JP2009071694A - Image processing device, image recording apparatus, program and record medium - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To solve the problem of degradation of image quality caused by dot of undesirable reproducibility generated in an electronic picture which performs multivalued outputting. <P>SOLUTION: A quantum memory 109 outputs output values Out(x-1, y), Out(x, y-1) in two pixels in a periphery of a noticed pixel as a quantum group q(x, y). A threshold setting part 108 sets a threshold group T(x, y) consisting of a first threshold T1 and a second threshold T2 in a noticed pixel position by using the output value Out in two pixels, and outputs the threshold group T(x, y) to a comparison determining part 103. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to an image processing apparatus, an image recording apparatus (image forming apparatus), a program, and a recording medium for printing multivalued image data with high definition and high gradation.

  There is an image input / output system that outputs multi-valued image data read by an input device such as a scanner or a digital camera to an output device such as a printer or a display. At that time, multi-valued image data read by the input device (for example, 256 gradations for 8-bit accuracy) is converted into image data of the number of gradations that can be output by the output device, and pseudo continuous gradation is expressed. As a method of doing this, there is a pseudo halftone process.

  In particular, binarization processing has been conventionally performed when the output device can express only binary values of dot on / off. Among these binarization processes, there are an error diffusion method and an average error minimum method that are excellent in both resolution and gradation. The error diffusion method and the minimum average error method are logically equivalent, only differing when the error diffusion operation is performed. Hereinafter, the error diffusion method will be described.

  There is a process in which quantization by the error diffusion method is applied not only to binary but also to the number of gradations of 3 or more. Similar to binarization, processing with excellent gradation and resolution is possible.

  By the way, in the electrophotographic process, an image structure in which isolated dots exist because the spatial frequency response deteriorates in each process of exposure, development, transfer, and fixing, including MTF (Modulation Transfer Function) of the photoreceptor. However, there is a problem that even if the signal is input as a recording signal, the reproducibility varies and sufficient gradation reproduction cannot be performed. In particular, in a multi-value writable electrophotographic process such as ternary (large, small dots) and quaternary (large, medium, small dots), the reproducibility of small dots in the low density area to medium density area is constant. It is very difficult to do.

  The error diffusion method is a halftone process in which dots are dispersed according to density by diffusing quantization errors to surrounding pixels when the dots are output. Therefore, many isolated dots are generated from the low density part to the medium density part. In the simple ternary error diffusion, gradation expression is performed with small dots and dot off, and after filling with small dots, gradation expression is performed with small dots and large dots. Such a method of frequently using small dots with poor reproducibility is not preferable for electrophotography.

  If the stability is required in electrophotography, binary writing is preferable, but texture is improved by changing from binary to ternary to quaternary. In addition, one dot generated by binary error diffusion and a large dot generated by ternary and quaternary error diffusion are the same, but small and medium dots are adjacent to a large dot of ternary and quaternary error diffusion to form a cluster. It has been found that it is more stable because it is not isolated than one dot of binary error diffusion.

  Thus, there has been a demand for gradation processing that provides good reproducibility even when multi-value writing such as ternary or quaternary is performed in the electrophotographic process. The following technologies are available to address such issues.

JP 2001-177722 A Japanese Patent No. 3480924 JP 2000-99718 JP 2004-112198 A JP 2005-198067 A

  In order to solve the above problem, Patent Document 1 discloses a technique in which dot-concentrated dither noise is superimposed on a threshold, and each dot quantized by error diffusion is gathered like a dot-concentrated dither superimposed on the threshold. Is disclosed. However, Patent Document 1 does not necessarily guarantee that small dots are not generated, and thus there is a problem that an unstable dot pattern is generated depending on the image type.

  Patent Document 2 discloses an image forming method in which input data of an m-value multi-tone image is quantized to an n value (3 ≦ n <m) by an error diffusion method, and the input data has a predetermined level. At this time, a method has been disclosed in which the interval between a plurality of thresholds is narrowed so that the probability of occurrence of small dots is lowered. In Patent Document 2, small dots are not used in the high density portion, and the same image as binary error diffusion is obtained, resulting in a stable image. However, it is not preferable in the low density portion because small dots are used in isolation.

  Patent Documents 3 to 5 are methods for determining whether a stable dot pattern is obtained by referring to the quantum state around the pixel of interest.

  Patent Document 3 discloses error diffusion in which when an unstable small dot is adjacent in the main scanning direction, the output value at the target pixel position is changed to a dot other than the small dot. When Patent Document 3 is used, it is possible to suppress continuous use of unstable pixels in the main scanning direction, but it is not guaranteed that small dots are not isolated in the low density portion, so when using electrophotography May result in unstable images.

  Patent Document 4 discloses an error diffusion technique in which, in multilevel error diffusion, a small dot is suppressed so that it is output only in a state where the dot is sandwiched in the main scanning direction. If Patent Document 4 is used, small dots will not appear at gradations of density that cannot be expressed in a state where the dots are held off in the main scanning direction, so that a stable image is obtained in the middle and high density portions. However, small dots are always isolated in the main scanning direction in the low density area, and are not suitable for electrophotography, as they are stable only when small dots are adjacent in the sub scanning direction from the low density area to the medium density area. A dot pattern is generated.

  Patent Document 5 discloses a technique that makes it easy for dots to form clusters by setting a threshold value in accordance with the quantum state in the vicinity of the target pixel. If patent document 5 is used, it will become easy to gather a dot in the middle-high density part of binary error diffusion, and it will become a stable image. However, when Patent Document 5 is used for ternary and quaternary error diffusion, clusters are formed with small dots in the low density portion, and medium dots are used after filling with small dots. The image becomes unstable.

  Therefore, there has been a demand for gradation processing that provides good reproducibility even when multi-value writing such as ternary or quaternary is performed in the electrophotographic process.

