JP2024019857A - Image processing device, image processing method, and program - Google Patents

Abstract

The present invention provides a technique that can maintain density as a region during quantization processing even in an image of a high frequency pattern. A first usage rate indicating the degree to which a two-dimensional matrix influences the quantization according to a pixel value for each pixel located in a target area including the pixel of interest; A second usage rate indicating the degree to which the cumulative error, which is the cumulative value of the quantization error values generated in the quantization for pixels around the quantization, influences the quantization is determined. Then, a first application usage rate to be applied to the quantization of the pixel of interest is determined based on the first usage rate of each pixel located in the target area, and the first usage rate of each pixel located in the target area is determined. determining a second application usage rate to be applied to the quantization of the pixel of interest based on the second usage rate, using the two-dimensional matrix according to the first application usage rate, and using the two-dimensional matrix according to the second application usage rate. and performing the quantization using the accumulated error. [Selection diagram] Figure 4

Description

The present invention relates to an image processing device, an image processing method, and a program that perform quantization processing.

In order to print an image using a printer, it is usually necessary to perform quantization processing on the image. Quantization processing is halftone processing that converts an image expressed in continuous gradations into the number of gradations that can be expressed by a printer. Conventionally, dither processing and error diffusion processing, for example, are known as quantization processing.

Further, conventionally, a method is known in which quantization is performed using both dither processing and error diffusion processing. For example, Patent Document 1 describes how to allocate the influence of dither processing and error diffusion processing using noise usage rate and error usage rate, which are parameters that indicate the degree to which dither matrix noise and cumulative error affect quantization processing. A technique for quantizing the data is disclosed.

International publication number WO2011/036735

However, for example, in an image with a high frequency pattern such as a hatching pattern of intermediate tone values, the noise usage rate and the error usage rate vary within the area. For this reason, even though processing is performed using error diffusion, there are cases where the error propagation is small or no error propagates depending on the error usage rate, and the density is not maintained as a region.

The present invention has been made in view of the above problems, and it is an object of the present invention to provide a technique that can maintain the density of a region during quantization processing even in an image of a high frequency pattern.

In order to achieve the above object, one embodiment of the present invention is a program that causes a computer to function as an image processing device that quantizes the pixel value of each pixel in an input image, the program that causes the computer to perform the quantization. a pixel selection means for selecting a pixel of interest to be a target; and at least a degree to which the two-dimensional matrix influences the quantization according to a pixel value of each pixel located in a target area including the pixel of interest; and a second usage rate that indicates the degree to which the quantization is affected by the cumulative error that is the cumulative value of quantization error values generated in the quantization for pixels surrounding the pixel of interest. a usage rate determination means for determining a first application usage rate to be applied to the quantization of the pixel of interest based on the first usage rate of each pixel located in the target area; applied usage rate determining means for determining a second applied usage rate to be applied to the quantization of the pixel of interest based on the second usage rate of each pixel; and the cumulative error corresponding to the pixel of interest and the second application rate. an applied error value obtaining unit that obtains an applied error value to be applied to the quantization of the pixel of interest based on the usage rate; and a cumulative pixel value that obtains a cumulative pixel value based on the applied error value and the pixel value of the pixel of interest. cumulative pixel value acquisition means; threshold value acquisition means for acquiring a quantization threshold to be applied to the quantization of the pixel of interest based on the two-dimensional matrix and the first applied usage rate; The method is characterized in that it functions as a quantization value acquisition means for acquiring a quantization value and a quantization error value based on the quantization threshold value.

According to the present invention, even in the case of an image with a high frequency pattern such as a hatching pattern of intermediate gradation values, the density as a region can be maintained during quantization processing.

Diagram showing an overview of quantization processing executed by the printing system and image processing device Diagram showing an example of an image obtained from an input image and quantization processing results A diagram showing an example of an input value in an input image and an output value after quantization processing Flowchart showing the processing routine of quantization processing Diagram explaining the pixel selection order Diagram explaining how to calculate noise usage rate and error usage rate Flowchart showing the processing routine of the first calculation process Flowchart showing the processing routine of the second calculation process Flowchart showing the processing routine of quantization execution processing Diagram showing the noise usage rate and error usage rate for each pixel in the target area Diagram showing an example of a diffusion filter and error distribution using a diffusion filter Flowchart showing the processing routine of error distribution processing Diagram showing another example of an image based on the input image and quantization processing results

Hereinafter, an example of an embodiment of an image processing device, an image processing method, and a program will be described in detail with reference to the accompanying drawings. Note that the following embodiments do not limit the present invention, and not all combinations of features described in the present embodiments are essential to the solution of the present invention. Further, the positions, shapes, etc. of the components described in the embodiments are merely examples, and the scope of the present invention is not intended to be limited thereto.

(Printing system configuration)
FIG. 1A is a diagram showing an example of a printing system including an image processing apparatus according to this embodiment. The printing system 10 in FIG. 1A includes a printing device 12 that prints on a print medium, and an image that can generate printable data that can be printed by the printing device 12 based on input print data. A processing device 14 is provided. The printing device 12 is, for example, an inkjet printer, and executes printing on a print medium based on printable data input from the image processing device 14. Further, the printing device 12 performs color printing using cyan ink, magenta ink, yellow ink, and black ink as process colors. Note that the printing device 12 may be configured to eject, in addition to process color ink, ink of another color, or a processing liquid that performs a predetermined process on the ejected ink.

Although not shown, the image processing device 14 includes at least a central processing unit (CPU), a ROM that stores programs for various processes executed by the CPU, and a RAM that is used as a work memory of the CPU. ing. Further, the image processing device 14 includes a storage unit capable of storing various types of information. The image processing device 14 executes image forming processing such as RIP (Raster Image Processor) processing, for example. Specifically, the image processing device 14 develops the original image represented by the print data to form printable data representing the image in a format that the printing device 12 can interpret.

In the present embodiment, the image processing device 14 performs quantization of at least a density value indicating color density, which is a pixel value of each pixel in the original image, in the image forming process. Therefore, in this embodiment, the image processing device 14 performs quantization for each process color used by the printing device 12, and forms a halftone image corresponding to each process color based on print data. Note that the image processing device 14 is, for example, a host device that controls the printing device 12, and operates as an image processing device according to a predetermined program. The image processing device 14 may receive print data from, for example, an external device connected to the image processing device 14. Alternatively, the image processing device 14 may be configured to allow a user to create print data.

(Overview of quantization)
Next, an overview of the quantization process executed by the image processing device 14 will be explained. FIG. 1(b) is a diagram showing an overview of the quantization process executed by the image processing device 14. The image processing device 14 forms a pseudo-halftone image, which is a halftone image, by quantizing the original image for each process color. This quantization is a process of converting the density value In (x, y) at each coordinate of the original image into a quantized value out (x, y) at the same coordinate of the pseudo halftone image.

The image processing device 14 performs hybrid error diffusion processing using both dither matrix noise D (i, j) and cumulative error E (x, y) as quantization. The image processing device 14 also sets values in the range of 0 or more and 1 or less, that is, 0% to 100%, as parameters related to the dither matrix noise D (i, j) and the cumulative error E (x, y). The noise usage rate Rn and the error usage rate Re are used.

