JP2005341142A - Image processor, processing method and program, and recording medium with the program stored - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an image processor in which bias of dot density is lessened, while suppressing overlapping of dots to be formed, and to provide an image processing method and program, or a recording medium with the program stored thereto. <P>SOLUTION: A difference data calculating section calculates the difference data between a first hue data binarized to predetermined gray scale data outputting a dot at a first gray scale converting section for each pixel and first hue data prior to binarization. Subsequently, a data-adding section processes the difference data thus calculated, through a predetermined filter to produce data where objective pixels for reducing the gray level around a pixel outputting a dot are spread isotropically and adds that data to second hue data. When the second hue data, having an increased/decreased gray level is subjected to gray sale conversion at at a second gray scale converting section, the output position of dot exhibiting proper dispersibility, without overlapping the dots corresponding to first and second hue data are obtained. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to halftone processing of image data represented by multi-tone values. Specifically, the present invention relates to halftone processing that suppresses overlapping of different types of dots when there are a plurality of types of output dots.

  2. Description of the Related Art Conventionally, an image output device such as a printer performs gradation conversion processing on gradation data having multi-value gradation values for each pixel into binary values indicating the presence / absence of dot output, and prints on a print sheet Printing is performed by forming dots. In general, a process of converting a multi-value gradation value into a binary value is referred to as a halftone process.

  When such halftone processing is performed on image data of a color image, dots of a plurality of colors are formed at the same position or in the vicinity of one position on the printing paper. On the other hand, there is a problem that the density of dots is uneven, such as sparse in some areas. For example, in a printer that performs printing by forming dots of cyan (C), magenta (M), and yellow (Y) colors, assuming that the gradation data of each color to be subjected to halftone processing are all the same. If halftone processing is performed in the same manner as each color, dots of all colors are formed at the same pixel position.

  In order to avoid such a problem, a technique has been disclosed in which dots of various hues are not formed at pixel positions where dots of one hue are formed (for example, Patent Document 1 and Patent Document 2).

  According to the technique disclosed in Patent Document 1, for example, magenta gradation data serving as one hue data is subjected to halftone processing by a systematic dither method, and the result value and dots are not formed when dots are formed. It binarizes to the result value of the case. Then, the difference value data between the binarized data and the magenta gradation data is added to the cyan gradation data serving as the other hue data. After that, when cyan tone data is halftone processed using an error diffusion technique, it becomes difficult to form cyan dots around magenta dots.

  Further, according to the technique disclosed in Patent Document 2, for example, halftone processing is performed on gradation data of one hue by a systematic dither method using a predetermined matrix to determine whether or not to form dots. To do. Then, except for the position corresponding to the pixel determined to form a dot, halftone processing is performed on the other hue gradation data by a systematic dither method using the same matrix so that different types of dots do not overlap. It is formed.

JP-A-10-81026 JP-A-10-157167

However, in Patent Document 1 or Patent Document 2, since the dot overlap suppression effect is given only to the pixels on which dots are formed, the problem that the above-described deviation in dot density occurs remains. In other words, in Patent Document 1, halftone processing of cyan tone data, which is the other hue, is performed using an error diffusion method, and therefore error data of one pixel that has an effect of suppressing dot overlap is It is diffused to the pixels in the direction in which the coefficients of the error diffusion matrix are arranged, and the effect of suppressing dot overlap is strongly generated in specific pixels. As a result, the effect of preventing dot overlap is biased, and the dot density is biased.

  Also, in Patent Document 2, as described above, since the dot suppression effect remains only in the pixels in which the dot formation of one hue is performed, the dots of the other hue may be formed in adjacent pixels, In this case, the dots are not dispersed, and a deviation in dot density occurs as in Patent Document 1.

  In order to solve such problems, a first object of the present invention is to provide an image processing apparatus, an image processing method, an image processing program, or an image processing apparatus that suppresses overlapping of formed dots and reduces the unevenness of dot density. It is to provide a recording medium on which a program is recorded. A second object of the present invention is to provide a halftone processing method that faithfully binarizes without losing the gradation value of the original image data.

  The first image processing apparatus of the present invention that achieves the above object provides a predetermined gradation for outputting dots of at least two or more types of hue data having different properties represented by multi-gradation values for each pixel. An image processing apparatus that performs gradation conversion into data, wherein a first gradation conversion unit that performs gradation conversion of first hue data among the hue data, and second hue data among the hue data includes gradation A second gradation conversion unit for conversion; a difference data calculation unit for calculating difference data between the predetermined gradation data converted by the first gradation conversion unit and the first hue data; A data addition unit that performs data processing on the calculated difference data using a predetermined filter and then adds the second difference data to the second hue data to obtain addition data; and the second gradation conversion The unit is configured to use the addition data as the second hue data. Characterized by gradation conversion to the predetermined grayscale data.

  According to such a first image processing apparatus, the first hue data for each pixel is converted into predetermined gradation data for outputting dots by the first gradation conversion unit and predetermined gradation data for not outputting dots. Binarize. Then, difference data between the binarized gradation data and the first hue data before binarization is calculated. This difference data basically keeps the sum of difference data for all pixels at almost zero, while reducing the gradation value for pixels where dots are output, and increasing the gradation value for pixels where dots are not output. Will have a data value. After that, the difference data is subjected to data processing by using a predetermined filter to data that isotropically expands the target pixel whose gradation value is to be reduced, centering on the pixel where the dot is output, and then converted into the second hue data. to add. As a result, in the second hue data, the gradation value of a predetermined pixel corresponding to the position of the pixel from which the dot is output for the first hue data is reduced, and the gradation level of the other pixels is reduced by this reduced gradation value. The tone value data has an increased tone value. Therefore, if the second hue data whose gradation value has been increased or decreased is subjected to gradation conversion, the dots corresponding to the first hue data and the dots corresponding to the second hue data do not overlap and dispersibility. A good dot output position can be obtained.

  Further, the data adding unit has the predetermined filter characteristic having a filter characteristic specified by a lightness ratio between dots output according to the first hue data and dots output according to the second hue data. These filters may be used.

  Usually, the brightness of the dots output corresponding to the first hue data is different from that of the dots output corresponding to the second hue data. For example, the brightness of the dots actually formed on the printing paper differs depending on the difference in the properties of the ink used in the case of an ink jet printer and the difference in the properties of the toner used in the case of a laser printer. Therefore, by using a filter having a filter characteristic specified by the lightness ratio of the dots, a pixel range to which the gradation value is reduced is set for the second hue data. As a result, for example, it is possible to form dots corresponding to each hue data in which the brightness per unit area such as on a printing paper is kept substantially constant.

  Here, the second gradation conversion unit may perform gradation conversion on the second hue data using an error diffusion technique. In this way, for the second hue data whose gradation value has been increased or decreased, an error of the gradation value accompanying binarization is diffused in a specific direction, but basically 2 based on the increased or decreased gradation value. In order to perform the value processing, dots corresponding to the second hue data (hereinafter simply referred to as “second”) that do not overlap with the dots formed corresponding to the first hue data (hereinafter simply referred to as “first dot”). Dot ") can be output.

  Alternatively, the second gradation conversion unit generates a pixel group that sequentially generates pixel groups by selecting pixels until the sum of gradation values of the second hue data for each pixel becomes a predetermined threshold value or more. And a data adding unit that applies the predetermined gradation data to predetermined pixels in the pixel group that are sequentially generated.

  According to such a second gradation conversion unit, for pixels having gradation values to be subjected to gradation conversion processing, gradation conversion unprocessed pixels are sequentially selected to form a pixel group (hereinafter referred to as “cell”). Call). Then, when the total gradation value of the pixels in the generated cell is equal to or greater than a predetermined threshold, predetermined gradation data for outputting a dot is given to the pixel at a predetermined position in the cell. Therefore, for example, if a dot is output at a predetermined position that is approximately the center of the generated cell, it is possible to form a second dot with good dispersibility that does not overlap with the first dot and maintains the distance between dots. .

  Alternatively, the second gradation conversion unit may determine, based on the first reference point for pixel selection, until the sum total of gradation values of the second hue data for each pixel is equal to or greater than a predetermined threshold value. A pixel group generation unit that sequentially selects pixels to generate a pixel group, a reference point determination unit that determines a second reference point for the generated pixel group, and the determined second reference point A data adding unit that applies the predetermined gradation data to a pixel, and the pixel group generation unit updates the first reference point and selects the pixel based on the first reference point. The pixel group is generated, and when the sum of gradation values of the second hue data in the generated pixel group is equal to or greater than the predetermined threshold, the data adding unit positions the second reference point. The predetermined gradation data may be given to the pixel to be processed.

  According to the second gradation conversion unit, for pixels having gradation values to be subjected to gradation conversion processing, a cell is generated by sequentially selecting gradation conversion unprocessed pixels based on the first reference point. To do. When the total gradation value of the pixels in the generated cell is equal to or greater than a predetermined threshold value, predetermined gradation data for outputting a dot is given to the pixel located at the second reference point in the cell. To do. In this way, since the reference point for determining the shape of the cell to be generated and the reference point for determining the position of the dot to be output can be set separately, it becomes possible to generate the cell by paying attention only to the shape of the cell. . Therefore, for example, by setting the first reference point so as to generate a circular cell, the probability that dots that are output adjacently overlap with each other can be reduced, and dots with good dispersibility can be formed.

  Here, when the sum total of the gradation values of the second hue data for each pixel in the pixel group exceeds the predetermined threshold, the pixel group generation unit sets the gradation value for the excess to the pixel. It is good also as returning to the pixel selected last by the group production | generation part.

  In this way, the gradation value of the hue data before the halftone process is lost because the gradation value exceeding the predetermined threshold value is returned to the last pixel incorporated in the cell exceeding the predetermined threshold value. In addition, the gradation value error associated with the binarization is not diffused in a specific direction. As a result, it is possible to reduce the bias of the effect of suppressing the overlapping of dots, which is the problem described above.

  Furthermore, the pixel group generation unit may randomly select a pixel to be selected when there are a plurality of pixels to be selected based on the first reference point. In this way, the shape of the cell to be generated is likely to be random, and as a result, the positions of the dots to be output will vary, and it can be expected that the dispersibility of the formed dots will be improved.

  The reference point determination unit may determine a center of gravity position calculated from the pixel position and the gradation value of the pixel in the pixel group as the second reference point. In this way, dots are output at positions that are almost faithful to the distribution of gradation values in the hue data before binarization for the generated cells. Therefore, it is possible to form dots having faithful reproducibility and dispersion of gradation values.

  The first reference point may be a barycentric position calculated from a pixel position in the pixel group and a gradation value of the second hue data included in the pixel. In this way, in the cell generation process, for example, if the pixel closest to the center of gravity of the cell is always selected and incorporated into the cell, the shape of the generated cell is approximately circular around the center of gravity of the cell. become. Therefore, dots formed according to such circular cells do not overlap, and the dot dispersibility can be improved.

  Further, in the first image processing apparatus of the present invention, the first gradation converting unit is configured to calculate the first hue data level for each pixel based on a third reference point for pixel selection. A pixel group generation unit that sequentially selects pixels to generate a pixel group until the sum of the tone values is equal to or greater than a predetermined threshold; a reference point determination unit that determines a fourth reference point for the generated pixel group; A data adding unit that applies the predetermined gradation data to a pixel located at the determined fourth reference point, and the pixel group generation unit updates the third reference point, The pixel group is generated by selecting the pixel based on a reference point of 3, and the data providing unit has a total sum of gradation values of the first hue data in the generated pixel group equal to or greater than the predetermined threshold value. The predetermined gradation is applied to the pixel where the fourth reference point is located. Characterized in that it granted over data.

  According to such a first gradation conversion unit, for pixels having gradation values to be subjected to gradation conversion processing, gradation conversion unprocessed pixels are sequentially selected based on the third reference point, for example, Generate a circular cell. Then, when the total gradation value of the pixels in the generated cell is equal to or greater than a predetermined threshold, predetermined gradation data for outputting dots is assigned to the pixel located at the fourth reference point in the cell. Give. Therefore, if dots are output at a predetermined position that is approximately the center of the generated circular cell, it is possible to form the first dots with good dispersibility while maintaining the distance between the dots. As a result, both the first dot and the second dot have a high probability of forming a highly dispersive dot.

  Further, the pixel group generation unit in the first gradation conversion unit may include an amount that exceeds when the sum of gradation values of the first hue data for each pixel in the pixel group exceeds the predetermined threshold. The gradation value may be returned to the pixel last selected by the pixel group generation unit.

  In this way, since the gradation value that exceeds the predetermined threshold value is returned to the pixel that was last incorporated in the cell that exceeded the predetermined threshold value, the gradation value of the first hue data is not lost. Toned. Therefore, the sum of the difference data between the first hue data and the binarized predetermined gradation data is kept almost zero, and the sum of the gradation values of the second hue data is the same before and after the addition of the difference data. Does not change. As a result, the second hue data is fully halftoned to the original gradation value.