The present invention has been made in view of such problems,
An object of the present invention is to provide an image processing apparatus, an image recording apparatus, a program, and a recording medium that can solve the image quality degradation problem caused by unreproducible dots that occur in an electrophotography capable of multi-value output.

  In the present invention, in multi-level error diffusion, the threshold value group is controlled according to the quantum data in the vicinity of the pixel of interest, thereby controlling the ease with which dots are produced, and dots with undesirable reproducibility are also used stably. As a result, image quality deterioration is made inconspicuous, and an output image result with good image quality is output. A dot smaller than a large dot is used adjacent to the large dot. Further, dots smaller than the large dots are used adjacent to the large dots from the highlight portion to the medium density portion.

  According to the present invention, in multi-level error diffusion, by controlling the threshold value group according to the quantum data in the vicinity of the pixel of interest, it is possible to control the ease with which dots are generated, so that dots with undesirable reproducibility are also stable. The image quality deterioration can be made inconspicuous, and an output image result with good image quality can be obtained.

  In addition, the history coefficient obtained according to the image data of the pixel of interest is a high value in the low density area and a low value in the high density area, and controls the ease of dot gathering. Can be obtained.

  Further, the variable threshold value obtained according to the image data of the target pixel is divided into N−1 threshold values in the middle and low density portions, and the N−1 threshold values become closer to the higher density portion, and the higher the threshold value, the higher the threshold value. Since the N-1 threshold values are the same value in the density portion, image quality stability equivalent to binary error diffusion can be obtained in the high density portion.

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

Example 1:
FIG. 1 shows a configuration of an image processing apparatus according to a first embodiment of the present invention. FIG. 2 shows the configuration of an image recording apparatus to which the present invention is applied. FIG. 3 shows a configuration of an image input / output system configured using the image processing apparatus of the present invention. In FIG. 3, an image input device 301 indicates an input device such as a scanner or a digital camera. For example, if the input image has 8-bit accuracy, it is captured as image data of 256 gradations. This multivalued image data is input to the image processing apparatus 302 of the present invention.

  The image processing apparatus 302 performs processing for converting the 256 gradation image data input from the image input apparatus 301 into the number of gradations that can be output by the subsequent image recording apparatus (image output apparatus) 303. In this gradation number conversion processing, multilevel error diffusion or a multilevel average error minimum method may be used. The image data quantized by the image processing apparatus 302 is sent to an image recording apparatus (image forming apparatus, image output apparatus) 303 as shown in FIG. Further, the image recording apparatus 303 can apply the processing method according to the present invention even when an image is recorded (image formation) using an ink jet method or gravure printing.

  In FIG. 2, the paper on which an image is to be formed is set on the main body tray 201 or the manual feed tray 202, and the conveyance of the paper is started from the tray 201 or 202 by the paper feed roller 203. Prior to the conveyance of the sheet by the sheet feeding roller 203, the photosensitive member (photosensitive drum) 204 rotates, the surface of the photosensitive member 204 is cleaned by the cleaning blade 205, and then uniformly charged by the charging roller 206. The Here, the laser beam modulated in accordance with the image signal is exposed from the laser optical system unit 207, developed by the developing roller 208, and the toner adheres. Made. The sheet fed from the sheet feeding roller 203 is conveyed while being sandwiched between the photosensitive drum 204 and the transfer roller 209, and at the same time, a toner image is transferred to the sheet. The transferred toner remaining on the photosensitive member 204 is scraped off again by the cleaning blade 205. A toner density sensor 210 is provided in front of the cleaning blade 205, and the density of the toner image formed on the photoconductor 204 can be measured by the toner density sensor 210. The paper on which the toner image is placed is transported to the fixing unit 211 along the transport path, and the toner image is fixed on the paper in the fixing unit 211. The printed sheets finally pass through the paper discharge roller 212 and are discharged in page order with the recording surface down.

  By the way, a video control unit 271 and an LD drive circuit 272 are connected to the laser optical system unit 207, and the video control unit 271 controls an image signal from a personal computer or a workstation, or an evaluation chart held inside. Generate a (test pattern) signal. Further, the developing roller 208 is applied with a high voltage bias by the bias circuit 214, and the overall density of the image is controlled by controlling the bias in the bias circuit 214.

  FIG. 4 shows a configuration example of the laser optical system unit, and shows an example of a positional relationship between the laser optical system unit of FIG. 2 and a photosensitive drum as a latent image carrier on which the emitted light beam is written.

  In FIG. 4, 11 and 12 are laser diodes (semiconductor lasers), 13 and 14 are collimating lenses, 15 is an optical member for optical path synthesis, 16 is a quarter-wave plate, and 17 and 18 are beam shaping optical systems. Each of these optical elements 11 to 18 constitutes a laser light source section (beam light source) Sou. The two light beams P1 emitted from the laser light source unit Sou are converted into parallel light beams by the collimating lenses 13 and 14 and guided to the polygon mirror 19 constituting a part of the scanning optical system. Reflected and deflected in the main scanning direction Q1 by 20a to 20f.

  The reflected and deflected light beam is guided to reflecting mirrors 21 and 22 constituting a part of the fθ optical system, and the reflected and deflected light beam passes through the fθ optical system 23 and is obliquely reflected. 24 and is guided to the surface 26 of the photosensitive drum 25 as a latent image carrier by the oblique reflection mirror 24. The surface 26 of the photosensitive drum 25 is linearly scanned in the main scanning direction Q1 by the light beam P1. This surface 26 is a surface to be scanned by the light beam P1, and writing is performed on this surface to be scanned.

  The laser optical system unit 207 is provided with synchronization sensors 27 and 28 on both sides in the longitudinal direction of the reflection mirror 24 (main scanning direction Q1 of the light beam). The synchronization sensor 27 is used for determining the write start timing, and the sync sensor 28 is used for determining the write end timing.