The noise usage rate Rn is a parameter indicating the degree to which the dither matrix noise D(i, j), which is a two-dimensional matrix, influences the quantization process. This noise usage rate Rn is determined according to the density value. In this embodiment, the applied noise usage rate Rna to be applied to the pixel to be quantized is obtained. Note that the applied noise usage rate Rna is calculated from the noise usage rate Rn according to each density value In(x,y) of the pixel to be quantized and its surrounding pixels. The image processing device 14 does not use the dither matrix noise D(i, j) as it is, but performs quantization using the product of the dither matrix D(i, j) and the applied noise usage rate Rna. That is, the image processing device 14 uses dither matrix noise D(i, j) according to the applied noise usage rate Rna corresponding to the pixel to be quantized.

The error usage rate Re is a parameter indicating the degree to which the cumulative error E(x, y) influences the quantization process. This error usage rate Re is determined according to the density value. In this embodiment, the applied error usage rate Rea to be applied to the pixel to be quantized is obtained. Note that the applied error usage rate Rea is calculated from the error usage rate Re that corresponds to the respective density values In(x, y) of the pixel to be quantized and its surrounding pixels. The image processing device 14 uses the product of the cumulative error E(x, y) and the applied error usage rate Rea to calculate the density value In(x , y) is calculated. Then, the image processing device 14 performs a quantization process using the calculated error-corrected input value In′ (x, y). , using the cumulative error E(x,y). Note that details of the quantization process will be described later.

(Concerns regarding known technology)
Here, a printing result when printing using data that has been quantized using a known technique will be described. In a known technique (for example, the technique disclosed in Patent Document 1), the noise usage rate Rn and error usage rate Re for a pixel are determined by the density value In(x, y) of that pixel. FIG. 2 is a diagram showing differences in print results depending on processing results for input images. FIG. 2(a) is a diagram showing an input image printed in a predetermined process color. FIG. 2(b) is a diagram showing a print result according to a result of processing an input image using a known technique. FIG. 2C is a diagram showing a print result according to the processing result of using the technique according to this embodiment on an input image. FIG. 3 is a diagram showing the result of quantization on an image cut out by 5×5 pixels from the input image of FIG. 2(a). FIG. 3(a) shows an image near the halftone of the input image of FIG. 2(a) (the density value of a pixel having density is 125 (8 bits)). FIG. 3(b) shows the result of quantization using a known technique. FIG. 3(c) shows the result of quantization according to this embodiment.

In the input image, pixels with a density of 0 and pixels with a density are arranged in a staggered manner, and the pixels with a density gradually increase in density from one side of the image to the other (Figure 2 (a) reference). In an image obtained by cutting out the vicinity of the halftone of this input image into 5×5 pixels, pixels with a density value of 0 are located around a pixel with a density value of 125, as shown in FIG. 3(a). A pixel with density (density value 125) has an error usage rate of 100% and a noise usage rate of 0%. As the diffusion filter, a diffusion filter 102 shown in FIG. 1(b) is used. Note that the ratio of the error usage rate and the noise usage rate to the density value is as shown in FIG. 6, for example, and the details will be described later. The result of quantization of the image in FIG. 3(a) using the known technique is as shown in FIG. 3(b).

That is, in FIG. 3A, the pixel 302 has a density value of 0. Therefore, in the pixel 302, the noise usage rate is 100% and the error usage rate is 0%. When pixel 302 is quantized, since there is a pixel 304 with a density value of 125 in the vicinity, the cumulative error E applied to pixel 302 becomes a non-zero value due to the propagation of errors generated in the surrounding pixels. However, since the error usage rate is 0% in the pixel 302, the cumulative error E is not reflected in the input value, and the error-diffused input value becomes 0 (0+0). Therefore, if the value of the dither matrix of pixel 302 is -108 and the initial threshold is 128, then the noise-corrected threshold is 20 (=-108×1.0+128). As a result, the input value (density value) 0 is compared with the threshold value 20, and since the input value is less than the threshold value, the quantization result is 0 (see FIG. 3(b)). Since 0 (0-0) is calculated as the error, there is no error that spreads to the surroundings.

On the other hand, in FIG. 3A, the pixel 304 has a density value of 125. Therefore, in the pixel 304, the noise usage rate is 0% and the error usage rate is 100%. Since the pixels around the pixel 304 have a density of 0, there is no error caused by quantization of the surrounding pixels. Therefore, the cumulative error E applied to pixel 304 is zero. In the pixel 304, the error usage rate is 100%, but since the cumulative error E is 0, the error-diffused input value is 125 (125+0). Therefore, if the value of the dither matrix of the pixel 304 is -64 and the initial threshold is 128, then the noise-corrected threshold is 128 (=-64×0.0+128). As a result, the input value (density value) 125 and the threshold value 128 are compared, and since the input value is less than the threshold value, the quantization result is 0 (see FIG. 3(b)). Then, +125 (125-0) is calculated as an error, and the error is diffused to surrounding pixels by a diffusion filter.

In this way, with the quantization using the known technology, the image in FIG. 3(a) is created as shown in FIG. 3(b) because the error generated in the pixel with density is diffused but not applied to the surrounding pixels. , all the output values in the region become 0, and the density is no longer maintained as a region. Therefore, in the case of an input value (density value) with an error usage rate of 100% and a noise usage rate of 0%, in error diffusion processing, the density due to the quantization result changes rapidly after the reference threshold, and the noise usage rate At gradation levels with low quantization, the density may not be maintained as a region as a result of quantization.

As shown in Figure 2(b), in the bright side (left side in the figure), part of the quantization result is 255, and in the dark side (right side in the figure), the quantization result is 255. In some parts, the value is 0 where it should be 255. This is because as the noise usage rate approaches 100%, there are pixels where the bright area: noise corrected threshold value<pixel density value, and the dark area: noise corrected threshold value>pixel density value. In other words, when comparing FIG. 2(a) and FIG. 2(b), we find that in FIG. 2(b), the density gradation is not maintained, and not only does the density change sharply near the center, but also in the bright area. It can be seen that there are places where the density inversion occurs in dark areas.

(Summary of technology according to this embodiment)
Therefore, in this embodiment, the applied noise usage rate Rna to be applied to the pixel to be quantized is determined from the noise usage rate Rn and error usage rate Re corresponding to the respective density values of the pixel to be quantized and the surrounding pixels. The application error usage rate Rea is determined. Then, using the determined applied noise usage rate Rna and applied error usage rate Rea, the quantization process for the target pixel is executed.

Thereby, for example, it is possible to influence the spatial frequency characteristics (dither characteristics) of dither processing on the spatial frequency characteristics (error diffusion characteristics) of error diffusion processing. Further, for example, quantization processing can be performed by a method that incorporates a dither characteristic into an error diffusion characteristic. In this case, unlike the case of simply switching between dither processing and error diffusion processing, it is possible to suppress the occurrence of boundary lines due to processing switching.

Further, an applied noise usage rate Rna and an applied error usage rate Rea that take into account the maintenance of the density of the region can be applied to the pixels to be quantized. As a result, in an image of a high frequency pattern such as a hatching pattern of halftone values in which the noise usage rate Rn and the error usage rate Re vary within the area, the density as a region can be maintained. Therefore, for example, it becomes possible to appropriately utilize the strengths of error diffusion processing and dither processing, and to perform quantization processing while maintaining the density of the region.

