JP4549306B2 - Image processing method, image processing apparatus, image forming apparatus, and computer program - Google Patents

Description

  The present invention relates to an image processing method for performing image processing by changing a coefficient of a spatial frequency component of image data, an image processing apparatus, an image forming apparatus including the image processing apparatus, and a computer program for realizing the image processing method.

  As a method of reducing the number of gradations of an image including halftones, for example, a method of binarizing a 256 gradation image and generating a two gradation image, the gradation value of the image and a predetermined value (threshold) are used. A binarization method, a dither method, an error diffusion method, and the like are known.

  FIG. 16 is a schematic diagram illustrating an example of a 4 × 4 dither matrix used in a dither method for binarizing a 16-gradation image. In the dither matrix, a predetermined value of 0 to 240 is set according to the position of the pixel. In the dither method, the gradation value of the input 256 gradation image data is compared with a predetermined value set in the dither matrix for each pixel, and if the gradation value is equal to or larger than the predetermined value, the gradation value is determined. When the gradation value is less than the predetermined value, the image is binarized by setting the gradation value to 0.

  The error diffusion method is a quantization error that occurs when binarizing each pixel of input image data, that is, a difference between a binarized gradation value and a gradation value of a pixel before binarization, This is a method of binarizing by distributing to pixels that have not been binarized yet. When the pixel to be binarized is the target pixel, the quantization error of the target pixel is weighted according to the relative position from the target pixel and then before the binarization process positioned around the target pixel. It is added to the gradation value of the pixel.

  FIG. 17 is a schematic diagram showing an example of a weighting coefficient matrix used in the error diffusion method. In the example of FIG. 17, the horizontal direction (left and right direction in the figure) is the X direction, the vertical direction (up and down direction in the figure) is the Y direction, and a 2 × 3 weight coefficient matrix including the pixel of interest (IX, IY) is obtained. It is shown. The weighting coefficient matrix indicates the weighting coefficients of the lower left neighbor, the lower neighbor, the lower right neighbor, and the right neighbor with reference to the target pixel (IX, IY). For example, the gradation value of the target pixel (IX, IY) is compared with a predetermined value, and when the gradation value is equal to or larger than the predetermined value, the gradation value of the target pixel (IX, IY) is set to 255, which is less than the predetermined value. In this case, the gradation value of the target pixel (IX, IY) is set to 0.

  Next, the difference between the binarized 255 or 0 gradation value and the gradation value of the target pixel (IX, IY) before binarization, that is, the quantization error is calculated based on the weighting coefficient matrix. Allocate to pixels before binarization. However, since the pixel (IX-1, IY) adjacent to the left of the target pixel (IX, IY) is quantized before the target pixel (IX, IY), the quantization error is not distributed. When the quantization error is Err, each of four neighboring (IX + 1, IY), (IX + 1, IY + 1), (IX, IY + 1), and (IX-1, IY + 1) of the pixel of interest (IX, IY) is Err × (7/16), Err × (1/16), Err (5/16), Err (3/16) are allocated.

  The error diffusion method distributes quantization errors to surrounding unprocessed pixels based on a weight coefficient matrix, and therefore has an advantage that a moiré pattern is less likely to appear in a binarized image.

  In addition to the dither method and error diffusion method described above, the image data is converted into image data having a spatial frequency component, and the halftone spatial frequency domain determined in advance for the converted coefficient of the spatial frequency component. There has been proposed a method of performing halftone processing of an image using data converted into (see, for example, Patent Document 1).

  However, according to the dither method, since binarization is performed using a dither matrix having the same pattern, there is a problem that a texture specific to dither, that is, a periodic pattern image is generated in the binarized image.

  Further, according to the method described in Patent Document 1, since the halftone data determined in advance is used, there is a problem that the same texture generation as that of the error diffusion method or the dither method occurs. ing. That is, in the spatial frequency domain, only the halftone process is performed using the same method as the above-described conventional method, and the same problem as described above occurs.

  In the error diffusion method, the error is diffused based on the same matrix for each pixel, so that a large quantization error is diffused in a chained manner in a highlight portion having a large gradation value, and the pixels appear to be connected. was there. That is, there is a problem that pixels having different gradation values have the same gradation value, and an image can be obtained in which the pixels appear to be partially connected.

  In addition, in a multi-valued image such as a quaternary image, there is a problem that a dot pattern becomes uniform in an intermediate density region and a tone gap (a phenomenon in which gradation changes discontinuously) occurs. For example, when the output value is 0, 85, 171, or 255, a tone gap is likely to occur near the intermediate density portion 85, 171 due to concentration of the same density. A tone gap always occurs as long as quantization processing is performed with a plurality of threshold values. For example, even in the case of binary output, a tone gap occurs near 0 or 255. However, in the case of multi-value output, there is an intermediate density output value that is easily noticeable by humans. Easy to stand out. It is difficult to completely prevent the tone gap in the intermediate density portion by an algorithm such that a plurality of threshold values used in the intermediate density region are appropriately selected and quantized.

  Therefore, the present applicant suppresses the tone gap problem that occurs near the output value of the image data by changing the high-frequency component of the image data, and generates the texture and error diffusion method that occurs in the dither method. A method has been proposed in which generation of a worm that has been suppressed can be suppressed and a high-quality binary image and multi-valued image can be generated (see, for example, Patent Document 2).

The image processing method described in Patent Document 2 converts image data into a spatial frequency component, changes the spatial frequency component of a predetermined frequency region with respect to the converted spatial frequency component, and performs the change processing on the spatial frequency. Halftone processing is performed by inversely converting the components into image data and reducing the number of gradations of the inversely converted image data based on a threshold value. Here, the spatial frequency component change processing described above detects the direct current component included in the spatial frequency component, the number of changes is determined according to the detected direct current component, and the number of spatial frequencies according to the determined number of changes. It is to change the ingredients.
Japanese Patent Laid-Open No. 2002-10085 JP 2005-50296 A

  As described above, in the method described in Patent Document 2, when changing the spatial frequency component, the DC component included in the spatial frequency component is detected, and the number of changes is determined according to the detected DC component, The number of spatial frequency components corresponding to the determined number of changes is changed. However, the magnitude of the change, that is, the magnitude of the amplitude is defined within a predetermined range, and the amplitude cannot be increased locally. For this reason, when the image data in the density area is output in four values, the bias of the dots having a constant density tends to increase near the two levels (8 levels and 171 levels) other than 0 and 255. There is a problem that the tone gap in this vicinity is not solved.