  Now, the image output device (image recording device) 303 shown in FIG. 3 uses a PWM (Pulse Width Modulation) signal as shown in FIG. 13 to vary the pulse duty, thereby reproducing large dots and small dots. The gradation values of large and small dots are 255 and 128, respectively.

  Further, in the system configuration of FIG. 3, each device is shown as independent depending on the processing. However, the present invention is not limited to this, and the form in which the function of the image processing device 302 exists in the image input device 301 or the image output Some forms exist in the device 303.

  FIG. 1 shows the configuration of the image processing apparatus 302 shown in FIG. The input terminal 101 receives multi-value image data from the image input device 301. Here, in order to represent two-dimensional image data, it is represented as In (x, y) (x represents an address in the main scanning direction of the image, and y represents an address in the sub scanning direction).

  Next, the input data In (x, y) is input to the adder 102. The adder 102 adds the input data In (x, y) and the error component E (x, y) input from the error memory 106, calculates the correction data C (x, y), and calculates the correction data C (x, y). y) is output to the comparison determination unit 103 and the subtractor 105.

  The comparison / determination unit 103 outputs an output value as shown in (1) below based on the correction data C (x, y) input from the adder 102 and the threshold group T (x, y) input from the threshold setting unit 108. Out (x, y) is determined. The threshold value group T (x, y) is a threshold value group consisting of a first threshold value T1 (x, y) and a second threshold value T2 (x, y). The first threshold value T1 (x, y) The threshold for determining dot output and the second threshold T2 (x, y) are thresholds for determining the output of small dots and large dots.

If (C (x, y) <T1)
then Out (x, y) = 0
Else If (C (x, y) <T2)
then Out (x, y) = 128
Else
then Out (x, y) = 255 (1)
This Out (x, y) is output from the output terminal 104 to the image recording device 303.

  The output value Out (x, y) is input to the quantum memory 109 and the subtractor 105. The subtracter 105 subtracts the correction data C (x, y) and the output value Out (x, y) as shown in the following equation (2), and calculates an error e (x, y) generated in the current pixel. .

e (x, y) = C (x, y) -Out (x, y) (2)
Next, the error diffusion unit 107 distributes the error e (x, y) based on a preset diffusion coefficient and adds it to the error data E (x, y) stored in the error memory 106. . Here, for example, when a coefficient as shown in FIG. 5 is used as the diffusion coefficient, the error diffusion unit 107 performs the following processes (3) to (6).

E (x + 1, y) = E (x + 1, y) + e (x, y) × 7/16 (3)
E (x-1, y + 1) = E (x-1, y + 1) + e (x, y) * 5/16 (4)
E (x, y + 1) = E (x, y + 1) + e (x, y) × 3/16 (5)
E (x + 1, y + 1) = E (x + 1, y + 1) + e (x, y) × 1/16 (6)
Further, the quantum memory 109 outputs a quantum group q (x, y), which is a group of a plurality of quantum states around the target pixel, to the threshold setting unit 108 with respect to the accumulated output value. Here, the quantum memory 109 outputs the output values Out (x−1, y) and Out in two pixels (x−1, y) and (x, y−1) adjacent to the target pixel (x, y). Assume that (x, y-1) is output as the quantum group q (x, y).

  The threshold value setting unit 108 outputs the output value Out (x−1) of the quantum group q (x, y) input from the quantum memory 109, that is, two pixels of the pixels (x−1, y) and (x, y−1). , Y), Out (x, y−1), and a threshold value group including a first threshold value T1 (x, y) and a second threshold value T2 (x, y) at the target pixel position as shown in (7) below. T (x, y) is set, and the threshold value group T (x, y) is output to the comparison determination unit 103.

If (Out (x-1, y) = 255)
then T1 (x, y) = 64,
T2 (x, y) = 127
Else If (Out (x, y-1) = 255)
then T1 (x, y) = 64,
T2 (x, y) = 127
Else
then T1 (x, y) = 127,
T2 (x, y) = 127 (7)
As described above, the multi-value error diffusion process in the image processing unit is performed by the configuration of FIG.

  Next, why such a process is effective will be described. As shown in Expression (7), the first threshold T1 (x, y) is determined by T1 (x, y-1) based on the output values Out (x-1, y) and Out (x, y-1) of the pixels adjacent to the target pixel. y) = different from 64 or 127. When the output values of both pixels in the two pixels adjacent to the target pixel are not large dots, the first threshold value T1 (x, y) and the second threshold value T2 (x, y) are the same value (127), and the binary error As with diffusion, only dot off or large dots are output, and isolated small dots are not output. The first threshold value T1 (x, y) is different from the second threshold value T2 (x, y) only when the output value of at least one of two pixels adjacent to the target pixel is 255, that is, a large dot. . At this time, since a large dot is output in an adjacent pixel, it becomes difficult to output a dot due to the propagation of a negative error, but a small dot can be output if a sufficient error is accumulated. Although difficult in the low density portion, small dots can be output adjacent to the large dots from the middle density portion to the high density portion.

  The threshold value setting unit 108 uses the output values Out (x−1, y) and Out (x, y−1) of the pixels adjacent to the target pixel, but even if the setting is changed according to the stability of the output device. Good. Specifically, in an output device that is stable not only adjacent in the main / sub-scanning direction but also continuously diagonally to the right and left, output values Out (x + 1, y−1), Out at the upper right and upper left of the target pixel. What is necessary is just to change so that (x-1, y-1) may be referred.

  In this embodiment, the ternary error diffusion has been described. However, quaternary error diffusion is also possible. Three threshold values used in the four-value error diffusion, the first threshold value T1 (x, y) is a threshold value for determining dot off and small dot output, and the second threshold value T2 (x, y) is a small dot and a medium dot The third threshold value T3 (x, y) is a threshold value for determining the output of medium dots and large dots. If equation (7) is modified as follows, all three threshold values are made the same so that binary error diffusion is achieved when no large dot is output in any pixel in the vicinity of the target pixel. When large dots are output to neighboring pixels, the three threshold values may be set to different values.

