatus according to claim 1 and printing an image by forming a plurality of dots on a print medium.
A printing apparatus that performs printing by forming dots having at least one of density and size corresponding to the plurality of gradation data. An image processing method for performing gradation conversion on input data having any one of P (P is an integer of 2 or more) kinds of gradation values for each pixel,
First gradation conversion is performed on the gradation value of the input data, which is converted into gradation data composed of Q (Q <P, Q is an integer of 2 or more) kinds of gradation values for each pixel, A first gradation conversion step for obtaining gradation data;
Error data consisting of M (M is an integer of 2 or more) types of gradations, which is an error between the gradation data acquired by the first gradation conversion means and the input data for each pixel. Then, new pixels are selected one after another until the sum of the gradation values becomes equal to or greater than the threshold value, and a pixel group is generated. Among the pixel groups, N (N is an integer of 2 or more) types of predetermined pixels. An image processing method comprising: a second gradation conversion step for assigning gradation values. An image processing program for performing gradation conversion on input data having any one of P (P is an integer of 2 or more) kinds of gradation values for each pixel,
Computer
First gradation conversion is performed on the gradation value of the input data, which is converted into gradation data composed of Q (Q <P, Q is an integer of 2 or more) kinds of gradation values for each pixel, First gradation conversion means for obtaining gradation data;
Error data consisting of M (M is an integer of 2 or more) types of gradations, which is an error between the gradation data acquired by the first gradation conversion means and the input data for each pixel. Then, new pixels are selected one after another until the sum of the gradation values becomes equal to or greater than the threshold value, and a pixel group is generated. N (N is an integer of 2 or more) types of predetermined pixels in the pixel group An image processing program that functions as a second gradation conversion unit that assigns gradation values. A computer-readable recording medium on which the image processing program according to claim 13 is recorded.

Images