Note that in this embodiment, the dither matrix noise D(i, j) is a two-dimensional matrix, and is, for example, a value specified by a preset dither matrix. Further, the dither matrix noise D(i, j) may be the same as or similar to the dither matrix noise used in conventional dither processing, for example. Furthermore, it is preferable to use, for example, noise with blue noise characteristics as the dither matrix noise D(i, j). In the image processing device 14, it is preferable that the dither matrix used is changed for each process color.

Furthermore, in this embodiment, the cumulative error E(x, y) is the cumulative value of the quantization errors Q(x, y), which are errors that occur during quantization of surrounding pixels (that is, the cumulative value of the quantization error values). value) and is calculated using a preset diffusion filter (diffusion matrix). Further, the calculated cumulative error E(x, y) is stored in, for example, a cumulative error buffer. The image processing device 14 calculates the cumulative error E(x, y) using, for example, the same or similar method to the cumulative error used in conventional error diffusion processing.

(Quantization processing)
Next, the quantization process executed in the image processing device 14 according to this embodiment will be explained. This quantization process is a process of performing quantization processing on each pixel in the original image. FIG. 4 is a flowchart showing a detailed processing routine of the quantization process executed by the image processing device 14. Note that the image processing device 14 executes the quantization process shown in FIG. 4 for each process color. The series of processes shown in the flowchart of FIG. 4 is performed by the CPU loading the program code stored in the ROM into the RAM and executing it. Alternatively, the functionality of some or all of the steps in FIG. 4 may be performed in hardware, such as an ASIC or electrical circuit. Note that the symbol S in the description of each process means a step in a flowchart (the same applies hereinafter in this specification).

When the quantization process is started, first, in S402, the CPU executes a pixel selection process to select a pixel of interest, which is a pixel to be quantized, from the original image. In this manner, in this embodiment, the CPU of the image processing device 14 functions as a pixel of interest selection section. Next, in S404, the CPU calculates the noise usage rate Rn and error usage rate Re according to the density value, which is the pixel value of each pixel, for each pixel in the target area including the pixel to be quantized. Execute rate calculation processing. In this manner, in this embodiment, the CPU of the image processing device 14 functions as a usage rate determination unit that determines the noise usage rate Rn and the error usage rate Re depending on the density value of the pixel. Thereafter, in S406, the CPU uses the calculated noise usage rate Rn and error usage rate Re to determine the applied noise usage rate Rna and applied error usage rate Rea to be applied to the pixels to be quantized. Execute. As described above, in this embodiment, the CPU of the image processing device 14 calculates the applied noise usage rate Rna and applied error corresponding to the pixel to be quantized from the noise usage rate Rn and error usage rate Re of each pixel in the target area. It functions as an applied usage rate determination unit that determines the usage rate Rea. Note that details of the above-described pixel selection process, usage rate calculation process, and applied usage rate determination process will be described later.

Then, in S408, the CPU calculates the error-corrected input value In'(x,y), which is the density value after correction based on the cumulative error E(x,y). In this process, for example, the applied error value that is the product of the applied error usage rate Rea and the cumulative error E(x, y) corresponding to the pixel selected in the pixel selection process of S402 is converted to the density value In( x, y). Then, the value after the addition is obtained as the error-corrected input value In'(x,y), which is the cumulative pixel value. In this manner, in this embodiment, the CPU of the image processing device 14 functions as an applied error value acquisition unit that acquires applied error values. Further, in this embodiment, the CPU of the image processing device 14 functions as a cumulative pixel value acquisition unit (error-corrected input value calculation unit) that acquires cumulative pixel values (error-corrected input values).

Further, in S410, the CPU calculates a noise-corrected threshold Th', which is a threshold that reflects the dither matrix noise D(i, j), as a threshold used in quantization. In this process, for example, the product of the applied noise usage rate Rna corresponding to the pixel selected in the pixel selection process of S402 and the dither matrix noise D(i, j) is added to a preset initial threshold Th. Then, the value after the addition is obtained as the noise-corrected threshold Th' which is the quantization threshold. In this way, in this embodiment, the CPU of the image processing device 14. It functions as a threshold acquisition unit (noise-corrected threshold calculation unit) that acquires a quantization threshold (noise-corrected threshold).

Next, in S412, the CPU compares the calculated noise-corrected threshold Th' with the error-corrected input value In'(x,y), and performs quantization on the pixel selected in the pixel selection process of S402. A quantization execution process is performed to calculate a quantized value by executing . Furthermore, in S412, the quantization error Q(x,y) is also calculated. Thereafter, in S414, the CPU performs error distribution processing S107 to diffuse the quantization error Q(x,y) caused by the quantization of this pixel to surrounding pixels according to the diffusion filter. In this error distribution process, the quantization error Q (x, y) is multiplied by the value of the diffusion filter corresponding to the distribution destination coordinates of the surrounding pixels, and the cumulative error E (x, y) corresponding to each of the surrounding pixels is calculated. Update the value. In this manner, in this embodiment, the CPU of the image processing device 14 functions as a quantization value acquisition unit that acquires quantization values and quantization error values.

Then, in S416, the CPU determines whether the quantized pixel is the final pixel of the original image. In S416, if it is determined that the pixel is the final pixel, this quantization process ends. If it is determined in S416 that the pixel is not the final pixel, the process returns to pixel selection processing in S402, selects the next pixel, and executes subsequent processing. Note that details of the quantization execution process and the error distribution process will be described later.

<Pixel selection process>
FIG. 5 is a diagram illustrating an example of the order of pixels selected in the pixel selection process. In the pixel selection process of S402, for example, as shown in FIG. 5, pixel lines are sequentially selected, and pixels in the selected lines are sequentially selected along a predetermined processing direction. The processing direction is switched for each adjacent line. For example, pixels are sequentially selected from one side to the other on odd lines and from the other side to one side on even lines. In this way, when performing quantization processing in a bidirectional manner in which the order of quantization into pixels is reversed between adjacent lines of the original image, the direction of error diffusion is not constant, so dots can be dispersed more appropriately. As a result, for example, compared to the case where quantization processing is performed in unidirectional processing, where the order of quantization processing for pixels is only in one direction between adjacent lines of the original image, dot delay and worm This makes it possible to suppress the generation of noise.

<Usage rate calculation process>
Next, the usage rate calculation process executed in S404 will be described with reference to FIGS. 6 to 8. FIG. 6 is a diagram showing an example of a graph and a calculation formula that associate the error usage rate Re and the noise usage rate Rn with density values. FIG. 7 is a flowchart showing a detailed processing routine of the first calculation process for calculating the error usage rate Re. Further, FIG. 8 is a flowchart showing a detailed processing routine of the second calculation process for calculating the noise usage rate Rn. The series of processes shown in the flowcharts of FIGS. 7 and 8 are performed by the CPU loading the program code stored in the ROM into the RAM and executing it. Alternatively, the functionality of some or all of the steps in FIGS. 7 and 8 may be performed in hardware such as an ASIC or electrical circuit.