If (Out (x-1, y) = 255)
then T1 (x, y) = 43,
T2 (x, y) = 128,
T3 (x, y) = 213
Else If (Out (x, y-1) = 255)
then T1 (x, y) = 43,
T2 (x, y) = 128,
T3 (x, y) = 213
Else
then T1 (x, y) = 127,
T2 (x, y) = 127,
T3 (x, y) = 127
By setting a threshold value with reference to the quantum state in the vicinity of the target pixel in this way, dots smaller than large dots, such as small and medium dots, are output adjacent to the large dots, and texture is added to the medium and high density portions An improved image with good reproducibility can be obtained.

Example 2:
FIG. 6 shows a configuration of an image processing apparatus according to the second embodiment of the present invention.
The input terminal 601 receives multi-value image data from the image input device 301. Here, in order to represent two-dimensional image data, it is represented as In (x, y) (x represents an address in the main scanning direction of the image, and y represents an address in the sub scanning direction).

  Next, the input data In (x, y) is input to the adder 602 and the variable threshold value setting unit 608. The adder 602 adds the input data In (x, y) and the error component E (x, y) input from the error memory 606, calculates the correction data C (x, y), and calculates the correction data C (x, y). y) is output to the comparison determination unit 603 and the subtracter 605.

  Further, the input data In (x, y) is input to the variable threshold setting unit 608. In the variable threshold value setting unit 608, as shown in FIG. 7, a variable threshold value group consisting of a first variable threshold value To1 (x, y) and a second variable threshold value To2 (x, y) according to input data In (x, y). To (x, y) is set and output to the threshold setting unit 609.

  The comparison determination unit 603 outputs an output value as shown in Expression (1) based on the correction data C (x, y) input from the adder 602 and the threshold value group T (x, y) input from the threshold setting unit 609. Out (x, y) is determined. This Out (x, y) is output from the output terminal 604 to the image recording device 303.

  The output value Out (x, y) is input to the quantum memory 610 and the subtracter 605. The subtractor 605 subtracts the correction data C (x, y) and the output value Out (x, y) as shown in the equation (2), and calculates an error e (x, y) generated in the current pixel.

  Next, the error diffusion unit 607 distributes the error e (x, y) and adds it to the error data E (x, y) stored in the error memory 606 as shown in the equations (3) to (6). To go.

  Further, the quantum memory 610 outputs, to the threshold setting unit 609, a quantum group q (x, y) in which a plurality of quantum states around the target pixel are collected with respect to the accumulated output value. Here, the quantum memory 610 uses the output values Out (x−1, y) and Out (x, y−1) in two pixels of the pixels (x−1, y) and (x, y−1) as the quantum group q. Assume that (x, y) is output.

  The threshold setting unit 609 outputs the output value Out (x−1) of the quantum group q (x, y) input from the quantum memory 610, that is, two pixels of the pixel (x−1, y) and (x, y−1). , Y), Out (x, y-1), a variable threshold value group To () including a first variable threshold value To1 (x, y) and a second variable threshold value To2 (x, y) input from the variable threshold value setting unit 608. (x, y) is used to set a threshold value group T (x, y) consisting of a first threshold value T1 (x, y) and a second threshold value T2 (x, y) at the target pixel position as shown in (8) below. Then, the threshold value group T (x, y) is output to the comparison determination unit 603.

If (Out (x-1, y) = 255)
then T1 (x, y) = To1 (x, y),
T2 (x, y) = To2 (x, y)
Else If (Out (x, y-1) = 255)
then T1 (x, y) = To1 (x, y),
T2 (x, y) = To2 (x, y)
Else
then T1 (x, y) = To2 (x, y),
T2 (x, y) = To2 (x, y) (8)
As described above, the multi-value error diffusion processing in the image processing unit is performed by the configuration of FIG.

  Next, why such a process is effective will be described. As shown in FIG. 7, the first variable threshold value To1 (x, y) takes different values depending on the input data In (x, y). First, when the gradation value is 0, the first variable threshold value To1 (x, y) is a value of 64. As the input value increases up to the gradation value 191, the first variable threshold value To1 (x, y) also increases, and After the adjustment value 192, the value is 127, which is the same as the second variable threshold value To2 (x, y). The second variable threshold value To2 (x, y) is a constant value (127) regardless of the input value.

  When the output values of both pixels are not large dots in the two pixels adjacent to the target pixel, the first threshold value T1 (x, y) and the second threshold value are also obtained in the second embodiment, as in the first embodiment. T2 (x, y) becomes the same value, and only dot-off or large dots are output as in binary error diffusion, and isolated small dots are not output.

  The first threshold value T1 (x, y) is different from the second threshold value T2 (x, y) only when the output value of at least one of two pixels adjacent to the target pixel is 255, that is, a large dot. . Since the first variable threshold value To1 (x, y) is a low value of about 64 at the gradation value 1, a large dot is output in an adjacent pixel. It becomes easier to output dots, and small dots are likely to be adjacent to large dots. In the vicinity of the gradation value 191, the first variable threshold value To1 (x, y) is about 126, and the difference from the second variable threshold value To2 (x, y) is only small. Dots may be output. Further, when the gradation value is 192 or more, the first variable threshold value To1 (x, y) and the second variable threshold value To2 (x, y) have the same value. Only an output is made, and an isolated small dot is not output.

  In the first embodiment, the dot pattern is such that large dots are discrete like binary error diffusion in the low density portion. However, in the second embodiment, since the small dots are likely to be adjacent to the large dots even in the low density portion, the low density is low. The image reproducibility of the part is improved. Further, in the high density portion, gradation representation is performed with large dots and dot off as in binary error diffusion, and small dots are not used, so that the image reproducibility is good. On the other hand, in Example 1, gradation expression is performed by mixing large dots and small dots in the high density portion, and a dot pattern in which small dots are surrounded by large dots occurs everywhere. Theoretically, gradation expression using large dots and small dots is preferable from the viewpoint of image quality of texture rather than gradation expression using large dots and dots off. However, in some electrophotography, a pattern in which small dots are surrounded by large dots may be developed in the same way as a pattern filled with large dots. In the case of outputting with such a printer, the system as in the second embodiment is preferable.