  The present invention has been made in view of such circumstances, and when the DC component of the spatial frequency component is not within a predetermined range, the first data is added to the AC component, and the DC component of the spatial frequency component is Image processing capable of generating an image in which tone gap is not conspicuous even in a gradation pattern by adopting a configuration in which the second data in which the value to be added is emphasized is added to the AC component when it is within the predetermined range It is an object to provide a method, an image processing apparatus, an image forming apparatus provided with the image processing apparatus, and a computer program for realizing the image processing method.

  Another object of the present invention is to determine whether a pixel having a maximum or minimum gradation value among the possible gradation values is included in the image data, and to determine the maximum or minimum gradation value. When it is determined that a pixel having a pixel value is included, an image in which a gradation reproduction process image that is more faithful to the input image can be obtained by changing the gradation value of pixels other than the pixel based on a threshold value A processing method, an image processing apparatus, and an image forming apparatus including the image processing apparatus are provided.

An image processing method according to the present invention is an image processing method for performing orthogonal transformation on image data to generate data composed of spatial frequency components, and performing image processing based on the generated data, and the spatial frequency components constituting the data Determining whether or not the magnitude of the direct current component is within a predetermined range, and determining that the direct current component is not within the predetermined range, a blue noise characteristic with respect to the alternating current component of the spatial frequency component is determined. Adding noise data of a spatial frequency domain having, adding the data obtained by multiplying the noise data by a constant larger than 1 to the alternating current component when determined to be within the predetermined range; and a step of performing inverse orthogonal transformation on the data obtained by adding the data obtained by multiplying the constant noise data or the noise data, the inverse orthogonal transform And having a step of decreasing with the number of gradations of one or more thresholds of image data obtained Ri.

  In the present invention, when the direct current component of the spatial frequency component is not within the predetermined range, the first data is added to the alternating current component, and when the direct current component of the spatial frequency component is within the predetermined range, Since the second data emphasizing the value to be added is added to the AC component, the tone gap in the vicinity of the output value other than the output value at which the gradation value is maximum or minimum is eliminated.

  In the present invention, the second data to be added when the DC component is within a predetermined range is obtained by calculation. Further, the tone gap in the vicinity of the output value other than the output value at which the gradation value is maximum or minimum is eliminated.

  In the image processing method according to the present invention, the image data to be subjected to the orthogonal transformation is data including a plurality of pixels in which each pixel has an appropriate gradation value, and the maximum or minimum value that the gradation value can take. A step of determining whether or not a pixel having a gradation value to be included in the image data and a pixel having a maximum or minimum gradation value are included. And changing the gradation value of the pixel based on the threshold value.

  In the present invention, it is determined whether or not a pixel having the maximum or minimum gradation value among the possible gradation values is included in the image data, and the maximum or minimum gradation value is determined. When it is determined that the pixel having the pixel is included, the gradation value of the pixel other than the pixel is changed based on the threshold value, so that a gradation reproduction process image more faithful to the input image can be obtained.

An image processing device according to the present invention generates data composed of spatial frequency components by performing orthogonal transformation on image data, and performs image processing based on the generated data. Means for determining whether or not the magnitude of the DC component is within a predetermined range, and having a blue noise characteristic with respect to the AC component of the spatial frequency when it is determined that the magnitude is not within the predetermined range adding the noise data in the spatial frequency domain, if it is determined that within said predetermined range, and means for adding data obtained by multiplying the constant greater than 1 in the noise data on the AC component, the noise data or and means for performing inverse orthogonal transformation on the data obtained by adding the data obtained by multiplying the constant to the noise data, the gradation of the image data obtained by the inverse orthogonal transform The characterized in that it comprises a means for reducing using one or more thresholds.

  In the present invention, when the direct current component of the spatial frequency component is not within the predetermined range, the first data is added to the alternating current component, and when the direct current component of the spatial frequency component is within the predetermined range, Since the second data emphasizing the value to be added is added to the AC component, the tone gap in the vicinity of the output value other than the output value at which the gradation value becomes the maximum value or the minimum value is eliminated.

  In the present invention, the second data to be added when the DC component is within a predetermined range is obtained by calculation. Further, the tone gap in the vicinity of the output value other than the output value at which the gradation value is maximum or minimum is eliminated.

  In the image processing apparatus according to the present invention, the image data to be subjected to the orthogonal transformation is data including a plurality of pixels in which each pixel has an appropriate gradation value, and the maximum or minimum value that the gradation value can take Means for determining whether or not a pixel having a gradation value to be included is included in the image data, and if it is determined that a pixel having a maximum or minimum gradation value is included, And means for changing the gradation value of the pixel based on the threshold value.

  In the present invention, it is determined whether or not a pixel having the maximum or minimum gradation value among the possible gradation values is included in the image data, and the maximum or minimum gradation value is determined. When it is determined that the pixel having the pixel is included, the gradation value of the pixel other than the pixel is changed based on the threshold value, so that a gradation reproduction process image more faithful to the input image can be obtained.

  An image forming apparatus according to the present invention includes the image processing apparatus according to any one of the above-described inventions, and a unit that forms an image on a sheet based on image data obtained by the image processing apparatus. It is characterized by.

  In the present invention, since any one of the image processing apparatuses described above is provided and an image is formed on the sheet based on the image data processed by the image processing apparatus, the tone gap is eliminated. An image is formed on the sheet.

A computer program according to the present invention is a computer program that causes a computer to perform orthogonal transformation on image data to generate data composed of spatial frequency components, and to perform image processing based on the generated data. And determining whether or not the magnitude of the DC component is within a predetermined range among the spatial frequency components constituting the A, and if the computer determines that the magnitude is not within the predetermined range, the alternating current of the spatial frequency component Obtained by multiplying the noise data by a constant larger than 1 when the computer determines that the noise data is in the predetermined range, and a step of adding noise data in a spatial frequency region having blue noise characteristics to the component. a step of adding data to the AC component, the computer, the Noizude Or the steps that subjected to the inverse orthogonal transformation on the data obtained by multiplying the constant noise data to data obtained by adding, to the computer, the number of gradations of one or more image data obtained by the inverse orthogonal transform And a step of decreasing using a threshold value.

  In the present invention, when the direct current component of the spatial frequency component is not within the predetermined range, the first data is added to the alternating current component, and when the direct current component of the spatial frequency component is within the predetermined range, Since the second data emphasizing the value to be added is added to the AC component, the tone gap in the vicinity of the output value other than the output value at which the gradation value is maximum or minimum is eliminated.

  According to the present invention, when the DC component of the spatial frequency component is not within the predetermined range, the first data is added to the AC component, and when the DC component of the spatial frequency component is within the predetermined range, the addition is performed. The second data emphasizing the value to be added is added to the AC component. Therefore, the tone gap can be eliminated in the vicinity of the output value other than the output value at which the gradation value is maximum or minimum.