= Overview of usage rate calculation process =
The error usage rate Re and the noise usage rate Rn are calculated based on a function that continuously changes with respect to density values in a range from 0, which is the minimum input value MinIn, to the maximum input value MaxIn. The maximum input value MaxIn and the minimum input value MinIn are, for example, the maximum and minimum values of the range that the density value that is the input value can take. Furthermore, in this function, the following three criteria are used to indicate the density range in the highlighted portion.
・Highlight side error usage rate minimum density value Hes, which is an example of the third highlight reference value
・Highlight side noise usage rate maximum density value Hn, which is an example of the first highlight reference value
・Highlight side error usage rate maximum density value He, which is an example of the second highlight reference value

Furthermore, the following three criteria are used to indicate the density range in the shadow portion.
・Shadow side error usage rate maximum density value Se, which is an example of the first shadow reference value
・Shadow side noise usage rate maximum density value Sn, which is an example of the second shadow reference value
・Shadow side error usage rate minimum density value Ses, which is an example of the third shadow reference value
Further, as standards indicating the density range sandwiching the initial threshold Th at the center of the halftone part, a highlight side noise usage rate 0% density value Hnz, a shadow side noise usage rate 0% density value Snz, and a first halftone reference value C1 are used. , and the second halftone reference value C2 are used.

Further, these parameters are set so that at least Hes≦Hn<He<Se<Sn≦Ses and Hn<Hnz<Snz<Sn. In addition, in this embodiment, these parameters have the magnitude relationship shown in the graph as 0(MinIn)<Hes≦Hn<He<C1<Hnz<Th<Snz<C2<Se<Sn≦Ses<MaxIn. It is set so that

Then, based on the calculation formula shown in FIG. 6, the error usage rate Re and the noise usage rate Rn are obtained. Note that if the noise usage rate Rn calculated by the formula shown in FIG. 6 is smaller than the predetermined minimum noise usage rate RnMin, the noise usage rate Rn is set to the minimum noise usage rate RnMin. As a result, the noise usage rate Rn becomes a value equal to or higher than the minimum noise usage rate RnMax regardless of the density value of the pixel. Note that the error usage rate Re and the noise usage rate Rn are set to values within the range of 0 to 100% (values of 0 to 1). In the calculation formula shown in FIG. 6, if it is 100% or more, it is set to 100%, and if it is 0% or less, it is set to 0%.

Further, the minimum noise usage rate RnMin is preset to a value larger than 0, for example, during adjustment when setting parameters. The minimum noise usage rate RnMin may be, for example, a value of 0.1 (10%) or more. The minimum noise usage rate RnMin is preferably set to, for example, 0.1 to 0.2 (10 to 20%). Furthermore, as can be seen from the graph in FIG. 6, when the input value In is equal to the first halftone reference value C1 or the second halftone reference value C2, the noise usage rate Rn calculated by the calculation formula is equal to the lowest noise It becomes equal to the usage rate RnMin.

By the above method, for example, if the input value In is greater than or equal to the highlight side error usage rate maximum density value He and is less than the shadow side error usage rate maximum density value Se, the error usage rate Re becomes 1 (100%). Set. Further, for example, when the input value In is less than or equal to the highlight side error usage rate minimum density value Hes, the error usage rate Re is set to 0. Furthermore, when the input value In is greater than or equal to the highlight side error usage rate minimum density value Hes and less than the highlight side error usage rate maximum density value He, the error usage rate Re is (In-Hes)/(He- Hes). As a result, for example, when the input value In is less than or equal to the highlight side error usage rate maximum density value He, the error usage rate Re is a value between 0 and 1 (100%), and the highlight side error usage rate is the maximum. It is set to a value that is gradually decreased from 1 according to the difference between the density value He and the input value In.

For example, when the input value In is greater than or equal to the shadow side error usage rate maximum density value Se and less than the shadow side error usage rate minimum density value Ses, the error usage rate Re is (Ses-In)/(Ses-Se ) is set to the value calculated by Further, when the input value In is equal to or greater than the shadow side error usage rate minimum density value Ses, the error usage rate Re is set to zero. As a result, for example, when the input value In is greater than or equal to the shadow side error usage rate maximum density value Se, the error usage rate Re is a value between 0 and 1 (100%), and the input value In and the shadow side error The usage rate is set to a value that is gradually decreased from 1 according to the difference from the maximum density value Se.

In this case, the error usage rate Re from the highlight part to the halftone part gradually increases with respect to the input value In, for example, from the highlight side error usage rate minimum density value Hes, and The maximum value is reached at the value He. In addition, the error usage rate Re from the halftone part to the shadow part gradually decreases from the shadow side error usage rate maximum density value Se with respect to the input value In, and reaches the minimum value at the shadow side error usage rate minimum density value Ses. Become.

As a result, for example, the error usage rate Re when the input value In is a density value corresponding to either a highlight part or a shadow part is lower than the error usage rate Re when the input value In is a density value corresponding to a halftone part. is also set to a small value. Further, in this case, the error usage rate Re is, for example, 0 (0%) at both ends of the density range, and in the range of texture generation parts Hd and Sd, which is the density range where a texture specific to dither processing occurs. It changes to a value of 1 (100%). With this configuration, for example, a configuration in which the error usage rate Re is mainly used for halftones can be appropriately realized.

Further, for example, if the input value In is less than or equal to the maximum density value Hn of the noise usage rate on the highlight side or the maximum density value Sn of the noise usage rate on the shadow side, the noise usage rate Rn is set to 1 (100%). . Furthermore, when the input value In is greater than or equal to the first halftone reference value C1 and less than or equal to the second halftone reference value C2, the noise usage rate Rn is set to the lowest noise usage rate RnMin. Further, for example, when the input value In is greater than or equal to the highlight side noise usage rate maximum density value Hn and less than the first halftone reference value C1, the noise usage rate Rn is greater than or equal to the minimum noise usage rate RnMin and 1 ( 100%) or less. Then, it is set to a value that is gradually decreased from 1 according to the difference between the input value In and the highlight side noise usage rate maximum density value Hn. Furthermore, for example, when the input value In is greater than or equal to the second halftone reference value C2 and less than or equal to the shadow side noise usage rate maximum density value Sn, the noise usage rate Rn is greater than or equal to the minimum noise usage rate RnMin and 1 (100 %) below. Then, it is set to a value that is gradually increased from the lowest noise usage rate RnMin according to the difference between the input value In and the second halftone reference value C2.

As a result, for example, the noise usage rate Rn when the input value In is a density value corresponding to either a highlight part or a shadow part is lower than the noise usage rate Rn when the input value In is a density value corresponding to a halftone part. is also set to a large value. Further, in this case, the noise usage rate Rn has a value of 1 (100%) in the highlight portion and the shadow portion, for example, and changes so that the value gradually decreases as the gray level approaches.

In this way, by obtaining the error usage rate Re and the noise usage rate Rn, it is possible to obtain the , the noise usage rate Rn is increased and the error usage rate Re is decreased. This increases the influence of the dithering characteristic that allows dots to be arranged in a dispersed manner in the highlight and shadow areas, making it possible to suppress the occurrence of dot delay. Furthermore, if there is a first pixel value that is brighter (or darker) than the intermediate value and a second pixel value that is closer to the intermediate value than the first pixel value, the error usage rate for the first pixel value is the second pixel value. lower than the error usage rate for . In addition, there is a set of pixels in which the noise usage rate for the first pixel value is higher than the noise usage rate for the second pixel value. When the input value is in the range of 0 to 255 as in this embodiment, the intermediate value is defined as the intermediate value, for example, 128. That is, in this embodiment, the noise usage rate Rn is higher when the first pixel value is different from the intermediate value than when the second pixel value is closer to the intermediate value than the first pixel value. Thus, the error usage rate Re is lower at the first pixel value than at the second pixel value.