  Further, if the gradation expression is expressed by binary error diffusion in the high density portion, the first variable threshold value To1 (x, y) and the second variable threshold value are used at a certain gradation without using the variable threshold value as shown in FIG. Even if the variable threshold value To2 (x, y) is different / same, it is possible to switch only. In this case, however, dot off, small dots, and large dots were used for gradation values lower than the gradation value to be switched. Differently, tone jump occurs, and when a gradation image is output, a pseudo contour is generated at a gradation in which processing is switched.

  On the other hand, as shown in FIG. 7, when the difference between the first variable threshold value To1 (x, y) and the second variable threshold value To2 (x, y) is gradually eliminated, immediately before the same gradation level. Since there is almost no difference between the first variable threshold value To1 (x, y) and the second variable threshold value To2 (x, y), small dots are rarely output, so that tone jump, that is, pseudo contour is less likely to occur.

Example 3:
FIG. 8 shows the configuration of an image processing apparatus according to the third embodiment of the present invention.
The input terminal 801 receives multi-value image data from the image input device 301. Next, the input data In (x, y) is input to the adder 802. The adder 802 adds the input data In (x, y) and the error component E (x, y) input from the error memory 806, calculates the correction data C (x, y), and calculates the correction data C (x, y). y) is output to the comparison determination unit 803 and the subtractor 805.

  The comparison determination unit 803 outputs an output value as shown in Expression (1) based on the correction data C (x, y) input from the adder 802 and the threshold group T (x, y) input from the threshold setting unit 808. Out (x, y) is determined, and Out (x, y) is output from the output terminal 804 to the image recording device 303.

  The output value Out (x, y) is input to the quantum memory 809 and the subtracter 805. The subtracter 805 subtracts the correction data C (x, y) and the output value Out (x, y) as shown in the equation (2), and calculates an error e (x, y) generated in the current pixel.

  Next, the error diffusion unit 807 distributes the error e (x, y) and adds it to the error data E (x, y) stored in the error memory 806 as shown in the equations (3) to (6). To go.

  In addition, the quantum memory 809 uses the quantum reference unit 811 and the threshold value setting unit 808 to combine the output values stored in the quantum reference unit 811 together with a plurality of quantum states around the target pixel required by the quantum reference unit 811. Output to. Here, the quantum memory 809 includes output values Out (x−1, y) and Out (x, y−1) in two pixels of the pixels (x−1, y) and (x, y−1) illustrated in FIG. 9. ) As a quantum group q (x, y).

  In the quantum reference unit 811, the quantum group q (x, y) input from the quantum memory 809, here, the output values Out (x−1, y) and Out (x, y−1) are set in advance. A weighted average value Q (x, y) obtained by weighted reference to the quantum state around the target pixel is output based on the reference coefficient. Here, for example, when the coefficient as shown in FIG. 9 is used as the reference coefficient, the quantum reference unit 811 performs the following process (9). The weighted average value Q (x, y) is output to the history value calculation unit 810.

Q (x, y) = Out (x-1, y) × 1/2 + Out (x, y−1) × 1/2 (9)
The history value calculation unit 810 uses the weighted average value Q (x, y) output from the quantum reference unit 811 and the history coefficient h set in advance to obtain the history value R (x, y) as shown in Expression (10). Calculate and output to the threshold setting unit 808. Now, the history coefficient h is set to 0.5.

R (x, y) = h × Q (x, y) (10)
The threshold value setting unit 808 outputs the output value Out (x−1) in the quantum group q (x, y) input from the quantum memory 809, that is, two pixels of the pixel (x−1, y) and (x, y−1). , Y), Out (x, y−1) and the history value R (x, y) input from the history value calculation unit 810, the first threshold T1 ( A threshold value group T (x, y) composed of x, y) and the second threshold value T2 (x, y) is set, and the threshold value group T (x, y) is output to the comparison determination unit 803.

If (Out (x-1, y) = 255)
then T1 (x, y) = 64−R (x, y),
T2 (x, y) = 127-R (x, y)
Else If (Out (x, y-1) = 255)
then T1 (x, y) = 64−R (x, y),
T2 (x, y) = 127-R (x, y)
Else
then T1 (x, y) = 127−R (x, y),
T2 (x, y) = 127−R (x, y) (11)
As described above, the multi-value error diffusion processing in the image processing unit is performed by the configuration of FIG.

  Next, why such a process is effective will be described. The third embodiment is different from the first embodiment in that the history value calculation unit 810 uses the weighted average value Q (x, y) obtained by weighted reference to the quantum state around the pixel of interest according to the history value R (x, y). The threshold is corrected. In Expression (11), when the output values Out (x−1, y) and Out (x, y−1) of two pixels adjacent to the target pixel are both 255, the weighted average value Q is calculated from Expression (9). (X, y) outputs 255. If the history coefficient h is 0.5, the history value R (x, y) is 127 according to the equation (10). Since this history value is subtracted from the first threshold value T1 (x, y) and the second threshold value T2 (x, y) used in the first embodiment in Expression (11), the pixel position adjacent to the target pixel If large and small dots are output, the threshold is set lower than in the first embodiment, and the dots are likely to be adjacent even if the error is not sufficiently accumulated. In particular, the ease with which dots are adjacent to each other in the low density portion is more preferable than that in the first embodiment. In an output machine in which it is better to form a cluster with large and small dots without being isolated even though they are large dots, the output of the third embodiment. The scheme gives favorable results.