  Here, the predetermined filter used by the data adding unit may have a filter characteristic specified based on the number of pixels in the pixel group generated by the first gradation converting unit.

  According to the first gradation conversion unit described above, the number of pixels in the generated cell increases when the gradation value of each pixel is small, and decreases when the gradation value of each pixel is large. That is, the distance between dots formed is large when the number of pixels constituting the cell is large, and the distance between dots formed is small when the number of pixels constituting the cell is small. Therefore, by using a filter having a filter characteristic specified by the number of pixels constituting such a cell, a target pixel range for reducing the gradation value is set for the second hue data. As a result, a range for suppressing the formation of the second dots can be set in accordance with the formation density of the first dots.

  According to the first image processing method of the present invention, at least two types of hue data having different properties represented by multi-gradation values are converted into predetermined gradation data for outputting dots for each pixel. A first gradation conversion step for gradation-converting the first hue data of the hue data, and a second gradation conversion of the second hue data of the hue data. A gradation conversion step, a difference data calculation step of calculating difference data between the predetermined gradation data subjected to gradation conversion by the first gradation conversion step and the first hue data, and the calculated A data addition step of adding difference data to the second hue data after performing data processing using a predetermined filter and obtaining addition data, wherein the second gradation conversion step includes pixel selection. Based on the first reference point for A pixel group generating step of generating pixels by sequentially selecting pixels until the sum of gradation values of the addition data for each pixel becomes equal to or greater than a predetermined threshold, and a second reference for the generated pixel group A reference point determining step for determining a point; and a data adding step for applying the predetermined gradation data to a pixel located at the determined second reference point, wherein the pixel group generating step includes: Updating one reference point and selecting the pixel based on the first reference point to generate the pixel group, and the data adding step includes a step of calculating the second hue data in the generated pixel group. When the total sum of tone values is equal to or greater than the predetermined threshold value, the predetermined gradation data is given to a pixel where the second reference point is located.

  The first image processing method of the present invention may be a computer program or a recording medium on which the program is recorded. That is, an image processing program for gradation-converting at least two types of hue data having different properties represented by multi-gradation values into predetermined gradation data for outputting dots for each pixel, Of the hue data, a first gradation conversion process for gradation conversion of the first hue data, a second gradation conversion process for gradation conversion of the second hue data of the hue data, and the first A difference data calculation process for calculating difference data between the predetermined gradation data subjected to gradation conversion by the gradation conversion process and the first hue data, and the calculated difference data using a predetermined filter. After the data processing is performed, the computer is caused to execute a data addition process for adding the second hue data to obtain the added data, and the second gradation conversion process is performed by the first gradation selection process. Based on reference point In addition, a pixel group generation process for generating pixels by sequentially selecting pixels until the sum of gradation values of the addition data for each pixel is equal to or greater than a predetermined threshold, A reference point determining process for determining a reference point; and a data adding process for adding the predetermined gradation data to a pixel located at the determined second reference point, wherein the pixel group generating process includes: The first reference point is updated, the pixel is selected based on the first reference point, and the pixel group is generated. The data providing process is performed on the second hue data in the generated pixel group. When the total sum of gradation values is equal to or greater than the predetermined threshold, the predetermined gradation data is given to the pixel where the second reference point is located. 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.

  Next, the second image processing apparatus of the present invention that achieves the above object provides at least two types of hue data having different properties represented by multi-tone values for each pixel, for outputting dots. An image processing apparatus that performs gradation conversion into gradation data of the first hue conversion section, wherein a first gradation conversion section that performs gradation conversion of the first hue data of the hue data and second hue data of the hue data A second gradation conversion unit that performs gradation conversion on the pixel, and the predetermined gradation data that has been converted by the first gradation conversion unit using a pixel to which dots are output according to a predetermined profile as a reference point A dot profile conversion unit for converting into distributed data dispersed in surrounding pixels, a difference data calculation unit for calculating difference data between the converted distributed data and the first hue data, and the calculated difference data , The second hue And a data addition unit for obtaining addition data by adding to the data, wherein the second gradation conversion unit converts the addition data into the predetermined gradation data as the second hue data. It is characterized by.

  According to the second image processing apparatus, the first hue data for each pixel is converted into predetermined gradation data for outputting dots by the first gradation conversion unit and predetermined gradation data for not outputting dots. Binarize. Then, predetermined gradation data for outputting dots among the binarized gradation data is distributed to peripheral pixels according to a predetermined profile centering on the pixels to which the dots are output. Then, difference data between the predetermined gradation data after distribution and the first hue data is calculated, and the calculated difference data is added to the second hue data. This difference data basically keeps the sum of the difference data for all the pixels at almost zero, reduces the gradation value for the peripheral pixels of the pixel to which the dot is output, and sets the gradation values for the other pixels. You will have increasing data values. Thereafter, this difference data is added to the second hue data. As a result, in the second hue data, the gradation value of a predetermined pixel corresponding to the position of the pixel from which the dot is output for the first hue data is reduced, and the gradation level of the other pixels is reduced by this reduced gradation value. The tone value data has an increased tone value. Therefore, if the second hue data whose gradation value has been increased or decreased is subjected to gradation conversion, the dots corresponding to the first hue data and the dots corresponding to the second hue data do not overlap and dispersibility. A good dot output position can be obtained.

  Here, the second gradation conversion unit in the second image processing apparatus may perform gradation conversion on the second hue data using an error diffusion technique. In this way, for the second hue data whose gradation value has been increased or decreased, an error of the gradation value accompanying binarization is diffused in a specific direction, but basically 2 based on the increased or decreased gradation value. Since the digitization process is performed, the second dot that does not overlap the first dot can be output.

  Alternatively, the second gradation conversion unit in the second image processing apparatus selects pixels until the sum of gradation values of the second hue data for each pixel becomes a predetermined threshold value or more. It is good also as providing the pixel group production | generation part which produces | generates a group sequentially, and the data provision part which provides the said predetermined gradation data with respect to the predetermined pixel in the said pixel group produced | generated sequentially.

  According to such a second gradation conversion unit, a cell is generated by sequentially selecting gradation conversion unprocessed pixels for pixels having gradation values to be subjected to gradation conversion processing. Then, when the total gradation value of the pixels in the generated cell is equal to or greater than a predetermined threshold, predetermined gradation data for outputting a dot is given to the pixel at a predetermined position in the cell. Therefore, for example, if a dot is output at a predetermined position that is approximately the center of the generated cell, it is possible to form a second dot with good dispersibility that does not overlap with the first dot and maintains the distance between dots. .

  Alternatively, the second gradation conversion unit in the second image processing apparatus may calculate a sum of gradation values of the second hue data for each pixel based on a first reference point for pixel selection. A pixel group generation unit that generates pixels by sequentially selecting pixels until a predetermined threshold value is exceeded, a reference point determination unit that determines a second reference point for the generated pixel group, and the determined A data adding unit that applies the predetermined gradation data to a pixel located at a second reference point, and the pixel group generation unit updates the first reference point to the first reference point. The pixel is selected on the basis of the pixel group to generate the pixel group, and when the sum of gradation values of the second hue data in the generated pixel group is equal to or greater than the predetermined threshold, The predetermined gradation data is attached to the pixel where the second reference point is located. It is also possible to be.

  According to the second gradation conversion unit, for pixels having gradation values to be subjected to gradation conversion processing, a cell is generated by sequentially selecting gradation conversion unprocessed pixels based on the first reference point. To do. When the total gradation value of the pixels in the generated cell is equal to or greater than a predetermined threshold value, predetermined gradation data for outputting a dot is given to the pixel located at the second reference point in the cell. To do. In this way, since the reference point for determining the shape of the cell to be generated and the reference point for determining the position of the dot to be output can be set separately, it becomes possible to generate the cell by paying attention only to the shape of the cell. . Therefore, for example, by setting the first reference point so as to generate a circular cell, the probability that adjacent dots overlap can be reduced, and dots with good dispersibility can be formed.

  Here, the pixel group generation unit in the second image processing apparatus is configured so that when the total sum of gradation values of the second hue data for each pixel in the pixel group exceeds the predetermined threshold, The gradation value may be returned to the pixel last selected by the pixel group generation unit.

  In this way, the gradation value of the hue data before the halftone process is lost because the gradation value exceeding the predetermined threshold value is returned to the last pixel incorporated in the cell exceeding the predetermined threshold value. In addition, the gradation value error associated with the binarization is not diffused in a specific direction. As a result, it is possible to reduce the bias of the effect of suppressing the overlapping of dots, which is the problem described above.

  Furthermore, the pixel group generation unit in the second image processing device may randomly select a pixel to be selected when there are a plurality of pixels to be selected based on the first reference point. In this way, the shape of the cell to be generated is likely to be random, the positions of the dots to be output will vary, and it can be expected that the dot dispersibility will be improved.

  The reference point determination unit in the second image processing apparatus may determine a center of gravity calculated from a pixel position and a pixel gradation value in the pixel group as the second reference point. Good. In this way, dots are output at positions that are almost faithful to the distribution of gradation values in the hue data before binarization for the generated cells. Therefore, it is possible to form dots having faithful reproducibility and dispersion of gradation values.

  The first reference point in the second image processing apparatus is a barycentric position calculated from the pixel position in the pixel group and the gradation value of the second hue data that the pixel has. It is good. In this way, in the cell generation process, for example, if the pixel closest to the center of gravity of the cell is always selected and incorporated into the cell, the shape of the generated cell is approximately circular around the center of gravity of the cell. become. Therefore, dots formed according to such circular cells do not overlap, and the dot dispersibility can be improved.

  Further, in the second image processing apparatus of the present invention, the first gradation conversion unit is configured to calculate the first hue data level for each pixel based on a third reference point for pixel selection. A pixel group generation unit that sequentially selects pixels to generate a pixel group until the sum of the tone values is equal to or greater than a predetermined threshold; a reference point determination unit that determines a fourth reference point for the generated pixel group; A data adding unit that applies the predetermined gradation data to a pixel located at the determined fourth reference point, and the pixel group generation unit updates the third reference point, The pixel group is generated by selecting the pixel based on a reference point of 3, and the data providing unit has a total sum of gradation values of the first hue data in the generated pixel group equal to or greater than the predetermined threshold value. The predetermined gradation is applied to the pixel where the fourth reference point is located. Characterized in that it granted over data.

  According to the first gradation conversion unit in the second image processing apparatus, for pixels having gradation values to be subjected to gradation conversion processing, the gradation conversion unprocessed pixels are sequentially set based on the third reference point. For example, a circular cell is generated. Then, when the total gradation value of the pixels in the generated cell is equal to or greater than a predetermined threshold, predetermined gradation data for outputting dots is assigned to the pixel located at the fourth reference point in the cell. Give. Therefore, if dots are output at a predetermined position that is approximately the center of the generated circular cell, it is possible to form the first dots with good dispersibility while maintaining the distance between the dots. As a result, it is possible to form dots with good dispersibility for both the first dots and the second dots.

  Further, the pixel group generation unit in the second image processing apparatus, when a sum of gradation values of the first hue data for each pixel in the pixel group exceeds the predetermined threshold, The gradation value may be returned to the pixel last selected by the pixel group generation unit.

  In this way, since the gradation value that exceeds the predetermined threshold value is returned to the pixel that was last incorporated in the cell that exceeded the predetermined threshold value, the gradation value of the first hue data is not lost. Toned. Therefore, the sum of the difference data between the first hue data and the binarized predetermined gradation data is kept almost zero, and the sum of the gradation values of the second hue data is the same before and after the addition of the difference data. Does not change. As a result, the second hue data is fully halftoned to the original gradation value.

  Here, the dot profile conversion unit in the second image processing apparatus sets the number of pixels in the pixel group for each pixel group generated by the first gradation conversion unit in the second image processing apparatus. The predetermined gradation data may be distributed according to the predetermined profile specified on the basis of the predetermined profile.

  According to the first gradation conversion unit described above, the number of pixels in the generated cell increases when the gradation value of each pixel is small, and decreases when the gradation value of each pixel is large. That is, the distance between dots formed is large when the number of pixels constituting the cell is large, and the distance between dots formed is small when the number of pixels constituting the cell is small. Therefore, a target pixel range for reducing the gradation value is set for the second hue data according to a profile specified by the number of pixels constituting such a cell. As a result, a range for suppressing the formation of the second dots can be set in accordance with the formation density of the first dots.

  Also, the second image processing method of the present invention converts at least two or more types of hue data having different properties represented by multi-gradation values into predetermined gradation data for outputting dots for each pixel. An image processing method for tone conversion, comprising: a first tone conversion step for tone-converting first hue data in the hue data; and a second tone-converting tone for second hue data in the hue data. The predetermined gradation data subjected to gradation conversion in the second gradation conversion process and the first gradation conversion process is distributed to peripheral pixels having a pixel to which a dot is output as a reference point according to a predetermined profile. A dot profile conversion step for converting into distributed data, a difference data calculation step for calculating difference data between the converted distributed data and the first hue data, and the calculated difference data as the second hue De A data adding step for obtaining added data by adding to the data, wherein the second gradation converting step includes a gradation value of the added data for each pixel based on a first reference point for pixel selection. A pixel group generating step of sequentially selecting pixels to generate a pixel group until a total sum of a predetermined threshold or more is reached, a reference point determining step of determining a second reference point for the generated pixel group, A data providing step of assigning the predetermined gradation data to a pixel located at the determined second reference point, wherein the pixel group generation step updates the first reference point to update the first reference point. The pixel group is generated by selecting the pixel based on the reference point, and the data adding step is performed such that a sum total of gradation values of the second hue data in the generated pixel group is not less than the predetermined threshold value. The second reference point is located Characterized by applying the predetermined gradation data.