Further, by increasing the influence of the error diffusion characteristic and performing quantization processing in the halftone portion, for example, more natural pseudo gradations can be obtained. Further, for example, by lowering the noise usage rate Rn and increasing the error usage rate Re in the density range where the texture occurs, it is possible to change the arrangement of dots. Moreover, this makes it possible to suppress the occurrence of texture. Further, by setting a minimum noise usage rate RnMin and preventing the noise usage rate Rn from becoming 0, for example, in the halftone part, the influence of the error diffusion characteristic can be increased while giving a slight influence of the dither characteristic. Thereby, generation of pattern noise can be suppressed.

In addition, the magnitude of the influence of the dither characteristic and the magnitude of the influence of the error diffusion characteristic are gradually changed according to calculation formulas for calculating the error usage rate Re and the noise usage rate Rn. As a result, it is possible to suppress the occurrence of a boundary line at the switching portion between the region where the dither characteristic is dominant and the region where the error diffusion characteristic is dominant. This allows smooth switching of the quantization processing method. Therefore, according to the present embodiment, each of the error usage rate Re and the noise usage rate Rn can be appropriately set depending on the density value of the pixel, for example. This makes it possible to perform quantization processing that more appropriately utilizes the strengths of error diffusion processing and dither processing.

=First calculation process and second calculation process=
Here, the specific processing contents of each process for calculating the error usage rate Re and the noise usage rate Rn will be explained. In S404, the first calculation process and the second calculation process are executed to calculate the error usage rate Re and the noise usage rate Rn according to the density value for each of the pixel to be quantized and the surrounding pixels. .

- First calculation process for calculating the error usage rate Re When the first calculation process is started, first in S702, the CPU determines that the input value In, which is the density value of the target pixel, has the highest error usage rate on the highlight side. It is determined whether or not the density value He is greater than or equal to the shadow side error usage rate and less than or equal to the maximum density value Se. If it is determined in S702 that the error is within the range, that is, He≦In≦Se, the process proceeds to S704, and the CPU sets the error usage rate Re to 1 (100%), which is the maximum usage rate.

If it is determined in S702 that the value is not within the range, that is, He≦In≦Se, the process advances to S706. In S706, the CPU determines whether the input value In is within a range that is greater than the highlight side error usage rate minimum density value Hes and smaller than the highlight side error usage rate maximum density value He. In S706, if it is determined that Hes<In<He is within the range, that is, Hes<In<He, the CPU proceeds to S708 and calculates the error usage rate Re by (In-Hes)/(He-Hes). Set to value.

Further, if it is determined in S706 that it is not within the range, that is, Hes<In<He, the process advances to S710. In S710, the CPU determines whether the input value In is within a range that is greater than the shadow side error usage rate maximum density value Se and smaller than the shadow side error usage rate minimum density value Ses. If it is determined in S710 that it is within the range, that is, that Se<In<Ses is satisfied, the process proceeds to S712, and the CPU calculates the error usage rate Re by (Ses-In)/(Ses-Se). Set to value.

Further, if it is determined in S710 that it is not within the range, that is, Se<In<Ses is not satisfied, the process proceeds to S714, and the CPU sets the error usage rate Re to 0 (0%). Then, when the error usage rate Re is set in S704, S708, S712, or S714, the process proceeds to S716, and the CPU sets the set error usage rate Re to the input value In. This is adopted as the error usage rate Re of the pixel to be used as the density value.

- Second calculation process for calculating the noise usage rate Rn When the second calculation process is started, first in S802, the CPU determines that the input value In, which is the density value of the target pixel, has the highest noise usage rate on the highlight side. It is determined whether the density value Hnz is greater than the density value Hn and smaller than the highlight side noise usage rate 0% density value Hnz. If it is determined in S802 that it is within the range, that is, that Hn<In<Hnz is satisfied, the process proceeds to S804, and the CPU calculates the noise usage rate Rn by (Hnz-In)/(Hnz-Hn). Set to value.

Further, if it is determined in S802 that it is not within the range, that is, that Hn<In<Hnz is not satisfied, the process advances to S806. In S806, the CPU determines whether the input value In is in a range that is greater than the shadow side noise usage rate 0% density value Snz and smaller than the shadow side noise usage rate maximum density value Sn. If it is determined in S806 that it is within the range, that is, that Snz<In<Sn is satisfied, the process proceeds to S808, and the CPU calculates the noise usage rate Rn by (In-Snz)/(Sn-Snz). Set to value.

When the noise usage rate Rn is set in S804 or S808, the process proceeds to S810, and the CPU determines whether the set noise usage rate Rn is larger than the minimum noise usage rate RnMin, which is the minimum usage rate. . If it is determined in S810 that Rn>RnMin is satisfied, the process advances to S816, which will be described later. If it is determined in S810 that Rn>RnMin is not satisfied, the CPU proceeds to S812, changes the noise usage rate Rn to the lowest noise usage rate RnMin, and then proceeds to S816.

If it is determined in S806 that it is not within the above range, that is, that Snz<In<Sn is not satisfied, the process proceeds to S814, and the CPU sets the noise usage rate Rn to 1 (100%), which is the maximum usage rate. After setting, the process advances to S816. In S816, the CPU adopts the set noise usage rate Rn as the noise usage rate Rn of the pixel that corresponds to the input value In, that is, the density value is the input value In.

<Quantization execution process>
Next, the quantization execution processing executed in S412 will be explained. FIG. 9 is a flowchart showing a detailed processing routine of the quantization execution process. The series of processes shown in the flowchart of FIG. 9 is performed by the CPU loading the program code stored in the ROM into the RAM and executing it. Alternatively, the functionality of some or all of the steps in FIG. 9 may be performed in hardware, such as an ASIC or electrical circuit.

When the quantization execution process is started, first, in S902, the CPU determines whether the input value In and the maximum input value MaxIn match. If it is determined in S902 that the input value In and the maximum input value MaxIn match, the process proceeds to S904, and the CPU sets an output value (quantized value) indicating the quantization result to 1. Note that the value "1" set as the output value in S904 is an example of a value that should be output when the input value In is larger than the threshold Th. In S904, the CPU sets the output value and also sets the error value used in the error distribution process executed in S414 to In'-MaxIn, which is the difference between the error-corrected input value In' and the maximum input value MaxIn. Set to .

If it is determined in S902 that the input value In and the maximum input value MaxIn do not match, the process proceeds to S906, and the CPU determines whether the input value In and the minimum input value MinIn match. If it is determined in S906 that the input value In and the minimum input value MinIn match, the process proceeds to S908, and the CPU sets the output value to 0. Note that the value "0" set as the output value in S908 is an example of a value that should be output when the input value In is smaller than the threshold Th. Further, in S908, the CPU sets the output value and also sets the error value to the error-corrected input value In'.