  In the above description, the history coefficient h is set to 0.5. However, as the history coefficient h is increased, the first threshold T1 (x, y) and the second threshold T2 (x, y) are sufficiently low. Even if there is a negative error due to adjacent pixels, small dots can be easily output. The history coefficient h may be set according to the stability of the output machine.

  Further, although the weighted average value Q (x, y) is obtained using the pixel position and coefficient shown in FIG. 9, the pixel position to be referred to may be expanded according to the stability of the output device. Specifically, in an output device that is stable not only adjacent in the main / sub-scanning direction but also continuously diagonally right and left, the output values Out (x + 1, y−1) and Out ( What is necessary is just to change so that x-1, y-1) may be referred.

Example 4:
FIG. 10 shows a configuration of an image processing apparatus according to the fourth embodiment of the present invention. The input terminal 1001 receives multi-value image data from the image input device 301. Next, the input data In (x, y) is input to the adder 1002. The adder 1002 adds the input data In (x, y) and the error component E (x, y) input from the error memory 1006 to calculate correction data C (x, y), and calculates the correction data C (x, y). ) Is output to the comparison determination unit 1003 and the subtractor 1005.

  The input data In (x, y) is input to the history coefficient setting unit 1010. The history coefficient setting unit 1010 sets a history coefficient h (x, y) corresponding to the input data In (x, y) as shown in FIG. 11 and outputs it to the history value calculation unit 1011.

  The comparison determination unit 1003 outputs an output value as shown in Expression (1) based on the correction data C (x, y) input from the adder 1002 and the threshold value group T (x, y) input from the threshold setting unit 1008. Out (x, y) is determined, and Out (x, y) is output from the output terminal 1004 to the image recording apparatus 303.

  The output value Out (x, y) is input to the quantum memory 1009 and the subtracter 1005. The subtracter 1005 subtracts the correction data C (x, y) and the output value Out (x, y) as shown in the equation (2), and calculates an error e (x, y) generated in the current pixel.

  Next, the error diffusion unit 1007 distributes the error e (x, y) and adds it to the error data E (x, y) stored in the error memory 1006 as shown in the equations (3) to (6). To go.

  In addition, the quantum memory 1009 uses a quantum reference unit 1012 and a threshold setting unit 1008 for a quantum group q (x, y) in which a plurality of quantum states around a pixel of interest that are necessary for the quantum reference unit 1012 are stored in the quantum memory 1009. Output to. Here, the quantum memory 1009 outputs the output values Out (x−1, y) and Out (x, y−1) in the two pixels (x−1, y) and (x, y−1) shown in FIG. 9. Are output as the quantum group q (x, y).

  In the quantum reference unit 1012, the quantum group q (x, y) input from the quantum memory 1009, here, the output values Out (x-1, y) and Out (x, y-1) are set in advance. A weighted average value Q (x, y) obtained by weighted reference to the quantum state around the target pixel is output based on the reference coefficient. Here, for example, when a coefficient as shown in FIG. 9 is used as the reference coefficient, the quantum reference unit 1012 performs processing such as Expression (9). The weighted average value Q (x, y) is output to the history value calculation unit 1011.

  The history value calculation unit 1011 is expressed by Equation (12) from the weighted average value Q (x, y) output from the quantum reference unit 1012 and the history coefficient h (x, y) output from the history coefficient setting unit 1010. The history value R (x, y) is calculated and output to the threshold setting unit 1008.

R (x, y) = h (x, y) × Q (x, y) (12)
The threshold setting unit 1008 outputs the output value Out (x−1) of the quantum group q (x, y) input from the quantum memory 1009, that is, two pixels of the pixel (x−1, y) and (x, y−1). , Y), Out (x, y-1) and the history value R (x, y) input from the history value calculation unit 1011, the first threshold T1 ( A threshold value group T (x, y) composed of x, y) and the second threshold value T2 (x, y) is set, and the threshold value group T (x, y) is output to the comparison determination unit 1003.

  As described above, the multi-value error diffusion processing in the image processing unit is performed by the configuration of FIG.

  Next, why such a process is effective will be described. The fourth embodiment is different from the third embodiment in that a history coefficient h (x, y) corresponding to the input data In (x, y) is used. When the history coefficient is fixed as in the third embodiment, there is a tendency that dots are collected too much in the middle density portion. If both the output values Out (x−1, y) and Out (x, y−1) at the pixel position shown in FIG. 9 are large dots, the history value is large, and the first threshold value T1 (x , Y) and the second threshold value T2 (x, y) are low values. However, if the output values Out (x-1, y) and Out (x, y-1) are both large dots, many negative errors are accumulated at the target pixel position. In order to output large and small dots at the target pixel position, the correction value composed of the peripheral error and the input value is larger than the threshold value. Even if the threshold value is lowered by the history value, if the error obtained from the periphery is negative, large and small dots are not output unless the input value is large. Since the input value is small in the low density area, dots do not adjoin excessively, but in the middle and high density areas, the input value is large, so even if the error in the surrounding pixels is negative, the correction value is appropriate. When the value is lower than a predetermined threshold due to the history value, large and small dots may be output. If the history value is fixed in this way, the number of adjacent dots increases in the medium and high density portions, which is stable, but may not be preferable for image design such as graininess and texture. In such a case, the history coefficient h (x, y) corresponding to the input data In (x, y) may be used as in the fourth embodiment.

Example 5:
FIG. 12 shows the configuration of an image processing apparatus according to the fifth embodiment of the present invention.
The input terminal 1201 receives multi-value image data from the image input device 301. Next, the input data In (x, y) is input to the adder 1202, the history coefficient setting unit 1211, and the variable threshold value setting unit 1208. The adder 1202 adds the input data In (x, y) and the error component E (x, y) input from the error memory 1206, calculates correction data C (x, y), and corrects the correction data C (x, y). y) is output to the comparison determination unit 1203 and the subtractor 1205.