  The second image processing method of the present invention may be a computer program or a recording medium on which the program is recorded. That is, an image processing program for gradation-converting at least two types of hue data having different properties represented by multi-gradation values into predetermined gradation data for outputting dots for each pixel, Of the hue data, a first gradation conversion process for gradation conversion of the first hue data, a second gradation conversion process for gradation conversion of the second hue data of the hue data, and the first Dot profile conversion processing for converting the predetermined gradation data subjected to gradation conversion by the gradation conversion processing of the above into distributed data dispersed in peripheral pixels having a pixel output as a reference point according to a predetermined profile; A difference data calculation process for calculating difference data between the converted dispersion data and the first hue data, and the calculated difference data is added to the second hue data to obtain an addition data. And the second gradation conversion process calculates the sum of gradation values of the addition data for each pixel based on the first reference point for pixel selection. A pixel group generation process for generating a pixel group by sequentially selecting pixels until a predetermined threshold value is exceeded, a reference point determination process for determining a second reference point for the generated pixel group, and the determined A data adding process for adding the predetermined gradation data to a pixel located at a second reference point, wherein the pixel group generation process updates the first reference point to the first reference point The pixel group is generated by selecting the pixel based on the image data, and the data adding process is performed when a sum of gradation values of the second hue data in the generated pixel group is equal to or greater than the predetermined threshold value. , The pixel where the second reference point is located Characterized by applying the predetermined gradation data. 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.

  Furthermore, in the first or second image processing apparatus, image processing method, image processing program, or recording medium of the present invention, at least two or more types of hue data having different properties are each (1) the size of a dot. Hue data corresponding to output dots having different hues, (2) hue data corresponding to output dots having different densities per unit area, (3) hue data corresponding to output dots having different hues, and (4) different light reflectances. It may be hue data satisfying at least one of the hue data corresponding to the output dots.

  In this way, (1) halftone processing for outputting dots when performing gradation display by the halftone dot method, and (2) outputting dots when performing gradation display using dots of different shades. Halftone processing, (3) halftone processing to output dots when performing gradation display using dots of different colors, and (4) gradation display using dots with different surface gloss The present invention can be applied to a halftone process that satisfies at least one of the halftone processes for outputting the dots.

  Next, the best mode for carrying out the present invention will be described. FIG. 1 is a configuration diagram showing an example of an entire system including an image processing apparatus to which the present invention is applied. The image input device 10, the image processing device 20, and the image output device 30 are configured as a whole. These devices are basically computers or devices controlled by a computer, and operate by executing a predetermined program under a predetermined operating system.

  The image input device 10 includes an application unit 11 and a rasterizing unit 12. The application unit 11 generates data to be printed such as character data, graphic data, and bitmap data. For example, the image input device 10 uses an application program such as a word processor or a graphic tool to generate character data, graphic data, and the like by operating a keyboard (not shown). The generated data is output to the rasterizing unit 12. Of course, the image input apparatus 10 receives data output from a device that acquires image data such as a scanner or a digital still camera (not shown), and outputs this data to the rasterization unit 12 as data generated by the application unit 11. There is no problem.

  The rasterizing unit 12 converts the print target data output from the application unit 11 into a plurality of types of hue data for each pixel and outputs the data. In the present embodiment, hue data represented by multi-gradation values of 8 bits (24 bits in total) for each pixel is output for each color of R (red), G (green), and B (blue). Therefore, the output hue data has 256 kinds of gradation values from 0 to 255 for each pixel. The hue data generating process in the rasterizing unit 12 is actually performed by a driver (not shown) installed in the image input apparatus 10. The hue data output from the rasterizing unit 12 in this way is output to the image processing apparatus 20.

  The image processing apparatus 20 is basically a computer and includes a color conversion unit 21 and a halftone processing unit 22. In the image processing device 20, color conversion processing and halftone processing are performed on the hue data output from the image input device 10, and processing for outputting predetermined gradation data DR to the image output device 30 is performed.

  The color conversion unit 21 receives hue data of 8 bits for each of RGB (total of 24 bits) having 256 types of gradation values output from the image input device 10, and performs color matching with the color components output by the image output device 30. Perform the conversion process. In the present embodiment, it is assumed that color conversion processing is performed on RGB to CMY hue data DT. Here, C represents cyan, M represents magenta, and Y represents yellow.

  The halftone processing unit 22 converts the CMY hue data DT output from the color conversion unit 21 into quantized data (multivalued values such as binary or quaternary values) using a predetermined threshold value, Predetermined gradation data DR that is the converted quantized data is output to the image output device 30. As the halftone processing, in the present embodiment, processing by an error diffusion method, or a cell composed of a plurality of pixels is configured using the center of gravity of a pixel group, and a dot is generated at the center of gravity of the cell (Circular Cell method, Hereinafter, the CC method) is used. Specific contents of the error diffusion method and the CC method will be described later.

  The image output apparatus 30 includes a pulse width modulation unit 31 and a print engine 35. The print engine 35 includes a laser driver 36 and a laser diode (LD) 37. In the image output device 30, the print engine 35 is driven in accordance with the predetermined gradation data DR output from the image processing device 20, and dots of each color component are formed on a recording medium such as print paper by the print engine 35. Printing is performed.

  The pulse width modulation unit 31 receives the predetermined gradation data DR output from the halftone processing unit 22, and uses the predetermined gradation data DR as a drive data for a laser drive pulse having a predetermined number of pulses and a pulse width. Generate as Then, this drive data is output to the laser driver 36.

  The laser driver 36 generates control data for driving the laser diode 37 from the input drive data, and outputs the control data to the laser diode 37. The laser diode 37 is driven based on the control data output from the laser driver 36, and further, a photosensitive drum and a transfer belt (not shown) are driven, and finally a recording medium such as a printing paper has a pulse width and a pulse number. In response, a predetermined number of dots of a predetermined color component are output. Thus, each color dot is formed on the recording medium, and the data from the image input device 10 is printed.

  Next, a specific hardware configuration of the image processing apparatus 20 to which the present invention is applied will be described with reference to FIG. Here, the color conversion unit 21 and the halftone processing unit 22 constituting the image processing apparatus 20 in FIG. 1 correspond to the CPU 26, the RAM 27, and the ROM 28 in FIG.

  As a whole, the image processing apparatus 20 includes an input / output interface (I / F) 25, a CPU 26, a RAM 27, a ROM 28, and a hard disk 29, which are connected to each other via a bus. The input / output I / F 25 serves as an interface between the image input device 10 and the image processing device 20 and an interface between the image processing device and the image output device. The input / output I / F 25 receives the RGB hue data from the image input device 10 transmitted by a predetermined transmission method, and converts it into data that can be processed by the image processing device 20. The RGB hue data is temporarily stored in the RAM 27. Then, the predetermined gradation data DR is sent from the input / output I / F 25 to the image output apparatus 30 using a predetermined transmission method.

  The CPU 26 reads out a program stored in the hard disk 29 or the ROM 28 via the bus and executes the read program under a predetermined operating system, thereby functioning as the image processing device 20 and executing a predetermined process. To do. In particular, it functions as a first gradation conversion unit, a second gradation conversion unit, a dot profile conversion unit, a difference data calculation unit, and a data addition unit described in the claims, and performs halftone processing in each embodiment described later. Execute.

  The program in which these processes are recorded may be stored in advance in the hard disk 29 or the ROM 28, 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. It may be stored by being stored in the hard disk 29 via the RW drive. Of course, the program may be stored in the hard disk 29 by accessing a server or the like that supplies a program via network means such as the Internet and downloading data.

  The RAM 27 serves as a working memory for each process executed under the control of the CPU 26, and stores various data after the process. A specific configuration of the RAM 27 will be described later.

Next, regarding the halftone processing performed in the image processing apparatus 20 configured as described above, the CC method and the error diffusion method, which are halftone processing methods used in the embodiments described later, will be described first. This will be described in the following order.
(A) Halftone processing by CC method (B) Halftone processing by error diffusion method (C) First embodiment of halftone processing (data processing by filter)
(C1) Halftone processing using error calculation diffusion method (C2) Halftone processing using CC method (D) Second embodiment of halftone processing (dispersion processing by profile)
(E) Modification In this embodiment, the CMY hue data DT output from the color conversion unit 21 is data represented by multi-gradation values of 8 bits for each color, and each color “0” is used. It is assumed that there are 256 kinds of gradation values up to “255”. In the present embodiment, the halftone processing unit 22 is described as performing a quantization process for controlling whether or not to output each color component dot, and a predetermined floor output by the halftone processing unit 22 is described. The tone data DR is binary data that is quantized (binarized) to a gradation value “0” or a gradation value “255” for each pixel for each of the C, M, and Y color components. To do. Finally, as described above, the image output device 30 generates the pulse width and the number of pulses corresponding to all the pixels quantized to the output predetermined gradation data, that is, the gradation value “255”. Then, printing is performed by controlling the output of dots of each color component.

(A) Halftone Processing by CC Method First, halftone processing by the CC method will be described with reference to the flowchart of FIG. 3 and the schematic diagrams of FIGS. When this processing is performed, first, in step S301, processing for reading an input image to be subjected to halftone processing, that is, gradation conversion processing is performed. The input image is assumed to be composed of square dot matrix pixels, and here, as an example, hue data of the color component cyan is read as an input image. An example of the read hue data is shown in FIG. For example, hue data having a gradation value of “50” is given to a pixel in the m-th row and the n-th column (hereinafter referred to as (n, m)).

  Next, in step S302, a process for searching for a pixel for which gradation conversion has not been processed (hereinafter, an unprocessed pixel) is performed, and a process for determining whether there is a search unprocessed pixel is performed. In search of unprocessed pixels, search scanning is always performed in the horizontal direction first, and then search scanning is performed in the vertical direction. That is, when there is an unprocessed pixel, the leftmost pixel is searched for among the uppermost pixels in the entire input image. Of course, at the start of the gradation conversion process, the pixel in the first row and first column is searched.

  Next, when there is an unprocessed pixel as a result of the determination (YES), the process proceeds to the next step S304. On the other hand, if there is no unprocessed pixel as a result of the determination (NO), it is assumed that the entire input image has been subjected to gradation conversion, and processing for outputting binarized data is performed (step S303), and the gradation conversion processing routine is executed. finish.

  In step S304, it is determined whether or not a cell expansion end condition is satisfied. If the result of determination is NO, processing for enlarging the cell is performed (step S305). In the present embodiment, a condition for enlarging the cell is that the total gradation value of the pixels in the cell is less than the threshold “255”. Further, the cell is expanded by incorporating the pixel closest to the center of gravity of the cell into the cell. On the other hand, if the result of determination is YES, cell expansion is terminated, and the process proceeds to step S306.

  The processing in step S304 and step S305 will be specifically described with reference to FIG. Now, assume that the searched unprocessed pixel is the pixel (n, m) in the m-th row and the n-th column indicated by the shaded portion in FIG. 4A, and all the other pixels are also unprocessed pixels. At this time, a cell CL of (n, m) 1 pixel is generated.

  Next, since the sum of the gradation values of the pixels in the cell CL is “50”, which is less than “255”, the center of gravity position of the cell CL is calculated. The calculated position of the center of gravity GC of the cell CL is the center of the pixel (n, m), and the unprocessed pixel closest to the calculated position of the center of gravity GC is selected as shown by the shaded portion in FIG. Incorporate into cell CL. In this case, two of (n + 1, m) (n, m + 1) exist as pixels closest to the center of gravity. In the present embodiment, when there are a plurality of such pixels to be selected, they are selected at random. Here, it is assumed that (n + 1, m) is incorporated in the cell and the cell CL is enlarged. When there are a plurality of pixels to be selected in this way, it becomes difficult to continuously generate cells having the same shape by selecting pixels at random. As a result, the dispersibility of dots formed at the center of gravity of the cells described later is improved. Of course, it is possible to select a predetermined selection order without making it random.

  Next, the sum of the gradation values of the pixels in the enlarged cell CL (the portion within the thick frame in FIG. 4C) is “100”, which is less than the threshold value “255”. The position of the center of gravity GC of the cell CL is calculated from the gradation value of the pixel. Then, as shown by the shaded portion in FIG. 4 (c), pixels (n, m + 1) (n + 1, m + 1) closest to the calculated position of the center of gravity GC are selected at random, and here (n, m + 1) is incorporated into the cell and the cell CL is enlarged.