Further, if it is determined in S906 that the input value In and the minimum input value MinIn do not match, the process proceeds to S910, and the CPU determines whether the error-corrected input value In' is larger than the noise-corrected threshold Th'. judge. If it is determined in S910 that the error correction input value In' is larger than the noise corrected threshold Th', the process proceeds to S912, where the CPU sets the output value to 1 and sets the error value to In'-MaxIn. do. If it is determined in S910 that the error correction input value In' is equal to or less than the noise corrected threshold Th', the process proceeds to S914, where the CPU sets the output value to 0 and sets the error value to In'. do. Then, when the output value (quantized value) and error value are set in S904, S908, S912, or S914, the process proceeds to S916, where the CPU acquires the set output value and error value, and performs quantization execution processing. end.

<Applicable usage rate determination process>
Next, the applied usage rate determination process executed in S406 will be described. FIG. 10 is a diagram showing the noise usage rate Rn and the error usage rate Re in the 3×3 pixel area of FIG. 3(a). The applied noise usage rate Rna and the applied error usage rate Rea are calculated from the density usage rate Rn and error usage rate Re according to the density value of each pixel in the target area including the pixel to be quantized and pixels around the pixel. Calculated. In this embodiment, the target area is 3×3 pixels including the pixel to be quantized and one pixel around the pixel. Therefore, as shown in FIG. 3A, when the pixel to be quantized is the pixel 304, the target area is the area 306.

Therefore, in order to determine the applied noise usage rate Rna and the applied error usage rate Rea in the pixel 304, the noise usage rate Rn and the error usage rate Re of all the pixels in the region 306 are used. Note that the noise usage rate Rn and error usage rate Re of each pixel are calculated by usage rate calculation processing. The noise usage rate Rn and error usage rate Re of each pixel in the region 306 are as shown in FIG. Specifically, in the pixel 304, the noise usage rate Rn is 0% and the error usage rate Re is 100%. Further, in each pixel surrounding the pixel 304, the noise usage rate Rn is 100% and the error usage rate is 0%.

In this embodiment, the applied noise usage rate Rn and the applied error usage rate Re are calculated from the respective averages of the noise usage rate Rn and the error usage rate Re of each pixel. Specifically, the applied noise usage rate Rna and the applied error usage rate Rea are, for example, 1/9 the noise usage rate Rn and error usage rate Re of each pixel in the target area, as shown in FIG. 1(b). Multiply the values and add the resulting values. Note that in FIG. 1(b), the Rn filter 104 and the Re filter 106 are written because they are similar to the filter calculations. That is, the applied noise usage rate Rna of the pixel 304 is (100×1/9)×8+0×1/9=89% (in this embodiment, the decimal point is rounded off). Further, the applied error usage rate Rea of the pixel 304 is (0×1/9)×8+100×1/9=11% (in this embodiment, the fraction below the decimal point is rounded).

When performing quantization on the pixel 304, if it is a known technique, a noise usage rate Rn of 0% and an error usage rate Re of 100% are applied, but in this embodiment, an applied noise usage rate Rn of 89% and an applied error usage rate of 89% are applied. A usage rate of 11% applies. Therefore, if the value of the dither matrix of the pixel 304 is -64 and the initial threshold is 128, then in the known technique, the noise-corrected threshold is 128 (=-64×0.0+128). As a result, the input value 125 (density value of the pixel 304) is compared with the threshold value 128, and since the input value is less than the threshold value, the quantization result is 0 (see FIG. 3(b)). On the other hand, in this embodiment, the noise-corrected threshold is 71 (-64×0.89+128). As a result, the input value 125 and the threshold value 71 are compared, and since the input value is greater than or equal to the threshold value, the quantization result is 255 (see FIG. 3(c)). As a result, when the input images are shown in FIGS. 2(a) and 3(a), the known technology results in the input images as shown in FIG. 2(b) and FIG. and as shown in FIG. 3(c). In other words, the technique according to the present embodiment makes it possible to obtain a quantization result that maintains the density of the region compared to the known technique.

Note that as the applied noise usage rate Rna and the applied error usage rate Rea, the average value of the noise usage rate Rn and the error usage rate Re of the pixel to be quantized and its surrounding pixels (that is, each pixel in the target area) is used. I did it like that. However, the method for determining the applied noise usage rate Rna and the applied error usage rate Rea is not limited to this. That is, any method may be used as long as it is possible to obtain the applied noise usage rate Rna and the applied error usage rate Rea corresponding to the density of the target area including the pixel to be quantized and its surrounding pixels. For example, the applied noise usage rate Rna may be the median value when the noise usage rates Rn of each pixel in the target area are arranged in order, or the applied noise usage rate Rna may be the one that has the highest frequency among the noise usage rates Rn, that is, the one that exists the most. The value may be used as the applied noise usage rate Rna. Similarly, the median value when the error usage rates Re of each pixel in the target area are arranged in order may be used as the applied error usage rate Re, or the value with the highest frequency among the error usage rates Re may be used as the applied error usage rate. It may also be Rea. Alternatively, as shown in FIG. 1(b), filters for different area sizes may be created individually and used.

<Error distribution processing>
Next, the error distribution processing executed in S414 will be explained. FIG. 11 is a diagram illustrating error distribution processing, in which (a) is a diagram illustrating an example of a diffusion filter, and (b) is a diagram illustrating an example of an error distribution method. In this embodiment, the diffusion filter is, for example, a Jarvis, Judice & Ninke matrix. Different diffusion filters are used in each direction of bidirectional processing. FIG. 11A shows a diffusion filter used in the main scanning direction from one side of the image to the other side, and a diffusion filter used in the sub-scanning direction from the other side of the image to one side. Also, the position of the symbol * in the diffusion filter is the coordinates [0, 0] (origin) of the input value In, and the numerical value in each matrix of the diffusion filter is the distribution ratio when distributing the error to surrounding pixels. It is.

As shown in Figure 11(b), in the process of distributing errors to surrounding pixels, if the coordinates of the error distribution destination are outside the image width, the distribution destination coordinates are changed to the coordinates of the beginning of the next line. be done. Furthermore, when the coordinates on the opposite side are outside the range, similar processing is performed. Furthermore, if the line to which the distribution is to be made does not exist, the error is not distributed. Furthermore, even if the coordinates of the distribution destination are already processed pixels, the error is not distributed.

FIG. 12 is a flowchart showing detailed processing contents of error distribution processing. The series of processes shown in the flowchart of FIG. 12 is performed by the CPU loading the program code stored in the ROM into the RAM and executing the program code. Alternatively, the functionality of some or all of the steps in FIG. 12 may be performed in hardware, such as an ASIC or electrical circuit.

When the error distribution process is started, the CPU executes a loop in which the Y coordinate (coordinate in the height direction of the image) is sequentially changed between S1202 and S1226 in the flowchart of FIG. In this loop, the value of Y is increased by 1 between 0 and the diffusion height minus 1 (the value obtained by subtracting 1 from the value indicating the diffusion height). Here, the diffusion height (a value indicating the diffusion height) is, for example, a height calculated using the diffusion matrix height. The diffusion matrix height is the number of rows of a matrix used as a diffusion filter. Therefore, when using the diffusion filter shown in FIG. 11, the value of Y can be increased by 1 between "0" and "2", which is the value obtained by subtracting 1 from the value 3 indicating the diffusion height. Become.