  The input data In (x, y) is input to the history coefficient setting unit 1211. The history coefficient setting unit 1211 sets a history coefficient h (x, y) corresponding to the input data In (x, y) as shown in FIG. 11 and outputs it to the history value calculation unit 1212.

  Further, the input data In (x, y) is input to the variable threshold setting unit 1208. In the variable threshold value setting unit 1208, as shown in FIG. 7, a variable threshold value group comprising a first variable threshold value To1 (x, y) and a second variable threshold value To2 (x, y) according to input data In (x, y). To (x, y) is set and output to the threshold setting unit 1209.

  The comparison determination unit 1203 outputs an output value as shown in Expression (1) based on the correction data C (x, y) input from the adder 1202 and the threshold value group T (x, y) input from the threshold setting unit 1209. Out (x, y) is determined, and Out (x, y) is output from the output terminal 1204 to the image recording device 303.

  The output value Out (x, y) is input to the quantum memory 1210 and the subtractor 1205. The subtractor 1205 subtracts the correction data C (x, y) and the output value Out (x, y) as shown in the equation (2), and calculates an error e (x, y) generated in the current pixel.

  Next, the error diffusion unit 1207 distributes the error e (x, y) and adds it to the error data E (x, y) stored in the error memory 1206 as shown in the equations (3) to (6). To go.

  In addition, the quantum memory 1210 uses a quantum reference unit 1213 and a threshold setting unit 1209 for a quantum group q (x, y) in which a plurality of quantum states around the pixel of interest necessary for the quantum reference unit 1213 are stored as output values. Output to. Here, the quantum memory 1210 outputs the output values Out (x−1, y) and Out (x, y−1) for the two pixels (x−1, y) and (x, y−1) shown in FIG. 9. Are output as the quantum group q (x, y).

  In the quantum reference unit 1213, the quantum group q (x, y) input from the quantum memory 1210, here, the output values Out (x-1, y) and Out (x, y-1) are set in advance. A weighted average value Q (x, y) obtained by weighted reference to the quantum state around the target pixel is output based on the reference coefficient. Here, for example, when a coefficient as shown in FIG. 9 is used as the reference coefficient, the quantum reference unit 1210 performs processing as shown in Expression (9). The weighted average value Q (x, y) is output to the history value calculation unit 1212.

  The history value calculation unit 1212 is expressed by Equation (12) from the weighted average value Q (x, y) output from the quantum reference unit 1213 and the history coefficient h (x, y) output from the history coefficient setting unit 1211. The history value R (x, y) is calculated and output to the threshold setting unit 1209.

  The threshold setting unit 1209 outputs the output value Out (x−1) of the quantum group q (x, y) input from the quantum memory 1210, that is, the two pixels of the pixel (x−1, y) and (x, y−1). , Y), Out (x, y−1), the history value R (x, y) input from the history value calculation unit 1212, and the first variable threshold value To 1 (x, y) input from the variable threshold value setting unit 1208. ) And the second variable threshold value To2 (x, y) and the second threshold value To2 (x, y), as shown in (13) below, the first threshold value T1 (x, y) and the second threshold value of the target pixel position A threshold value group T (x, y) composed of the threshold value T2 (x, y) is set, and the threshold value group T (x, y) is output to the comparison determination unit 1203.

If (Out (x-1, y) = 255)
then T1 (x, y) = To1 (x, y) -R (x, y),
T2 (x, y) = To2 (x, y) -R (x, y)
Else If (Out (x, y-1) = 255)
then T1 (x, y) = To1 (x, y) -R (x, y),
T2 (x, y) = To2 (x, y) -R (x, y)
Else
then T1 (x, y) = To2 (x, y) -R (x, y),
T2 (x, y) = To2 (x, y) -R (x, y) (13)
As described above, the multi-value error diffusion process in the image processing unit is performed by the configuration of FIG.

  Next, why such a process is effective will be described. The fifth embodiment is configured by combining the second embodiment and the fourth embodiment. In the fourth embodiment, similar to the first embodiment, gradation expression is performed by mixing large dots and small dots in the high density portion, and a dot pattern in which small dots are surrounded by large dots is generated everywhere. In some electrophotography, a pattern in which small dots are surrounded by large dots may be developed in the same manner as a pattern in which large dots are filled with large dots. Therefore, input data In (x, It is better to use the first variable threshold value To1 (x, y) and the second variable threshold value To2 (x, y) according to y).

  Although the present invention is for error diffusion processing, it can also be applied to the minimum average error method. In addition, the present invention can be applied to a system (for example, a copier, a facsimile machine, etc.) consisting of a single device even if it is applied to a system composed of a plurality of devices (for example, a host computer, interface device, reader, printer, etc.). You may apply.

  In addition, the present invention supplies a storage medium storing software program codes for realizing the functions of the above-described embodiments to a system or apparatus, and the computer (CPU or MPU) of the system or apparatus is stored in the storage medium. This can also be achieved by reading and executing the program code. In this case, the program code itself read from the storage medium realizes the functions of the above-described embodiment.

  As a storage medium for supplying the program code, for example, a flexible disk, a hard disk, an optical disk, a magneto-optical disk, a magnetic tape, a nonvolatile memory card, a ROM, or the like can be used. Further, by executing the program code read by the computer, not only the functions of the above-described embodiments are realized, but also an OS (operating system) operating on the computer based on an instruction of the program code. A case where part or all of the actual processing is performed and the functions of the above-described embodiments are realized by the processing is also included. Further, after the program code read from the storage medium is written into a memory provided in a function expansion board inserted into the computer or a function expansion unit connected to the computer, the function expansion is performed based on the instruction of the program code. A case where the CPU or the like provided in the board or the function expansion unit performs part or all of the actual processing and the functions of the above-described embodiments are realized by the processing is included.