  Next, since the sum of the gradation values of the pixels in the enlarged cell CL (the portion within the thick frame in FIG. 4D) is “150”, which is less than the threshold value “255”, the pixel position in the cell CL The position of the center of gravity GC of the cell CL is calculated from the gradation value of the pixel. 4D, the pixel (n + 1, m + 1) closest to the calculated position of the center of gravity GC is incorporated into the cell, and the cell CL is enlarged.

  Next, since the sum of the gradation values of the pixels in the enlarged cell CL (the portion within the thick frame in FIG. 4E) is “205”, which is less than the threshold value “255”, the pixel position in the cell CL The position of the center of gravity GC of the cell CL is calculated from the gradation value of the pixel. Then, as shown in FIG. 4E, the pixel (n + 1, m + 2) closest to the calculated position of the center of gravity GC is incorporated into the cell, and the cell CL is enlarged.

  Next, the sum of the gradation values of the pixels in the enlarged cell CL (the portion within the thick frame in FIG. 4F) is “255”, which satisfies the cell enlargement end condition. The process ends, and the process proceeds to step S306 (FIG. 3).

  In step S306, a process for determining whether or not the total gradation value of the pixels in the cell is equal to the threshold value is performed. If it is equal to the threshold value as a result of the determination (YES), a process of calculating the position of the center of gravity GC of the cell from the pixel position in the cell and the gradation value of the pixel is performed (step S307). Then, a process of giving the same gradation value as the threshold value to the pixel existing at the calculated center of gravity position is performed (step S308), and the process returns to step S302 to repeat the process of searching for unprocessed pixels.

  As shown in FIG. 4F, in step S308, the same gradation value “255” as the threshold value is given to the pixel (n + 1, m + 1) existing at the position of the center of gravity GC calculated in step S307. An image is printed by forming dots corresponding to cyan hue data at the position of the pixel to which the gradation value “255” is given.

  On the other hand, if the result of determination in step S306 is not equal to the threshold value, that is, if the threshold value is exceeded (NO), the process of calculating the center of gravity of the cell ignoring the error data that is the gradation value that exceeds the threshold value This is performed (step S309). Then, the error data is returned to the last pixel incorporated in the cell (step S310), and the pixel to which the error data is returned is processed as an unprocessed pixel (step S311). Next, the process proceeds to step S308, and a process of giving the same gradation value as the threshold value to the pixel existing at the calculated center of gravity position is performed.

  The processing from step S309 to S311 will be specifically described with reference to FIG. In FIG. 5A, following the generation processing of the cell CL1 described in FIG. 4F, in step S302, the pixel (n + 2, m) is searched as an unprocessed pixel according to the scanning direction by a predetermined scanning method. Shows the state. Then, the same processing as that described in FIG. 4 is performed. In FIG. 5B, the pixel (n + 2, m + 1) is performed. In FIG. 5C, the pixel (n + 3, m + 1) is processed. In FIG. The cell is expanded by incorporating (n + 3, m + 2) into the cell CL2.

  As shown in FIG. 5E, when the pixel (n + 3, m + 2) is incorporated into the cell CL2, the total gradation value of the pixels in the cell CL2 is “265”, which exceeds the threshold value “255”. Therefore, the excess error data “10” (= 265−255) is ignored, and the center of gravity GC2 of the cell CL2 is calculated assuming that the pixel (n + 3, m + 2) has the gradation value “50” (= 60−10). To do.

  Then, as shown in FIG. 5F, the gradation value “255” is assigned to the pixel (n + 3, m + 1) existing at the calculated position of the center of gravity GC2. At this time, the error data “10” is returned to the pixel (n + 3, m + 2) last incorporated in the cell CL2. Then, the pixel (n + 3, m + 2) is set as an unprocessed pixel having the gradation value “10” and excluded from the cell CL2.

  The conversion processing described with reference to FIGS. 3, 4, and 5 is the basic processing content of the CC method in the present embodiment. According to the gradation conversion processing by the CC method, since a cell is always generated by incorporating the pixel closest to the center of gravity of the cell, a cell having a substantially circular shape is generated with respect to a pixel position to which a threshold value is applied. Therefore, when a dot is formed at a pixel position to which a threshold is given, the probability that adjacent dots are close to each other or overlap each other can be reduced, and a dot with good dispersibility can be formed.

  In the CC method, as described above, since the cell is expanded until the total gradation value of the pixels in the cell reaches the threshold value, a cell having a large number of pixels is generated in an input image region having many pixels having small gradation values. Conversely, in the input image area where there are many pixels with large gradation values, cells with a small number of pixels are generated. Therefore, the number of pixels constituting the generated cell is characterized in that it represents the magnitude of the gradation value of the input image, that is, the shade of the input image.

  Further, according to the gradation conversion by the CC method, the error data is returned to the original pixel, so that distortion of the input image data is reduced, and gradation conversion processing is performed relatively faithfully with respect to the gradation value of the input image. be able to.

  Next, with regard to gradation conversion by the CC method, specific processing contents in each hardware will be described with reference to the flowchart of FIG. 3 and the configuration diagram of FIG. 2 and the schematic diagrams of FIGS.

  First, the CPU 26 reads a program stored in the hard disk 29 or the ROM 28, and starts an input image reading process (step S301). The input image is read by fetching the hue data after the color conversion process stored in the hard disk 29 into the designated input buffer area of the RAM 27. For example, cyan hue data is stored in the cyan input buffer area, and magenta hue data is stored in the magenta input buffer area. Since the CC method basically performs the same processing for all hue data, the hue data will be described without distinction, and not only the input buffer prepared in the RAM 27 but also the output buffer described below. The description of cyan, magenta, etc. is also omitted for the working memory. An example of this is shown in FIG. Of course, the processing data for each hue data is stored in the corresponding areas of the input buffer, working memory, and output buffer.

  As illustrated in FIG. 6A, the RAM 27 includes an input buffer area 271, a working memory area 272, and an output buffer area 273. The input buffer area 271 has a two-dimensional structure. For example, for the pixel of the input image (hereinafter referred to as input pixel), the hue data of the input pixel located at (n, m) is stored in the input buffer area. Stored at the address (n, m). An input buffer area 271 is configured to correspond to the position of the input pixel. Actually, the tone value of the hue data is stored at this address. For example, when cyan hue data shown in FIG. 4A is input, the gradation value of each pixel is stored in the input buffer 271 as shown in FIG. The working memory area 272 stores the calculation result of the barycentric position and the calculation result of the gradation value, and the output buffer area 273 is configured to correspond to the hue data of each input pixel like the input buffer 271. Details will be described later.

  Next, the CPU 26 searches for and determines an initial pixel that is the first unprocessed pixel in cell generation (step S302), and determines whether or not the gradation value of the determined initial pixel is smaller than a threshold value (step S304). Specifically, the threshold value stored in the ROM 28 is read and compared with the gradation value of the initial pixel read from the input buffer 271 of the RAM 27. If the threshold value is smaller than the threshold value, the process proceeds to step S305. The process proceeds to step S306.

  For the determination of the initial pixel, the CPU 26 selects the pixel located on the upper left side in the position of the input pixel from the input image data stored in the input buffer 271 of the RAM 27. That is, in the example shown in FIG. 6A, the CPU 26 determines that the pixel located at the position corresponding to (n, m) is the initial pixel. Hereinafter, in order to simplify the description, (n, m) = (0, 0) will be described.

  If it is determined in step S304 that it is smaller than the threshold value (YES), the CPU 26 selects an unprocessed pixel and performs a process of enlarging the cell (step S305). Here, selection of unprocessed pixels is performed using the center of gravity of the cell. By selecting a pixel using the center of gravity, the shape of the cell that is finally generated becomes a circular shape. By forming dots at the center of gravity, the distance between dots is uniform and the dispersibility of the dots is good. A printed output can be obtained.

  The CPU 26 reads the gradation value of the pixel where the initial pixel is located and its address position, and calculates the center of gravity. Specifically, the CPU 26 calculates the center of gravity by calculating using the following calculation expression.

(Formula 1)
x _centroid = {{(x coordinate of centroid of cell) × (total gradation value of pixels in cell)} + {(x coordinate of selected unprocessed pixel) × (gradation value of selected unprocessed pixel) }} / {(Total gradation value of pixels in cell) + (gradation value of selected unprocessed pixel)}
y _centroid = {{(y coordinate of centroid of cell) × (total tone value of pixels in cell)} + {(y coordinate of selected unprocessed pixel) × (gradation value of selected unprocessed pixel) }} / {(Total gradation value of pixels in cell) + (gradation value of selected unprocessed pixel)}
(X _{center of gravity} , y _center of gravity are coordinates of the _center of gravity)
This arithmetic expression is stored in the ROM 28, and the CPU 26 reads out and performs arithmetic operation in step S305. Of course, in the case of the initial pixel, the x and y coordinates of the center of gravity of the cell are calculated as “0”. The CPU 26 stores the calculated barycentric position and the number of pixels constituting the cell “1” at the addresses “x”, “y”, and “gn” of the working memory area 272 described above (FIG. 6B). reference).

  If it is determined in step S304 that the gradation value of the initial pixel is smaller than the threshold value, the CPU 26 stores “0” in the output buffer 273 corresponding to the initial pixel, and in step S304, the gradation value of the initial pixel. Is determined to be greater than or equal to the threshold, “255” is stored in the output buffer 273 corresponding to the initial pixel at that time. This is because a process of outputting a dot at this pixel position is performed. Here, since the gradation value of the selected initial pixel is smaller than the threshold value, “0” is stored by the CPU 26 in the output buffer 273 of the RAM 27 corresponding to the selected pixel as shown in FIG. become.

  Subsequently, the CPU 26 stores the gradation value of the initial pixel at the address “sum” of the working memory 272 and stores “−1” in the input buffer 271 corresponding to the selected initial pixel. This is because the pixel is not selected again by selecting the unprocessed pixel. Thereafter, at the same time as the selection process of the unprocessed pixel or prior to the selection process, the sum of the gradation values of the pixels that have already constituted the cell so far is stored at the address “sum” of the working memory 272 and stored in the pixel. A process of storing “−1” in the address of the corresponding input buffer 271 is performed.

  The CPU 26 stores “−1” in the input buffer 271. However, the present invention is not limited to this, and other negative values may be used. Further, a memory area having the same configuration as that of the input buffer 271 may be prepared, and a flag may be set at a position corresponding to the selected pixel to determine whether or not the selected pixel has been processed.

  The unprocessed pixel selection process is performed by selecting a pixel closest to the center of gravity calculated in step S305. At this time, the tone value of the input pixel at the address (1, 0) or the tone value of the input pixel at the address (0, 1) from the tone value of the input image stored at a predetermined position of the input buffer of the RAM 27. A tone value is selected. This is because the two pixels are equidistant from the address (0, 0). Here, it is necessary to select one of the pixels, but in this embodiment, the CPU 26 randomly selects a pixel, and here, selects the gradation value of the pixel at the address (1,0) in the 0th row and the first column. To do. Thus, the process of enlarging the cell is performed.

  Next, the CPU 26 determines again whether or not the total gradation value of the pixels in the cell is less than the threshold value (step S304). That is, it is determined whether or not the total gradation value of the pixels in the cell including the gradation value of the selected unprocessed pixel is less than the threshold value “255”. Specifically, the CPU 26 reads out the gradation value of the pixel written in the address “sum” of the working memory 272 of the RAM 27 and the gradation value stored in the input buffer 271 of the pixel selected in step S305, The sum is calculated and compared with a threshold value (here, “255”) stored in the ROM 28. For example, in the example of FIG. 6C, the value “50” stored at the address “sum” of the working memory 272 and the gradation value “50” stored at the address (1, 0) of the input buffer 271. The total value is “100”. Therefore, the total value does not reach the threshold value.

  When the sum of the gradation values does not exceed the threshold value (step S304: YES), the CPU 26 calculates the center of gravity including the unprocessed pixel selected again (step S305). The specific calculation of the CPU 26 is performed using the above-described equation (Equation 1). The x and y coordinates of the center of gravity obtained so far are (0, 0), the tone value of the center of gravity is “50”, the x and y coordinates of the unprocessed pixel are (1, 0), and the tone value is “ Since it is 50 ″, the calculated center-of-gravity position is (0.5, 0). The barycentric position calculated in step S305 is stored in the working memory 272 of the RAM 27 together with the number of pixels of the cell under the control of the CPU 26. In the previous processing in step S305, the barycentric position of the initial pixel and the number of pixels of the initial pixel were stored in the working memory 272, but here the position is overwritten and stored. An example after the storage is shown in FIG.

  Here, as shown in FIG. 6C, the center-of-gravity position is not necessarily an integer value. At this time, in addition to the method of selecting the pixel closest to the center of gravity in the selection of the unprocessed pixel, the method of selecting the pixel closest to the pixel where the center of gravity is located, that is, the pixel closest to the center of the pixel where the center of gravity is located may be used. Good. In this way, the position of the center of gravity can be calculated as an integer value, and a reduction in the amount of calculation processed by the CPU 26 can be expected.