Furthermore, a loop is executed between S1204 and S1224 in which the X coordinate (coordinate in the width direction of the image) is sequentially changed. In this loop, the value of X is increased by 1 from the value indicating the diffusion width on the - side to the value indicating the diffusion width on the + side. Here, the value indicating the diffusion width is, for example, a value calculated by (diffusion matrix width - 1)/2. The diffusion matrix width is the number of columns of a matrix used as a diffusion filter. Therefore, when using the diffusion filter of FIG. 11, the diffusion matrix width is 5, so the value indicating the diffusion width is (5-1)/2, and the value of will be increased by 1.

Then, in S1206, the CPU sets the coordinates (X', Y') of the distribution destination and also sets the distribution ratio based on the diffusion filter. That is, if the coordinates of the input pixel (the pixel where the asterisk is located in FIG. 11(b)) are (x, y), then X'=x+X. Note that X is the value set in S1204. Moreover, Y'=y+Y. Note that Y is the value set in S1202, S1220, S1222, etc. Furthermore, the distribution ratio is a value associated with the matrix of coordinates (X, Y) in the diffusion filter.

Next, in S1208, the CPU determines whether the distribution ratio is greater than zero. If it is determined in S1208 that the distribution ratio is 0 or less, the process returns to S1204 without distributing the error. If it is determined in S1208 that the distribution ratio is greater than 0, the process proceeds to S1210, and the CPU determines whether the coordinate X' of the distribution destination is smaller than the image width.

If it is determined in S1210 that the coordinate X' of the distribution destination is smaller than the image width, the process proceeds to S1212, and the CPU determines whether the coordinate X' is greater than or equal to 0. If it is determined in S1212 that the coordinate X' is greater than or equal to 0, the process proceeds to S1214, and the CPU determines whether the coordinate Y' of the distribution destination is smaller than the image height. If it is determined in S1214 that the coordinate Y' of the distribution destination is smaller than the image height, the process proceeds to S1216, and the CPU sets the distribution error value (quantization error value) to the product of the error value and the distribution ratio. Then, in S1218, the CPU adds the set distribution error value to the cumulative error stored in the cumulative error buffer, and updates the cumulative error stored in the cumulative error buffer. Thereby, the cumulative error is updated according to the generated quantization error value.

If it is determined in S1210 that the coordinate X' is equal to or greater than the image width, the CPU sets the coordinate X'=X'-image width and the coordinate Y'=Y'+1 in S1220, and proceeds to S1214. Further, if it is determined in S1212 that the coordinate X' is smaller than 0, the CPU sets the coordinate X'=X'+image width and the coordinate Y'=Y'+1 in S1222, and proceeds to S1214. Furthermore, if it is determined in S1214 that the coordinate Y' is equal to or greater than the image height, the process proceeds to S1204 without distributing the error. In this way, in the error diffusion process, the quantization error value is multiplied by the value stored in the cumulative error buffer to calculate the cumulative error.

As described above, in this embodiment, the cumulative error E obtained by multiplying the applied error usage rate Rea obtained according to the density value In of the pixel to be quantized and its surrounding pixels is calculated as the density value In of the pixel to be quantized. The degree to which the error diffusion characteristics are affected can be adjusted by adding . In addition, the degree to which the dither characteristics are affected is adjusted by adding the dither matrix noise D, which is multiplied by the applied noise usage rate Rna obtained according to the density value of the pixel to be quantized and its surrounding pixels, to the initial threshold Th. I decided to do so. Thereby, for example, it is possible to set the degree to which each of the error diffusion characteristics and the dither characteristics is influenced depending on the density value In(x, y) that is the input value and the density values of surrounding pixels. Further, it is possible to perform quantization processing that appropriately takes advantage of the strengths of error diffusion processing and dither processing.

Further, when only typical error diffusion processing is performed, problems such as dot delay occur in, for example, highlight areas and shadow areas. Further, when only dithering is performed, problems such as textures, which are specific to dithering, occur. Furthermore, for example, when simply switching between dither processing and error diffusion processing between a highlight section and a halftone section, or between a halftone section and a shadow section, for example, the switching may cause a boundary line to appear. A problem arises. On the other hand, in the present embodiment, in which the noise usage rate and the error usage rate are gradually changed and switched to execute processing, it is possible to appropriately suppress dot delay in highlight areas and shadow areas, for example. Furthermore, it is possible to appropriately prevent the occurrence of textures in halftones and the occurrence of border lines due to switching. Therefore, according to this embodiment, quantization processing can be performed more appropriately.

Furthermore, in this embodiment, the applied noise usage rate and applied error usage rate of the pixel to be quantized are calculated from the noise usage rate and error usage rate according to the respective density values of the pixel to be quantized and the surrounding pixels. I tried to decide. Therefore, when quantizing pixels, it is possible to apply an applied noise usage rate and an applied error usage rate that take into account the maintenance of the density of the area. As a result, it is possible to obtain quantization results that maintain the density of the area better than before, even in images with high-frequency patterns such as hatching patterns of halftone values where the noise usage rate and error usage rate vary within the area. Become.

Although this embodiment has been described above, the technical scope of this embodiment is not limited to the above range. It will be apparent to those skilled in the art that various changes or improvements can be made to the embodiments described above. It is clear from the claims that such modifications or improvements may be included within the technical scope of the present invention.

Furthermore, in the above, the quantization process has been described for the case where the number of gradations after quantization is two gradations. However, quantization using a similar method can also be applied to processing when the number of gradations is three or more, for example. The case where the number of gradations is three or more means, for example, the case where three or more types of dot sizes are used. In this case, for example, the quantization process corresponding to each dot of each size is performed in accordance with the applied noise usage rate Rna and the applied error usage rate Rea, in the same or similar manner as in the case of two gradations, using dither matrix noise and This is done using both cumulative errors. Then, the output results corresponding to the dots of each size are compared, and the dot with the largest size is determined as the final output. In this way, for example, even when the number of gradations is three or more, the quantization process can be performed while maintaining the density of the area.

Further, in this embodiment, as shown in FIGS. 2A to 2C, processing is exemplified for a highlight side image in which pixels with a density of 0 and pixels with a density are arranged in a staggered manner. The technology according to the present embodiment is not limited to this, but also includes the processing of images on the shadow side as shown in FIGS. You can get the quantization result.

Furthermore, in this embodiment, the applied noise usage rate and the applied error usage rate are calculated in a 3×3 pixel target area centered on the pixel to be quantized, but the invention is not limited to this. In other words, the target area may have any size as long as it includes the pixel to be quantized and its surrounding pixels, and does not need to be rectangular as in this embodiment. By setting the size according to the error diffusion range, the applied noise usage rate and applied error usage rate can be used in consideration of the influence of error propagation, and the density reproducibility of the area may be improved. Furthermore, by setting the area to pixels that have already been processed, it is possible to reduce the memory capacity and speed up the processing in some cases.

Furthermore, in this embodiment, when calculating each usage rate (error usage rate Re and noise usage rate Rn) for a pixel to be quantized, each usage rate of the pixel to be quantized and its surrounding pixels is calculated. However, it is not limited to this. For example, calculate each usage rate for all pixels and save the results, and refer to the calculated usage rate for each pixel that is necessary when calculating each usage rate for the pixel to be quantized. You may also do so.

The present disclosure provides a system or device with a program that implements one or more functions of the embodiments described above via a network or a print medium, and one or more processors in a computer of the system or device reads and executes the program. This can also be achieved by processing. It can also be realized by a circuit (for example, ASIC) that realizes one or more functions.