  According to the present invention, the second data to be added when the DC component is within a predetermined range is obtained by calculation. Further, the tone gap can be eliminated in the vicinity of the output value other than the output value at which the gradation value is maximum or minimum.

  According to the present invention, the second data to be added when the DC component is within a predetermined range has a value that continuously changes with respect to the spatial frequency. In this case, since the emphasis method is changed so as to change gradually, the tone gap can be more clearly eliminated in the vicinity of the output value other than the output value at which the gradation value is maximum or minimum.

  According to the present invention, it is determined whether or not a pixel having the maximum or minimum gradation value among the possible gradation values is included in the image data, and the maximum or minimum gradation value is obtained. When it is determined that the pixel is included, the gradation value of the pixel other than the pixel is changed based on the threshold value. Therefore, a gradation reproduction processed image that is more faithful to the input image can be obtained.

  According to the present invention, any one of the above-described image processing apparatuses is provided, and an image is formed on a sheet based on image data processed by the image processing apparatus. Therefore, an image in which the tone gap is eliminated can be formed on the sheet.

Hereinafter, the present invention will be specifically described with reference to the drawings showing embodiments thereof.
Embodiment 1 FIG.
FIG. 1 is a block diagram illustrating a configuration example of an image processing apparatus according to the first embodiment. In the figure, reference numeral 101 denotes an image processing apparatus according to the first embodiment. The image processing apparatus 101 includes an image data storage unit 1 that stores input image data Pi (X, Y), and a frequency conversion unit that converts the input image data Pi (X, Y) into a spatial frequency component Qj (S, T). 2. DC component determination unit 4 for determining a DC component based on the converted spatial frequency component Qj (S, T), noise with respect to the spatial frequency component Qj (S, T) output by the frequency conversion unit 2 A noise adding unit 5 for adding data, an inverse frequency converting unit 6 for acquiring frequency domain image data by performing inverse frequency conversion on the spatial frequency component Qm (S, T) to which the noise data is added, and an inverse frequency converting unit 6 A threshold processing unit 7 that performs threshold processing on the output image data is provided.

  The frequency conversion unit 2 receives image data (hereinafter referred to as a processing block) having 8 × 8 pixels as one block from the image data storage unit 1 and performs DCT conversion, which is one of orthogonal transformations. The DCT-transformed spatial frequency component Qj (S, T) is output to the noise adding unit 5. Hereinafter, the DCT-transformed spatial frequency component Qj (S, T) is referred to as a DCT coefficient. Here, a description will be given using a typical discrete cosine transform (DCT). The expression for the discrete cosine transform is given by the following expression when the input image is A, the output image is B, and the row and column sizes of the input image A are M and N.

  In the present embodiment, DCT conversion is performed in the processing block unit in the right direction (X direction) from the processing block including the upper left pixel in the two-dimensional image, and finally the line is changed while processing unit is changed. DCT conversion is performed up to the final processing block including the lower right pixel.

  The DC component determination unit 4 determines the size of the DC component in the processing block unit data converted into the spatial frequency component. The magnitude of the direct current component represents the average density value in the processing block. Here, it is assumed that the image data is 8 bits (density value: 0 to 255) and is output in four values of 0, 85, 171, and 255. In this case, for example, when a four-value error diffusion process is performed on a gradation pattern, a tone gap becomes conspicuous in the vicinity of the intermediate output values 85 and 171. In this case, the density levels where the tone gap is conspicuous are between 80 and 90 and between 165 and 175.

  In the error diffusion process of quaternary output, values 43, 128, and 213 between 0 and 255 levels are used as threshold values, and the output value is determined while sequentially comparing the density value of the input pixel and the threshold value. As the output value, the closest level of selected 0, 85, 171 and 255 is selected and output, but is limited to any one of these four. In the error diffusion processing, a difference between the input value and the determined output value is calculated, and weighting is performed on the determined peripheral unprocessed pixels to perform error distribution processing. Here, when the density of the input pixel is extremely low, more output values of 0 level are easily selected, and when the density is extremely high, more output values of 255 level are selected. Even if the density is between these values and the output value is in the vicinity of the 85 or 171 level, the closer output value is selected, so the 85 and 171 levels are easily selected, and the same output level tends to be biased. is there. When the output value level is 0 and 255, when processing an image having gradation, the deviation of the 0 and 255 level dots is located at the end of the gradation area, so that it is not noticeable. However, since the output levels of 85 and 171 that are intermediate densities are present in the gradation area, the deviation of dots having the same density is easily noticeable.

  Here, when the image data represented by the density value is converted into the frequency space, the image data having a uniform density from 0 to 255 has a value of 0 to 2040 in the DC component. Therefore, the values of the direct current component that can cause the tone gap are approximately 639.2 to 720.8 and 1278.4 to 1441.6. Accordingly, when the magnitude of the DC component of a certain processing block is DC, if the condition of 639.2 <DC <720.8 or 1278.4 <DC <1441.6 is satisfied, the density at which a tone gap may occur It is determined that the level is high, and a control signal for enhancing and adding noise is output to the next noise adding unit 5.

Next, the noise adding unit 5 will be described. The noise adding unit 5 converts noise having a blue noise characteristic (noise expressed by a density value) into noise data in a spatial frequency domain by DCT conversion or the like, and converts the value into frequency-converted image data (in this case, a DCT coefficient). ). Blue noise characteristic data normalized after DCT conversion has a large value in a high frequency region. The image data converted into the spatial frequency domain, that is, the DCT coefficient Qj (S, T) is processed in units of processing blocks, and blue noise characteristic data is added to the data for each processing block corresponding to these. It is processed.
Here, when a control signal for emphasizing noise is sent from the DC component determination unit 4, the noise addition unit 5 emphasizes the entire noise using a predetermined constant for each processing block.

  In this way, the selection of the processing of whether the noise amplitude is emphasized and added or added without change is switched according to the result of the DC component determination unit 4. When the noise is enhanced and added, the result is data in which the amplitude of the blue noise is enhanced, but the characteristics of the blue noise are maintained and the data acts as good noise.

   FIG. 2 is a schematic diagram showing an example of blue noise. The example of FIG. 2 shows the blue noise characteristic data when the enhancement process is not performed, that is, when it is determined that the magnitude of the DC component does not belong to the above-described range. Have. When performing enhancement processing, each element is multiplied by a constant (for example, the constant is 1.1), and blue noise characteristic data to be added when it is determined that the magnitude of the DC component belongs to the above-described range is obtained.