  In this way, each time the processes in steps S304 and S305 are repeated, the CPU 26 configures the cell until the threshold value is reached while determining whether the total gradation value of the pixels in the cell calculated in step S304 is equal to the threshold value. I can go. If the threshold value has not been reached, as described above, the CPU 26 stores “0” in the position of the output buffer 273 corresponding to the position of the input pixel incorporated in the cell.

  FIG. 7A shows a state when the unprocessed pixel (1, 1) is selected. The “sum” value of the working memory 272 and the gradation value of the pixel (1, 1) are shown. The CPU 26 reads out the total value and the threshold value stored in the ROM 28 and compares them. Since the threshold value has not yet been reached, the pixel (1, 1) is incorporated in the cell, and the working memory 272 stores the position of the center of gravity and the cell. And “0” is stored in the output buffer 273 corresponding to the selected unprocessed pixel (1, 1).

  FIG. 7B shows a process performed subsequently to FIG. 7A and shows a state where the input pixel (1, 2) is selected as the pixel closest to the center of gravity position. At this time, a state is shown in which the sum of the gradation values of the cells is “255”. That is, since the total tone value “205” of the pixels incorporated in the cell is stored in the working memory 272 of the RAM 27, the total value of the value and the value of the pixel (1, 2) is set as the threshold value. By comparing, the CPU 26 determines that it is equal to the threshold value. In this case, the CPU 26 stores “0” at the position of the output buffer 273 corresponding to the position of the input pixel (1, 2) incorporated into the cell.

  If the CPU 26 determines that it is equal to the threshold value (step S306: YES), the process proceeds to step S307, the center of gravity position is calculated using (Equation 1), and the calculated center of gravity position and the number of pixels of the cell “5”. Is stored in the working memory 272. Then, a process for forming a dot of a predetermined color component on a pixel located at the center of gravity is performed. Specifically, as shown in FIG. 7B, a predetermined gradation data “255” for forming a dot at an address position corresponding to the position of the input pixel of the output buffer 273 of the RAM 27 is formed into a cell. The number of pixels to be written (in this embodiment, “G5”) is written. The structure of the output buffer is also a two-dimensional structure like the input buffer, and has an address space corresponding to the position of the input pixel. By writing “255” and “G5” at the address where the center of gravity is located, a dot can be actually formed at a position corresponding to that position.

  Next, the CPU 26 proceeds to a process of generating a new cell with the writing process of the data “255” as a trigger, and determines again whether there is an unprocessed pixel (step S302). That is, it is determined whether or not the above-described processing has been performed for all gradation values written in the input buffer 271 of the RAM 27. If there is an unprocessed pixel (YES), the processing proceeds to step S304 again, and the above-described processing is performed. Repeat the process. For example, FIG. 7C shows a state where the unprocessed pixel (2, 0) is searched for in accordance with the scanning method described above. If the process is completed for all the pixels (step S302: NO), the CPU 26 proceeds to step S303, and the process ends.

  On the other hand, when the sum of the gradation values of the pixels in the cell exceeds the threshold value in step S306 (NO), the process proceeds to step S309, and a centroid calculation process is performed when the threshold value is exceeded.

  FIG. 8A shows a state in which the processing of the CPU 26 is performed subsequent to the processing described in FIG. 7, and is stored in the RAM 27 when the input pixel (3, 1) is incorporated in the cell. Shows the state of the data. Next, as shown in FIG. 8B, when the pixel located at (3, 2) is selected, the calculation result of the total gradation value of the pixels in the cell is “265”, and the threshold value “ Will exceed 255 ". Therefore, in such a case (step S306: NO), the process proceeds to step S309.

  In step S309, a process of calculating the center of gravity of the cell while ignoring the gradation value exceeding the threshold value, that is, the error data is performed. For this reason, first, the CPU 26 performs an operation of subtracting the total tone value of the pixels in the cell from the threshold value, that is, the tone value of the working memory stored in step S305 so far from the threshold value. For example, in the example shown in FIG. 8, the threshold value is “255”, and the gradation value stored so far in step S305 is “205” as shown in FIG. 8B. A value of 205 = 50 is calculated.

  Then, the CPU 26 calculates the center of gravity using the value of “50” as the gradation value of the pixel (3, 2), so that the error data is ignored. Specifically, the following calculation formula is used for the calculation of the center of gravity.

(Formula 2)
x _centroid = {{(x coordinate of centroid of cell) × (total gradation value of pixels in cell)} + {(x coordinate of selected unprocessed pixel) × (gradation value of pixel in cell is threshold value) Tone value until}}} / {(total tone value of pixels in cell) + (tone value until tone value of pixel in cell becomes threshold)}
y _centroid = {{(y coordinate of centroid of cell) × (total tone value of pixels in cell)} + {(y coordinate of selected unprocessed pixel) × (tone value of pixel in cell is threshold) Gradation value until it becomes}}} / {(total level value of pixels in cell) + (gradation value until gradation value of pixel in cell becomes threshold value)}
(X _{center of gravity} , y _center of gravity are coordinates of the _center of gravity)
The difference from (Expression 1) is that the gradation value of the selected unprocessed pixel is not used as it is, but the gradation value until the sum of the cells becomes equal to the threshold value is used.

  This arithmetic expression is stored in the ROM 28 in advance as in the case of (Expression 1), and is executed by the CPU 26 reading it from the ROM 28 when executing this step. Here, the gradation value of the pixel finally selected in step S305 is calculated to be equal to the threshold value by summing up with the sum of the gradation values calculated so far, and based on that, the center of gravity is calculated. The position will be determined. Then, the CPU 26 writes the calculated gravity center position data in the working memory 272 of the RAM 27 again (see the working memory 272 in FIG. 8B).

  Further, the CPU 26 calculates a value (error data) obtained by subtracting the calculation result calculated in step S309 from the original gradation value of the finally selected pixel, and the value is again input to the input buffer of the selected pixel. Write (step S310). Thereby, the gradation value of the pixel is stored as return data. In the example shown in FIG. 8C, “50” calculated in step S309 is subtracted from the gradation value “60” of the finally selected pixel (3, 2) to obtain the value “10”. Is again written in the address (3, 2) of the input buffer 271 of the RAM 27 (see the input buffer 271 in FIG. 8C).

  Then, this pixel is not incorporated into the cell, and the gradation conversion is treated as unprocessed, and processing is performed to make an unprocessed pixel incorporated into a new cell (step S311). As described above, the error data is not returned to pixels other than the cell, but is returned to the pixel where the error data is generated and stored again, and the pixel is set as an unprocessed pixel. Dots can be formed at faithful positions.

  Thereafter, the process proceeds to step S308, and the above-described process is repeated. Incidentally, the CPU 26 stores “255” and “G4” in the output buffer 273 corresponding to the pixel in which the center of gravity exists. Thereby, a dot can be formed in the pixel in which a gravity center position exists by subsequent processing.

  As described above, halftone processing by the CC method is performed on each hue data by the hardware configuration shown in FIG.

(B) Halftone Processing by Error Diffusion Method Next, halftone processing by the error diffusion method will be described with reference to FIGS. The error diffusion method repeats main scanning in the horizontal direction (from the left to the right of the input image on the screen of the input image) for the pixels of the input image arranged in a dot matrix, while moving the scanning line in the vertical direction (from the top of the input image on the screen). The target pixel is sequentially scanned by executing sub-scanning (downward), and tone conversion is performed on the target hue data for all the pixels to a tone value having a predetermined type of value. By scanning the target pixel in this way, as shown in FIG. 9A, the upper row and the lower row of the target pixel are divided into a gradation-converted region and an unconverted region. Further, the left column and the right column of the pixel of interest are also divided into a tone converted region and a tone non-converted region. Basically, a difference value between a predetermined gradation value after gradation conversion and an original gradation value before gradation conversion, that is, an error value by quantization (hereinafter simply referred to as “error value”) By assigning them to the right and below, the error of the gradation value in the entire input image is reduced.

  Further, as shown in FIGS. 9B to 9D, when assigning the error value to be diffused, the error value is diffused by weighting a predetermined pixel with respect to the target pixel. In this way, by spreading weighted predetermined pixels, it is possible to set the degree of diffusion of the gradation value of the input image.

  Now, the tone conversion process (binarization) process using the error diffusion method will be described with reference to the flowchart of FIG. 10 and the schematic diagram of FIG. Note that the error value diffusion in this embodiment is performed using the weighting method of FIG. The error value generated in the target pixel is diffused at a ratio of 2 to the right adjacent pixel, 2 to the lower pixel, 1 to the lower right pixel, and 1 to the lower left pixel.

  In this process, attention is first focused on the pixel in the p-th row and the q-th column. The hue data of the C, M, and Y color components of the pixel is represented as data_C [p] [q] if it is a cyan (C) component, for example. This is binarized for each component of C, M, and Y. As an example, processing for binarizing the C component will be described below. Of course, the processing is basically the same for the M and Y components. Note that an error value generated by binarization of the pixels in the p-th row and the q-th column is represented as err [p] [q].

Similarly, in this process, the gradation value (pre_C) of the C component after the gradation conversion process is expressed as follows.
pre_C [0] = 0
pre_C [1] = 255

  The threshold value SH used for the gradation conversion process is set to a predetermined value that is larger than pre_C [0] and smaller than pre_C [1].

  When the process of FIG. 10 is started, first, in step S401, a process for determining whether or not the hue data data_C [p] [q] of the pixel in the p-th row and the q-th column as the target pixel is smaller than the threshold value SH. I do. As a result of the determination, if the gradation value of data_C [p] [q] of the pixel of interest is smaller than the threshold value SH (YES), the gradation value of pre_C is set to the value “0” of pre_C [0] (step S402). ).

  On the other hand, if the gradation value of data_C [p] [q] is larger than the threshold value SH (NO), whether or not the gradation value of data_C [p] [q] is equal to or less than the value of pre_C [1] in step S405. The process which determines is performed. If the result of determination is YES, the level value of pre_C is set to the value “255” of pre_C [1] (step S406).

  Basically, data_C [p] [q] is gradation-converted to either “0” or “255” by the above processing, but if it is not converted to either, that is, step If the determination result is NO in S405, the process ends as a data error.

  Next, a process of calculating err [p] [q] from the gradation value pre_C and data_C [p] [q] set to either “0” or “255” in steps S402 and S406 is performed. (Step S407). In the next step S408, the calculated err [p] [q] is diffused to predetermined pixels according to the weighting in FIG. 9B.

  The above processing is performed according to a predetermined scanning order, and it is determined whether or not all of p and q have been completed (step S409). If not completed (NO), p and q are updated (step S410), and sequentially. By performing tone conversion on the hue data of the pixels, the processing for all the pixels of the input image is finished (YES), and the tone conversion processing by the error diffusion method is finished. Of course, the pixel in the first row and the first column of the input image is set as the first target pixel, and then the target pixel is updated according to the main scanning and sub-scanning methods described above.

  The process shown in the flowchart of FIG. 10 will be described as an example with reference to the schematic diagram of FIG. Here, it is assumed that tone conversion is performed on the hue data of the cyan component shown in FIG. 4A by using a predetermined threshold “70”. Of course, the threshold value is not limited to this, and may be any value between the values “0” and “255” as described above.

  As shown in FIG. 11A, the pixel in the p-th row and the q-th column is a pixel of interest. Hereinafter, the pixel is expressed as (q, p). By comparison with the threshold “70”, (q, p) is converted into a gradation value “0” as shown in FIG. Then, according to the weighting method, the error value “50” (= 50−0) is “17” for each pixel (q + 1, p) and (q, p + 1) indicated by the shaded portion, and “8” for (q + 1, p + 1). Each error value is diffused. When the target pixel is located at an end portion such as the left end of the input image, the error value is distributed according to the respective weighting coefficients for the existing pixels among the pixels to be subjected to the error value diffusion. Accordingly, the gradation value corresponding to the weight for the nonexistent pixel is lost.

  Next, according to the scanning method, (q + 1, p) is set as the next pixel of interest (FIG. 11 (c), thick frame portion), and (q + 1, p) is converted into a gradation value “0” by comparison with a threshold value ( FIG. 11 (d)). Then, according to the weighting method, the error value “67” (= 67-0) is set to “22”, (q + 2) for each pixel (q + 2, p) and (q + 1, p + 1) indicated by the shaded portion in FIG. , P + 1) and (q, p + 1), the error value “11.5” is diffused.

  Next, according to the scanning method, (q + 2, p) is set as a pixel of interest (FIG. 11 (d) thick frame portion), and (q + 2, p) is converted into a gradation value “255” by comparison with a threshold value (FIG. 11). (E)). Then, according to the weighting method, the error value “−183” (= 72−255) is “−61” in (q + 3, p) and (q + 2, p + 1), and “−30” in (q + 3, p + 1) and (q + 1, p + 1). Each error value of .5 "is diffused.