1 shows a configuration of an image processing apparatus according to a first embodiment of the present invention. 1 shows a configuration of an image recording apparatus to which the present invention is applied. 1 shows a configuration of an image input / output system configured using an image processing apparatus of the present invention. The structural example of a laser optical system unit is shown. An example of an error diffusion coefficient is shown. 2 shows a configuration of an image processing apparatus according to a second embodiment of the present invention. The 1st and 2nd variable threshold value according to an input value is shown. 3 shows a configuration of an image processing apparatus according to a third embodiment of the present invention. An example of a reference coefficient is shown. 4 shows a configuration of an image processing apparatus according to a fourth embodiment of the present invention. Indicates the history coefficient according to the input value. 6 shows a configuration of an image processing apparatus according to a fifth embodiment of the present invention. Large dots and small dots reproduced using a PWM signal are shown.

Explanation of symbols

DESCRIPTION OF SYMBOLS 101 Input terminal 102 Adder 103 Comparison determination part 104 Output terminal 105 Subtractor 106 Error memory 107 Error diffusion part 108 Threshold setting part 109 Quantum memory

Claims (10)

  Multi-value (M-value) image data is quantized to N values (M> N> 2) using multi-value error diffusion or multi-value average error minimum method, and recorded using dots corresponding to each of the N values. An image processing apparatus that outputs a correction value obtained by adding an error obtained by adding a weighted product-sum from surrounding already quantized pixels to the multi-valued image data of the target pixel; and the N-value image data Means for calculating an error generated along with generation, means for weighting and spreading the error around the pixel of interest, means for storing the error subjected to weighted diffusion, and means for storing the quantum state around the pixel of interest An image processing apparatus comprising: means for setting a threshold value according to the quantum state; and means for comparing the threshold value with the correction value and outputting N-value image data.   Multi-value (M-value) image data is quantized to N values (M> N> 2) using multi-value error diffusion or multi-value average error minimum method, and recorded using dots corresponding to each of the N values. An image processing apparatus that outputs a correction value obtained by adding an error obtained by adding a weighted product-sum from surrounding already quantized pixels to the multi-valued image data of the target pixel; and the N-value image data Means for calculating an error generated along with generation, means for weighting and spreading the error around the pixel of interest, means for storing the weighted and diffused error, means for storing the quantum state around the pixel of interest, The means for setting a variable threshold according to the multi-valued image data of the pixel of interest, the means for setting a threshold based on the quantum state around the pixel of interest and the variable threshold, and comparing the threshold with the correction value N Means for outputting value image data The image processing apparatus characterized by was e.   Multi-value (M-value) image data is quantized to N values (M> N> 2) using multi-value error diffusion or multi-value average error minimum method, and recorded using dots corresponding to each of the N values. An image processing apparatus that outputs a correction value obtained by adding an error obtained by adding a weighted product-sum from surrounding already quantized pixels to the multi-valued image data of the target pixel; and the N-value image data Means for calculating an error generated along with generation, means for weighting and spreading the error around the pixel of interest, means for storing the weighted and diffused error, means for storing the quantum state around the pixel of interest, Means for outputting a weighted average value obtained by weighted product-sum of quantum states around the target pixel; means for calculating a history value based on the weighted average value; and a threshold value based on the quantum state and history value around the target pixel. A means of setting Compare the serial threshold and said correction-value image processing apparatus characterized by comprising a means for outputting an N-level image data.   Multi-value (M-value) image data is quantized to N values (M> N> 2) using multi-value error diffusion or multi-value average error minimum method, and recorded using dots corresponding to each of the N values. An image processing apparatus that outputs a correction value obtained by adding an error obtained by adding a weighted product-sum from surrounding already quantized pixels to the multi-valued image data of the target pixel; and the N-value image data Means for calculating an error generated along with generation, means for weighting and spreading the error around the pixel of interest, means for storing the weighted and diffused error, means for storing the quantum state around the pixel of interest, Means for outputting a weighted average value obtained by weighted product-sum of quantum states around the target pixel, means for setting a history coefficient according to the multi-valued image data of the target pixel, and based on the weighted average value and the history coefficient Calculate historical values Means for setting a threshold value based on the quantum state and history value around the pixel of interest, and means for comparing the threshold value with the correction value and outputting N-value image data. An image processing apparatus.   Multi-value (M-value) image data is quantized to N values (M> N> 2) using multi-value error diffusion or multi-value average error minimum method, and recorded using dots corresponding to each of the N values. An image processing apparatus that outputs a correction value obtained by adding an error obtained by adding a weighted product-sum from surrounding already quantized pixels to the multi-valued image data of the target pixel; and the N-value image data Means for calculating an error generated along with generation, means for weighting and spreading the error around the pixel of interest, means for storing the weighted and diffused error, means for storing the quantum state around the pixel of interest, Means for outputting a weighted average value obtained by weighted product-sum of quantum states around the target pixel, means for setting a history coefficient according to the multi-valued image data of the target pixel, and based on the weighted average value and the history coefficient Calculate historical values Means for setting a variable threshold according to multi-valued image data of the target pixel; means for setting a threshold based on a quantum state, a history value, and a variable threshold around the target pixel; and the threshold and the correction An image processing apparatus comprising means for comparing values and outputting N-value image data.   6. The image processing apparatus according to claim 4, wherein the history coefficient obtained according to the image data of the target pixel has a high value in a low density portion and a low value in a high density portion.   The variable threshold value obtained according to the image data of the target pixel is divided into N-1 threshold values in the middle and low density portions, and the N-1 threshold values become closer to the high density portion, and the high density value is increased. 6. The image processing apparatus according to claim 2, wherein the N-1 threshold values are the same in the unit.   An image recording apparatus having the functions of each unit of the image processing apparatus according to claim 1.   The program for making a computer implement | achieve the function of each means of the image processing apparatus of any one of Claims 1 thru | or 7.   A computer-readable recording medium on which a program for causing a computer to realize the functions of the respective units of the image processing apparatus according to claim 1 is recorded.

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