  In the case of blue noise, the low frequency component is almost zero, and the high frequency component has a large value component. Therefore, as shown in Japanese Patent Application Laid-Open No. 2005-50296, the change region for changing the DCT coefficient is changed. There is no need to divide the non-changed area, and blue noise can be added to the DCT coefficient of the processing block. In addition, since blue noise is added, image degradation can be suppressed without losing the original data. The added data is output to the next inverse frequency converter 6.

  The inverse frequency conversion unit 6 performs processing for converting the spatial frequency domain data sent from the noise addition unit 5 into density region data (image data). In this embodiment, since the DCT transformation is used as the orthogonal transformation, the inverse DCT transformation is performed as the inverse orthogonal transformation. The two-dimensional inverse DCT transform is given by the inverse transform of Equation 1. Since the noise of the blue noise characteristic is mainly added to the data in the high frequency region, the dot arrangement can be changed while minimizing the influence on the original image data.

The threshold processing unit 7 uses the plurality of threshold values for the density area data (image data) Pn (X, Y) received from the inverse frequency conversion unit 6, and multi-value density data (output image data) Po (X, Y). ). For example, in the case of quaternary output, using three threshold values,
If 0 <Pn (X, Y) ≦ 42, then Po (X, Y) = 0,
If 42 <Pn (X, Y) ≦ 127, Po (X, Y) = 85,
If 127 <Pn (X, Y) ≦ 212, Po (X, Y) = 171,
If 212 <Pn (X, Y) ≦ 255, Po (X, Y) = 255
Convert to

  In this way, the input image data Pi (X, Y) stored in the image data storage unit 1 is converted into the spatial frequency domain, and then the optimum noise is added and inversely converted. As a result, the output image data Po (X, Y) in which the number of gradations is reduced to four values or the like for all pixels is obtained.

Embodiment 2. FIG.
In the first embodiment, when noise enhancement processing is performed, the degree of enhancement is constant, but may be variable according to the magnitude of the DC component. For example, a beautiful gradation pattern with less noticeable tone gap can be obtained by gradually changing the enhancement level with respect to the magnitude of the DC component.

  FIG. 3 is a block diagram illustrating a configuration example of the image processing apparatus according to the second embodiment. The image processing apparatus 102 according to the second embodiment includes the image data storage unit 1, the frequency conversion unit 2, the DC component determination unit 4, the noise addition unit 5, the inverse frequency conversion unit 6, and the threshold processing unit described in the first embodiment. 7, a gain setting unit 8 is provided.

  The gain setting unit 8 sets the enhancement level variably when the DC component determination unit 4 requires noise enhancement processing. FIG. 4 is a graph for explaining the emphasis level by the gain setting unit 8. The vertical axis represents the enhancement level, and the horizontal axis represents the magnitude of the DC component. When the magnitude of the DC component of a certain processing block is DC, when the condition of 639.2 <DC <720.8 or 1278.4 <DC <1441.6 is satisfied, the tone level is a density level at which a tone gap can occur. It is determined that there is, and a control signal to be added with noise enhancement is output to the next noise adding unit 5. The DC determination condition is adjusted so that a smooth gradation image is obtained.

  In this case, the magnitude of the enhancement level depends on the magnitude of the DC component, and as shown in the graph, the enhancement level is gradually increased with respect to the value of the DC component, just around the output value level. The maximum value is taken with, and the emphasis level is gradually reduced after the output value level. In other words, processing is performed in which the noise enhancement level is variable so as to draw a gentle curve.

  As described above, in this embodiment, by gently emphasizing the amplitude of the noise, the gap at the boundary between the portion where the noise amplitude is not emphasized and the portion where the noise amplitude is performed can be eliminated. It is possible to provide a beautiful gradation pattern that is inconspicuous.

Embodiment 3 FIG.
In the first embodiment, the DC component determination unit 4 is configured to determine whether or not to perform noise enhancement processing based on the determination result. However, it is determined whether or not the processing block has the feature of the edge component. You may combine with the method of performing the emphasis process or smoothing process of the predetermined frequency component in a processing block based on a result.

  FIG. 5 is a block diagram illustrating a configuration example of the image processing apparatus according to the third embodiment. The image processing apparatus 103 according to the third embodiment includes the image data storage unit 1, the frequency conversion unit 2, the DC component determination unit 4, the noise addition unit 5, the inverse frequency conversion unit 6, and the threshold processing unit described in the first embodiment. 7, a frequency component determination unit 3 and a change unit 24 are provided.

  The frequency component determination unit 3 determines the size of the spatial frequency component at a position near the DC component in each processing block converted to the spatial frequency component. FIG. 6 is an explanatory diagram for explaining the determination method. In this explanatory diagram, a processing block (8 × 8 pixels) after being converted into a spatial frequency component is schematically shown. The upper left position is a direct current component (DC component), and the rest is called an alternating current component (AC component). The frequency component determination unit 3 obtains the magnitude of the absolute value of the DCT coefficient representing the frequency component at a position near the DC component, and determines whether at least one is larger or smaller than a predetermined value.

For example, the coordinate at the position of the DC component is Q11 (y1, x1), the magnitude of the component (coefficient) at that position is A, the coordinate at the right adjacent position is Q12 (y1, x1 + 1), and The size of the component is B, the coordinate at the position immediately to the right is Q13 (y1, x1 + 2), the size of the component at that position is C, and Q21 (y1 + 1, x1) in the same manner from the left side down one row. The component size at that position is D, the coordinate at the right adjacent position is Q22 (y1 + 1, x1 + 1), the component size at that position is E, and the coordinate at the right adjacent position is Q23 (y1 + 1, x1 + 2), the magnitude of the component at that position is F, and further down one line, similarly, Q31 (y1 + 2, x1), the magnitude of the component at that position is G, and the coordinate at the right adjacent position is Q33 (y1 + 2) , X1 + 1), the size of the component at that position is H, and further to the right Position Q33 (y1 + 2, x1 + 2) in, determines the magnitude of the component at that position expressed in I, when a predetermined value of the alpha (positive constant), the following conditional expression.
| B |> α or | C |> α or | D |> α or | E |> α or | F |> α, | G |> α or | H |> α or | I |> α

  Whether or not this condition is satisfied is used as a determination criterion, and determination data is output to the changing unit 24. Note that α, which is a predetermined positive constant, can be arbitrarily set. When the set value of α is small, it is easy to determine that the processing block includes an edge, and when the set value of α is large, the edge is included. It becomes difficult to judge. Here, α = 64 is set as an example. By setting this α, it is used as a material for determining whether or not an image in the density region includes an edge component in each processing block unit. If the above conditional expression is satisfied, data 1 with a flag set is output, and if not satisfied, data 0 without a flag set is sequentially output for each processing block. The image data frequency-converted by the frequency converting unit 2 is input to the changing unit 24 as it is.