  According to the scanning method, (q + 3, p) is the next pixel of interest (FIG. 11 (f), thick frame). At this time, the error value corresponding to the weighting of pixels (q + 2, p + 1) and (q + 3, p + 1) is This is the target pixel to be diffused (see the shaded portion in FIG. 11 (f)). As described above, the hue data conversion process is repeatedly performed for all the pixels of the input image, whereby the gradation conversion (binarization) process is performed on the target hue data.

  As described above, halftone processing using the error diffusion method diffuses the gradation value generated with quantization as an error value in a certain direction. This is a gradation conversion processing method that can perform gradation conversion while maintaining almost the original gradation value, although the gradation value is lost.

  The hardware configuration for the error diffusion method is basically the same as the hardware configuration in the gradation conversion by the CC method described above, such as the configuration of the input buffer and output buffer of the CPU 26, ROM 28 and RAM 27. Further, since the error diffusion method is a well-known technique, a description of the hardware configuration and the details of the control processing based on the erroneous calculation diffusion method performed by the CPU 26 is omitted here.

(C) First embodiment of halftone processing (data processing by filter)
(C1) Halftone Processing Using Error Calculation Diffusion Method Next, a first example of the halftone processing in this embodiment will be described with reference to the flowchart of FIG. In the first embodiment, when halftone processing of hue data of cyan and magenta components having two different hues is performed, the difference data between the hue data of the cyan component and the binarized data is processed by a predetermined filter. Are added to the hue data of the magenta component to form dots that do not overlap each other and have good dispersibility.

  When this processing is started, first, in step S201, processing for reading an input image is performed. Specifically, the process of reading the input image is performed by taking the hue data DT output from the color conversion unit 21 (FIG. 1) into the input buffer. Normally, all the hue data is read and halftone processing is performed to output dots. However, here, in order to facilitate the explanation, cyan (two hue data having a uniform gradation value and different hues) It is assumed that the component C) and the magenta (M) component are taken into the input buffers 271Ca and 271Ma, respectively. FIG. 13A shows a state in which hue data of a cyan component having a uniform gradation value “20” is captured. Further, in this embodiment, for the sake of simplicity of explanation, both the cyan component and magenta component that have been taken in are assumed to be hue data of 8 × 8 pixels (addresses (0, 0) to (7, 7)). To do.

  Next, in step S202, the gradation value of the cyan component is binarized using an error diffusion method. In the binarization process here, the threshold value is set to “45”, and the error value is diffused and gradation-converted by the weighting method of FIG. 9B. As a result, the binarized data shown in FIG. 13B is stored in the cyan output buffer 273Ca.

  Next, in step S203, calculation processing of difference data of the cyan component is performed. The difference data is obtained by subtracting the binarized data of the cyan component from the hue data of the cyan component. Specifically, the data of the cyan output buffer 273Ca is calculated from the data of the cyan input buffer 271Ca by performing a minus operation. The calculation result is stored in the cyan output buffer 273Cb as shown in FIG.

  The calculated difference data is obtained by greatly reducing the gradation value of only one pixel corresponding to the position where the dot is formed. Therefore, if this difference data is added to magenta in the subsequent processing steps as it is, the dot suppression effect remains only in the pixels in which the dot formation of one hue has been performed, as in the above-described Patent Document 2, and therefore, Dots are easily formed in adjacent pixels, and the dots are less likely to be dispersed. In step S204, the cyan component difference data is subjected to data processing using a predetermined filter. That is, using a predetermined filter matrix, a process of dispersing the dot suppression effect around the pixel positions where dots are formed is performed.

  14A to 14C illustrate filter matrices. In this embodiment, the filter matrix to be used is a matrix having a size corresponding to the ratio of the lightness of the first dots and the lightness of the second dots. For example, when the brightness of the first dot is high, a large matrix as illustrated in FIG. On the other hand, when the brightness of the first dot is low, a small matrix as illustrated in FIG. In this way, when the first dot is a dot with high brightness, that is, a dot that is more conspicuous, the second dot is formed at a position far from the position of the first dot, and the first dot is a dot with low brightness, that is, more In the case of a dot that is not noticeable, the second dot is formed at a position close to the position of the first dot. As a result, there is an effect of keeping the brightness per unit area constant on the printed paper surface.

  In this embodiment, it is assumed that the data processing is performed using the filter matrix shown in FIG. 14B from the brightness ratio of the cyan component output dots and the magenta component output dots. A predetermined filter matrix used according to the lightness ratio of dots output by the image output device 30 is stored in advance in the ROM 28 or the hard disk 29, and is read out and processed by the CPU 26 during data processing. Of course, a predetermined filter matrix may be used regardless of the brightness of the output dots.

  Data processing using the filter matrix will be described with reference to FIG. As shown in FIG. 15A, first, data processing is performed using a filter matrix indicated by a thick frame with the address (0, 0) of the cyan output buffer 273Cb as a target pixel. At this time, as shown in the figure, a portion that does not overlap with the address of the cyan output buffer 273Cb in which the difference data is stored is generated on the filter matrix, and data processing is performed with the gradation value of this portion set to “0”. As a result of the data processing, the gradation value of the address (0, 0) becomes “10” and is stored in the cyan output buffer 273Cc prepared in the RAM 27.

  Next, the target pixel moves to the address (1, 0), and data processing is performed in the same manner, and the gradation value “−11” is stored in the address (1, 0) of the cyan output buffer 273Cc. Thereafter, data processing is repeatedly performed using the filter matrix in accordance with the above-described main / sub-scanning order. FIG. 15C shows the progress of data processing. When the address (4, 1) is the target pixel, the gradation value “20” is the address (4, 1) of the cyan output buffer 273Cc. ) Is stored.

  When the data processing is completed for all addresses, the difference data of the cyan component is in the state of the cyan output buffer 273Cc shown in FIG. In the next step S205, processing for adding the difference data of the cyan component subjected to the data processing to the magenta component is performed. FIG. 16B shows the hue data of the magenta component taken into the magenta input buffer 271Ma prepared in the RAM 27, as with the cyan component. In this embodiment, it is assumed that the magenta component is also hue data having a uniform gradation value “20”. The addition process is performed by performing a plus operation on the data in the cyan output buffer 273Cc and the data in the magenta input buffer 271Ma. This result is stored in a magenta input buffer 271Mb prepared in the RAM 27 (see FIG. 16C).

  The hue data of the magenta component thus added is binarized in the next step S206. In the present embodiment, the binarization process of the hue data of the magenta component is also performed using the threshold “45” and the error diffusion method based on the weighting shown in FIG. .

  As a result of the binarization process in step S206, the magenta output buffer 273Ma becomes as shown in FIG. Then, a color dot (simply a magenta dot) corresponding to a magenta component is formed at a pixel position corresponding to an address in which the gradation value “255” is stored. On the other hand, the color dot corresponding to the cyan component (simply cyan dot) is located at the pixel position corresponding to the address of the gradation value “255” stored in the cyan output buffer 273Ca as shown in FIG. It is formed. Accordingly, the two types of dots are formed as shown in FIG.

  Although the processing of the first embodiment using the error diffusion method is completed as described above, as is clear from FIG. 17C, according to the halftone processing of the present embodiment in which data processing using a predetermined filter is performed. It can be seen that, for two types of hue data with different hues, the dots formed do not overlap and have good dispersibility. In this embodiment, if the hue data of cyan and magenta are binarized by the same miscalculation diffusion method without performing the processing of steps S203 to S205, since the input data is exactly the same, the cyan component is also magenta. Both components are the binarized data shown in FIG. 13B, and dots are formed at exactly the same pixel positions. According to this embodiment, dots are formed with good dispersibility without overlapping.

(C2) Halftone processing using CC method In FIG. 17C, four cyan dots are formed and six magenta dots are formed. Here, since the sum of the gradation values of all the pixels of each input hue data is “1280” (= 20 × 8 × 8), the number of gradation values “255” after binarization is When the gradation value is stored, “5” (= INT (1280/255): INT is a function for rounding down the decimal point), and a total of five dots should be formed. However, when the miscalculation diffusion method is used for the binarization process, the error value is diffused to the right side and the lower side of the entire image according to the weighting method as described above. A part of the tone value is lost, and as a result of the binarization processing of the cyan component, the number of addresses where the tone value “255” is stored becomes four, and the tone value of the binarized data is reduced. The gradation value of the component increases. As a result, cyan dots to be formed are reduced and magenta dots are increased, resulting in a difference in the number of dots.

(C2) Halftone Processing Using CC Method In view of the difference in the number of dots formed as described above, a first embodiment using halftone processing using the CC method will be described with reference to the flowchart of FIG. First, in step S201, the same cyan component tone value as shown in FIG. 13A is stored in the cyan input buffer 271Ca (see FIG. 18A). When this is binarized using the CC method in step S202 (FIG. 12), data of the cyan output buffer 273Ca shown in FIG. 18B is obtained. In the figure, a thick frame represents a generated cell, “255” represents a dot formation position, and “G13” and “G11” represent the number of pixels constituting the cell. As shown in FIG. 18B, according to the CC method, since the gradation value is stored, five cyan dots are formed.

  Next, when the cyan component difference data is calculated in step S203, the data of the cyan output buffer 273Cb shown in FIG. 18C is obtained. Next, when data in the cyan output buffer 273Cb is processed using the predetermined filter matrix shown in FIG. 14B in step S204, the data in the cyan output buffer 273Cc shown in FIG. obtain. Then, this data is added to the data in the magenta input buffer 271Ma shown in FIG. 19B (step S205), and gradation value data to be binarized is obtained in the magenta input buffer 271Mb (FIG. 19). 19 (c)).

  In step S206, the tone value of the magenta component stored in the magenta input buffer 271Mb is binarized using the CC method. FIG. 20A shows the state of the generated cell with a thick frame line. Then, the data of the binarization processing by the CC method is stored as binarized data at each address of the magenta output buffer 273Ma as shown in FIG. As a result, cyan dots and magenta dots are formed as shown in FIG.

  As described above, data processing is performed using a predetermined filter matrix, and dots corresponding to two types of hue data are formed without overlapping by binarization processing by the CC method. Also, the number of cyan dots is five.

  However, the number of magenta dots formed is still four, and the dispersibility of cyan dots and magenta dots is inferior compared with the error diffusion method shown in FIG. This is because the gradation value at the position where the filter matrix and the input buffer address do not overlap is processed as “0” when data processing by a predetermined filter is performed in step S204 of FIG. This is because the positive tone value of the cyan component is lost in the address, and as a result, the minus tone value is added to the magenta component in step S205, and the tone value of the magenta component is reduced.

(D) Second embodiment of halftone processing (distributed processing by profile)
Therefore, as a second embodiment, the dot data is not overlapped and an appropriate number of dots is formed by replacing the difference data with a method of performing data processing using a filter and dispersing binarized data with a predetermined profile. A binarization processing method using the CC method will be described.

  The halftone process performed in the second embodiment will be described with reference to the flowchart of FIG. In the second embodiment, when halftone processing of hue data of cyan and magenta components having two different hues is performed, data of gradation value “255” among binarized data of cyan components is used with a predetermined profile. The difference data between the dispersed data and the hue data before binarization processing is added to the hue data of the magenta component to form dots that do not overlap each other and have good dispersibility.

  When this process is started, first, in step S211, a process of reading an input image is performed. Here, as in the first embodiment described above, the cyan (C) component and the magenta (M) component, which are two hue data shown in FIG. 13A, are input to the input buffers 271Ca and 271Ma, respectively. Suppose that it was taken in.

  Next, in step S212, the gradation value of the cyan component is binarized using the CC method. By this processing, the binarized data shown in FIG. 22A is stored in the cyan output buffer 273Ca in the same manner as the binarization processing of the cyan component gradation value by the CC method in the first embodiment described above. Stored.

  Next, in step S213, a process of converting the gradation value “255” of the cyan dot into distributed data distributed according to a predetermined profile is performed. Specifically, the gradation value “255” is distributed to the peripheral pixels according to a predetermined profile with reference to the pixel position where the dot is formed. By this processing, the dot suppression effect is not limited to only one pixel corresponding to the position where the dot is formed, and the dot suppression effect is spread to the periphery thereof. Therefore, the formed first dot and second dot are dispersed. Is done.

  In this embodiment, the filter matrix shown in FIGS. 14A to 14C is used as the predetermined profile. Of course, different profiles may be used. Furthermore, in this embodiment, for each generated cell, a different profile is used according to the number of pixels constituting the cell. For example, when the number of pixels constituting the cell is small, the matrix shown in FIG. 14A is used, and when the number of pixels constituting the cell is large, the matrix shown in FIG. 14B is used. . As a result, the dot positions of the second dots formed for the second hue data are moved away according to the density of the first dots formed for the first hue data, so that the dot dispersibility is improved. .

  Specifically, in the cyan output buffer 273Ca, the profile shown in FIG. 14B is applied with reference to the address where “G13” indicating 13 pixels constituting the cell is stored. FIG. 22 (c) shows that the profile shown in FIG. 14 (a) is applied with reference to the address where “G11” indicating the number of pixels constituting the cell shown in FIG. Indicated. FIG. 23A shows the cyan output buffer 273Cb converted into distributed data in step S213.