  In the present embodiment, the determination is performed using six coefficients adjacent to the DC component. However, the predetermined position of the frequency component used for the determination is within the low frequency region if the hardware condition permits. It may be expanded to a wider range.

  The changing unit 24 changes the DCT coefficient Qj (S, T) in units of processing blocks. The change process is not performed for all DCT coefficients in the processing block, but only for a predetermined DCT coefficient among the 8 × 8 DCT coefficients in the processing block.

  FIG. 7 is a schematic diagram showing an example of a region where the DCT coefficient is changed. FIG. 7 shows the change area when the data of the flag 1, that is, the data determined that the processing block includes the edge area is sent by the frequency component determination unit 3. The change area is set in advance in the change unit 24 or a control unit (not shown), for example.

  Next, a method for changing the DCT coefficient will be described. When the flag is 1, the change area component shown in FIG. 7 (the frequency component only in the vertical direction, the frequency component only in the horizontal direction, and the frequency component at the intersection of both) is multiplied by a predetermined constant. . At this time, position information based on the position of each frequency component is used. Data is not changed for components other than the change region.

In the processing block determined by the frequency component determination unit 3 as a processing block including an edge, in the 8 × 8 pixels in the processing block, from the respective positions in order,
(1,1), (1,2), (1,3), (1,4), (1,5), (1,6), (1,7), (1,8)
(2,1), (2,2), (2,3), (2,4), (2,5), (2,6), (2,7), (2,8)
(3,1), (3,2), (3,3), (3,4), (3,5), (3,6), (3,7), (3,8)
(4,1), (4,2), (4,3), (4,4), (4,5), (4,6), (4,7), (4,8)
(5,1), (5,2), (5,3), (5,4), (5,5), (5,6), (5,7), (5,8)
(6,1), (6,2), (6,3), (6,4), (6,5), (6,6), (6,7), (6,8)
(7,1), (7,2), (7,3), (7,4), (7,5), (7,6), (7,7), (7,8)
(8,1), (8,2), (8,3), (8,4), (8,5), (8,6), (8,7), (8,8)
In this case, a value obtained by adding the row number and the column number is set as position information data at the processing position. For example, the position information data in the case of (1, 1) is 2 (= 1 + 1), the position information data in the case of (1, 2) is 3 (= 1 + 2), and the position information data in the case of (1, 8) is The position information data in the case of 9 (= 1 + 8) and (2, 1) is 3 (= 2 + 1), and the position information data in the case of (8, 1) is 9 (= 8 + 1). FIG. 8 is a schematic diagram showing position information data in the change area.

  Next, conversion processing is performed on the frequency data in the change region by multiplying the position information data for each position by a predetermined constant. That is, in the change region, (frequency component data after change) = (frequency component data before change) × (position information data) × (constant).

  If it is determined that the block includes an edge, for example, Qk (1,3) = Qj (1,3) × (1 + 3) × 0.35 is obtained because change processing is performed on data in 1 row and 3 columns. Since the change process is not performed for the data in row 3 column, Qk (2,3) = Qj (2,3). In this example, the constant used for the changing process is set to 0.35. However, the constant is not necessarily 0.35, and may be a value that is emphasized by a small value. In addition, since it is emphasized by this value, it may be determined while evaluating the image quality using an actual print sample in consideration of the overall balance such as whether or not the edge is excessively emphasized. Even when such processing is performed, since the DC component remains as it is, the average density of the entire processing block is substantially maintained.

  On the other hand, when flag 0 data is sent from the frequency component determination unit 3, it is determined that this processing block does not include an edge component. In this case, the processing in the changing unit 24 is smoothing processing. The smoothing process is a smoothing process using a CSF function using visual characteristics or a function having Gaussian characteristics. The image data that has been subjected to the respective change processes is transferred to the next noise adding unit 5.

  The processing contents executed by the noise adding unit 5, the inverse frequency converting unit 6, and the threshold processing unit 7 are exactly the same as those in the first embodiment.

  In the present embodiment, the gain setting unit 8 described in the second embodiment may be provided between the DC component determination unit 4 and the noise addition unit 5.

Embodiment 4 FIG.
FIG. 9 is a block diagram illustrating a configuration example of the image processing apparatus according to the fourth embodiment. The image processing apparatus 104 according to the fourth embodiment includes the image data storage unit 1, the frequency conversion unit 2, the DC component determination unit 4, the noise addition unit 5, the inverse frequency conversion unit 6, and the threshold processing unit described in the first embodiment. 7, a buffer unit 10 is provided.

  The buffer unit 10 temporarily stores data in the line buffer, and when the input image data is 255 or 0, the data information at that position is output to the threshold processing unit 7.

  Based on the data information sent from the buffer unit 10, the threshold processing unit 7 converts the data subjected to inverse frequency conversion into 0 or 255 data regardless of the threshold.

With these processes, it is possible to obtain a gradation reproduction image that is more faithful to the input image even in the image after the threshold processing.
The configuration may further include the gain setting unit 8 described in the second embodiment, the frequency component determination unit 3 and the changing unit 24 described in the third embodiment.

Embodiment 5 FIG.
In this embodiment, an image forming apparatus according to the present invention will be described. FIG. 10 is a block diagram illustrating a configuration example of the image forming apparatus according to the present embodiment. The image forming apparatus 300 operates as a digital color copying machine or a digital color multifunction peripheral. The image forming apparatus 300 includes a color image input device 30, a color image processing device 31, a color image output device 32, and an operation panel 33. The image forming apparatus 300 includes a control unit (not shown) for controlling each of the color image input device 30, the color image processing device 31, and the color image output device 32.

  The color image input device 30 includes, for example, a CCD (Charge Coupled Device), and a reflected light image from an original is read by the CCD, and RGB (R: Red, G: Green, B: Blue) analog signals are output. Generated. The generated RGB analog signal is output to the color image processing device 31.

  The color image processing apparatus 31 includes an A / D conversion unit 311, a shading correction unit 312, an input tone correction unit 313, a region separation processing unit 314, a color correction unit 315, a black generation and under color removal unit 316, and a spatial filter processing unit 317. An output tone correction unit 318 and a tone reproduction processing unit 319. The gradation reproduction processing unit 319 performs processing similar to that of the image processing apparatuses 101 to 104 described in the first to fourth embodiments.

  The color image processing device 31 converts the RGB analog signal received from the color image input device 30 into an RGB digital signal, performs various image processing such as correction processing, and performs CMYK (C: Cyan, M: Magenta, Y: Yellow). , K: Black), and the number of gradations of the generated CMYK digital signal is reduced to binary or quaternary. The binarized or quaternarized output image data is temporarily stored in a storage unit (not shown), and is output to the color image output device 32 at a predetermined timing.

  The A / D conversion unit 311 receives the RGB analog signal from the color image input device 30, converts the received RGB analog signal into an RGB digital signal, and sends the RGB digital signal to the shading correction unit 312. The shading correction unit 312 performs processing for removing various distortions generated in the illumination system, the imaging system, and the imaging system of the color image input device 30 on the RGB digital signal received from the A / D conversion unit 311. This is sent to the input tone correction unit 313. The input tone correction unit 313 adjusts the color balance for the RGB digital signal (RGB reflectance signal) received from the shading correction unit 312 and is processed by the image processing system employed in the color image processing device 31. It is converted into a density signal that can be easily processed and sent to the region separation processing unit 314.

  The region separation processing unit 314 separates each pixel in the image of the RGB digital signal received from the input tone correction unit 313 into one of a character region, a halftone dot region, and a photograph region, and based on the separation result, the pixel Is output to the color correction unit 315, the black generation and under color removal unit 316, the spatial filter processing unit 317, and the gradation reproduction processing unit 319. The RGB digital signal received from the input tone correction unit 313 is sent to the color correction unit 315 as it is.

  The color correction unit 315 converts the RGB digital signal sent from the input tone correction unit 313 into a CMY signal in order to perform color reproduction faithfully, and based on the spectral characteristics of the CMY color material including unnecessary absorption components. After the process of removing the color turbidity, it is sent to the black generation and under color removal unit 316. The black generation and under color removal unit 316 performs black generation processing for generating a black signal (K signal) from the three color signals (C signal, M signal, and Y signal) of the CMY signal received from the color correction unit 315, A new CMY signal is generated by subtracting the K signal obtained by the black generation process from the original CMY signal, and a CMYK four-color signal (CMYK signal) is sent to the spatial filter processing unit 317.

As a general black generation process, there is a method of generating black using skeleton black. In this method, the input / output characteristic of the skeleton curve is y = f (x), the input data is C, M, Y, the output data is C ′, M ′, Y ′, K ′, the UCR rate (UCR) : Under Color Removal) α (0 <α <1)
K ′ = f {min (C, M, Y)}
C ′ = C−αK ′
M ′ = M−αK ′
Y ′ = Y−αK ′
It is represented by

  The spatial filter processing unit 317 performs spatial filter processing using a digital filter on the image of the CMYK signal received from the black generation and under color removal unit 316 and corrects the spatial frequency characteristics to correct blur or Perform processing to improve graininess degradation. The output tone correction unit 318 performs output tone correction processing and the like, and the tone reproduction processing unit 319 performs predetermined processing on the image data of the CMYK signal based on the region identification signal.

  For example, a region separated as a character by the region separation processing unit 314 is emphasized at a high frequency by a sharp enhancement process included in the spatial filter processing performed by the spatial filter processing unit 317 in order to improve the reproducibility of black characters or color characters. Increase the amount. Further, the gradation reproduction processing unit 319 performs high-resolution binarization or multilevel conversion suitable for high-frequency reproduction.

  Further, with respect to the regions separated as halftone dots by the region separation processing unit 314, the spatial filter processing unit 317 performs low-pass filter processing for removing the input halftone component. The output tone correction unit 318 performs output tone correction processing for converting a signal such as a density signal into a halftone dot area ratio that is a characteristic value of the color image output device 32, and the tone reproduction processing unit 319 performs processing. Then, gradation reproduction processing (halftone generation) is performed so that the image is finally separated into pixels and binarized or multi-valued so that each gradation can be reproduced. Further, regarding the region separated into the photograph by the region separation processing unit 314, the gradation reproduction processing unit 319 performs binarization or multi-value conversion with emphasis on gradation reproducibility.

  The CMYK signal (image data) that has been binarized or multi-valued by the gradation reproduction processing unit 319 is sent to the color image output device 32. The color image output device 32 is a device that forms an image on a recording medium such as paper based on the CMYK signal received from the color image processing device 31. For example, an electrophotographic or inkjet color image output device can be used.

  The operation panel 33 is an input means for an operator to input an instruction by a key operation or the like. An operator instruction is output as a control signal from the operation panel 33 to the color image input device 30, the color image processing device 31, and the color image output device 32. According to an operator's instruction, an original image is read by the color image input device 30, and after data processing by the color image processing device 31, an image is formed on a recording medium by the color image output device 32, thereby functioning as a digital color copying machine. The above processing is controlled by a control unit (not shown).

Embodiment 6 FIG.
In the first to fifth embodiments, the image processing apparatus and the image forming apparatus according to the present invention have been described. However, the present invention may be implemented by a printer driver (computer program) installed in a computer.

  FIG. 11 is a block diagram illustrating a configuration example of the image forming system according to the present embodiment. The image forming system 400 includes a computer 40 and a printer 41. The printer 41 may be a digital multifunction machine having a copy function and a facsimile function in addition to a normal printer function. Further, the printer 41 performs electrophotographic or inkjet image formation.

  Image data acquired by an image input device such as a scanner or a digital camera is input to the computer 40 and stored in a storage device (not shown). The image data input to the computer 40 can be processed and edited by executing various application programs. Further, a printer driver 42 for generating image data to be output to the printer 41 is installed in the computer 40. The printer driver 42 includes a color correction unit 45 that performs color correction processing of image data, and gradation reproduction that performs threshold processing to reduce the number of gradations (for example, 256 gradations) of output image data to two values or four values. It operates as a processing unit 46 and a printer language translation unit 47 that converts output image data into a printer language. The color correction unit 45 also performs black generation and under color removal processing. The processing executed in the gradation reproduction processing unit 46 corresponds to the processing executed by the image processing apparatuses 101 to 104 described in the first to fourth embodiments. The data converted into the printer language by the printer language translation unit 47 is output to the printer 41 via the communication port 44 such as RS232C or LAN.

Embodiment 7 FIG.
In the sixth embodiment, the image processing apparatus according to the present invention is realized by the computer 40 in which the printer driver 42 is installed. However, such a computer program is provided by a recording medium such as an FD or a CD-ROM and recorded. The image processing method according to the present invention may be realized by executing a computer program read from a medium.

  FIG. 12 is a block diagram illustrating a configuration example of the image forming system according to the present embodiment. The image forming system 500 includes a computer 50 and a printer 41 as in the sixth embodiment. The computer 50 includes a CPU 51, and this CPU 51 controls each part of hardware such as a RAM 52, a hard disk 53, a communication port 54, an external storage unit 55, an input unit 56, and a display unit 57.