  Next, in step S214, processing for calculating difference data of the cyan component is performed. The difference data is obtained by subtracting the cyan component dispersion data from the cyan component hue data. Specifically, the data of the cyan output buffer 273Cb is calculated from the data of the cyan input buffer 271Ca by performing a minus operation. The calculation result is stored in the cyan output buffer 273Cc as shown in FIG.

  Next, in step S215, processing for adding the difference data of the cyan component calculated to the magenta component is performed. FIG. 23C shows the magenta input buffer 271Ma in which the tone values of the magenta component are stored. The difference data shown in FIG. 23B is added to this data. The addition result is shown in FIG. The added data is stored in the magenta input buffer 271Mb, and binarization processing by the CC method is performed on the added data in the next step S216.

  The binarized data processed in step S216 is shown in FIG. It can be seen that the magenta output buffer 273Ma stores the gradation value “255” at five addresses and forms five magenta dots. On the other hand, as shown in FIG. 22A, the cyan dot is formed at a pixel position corresponding to the address of the gradation value “255” stored in the cyan output buffer 273Ca. Accordingly, the two types of dots are formed as shown in FIG.

  Although the processing of the second embodiment is completed as described above, as is apparent from FIG. 24C, according to the present embodiment in which the distributed data conversion processing using a predetermined profile is performed, two types of hues having different hues Regarding the data, it can be seen that the number of dots to be formed is formed from the tone values of the input hue data, and the dots do not overlap and have good dispersibility.

  As described above, according to the first and second examples of the present embodiment, a predetermined filter or a predetermined profile is used to determine a predetermined value from the tone value of the cyan component that is the first hue data. By adding the data calculated by the processing to the tone value of the magenta component that is the second hue data, the magenta dot that is the second dot is centered on the pixel position where the cyan dot that is the first dot is formed. The inhibitory effect on the formation of can be expanded. As a result, two different types of dots do not overlap and are formed with good dispersibility.

  In the first and second embodiments, the cyan component is treated as the first hue data and the magenta component is treated as the second hue data. Conversely, the magenta component may be treated as the first hue data. Also, any two of the three color components including the yellow component may be used as the first hue data and the second hue data, respectively. Further, in the above-described embodiment, for example, three color components are used. However, among the plurality of color components output by the image output apparatus 30 including the black component, any two color components are respectively converted into the first hue data and the second Hue data.

  In the first and second embodiments described above, the case where halftone processing is performed on two hue data has been described. However, when halftone processing is performed on three or more hue data, two hue data is processed. This halftone process may be repeated. That is, if the halftone processing result for two hue data is considered as the processing result for one hue data, this is considered as the first hue data, and the third hue data is considered as the second hue data, then two It can be handled in exactly the same way as the hue data processing method.

  As described above, according to the first and second embodiments, halftone processing (binarization processing) is an error diffusion method or CC that is a processing method that performs gradation conversion by storing gradation values. Since the method is used, it is possible to binarize relatively faithfully to the original input image. In particular, in the CC method, error data of a selected pixel that occurs due to quantization is returned to the original selected pixel without being diffused to other pixels, so that it can be binarized almost faithfully to the input image. .

  Similarly, according to the halftone process by the CC method, since the pixel closest to the center of gravity of the cell is selected and incorporated into the cell, the shape of the generated cell is approximately circular around the center of gravity. Therefore, since the dot output to the center of gravity of the cell is located at the approximate center of the cell, the probability of overlapping dots is low, and it can be expected that dots with good dispersibility are formed.

  The embodiments of the present invention have been described in the first embodiment and the second embodiment. However, the present invention is not limited to these embodiments, and various modifications can be made without departing from the spirit of the present invention. Of course, it can be implemented in various forms. Hereinafter, a modification will be described.

(E) Modification “First Modification”
In the above-described embodiment, the same halftone processing method is used for the first gradation conversion processing and the second gradation conversion processing. However, the present invention is not particularly limited to this. The tone conversion processing method and the second tone conversion processing method may be different halftone processing methods.

  For example, the first gradation conversion process may be performed using the error diffusion method, and the second gradation conversion process may be performed using the CC method, or vice versa. FIG. 25 illustrates a case where the first gradation conversion process is performed using the CC method in the first embodiment, and then the second gradation conversion process is performed using the error diffusion method.

  FIG. 25A shows the same state as the magenta output buffer 271Mb which is the second gradation conversion target data shown in FIG. In the above embodiment, the binarization process is performed using the CC method, but in this modification, the binarization process is performed using the error diffusion method. For comparison, the threshold value “45” and the weighting method shown in FIG.

  As a result of the binarization process using the error diffusion method, the binarized data shown in FIG. 25B is stored in the magenta output buffer 273Ma. Accordingly, the dots formed in this case are as shown in FIG.

  As is apparent from FIG. 25C, both cyan dots and magenta dots are formed with good dispersibility without overlapping. Further, five magenta dots are formed, and the same number as the cyan dots. In the binarization process using the error diffusion method, the smaller the threshold value, the faster the dot output pixel becomes in the scanning order, and hence a negative error value is diffused thereafter. On the other hand, the CC method does not generate a negative error value as described above. For this reason, in this modification, as a result of the binarization processing by the error diffusion method, one magenta dot is added to the CC method. In this way, by changing the halftone processing method between the first gradation conversion process and the second gradation conversion process, it is possible to form dots with good dispersibility faithfully in the input image. is there.

"Second modification"
In the above embodiment, the error diffusion method or the CC method is used as the halftone processing method. However, the present invention is not limited to this, and other processing methods may be used. For example, a systematic dither method used in Patent Document 1 or 2 may be used. According to the halftone processing method according to the embodiment, as described above, since the suppression effect on the formation of the second dots is not limited to the pixels on which the first dots are formed, the halftone processing is systematically performed. Even when the dither method is used, the dispersibility of the dots can be improved.

“Third Modification”
Furthermore, in the above embodiment, the hue data having two different hues of the cyan component and the magenta component has been described as halftone processing. However, the present invention is not limited to hue data having different hues, and the dot size is not limited. It may be hue data with different properties such as hue data corresponding to different output dots, hue data corresponding to output dots having different densities per unit area, or hue data corresponding to output dots having different light reflectance. .

  In general, in an inkjet printer or a laser printer, inks or toners of yellow and black components are prepared in addition to cyan and magenta components, and yellow dots and black dots are formed in addition to cyan dots and magenta dots. In the inkjet printer, light cyan dots and light magenta dots having different densities (thinning) may be formed even with cyan and magenta dots having the same hue. Alternatively, the size of the dots to be formed may be a plurality of types instead of one. For example, an ink jet printer may form three types of dots for each color component. Further, a laser printer may employ a method (halftone dot method) in which gradation is expressed by the size of a dot. For example, 16 types of dots may be formed by pulse width modulation with 4 bits. Alternatively, there are cases where an image is expressed with a difference in the light reflectance of the formed dot surface.

  When dots corresponding to hue data having different properties as described above are formed on the same print medium, each dot data corresponding to the dots to be formed is subjected to the halftone process according to the above embodiment, so that each dot Do not overlap, and dots can be formed with good dispersibility.

"Fourth modification"
In the first embodiment, a predetermined filter matrix is used based on the brightness ratio between the first dot and the second dot. As a fourth modification, the first gradation conversion process is performed using the CC method. When it is performed, data processing may be performed using a filter having a filter characteristic corresponding to the number of pixels constituting the generated cell, that is, a matrix having a size corresponding to the number of pixels constituting the cell.

  For example, in the binarized data obtained by binarizing the first hue data, for each generated cell, a filter matrix specified based on the number of pixels constituting the cell is used, and output is performed corresponding to each cell. The difference data may be processed for each dot. Alternatively, the difference data may be data-processed using one filter matrix specified based on the average value of the number of pixels constituting the cell.

  By doing this, the filter matrix used for the data processing of the difference data is selected according to the dot density (number of dots formed per unit area) of the first dots output for the first hue data. As a result, for example, when the dot density of the first dots is high, a small matrix as illustrated in FIG. 14A is used, and when the dot density of the first dots is low, it is illustrated in FIG. By using such a large matrix, the position of the second dots output corresponding to the second hue data is moved away according to the density of the first dots, and the dispersibility of the dots is improved.

“Fifth Modification”
Further, as a fifth modification, the image processing apparatus in the above embodiment performs processing as an image reproduced and displayed by a laser printer. However, the present invention is not limited to this, and paper or the like is used. In addition to various printers, such as printers that eject ink onto a print medium and printers that print by thermal transfer of ink, images are reproduced and displayed on an electronic photograph or display. May be performed.

“Sixth Modification”
In the above embodiment, the image processing device 20 is independent from the image input device 10 and the image output device 30, but may be incorporated into the image output device 30 such as a printer or a display. The image input device 10 may be incorporated. Alternatively, the image processing apparatus according to the above embodiment is incorporated in various electronic devices such as a copier, a computer device, a word processor, a fax machine, and a mobile phone in which an image output device such as a printer or a display is incorporated. Also good.

The block diagram which shows the whole system in one Embodiment with which this invention is applied. 1 is a schematic diagram illustrating a hardware configuration of an image processing apparatus according to an embodiment. The flowchart explaining the halftone process by CC method in this embodiment. The schematic diagram for demonstrating the halftone process content by CC method. The schematic diagram for demonstrating the halftone process content by CC method. Explanatory drawing explaining the structure of RAM and the example of the data stored. Explanatory drawing explaining the structure of RAM and the example of the data stored. Explanatory drawing explaining the structure of RAM and the example of the data stored. Explanatory drawing explaining the scanning method and error diffusion method of a pixel about an error diffusion method. 6 is a flowchart for explaining halftone processing by an error diffusion method in the present embodiment. The schematic diagram for demonstrating the halftone process content by an error diffusion method. The flowchart explaining the halftone process by 1st Example. Explanatory drawing explaining the example of the data stored in RAM in 1st Example by an error diffusion method. An example of a filter matrix used for data processing in the first embodiment by the error diffusion method. Explanatory drawing explaining the process of the data processing using a filter matrix. Explanatory drawing explaining the example of the data stored in RAM in 1st Example by an error diffusion method. Explanatory drawing explaining the example of the dot formed in 1st Example by an error diffusion method. Explanatory drawing explaining the example of the data stored in RAM in 1st Example by CC method. Explanatory drawing explaining the example of the data stored in RAM in 1st Example by CC method. Explanatory drawing explaining the example of the dot formed in 1st Example by CC method. The flowchart explaining the halftone process by 2nd Example. Explanatory drawing explaining the profile used in 2nd Example by CC method. Explanatory drawing explaining the example of the data stored in RAM in 2nd Example by CC method. Explanatory drawing explaining the example of the dot formed in 2nd Example by CC method. Explanatory drawing explaining the example of the dot formed in the 1st modification.

Explanation of symbols

DESCRIPTION OF SYMBOLS 10 ... Image input device, 11 ... Application part, 12 ... Rasterization part, 20 ... Image processing apparatus, 21 ... Color conversion part, 22 ... Halftone processing part, 25 ... Input-output I / F, 26 ... CPU, 27 ... RAM 28 ... ROM, 29 ... hard disk, 30 ... image output device, 31 ... pulse width modulator, 35 ... print engine, 36 ... laser driver, 37 ... laser diode (LD), CL ... cell, CL1 ... cell, CL2 ... cell , GC ... centroid, GC2 ... centroid, MG ... unprocessed pixel, 271 ... input buffer, 271Ca ... cyan input buffer, 271Ma ... magenta input buffer, 271Mb ... magenta input buffer, 272 ... working memory, 273 ... output buffer, 273Ca ... Cyan output buffer, 273Cb ... Cyan output buffer, 273Cc ... Cyan output Buffer, 273Ma ... magenta output buffer, DT ... CMY hue data, DR ... predetermined gradation data, SH ... threshold.