  The RAM 52 is a volatile storage unit that temporarily stores various data generated when the CPU 51 controls the above-described hardware units. The hard disk 53 is a nonvolatile storage unit that stores image data before processing and after processing. The communication port 54 is a port for connecting the printer 41, and functions as a communication unit that transmits image data for output to the connected printer 41 and receives information notified from the printer 41.

  The external storage unit 55 includes a reading device for reading the computer program from the recording medium 60 on which the computer program of the present invention is recorded. As the recording medium 60, an FD, a CD-ROM, or the like can be used. The computer read through the external storage unit 55 is stored in the hard disk 53.

  The input unit 56 includes input means such as a keyboard and a mouse in order to select image data to be processed, input parameters necessary for image processing, an instruction to start image processing, and the like. The display unit 57 includes a liquid crystal display and a CRT display, and displays instructions necessary for various operations, image data before processing, image data after processing, and the like.

  As the recording medium 60 for recording the computer program according to the present invention, in addition to the above-mentioned FD and CD-ROM, optical recording medium such as MO, MD, DVD, magnetic recording medium such as hard disk, IC card, memory card Further, it is also possible to use a semiconductor memory such as a card-type recording medium such as an optical card, a mask ROM, an EPROM (Erasable Programmable Read Only Memory), an EEPROM (Electrically Erasable Programmable Read Only Memory), a flash ROM, or the like. Moreover, the structure which downloads from an external server not shown through a communication means may be sufficient.

  Hereinafter, the operation of the computer 50 will be described. FIG. 13 is a flowchart for explaining a procedure of processing executed by the computer 50. Here, a processing procedure for generating a quaternary output image by performing frequency conversion processing by DCT will be described. Image data to be processed, noise data to be added to the image data, and the like are stored in the hard disk 53, read into the RAM 52 when used for processing, and used by the CPU 51. First, the CPU 51 reads out an 8 × 8 pixel processing block from the image data read into the RAM 52 (step S101), and performs DCT processing on the read processing block, thereby converting the image data into spatial frequency component data. Conversion is performed (step S102).

  Next, the CPU 51 determines whether or not the DC component of the processing block belongs within a predetermined range (step S111). When it is determined that the DC component of the processing block belongs to the predetermined range (S111: YES), the CPU 51 executes noise amplitude enhancement processing (step S112). In the noise amplitude enhancement process, as described in the first embodiment, the noise data (blue noise) prepared in advance is multiplied by a constant to generate new noise data, or as described in the second embodiment. In addition, a process of changing the amplitude intensity of the noise is performed depending on the magnitude of the DC component.

  When it is determined that the DC component of the processing block does not belong within the predetermined range (S111: NO), noise data (blue noise) prepared in advance is added, and the DC component of the processing block falls within the predetermined range. If it is determined that they belong (S111: YES), the CPU 51 executes a noise addition process for adding the noise data whose amplitude is emphasized in step S112 (step S116).

  After executing the noise addition processing, the CPU 51 executes inverse DCT processing to convert the spatial frequency domain data into density domain data (step S117). Then, threshold processing is performed to obtain a quaternary output image (step S118). Although it is necessary to set three threshold values in order to obtain a four-value output image, these threshold values may be set as parameters in a computer program read from the recording medium 60.

  Next, the CPU 51 determines whether or not the processing has been completed for all the processing blocks (step S119). If it is determined that the processing has not ended (S119: NO), the CPU 51 returns the processing to step S101. If it is determined that the processing for all the processing blocks has been completed (S119: YES), the CPU 51 ends the processing according to this flowchart.

Embodiment 8 FIG.
In the seventh embodiment, the noise addition processing is performed based on the magnitude of the DC component. However, as in the third embodiment, the noise addition processing is combined with the enhancement processing or smoothing processing of a predetermined frequency component. It may be configured to execute. Note that the hardware configuration for realizing the image processing method in the present embodiment is the same as that described in the seventh embodiment. In other words, the computer program of the present invention is read from the recording medium 60, loaded onto the RAM 52, and executed by the CPU 51, thereby realizing the image processing method according to the present embodiment.

  FIG. 14 is a flowchart for explaining a procedure of processing executed by the computer 50. As in the seventh embodiment, it is assumed that image data to be processed, noise data to be added to the image data, and the like are stored in the hard disk 53 and read onto the RAM 52 when processing is performed. First, the CPU 51 reads an 8 × 8 pixel processing block from the image data read into the RAM 52 (step S101), and performs DCT processing on the read processing block to convert the image data into frequency component data. (Step S102).

  Next, the CPU 51 determines whether or not the magnitude of the DCT coefficient of the predetermined frequency component is larger than a predetermined value (step S103). When determining that the magnitude of the DCT coefficient of the predetermined frequency component is larger than the predetermined value (S103: YES), the CPU 101 performs a changing process of multiplying a predetermined constant and position information (step S104). If it is determined that the magnitude of the DCT coefficient of the predetermined frequency component is equal to or smaller than the predetermined value (S103: NO), a frequency changing process using human visual characteristics or the like is performed (step S105).

  Next, the CPU 51 determines whether or not the DC component of the processing block belongs within a predetermined range (step S111). When it is determined that the DC component of the processing block belongs to the predetermined range (S111: YES), the CPU 51 executes noise amplitude enhancement processing (step S112). In the noise amplitude enhancement process, as described in the first embodiment, the noise data (blue noise) prepared in advance is multiplied by a constant to generate new noise data, or as described in the second embodiment. In addition, a process of changing the amplitude intensity of the noise is performed depending on the magnitude of the DC component.

  When it is determined that the DC component of the processing block does not belong within the predetermined range (S111: NO), noise data (blue noise) prepared in advance is added, and the DC component of the processing block falls within the predetermined range. If it is determined that they belong (S111: YES), the CPU 51 executes a noise addition process for adding the noise data whose amplitude is emphasized in step S112 (step S116).

  After executing the noise addition processing, the CPU 51 executes inverse DCT processing to convert the spatial frequency domain data into density domain data (step S117). Then, threshold processing is performed to obtain a quaternary output image (step S118). In order to obtain a four-value output image, it is necessary to set three threshold values. These threshold values may be set as parameters in a computer program read from the recording medium 60.

  Next, the CPU 51 determines whether or not the processing has been completed for all the processing blocks (step S119). If it is determined that the processing has not ended (S119: NO), the CPU 51 returns the processing to step S101. If it is determined that the processing for all the processing blocks has been completed (S119: YES), the CPU 51 ends the processing according to this flowchart.