Claims (30)

An image processing apparatus that performs gradation conversion of at least two or more types of hue data having different properties represented by multi-gradation values into predetermined gradation data for outputting dots for each pixel,
A first gradation conversion unit for gradation-converting the first hue data of the hue data;
A second gradation conversion unit for gradation-converting second hue data of the hue data;
A difference data calculation unit for calculating difference data between the predetermined gradation data subjected to gradation conversion by the first gradation conversion unit and the first hue data;
A data adder that performs data processing on the calculated difference data using a predetermined filter and then adds to the second hue data to obtain added data;
The image processing apparatus according to claim 2, wherein the second gradation conversion unit performs gradation conversion of the addition data as the second hue data into the predetermined gradation data. The image processing apparatus according to claim 1,
The data adding unit includes the predetermined filter having a filter characteristic specified by a lightness ratio between a dot output according to the first hue data and a dot output according to the second hue data. An image processing apparatus using The image processing apparatus according to claim 1, wherein:
The image processing apparatus, wherein the second gradation conversion unit performs gradation conversion on the second hue data using an error diffusion technique. The image processing apparatus according to claim 1, wherein:
The second gradation conversion unit
A pixel group generation unit that sequentially generates pixel groups by selecting pixels until the sum of gradation values of the second hue data for each pixel is equal to or greater than a predetermined threshold;
A data providing unit that assigns the predetermined gradation data to predetermined pixels in the pixel group that are sequentially generated;
An image processing apparatus. The image processing apparatus according to claim 1, wherein:
The second gradation conversion unit
Pixels that generate a pixel group by sequentially selecting pixels based on the first reference point for pixel selection until the sum of the gradation values of the second hue data for each pixel is equal to or greater than a predetermined threshold value A group generator;
A reference point determination unit for determining a second reference point for the generated pixel group;
A data providing unit that provides the predetermined gradation data to a pixel located at the determined second reference point;
The pixel group generation unit updates the first reference point, selects the pixel based on the first reference point, and generates the pixel group,
When the sum of gradation values of the second hue data in the generated pixel group is equal to or greater than the predetermined threshold value, the data providing unit is arranged at the predetermined floor on the pixel where the second reference point is located. An image processing apparatus characterized by adding tone data. The image processing apparatus according to claim 4 or 5, wherein
The pixel group generation unit, when the sum total of the gradation values of the second hue data for each pixel in the pixel group exceeds the predetermined threshold, the pixel group generation unit Returns to the last selected pixel. The image processing apparatus according to claim 5, wherein:
The pixel group generation unit randomly selects a pixel to be selected when there are a plurality of pixels to be selected based on the first reference point. An image processing apparatus according to any one of claims 5 to 7,
The image processing apparatus, wherein the reference point determination unit determines a centroid position calculated from a pixel position and a pixel gradation value in the pixel group as the second reference point. The image processing apparatus according to any one of claims 5 to 8,
The image processing apparatus according to claim 1, wherein the first reference point is a barycentric position calculated from a pixel position in the pixel group and a gradation value of the second hue data of the pixel. An image processing apparatus according to any one of claims 1 to 9,
The first gradation conversion unit sequentially selects pixels based on a third reference point for pixel selection until the sum of gradation values of the first hue data for each pixel becomes a predetermined threshold value or more. A pixel group generation unit that selects and generates a pixel group;
A reference point determination unit for determining a fourth reference point for the generated pixel group;
A data providing unit that provides the predetermined gradation data to a pixel located at the determined fourth reference point;
The pixel group generation unit updates the third reference point, generates the pixel group by selecting the pixel based on the third reference point,
When the sum of gradation values of the first hue data in the generated pixel group is greater than or equal to the predetermined threshold, the data providing unit is configured to detect the predetermined floor at a pixel where the fourth reference point is located. An image processing apparatus characterized by adding tone data. The image processing apparatus according to claim 10,
The pixel group generation unit, when a total sum of gradation values of the first hue data for each pixel in the pixel group exceeds the predetermined threshold value, the pixel group generation unit displays the gradation value for the excess. Returns to the last selected pixel. The image processing apparatus according to claim 10 or 11,
The image processing apparatus, wherein the predetermined filter used by the data adding unit has a filter characteristic specified based on the number of pixels in a pixel group generated by the first gradation conversion unit. An image processing method for gradation-converting at least two types of hue data having different properties represented by multi-gradation values into predetermined gradation data for outputting dots for each pixel,
A first gradation conversion step for gradation-converting the first hue data of the hue data;
A second gradation conversion step of converting the second hue data among the hue data;
A difference data calculation step of calculating difference data between the predetermined gradation data subjected to gradation conversion by the first gradation conversion step and the first hue data;
A data addition step of adding the second difference data to the second hue data after performing data processing on the calculated difference data using a predetermined filter;
The second gradation conversion step includes
A pixel group generation step of generating a pixel group by sequentially selecting pixels based on a first reference point for pixel selection until the sum of the gradation values of the addition data for each pixel becomes equal to or greater than a predetermined threshold value When,
A reference point determining step for determining a second reference point for the generated pixel group;
A data adding step of adding the predetermined gradation data to the pixel located at the determined second reference point;
The pixel group generation step updates the first reference point, selects the pixel based on the first reference point, and generates the pixel group.
In the data adding step, when the sum of the gradation values of the second hue data in the generated pixel group is equal to or greater than the predetermined threshold, the predetermined floor is positioned on the pixel where the second reference point is located. An image processing method characterized by adding tone data. An image processing program that performs gradation conversion of at least two or more types of hue data having different properties represented by multi-gradation values into predetermined gradation data for outputting dots for each pixel,
A first gradation conversion process for gradation-converting the first hue data of the hue data;
A second gradation conversion process for gradation-converting second hue data of the hue data;
Difference data calculation processing for calculating difference data between the predetermined gradation data subjected to gradation conversion by the first gradation conversion processing and the first hue data;
After the calculated difference data is subjected to data processing using a predetermined filter, the computer is caused to execute data addition processing for adding to the second hue data to obtain addition data,
The second gradation conversion process includes:
Pixel group generation processing for generating a pixel group by sequentially selecting pixels based on a first reference point for pixel selection until the sum of the gradation values of the addition data for each pixel becomes equal to or greater than a predetermined threshold value When,
A reference point determination process for determining a second reference point for the generated pixel group;
A data providing process for adding the predetermined gradation data to a pixel located at the determined second reference point;
The pixel group generation process updates the first reference point, selects the pixel based on the first reference point, and generates the pixel group.
The data adding process is performed when the sum of the gradation values of the second hue data in the generated pixel group is equal to or greater than the predetermined threshold value, and the predetermined floor is positioned on the pixel where the second reference point is located. A program characterized by adding tone data.   A computer-readable recording medium on which the image processing program according to claim 14 is recorded. An image processing apparatus that performs gradation conversion of at least two or more types of hue data having different properties represented by multi-gradation values into predetermined gradation data for outputting dots for each pixel,
A first gradation conversion unit for gradation-converting the first hue data of the hue data;
A second gradation conversion unit for gradation-converting second hue data of the hue data;
Dot profile conversion that converts the predetermined gradation data that has undergone gradation conversion by the first gradation conversion unit into distributed data that is distributed to peripheral pixels having a pixel to which a dot is output as a reference point according to a predetermined profile And
A difference data calculation unit for calculating difference data between the converted dispersion data and the first hue data;
A data addition unit for adding the calculated difference data to the second hue data to obtain addition data;
The image processing apparatus according to claim 2, wherein the second gradation conversion unit performs gradation conversion of the addition data as the second hue data into the predetermined gradation data. The image processing apparatus according to claim 16,
The image processing apparatus, wherein the second gradation conversion unit performs gradation conversion on the second hue data using an error diffusion technique. The image processing apparatus according to claim 16,
The second gradation conversion unit
A pixel group generation unit that sequentially generates pixel groups by selecting pixels until the sum of gradation values of the second hue data for each pixel is equal to or greater than a predetermined threshold;
A data providing unit that assigns the predetermined gradation data to predetermined pixels in the pixel group that are sequentially generated;
An image processing apparatus further comprising: The image processing apparatus according to claim 16,
The second gradation conversion unit
Pixels that generate a pixel group by sequentially selecting pixels based on the first reference point for pixel selection until the sum of the gradation values of the second hue data for each pixel is equal to or greater than a predetermined threshold value A group generator;
A reference point determination unit for determining a second reference point for the generated pixel group;
A data adding unit for adding the predetermined gradation data to the pixel located at the determined second reference point;
The pixel group generation unit updates the first reference point, selects the pixel based on the first reference point, and generates the pixel group,
When the sum of gradation values of the second hue data in the generated pixel group is equal to or greater than the predetermined threshold value, the data providing unit is arranged at the predetermined floor on the pixel where the second reference point is located. An image processing apparatus characterized by adding tone data. The image processing apparatus according to claim 18 or 19,
The pixel group generation unit, when the sum total of the gradation values of the second hue data for each pixel in the pixel group exceeds the predetermined threshold, the pixel group generation unit Returns to the last selected pixel. The image processing apparatus according to claim 19 or 20,
The pixel group generation unit randomly selects a pixel to be selected when there are a plurality of pixels to be selected based on the first reference point. The image processing apparatus according to any one of claims 19 to 21,
The image processing apparatus, wherein the reference point determination unit determines a centroid position calculated from a pixel position and a pixel gradation value in the pixel group as the second reference point. The image processing apparatus according to any one of claims 19 to 22,
The image processing apparatus according to claim 1, wherein the first reference point is a barycentric position calculated from a pixel position in the pixel group and a gradation value of the second hue data of the pixel. The image processing apparatus according to any one of claims 16 to 23, wherein:
The first gradation conversion unit sequentially selects pixels based on a third reference point for pixel selection until the sum of gradation values of the first hue data for each pixel becomes a predetermined threshold value or more. A pixel group generation unit that selects and generates a pixel group;
A reference point determination unit for determining a fourth reference point for the generated pixel group;
A data providing unit that provides the predetermined gradation data to a pixel located at the determined fourth reference point;
The pixel group generation unit updates the third reference point, generates the pixel group by selecting the pixel based on the third reference point,
When the sum of gradation values of the first hue data in the generated pixel group is greater than or equal to the predetermined threshold, the data providing unit is configured to detect the predetermined floor at a pixel where the fourth reference point is located. An image processing apparatus characterized by adding tone data. The image processing apparatus according to claim 24, wherein
The pixel group generation unit, when a total sum of gradation values of the first hue data for each pixel in the pixel group exceeds the predetermined threshold value, the pixel group generation unit displays the gradation value for the excess. Returns to the last selected pixel. The image processing apparatus according to claim 24 or 25, wherein:
The dot profile conversion unit distributes the predetermined gradation data for each pixel group generated by the first gradation conversion unit according to the predetermined profile specified based on the number of pixels in the pixel group. An image processing apparatus. An image processing method for gradation-converting at least two types of hue data having different properties represented by multi-gradation values into predetermined gradation data for outputting dots for each pixel,
A first gradation conversion step for gradation-converting the first hue data of the hue data;
A second gradation conversion step of converting the second hue data among the hue data;
Dot profile conversion for converting the predetermined gradation data subjected to gradation conversion in the first gradation conversion step into distributed data distributed in peripheral pixels having a pixel to which a dot is output as a reference point according to a predetermined profile Process,
A difference data calculation step of calculating difference data between the converted dispersion data and the first hue data;
A data addition step of adding the calculated difference data to the second hue data to obtain addition data;
The second gradation conversion step includes
A pixel group generation step of generating a pixel group by sequentially selecting pixels based on a first reference point for pixel selection until the sum of the gradation values of the addition data for each pixel becomes equal to or greater than a predetermined threshold value When,
A reference point determining step for determining a second reference point for the generated pixel group;
A data adding step of adding the predetermined gradation data to the pixel located at the determined second reference point;
The pixel group generation step updates the first reference point, selects the pixel based on the first reference point, and generates the pixel group.
In the data adding step, when the sum of the gradation values of the second hue data in the generated pixel group is equal to or greater than the predetermined threshold, the predetermined floor is positioned on the pixel where the second reference point is located. An image processing method characterized by adding tone data. An image processing program that performs gradation conversion of at least two or more types of hue data having different properties represented by multi-gradation values into predetermined gradation data for outputting dots for each pixel,
A first gradation conversion process for gradation-converting the first hue data of the hue data;
A second gradation conversion process for gradation-converting second hue data of the hue data;
Dot profile conversion that converts the predetermined gradation data that has undergone gradation conversion by the first gradation conversion processing into distributed data that is distributed to peripheral pixels having a pixel to which a dot is output as a reference point according to a predetermined profile Processing,
Difference data calculation processing for calculating difference data between the converted dispersion data and the first hue data;
Causing the computer to execute a data addition process for adding the calculated difference data to the second hue data to obtain addition data;
The second gradation conversion process includes:
Pixel group generation processing for generating a pixel group by sequentially selecting pixels based on a first reference point for pixel selection until the sum of the gradation values of the addition data for each pixel becomes equal to or greater than a predetermined threshold value When,
A reference point determination process for determining a second reference point for the generated pixel group;
A data providing process for adding the predetermined gradation data to a pixel located at the determined second reference point;
The pixel group generation process updates the first reference point, selects the pixel based on the first reference point, and generates the pixel group.
The data adding process is performed when the sum of the gradation values of the second hue data in the generated pixel group is equal to or greater than the predetermined threshold value, and the predetermined floor is positioned on the pixel where the second reference point is located. A program characterized by adding tone data.   A computer-readable recording medium on which the image processing program according to claim 28 is recorded. An image processing apparatus, an image processing method, an image processing program, or a recording medium according to any one of claims 1 to 29,
At least two or more types of hue data having different properties are: (1) hue data corresponding to output dots having different dot sizes;
(2) Hue data corresponding to output dots having different densities per unit area;
(3) Hue data corresponding to output dots with different hues,
(4) Hue data corresponding to output dots having different light reflectivities,
An image processing apparatus, an image processing method, an image processing program, or a recording medium, characterized by being hue data satisfying at least one of them.

Images