Embodiment 9 FIG.
In the seventh embodiment and the eighth embodiment, the threshold value processing is performed using three threshold values in order to obtain a four-value output image. However, as in the fourth embodiment, the image data after the inverse DCT processing is used. It may be determined whether or not a pixel having a gradation value of 0 or 255 is included, and if included, the gradation value is retained. The hardware configuration is the same as that described in the seventh embodiment. In other words, the computer program of the present invention is read from the recording medium 60, loaded onto the RAM 52, and executed by the CPU 51, thereby realizing the image processing method according to the present embodiment.

  FIG. 15 is a flowchart for explaining a procedure of processing executed by the computer 50. Note that the processing described with reference to this flowchart is executed instead of the threshold processing in step S118 described with reference to FIGS. The CPU 51 determines whether or not the gradation value of each pixel included in the processing block after the inverse DCT process is 0 or 255 (step S201). When it is determined that the gradation value of the target pixel is 0 or 255 (S201: YES), the gradation value is set to 0 or 255 regardless of the threshold value at the same pixel position in the processing block (step S202). .

  On the other hand, when it is determined that the gradation value of the target pixel is not 0 or 255 (S201: NO), normal threshold processing is performed (step S203). That is, when four values are output, three threshold values are set, the magnitude relationship between these threshold values and the gradation value of the target pixel is determined, and the four gradation values are determined based on the determination result. Classify.

  Next, the CPU 51 determines whether or not processing has been completed for all pixels (step S204). If it is determined that processing has not been completed for all pixels (S204: NO), the process returns to step S201, and all the pixels are processed. If it is determined that the process has been completed for the pixel (S204: YES), the process according to this flowchart is terminated.

  With these processes, it is possible to obtain a gradation reproduction image that is more faithful to the input image even in the image after the threshold processing.

1 is a block diagram illustrating a configuration example of an image processing apparatus according to a first embodiment. It is a schematic diagram which shows an example of blue noise. 6 is a block diagram illustrating a configuration example of an image processing device according to Embodiment 2. FIG. It is a graph explaining the emphasis level by a gain setting part. 10 is a block diagram illustrating a configuration example of an image processing apparatus according to Embodiment 3. FIG. It is explanatory drawing explaining the determination method. It is a schematic diagram which shows an example of the area | region which changes a DCT coefficient. It is a schematic diagram which shows the positional information data in a change area | region. FIG. 10 is a block diagram illustrating a configuration example of an image processing device according to a fourth embodiment. 1 is a block diagram illustrating a configuration example of an image forming apparatus according to an exemplary embodiment. 1 is a block diagram illustrating a configuration example of an image forming system according to an embodiment. 1 is a block diagram illustrating a configuration example of an image forming system according to an embodiment. It is a flowchart explaining the procedure of the process which a computer performs. It is a flowchart explaining the procedure of the process which a computer performs. It is a flowchart explaining the procedure of the process which a computer performs. It is a schematic diagram which shows an example of the 4x4 dither matrix used for the dither method which binarizes a 16 gradation image. It is a schematic diagram which shows an example of the weighting coefficient matrix used for the error diffusion method.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Image data memory | storage part 2 Frequency conversion part 3 Frequency component determination part 4 DC component determination part 5 Noise addition part 6 Inverse frequency conversion part 7 Threshold processing part 8 Gain setting part 10 Buffer part 101,102,103,104 Image processing apparatus

Claims (6)

In an image processing method of performing orthogonal transformation on image data to generate data composed of spatial frequency components and performing image processing based on the generated data,
The step of determining whether or not the magnitude of the DC component of the spatial frequency components constituting the data is within a predetermined range, and the AC component of the spatial frequency component when it is determined that it is not within the predetermined range Adding noise data in the spatial frequency domain having blue noise characteristics to the data, and determining that the noise data is within the predetermined range, the data obtained by multiplying the noise data by a constant larger than 1 is used as the AC Adding to the component; performing inverse orthogonal transformation on the noise data or data obtained by multiplying the noise data by the constant; and stepping the image data obtained by the inverse orthogonal transformation. Reducing the logarithm using one or more thresholds.   The image data to be subjected to the orthogonal transformation is data composed of a plurality of pixels in which each pixel has an appropriate gradation value, and a pixel having a maximum or minimum gradation value among values that the gradation value can take. A step of determining whether or not the pixel is included in the image data; and if it is determined that a pixel having a maximum or minimum gradation value is included, the gradation value of a pixel other than the pixel is set as the threshold value. The image processing method according to claim 1, further comprising a step of changing based on the step. In an image processing apparatus that performs orthogonal transformation on image data to generate data composed of spatial frequency components, and performs image processing based on the generated data,
Among the spatial frequency components constituting the data, a means for determining whether or not the magnitude of the direct current component is within a predetermined range, and when determining that the direct current component is not within the predetermined range, On the other hand, when noise data in the spatial frequency region having blue noise characteristics is added and it is determined that the noise data is within the predetermined range, data obtained by multiplying the noise data by a constant larger than 1 is added to the AC component. Means for performing inverse orthogonal transformation on the noise data or data obtained by adding the noise data multiplied by the constant, and the number of gradations of the image data obtained by the inverse orthogonal transformation. An image processing apparatus comprising: means for decreasing using one or a plurality of threshold values.   The image data to be subjected to the orthogonal transformation is data composed of a plurality of pixels in which each pixel has an appropriate gradation value, and a pixel having a maximum or minimum gradation value among values that the gradation value can take. In the case where it is determined that a pixel having a maximum or minimum gradation value is included and means for determining whether or not the image data is included, the gradation value of a pixel other than the pixel is set as the threshold value. The image processing apparatus according to claim 3, further comprising a changing unit based on the image processing unit.   An image forming apparatus comprising: the image processing apparatus according to claim 3; and means for forming an image on a sheet based on image data obtained by the image processing apparatus. In a computer program for causing a computer to perform orthogonal transformation on image data to generate data composed of spatial frequency components, and to perform image processing based on the generated data,
A step of causing a computer to determine whether or not a magnitude of a direct current component among spatial frequency components constituting the data is within a predetermined range; and when the computer determines that the magnitude is not within the predetermined range, A step of adding noise data of a spatial frequency region having blue noise characteristics to an alternating current component of the spatial frequency component, and if the computer determines that the noise data is within the predetermined range, a constant greater than 1 is added to the noise data Adding the data obtained by multiplying the AC component to the AC component, and causing the computer to perform inverse orthogonal transformation on the noise data or the data obtained by adding the noise data multiplied by the constant ; The computer reduces the number of gradations of the image data obtained by the inverse orthogonal transformation using one or more threshold values. Computer program, characterized in that a step.

Images