The disclosure of the above embodiments includes the following configurations and methods.

(Configuration 1)
A program that causes a computer to function as an image processing device that quantizes the pixel value of each pixel in an input image, the program comprising:
pixel selection means for selecting the pixel of interest to be quantized;
At least, for each pixel located in the target area including the pixel of interest, a first usage rate indicating the degree to which the two-dimensional matrix influences the quantization according to the pixel value, and a peripheral area of the pixel of interest. usage rate determining means for determining a second usage rate indicating the degree to which the cumulative error, which is the cumulative value of quantization error values generated in the quantization for the pixels, influences the quantization;
determining a first application usage rate to be applied to the quantization of the pixel of interest based on the first usage rate of each pixel located in the target area; and determining the second usage rate of each pixel located in the target area. applied usage rate determining means for determining a second applied usage rate to be applied to the quantization of the pixel of interest based on the quantization rate;
applied error value acquisition means for acquiring an applied error value to be applied to the quantization of the pixel of interest based on the cumulative error corresponding to the pixel of interest and the second applied usage rate;
Cumulative pixel value obtaining means for obtaining a cumulative pixel value based on the applied error value and the pixel value of the pixel of interest;
Threshold acquisition means for acquiring a quantization threshold to be applied to the quantization of the pixel of interest based on the two-dimensional matrix and the first applied usage rate;
A program that functions as a quantization value acquisition means for acquiring a quantization value and a quantization error value based on the cumulative pixel value and the quantization threshold.

(Configuration 2)
The first usage rate is higher when the first pixel value is different from the intermediate value than when the second pixel value is closer to the intermediate value than the first pixel value,
The program according to configuration 1, wherein the second usage rate is lower when the pixel value is the first pixel value than when the second pixel value is the second usage rate.

(Configuration 3)
3. The program according to configuration 1 or 2, wherein the target area includes at least 3×3 pixels centered on the pixel of interest.

(Configuration 4)
The first applied usage rate is an average value of the first usage rates of each pixel in the target area,
4. The program according to any one of configurations 1 to 3, wherein the second applied usage rate is an average value of the second usage rates of each pixel in the target area.

(Configuration 5)
The first applied usage rate is a median value of the first usage rates of each pixel in the target area,
4. The program according to any one of configurations 1 to 3, wherein the second applied usage rate is a median value of the second usage rates of each pixel in the target area.

(Configuration 6)
The first applied usage rate is the value that occurs most frequently among the first usage rates of each pixel in the target area,
4. The program according to any one of configurations 1 to 3, wherein the second applied usage rate is a value that occurs most frequently among the second usage rates of each pixel in the target area.

(Configuration 7)
pixel of interest selection means for selecting a pixel of interest to be quantized from each pixel in the input image;
At least, for each pixel located in the target area including the pixel of interest, a first usage rate indicating the degree to which the two-dimensional matrix influences the quantization according to the pixel value, and a peripheral area of the pixel of interest. usage rate determining means for determining a second usage rate indicating the degree to which the cumulative error, which is the cumulative value of quantization error values generated in the quantization for the pixels, influences the quantization;
determining a first application usage rate to be applied to the quantization of the pixel of interest based on the first usage rate of each pixel located in the target area; and determining the second usage rate of each pixel located in the target area. applied usage rate determining means for determining a second applied usage rate to be applied to the quantization of the pixel of interest based on the quantization rate;
applied error value acquisition means for acquiring an applied error value to be applied to the quantization of the pixel of interest based on the cumulative error corresponding to the pixel of interest and the second applied usage rate;
Cumulative pixel value obtaining means for obtaining a cumulative pixel value based on the applied error value and the pixel value of the pixel of interest;
Threshold acquisition means for acquiring a quantization threshold to be applied to the quantization of the pixel of interest based on the two-dimensional matrix and the first applied usage rate;
An image processing device comprising: quantization value acquisition means for acquiring a quantization value and a quantization error value based on the cumulative pixel value and the quantization threshold.

(Configuration 8)
An image processing method in an image processing device that quantizes the pixel value of each pixel in an input image, the method comprising:
Selecting the pixel of interest to be quantized,
At least, for each pixel located in the target area including the pixel of interest, a first usage rate indicating the degree to which the two-dimensional matrix influences the quantization according to the pixel value; determining a second usage rate indicating the degree to which the cumulative error, which is the cumulative value of quantization error values generated in the quantization for the pixel, influences the quantization;
determining a first application usage rate to be applied to the quantization of the pixel of interest based on the first usage rate of each pixel located in the target area; and determining the second usage rate of each pixel located in the target area. determining a second applied usage rate to be applied to the quantization of the pixel of interest based on the rate;
obtaining an applied error value to be applied to the quantization of the pixel of interest based on the cumulative error corresponding to the pixel of interest and the second applied usage rate;
obtaining a cumulative pixel value based on the applied error value and the pixel value of the pixel of interest;
obtaining a quantization threshold to be applied to the quantization of the pixel of interest based on the two-dimensional matrix and the first applied usage rate;
An image processing method, comprising: obtaining a quantization value and a quantization error value based on the cumulative pixel value and the quantization threshold.

(Configuration 9)
A program that causes a computer to quantize a density value indicating the color density of each pixel in an image, the program comprising:
pixel selection processing that sequentially selects pixels to be quantized;
Accumulates the noise usage rate that indicates the degree to which dither matrix noise, which is noise specified by a preset dither matrix, influences the quantization process, and the quantization error, which is the error that occurs in the quantization of surrounding pixels. The noise usage rate and the error usage rate that indicate the degree to which the accumulated error affected the quantization process are determined according to the density values of the pixels that are sequentially selected in the pixel selection process. a usage rate determination process that determines the error usage rate;
During the quantization for the pixel selected in the pixel selection process, from the noise usage rate and the error usage rate corresponding to each of the pixel selected in the pixel selection process and the surrounding pixels. an applied usage rate determination process that determines an applied noise usage rate and an applied error usage rate to be applied;
This is a process of performing the quantization on the density values of the pixels that are sequentially selected in the pixel selection process, and uses dither matrix noise according to the applied noise usage rate corresponding to the pixel, and causing the computer to execute a quantization execution process of performing the quantization using the cumulative error according to the corresponding applied error usage rate;
The program includes, for the pixels sequentially selected in the pixel selection process,
This is a process of calculating an error-corrected input value, which is a density value after correction based on the cumulative error, and the product of the applied error usage rate corresponding to the pixel and the cumulative error is calculated as the density value of the pixel. an error-corrected input value calculation process of calculating the added value as the error-corrected input value;
This is a process of calculating a noise-corrected threshold that is a threshold that reflects the dither matrix noise as a threshold used in the quantization, and the product of the applied noise usage rate corresponding to the pixel and the dither matrix noise is calculated in advance. further causing the computer to execute a noise-corrected threshold calculation process of calculating a value added to the set initial threshold as the noise-corrected threshold;
The quantization execution process performs the quantization by comparing the noise-corrected threshold and the error-corrected input value,
The usage rate determination process includes, in determining the noise usage rate and the error usage rate,
The noise usage rate when the density value of the pixel is a density value corresponding to either a highlight part or a shadow part is a density value corresponding to a halftone part between the highlight part and the shadow part. Make the value larger than the above noise usage rate in case,
The error usage rate when the density value of the pixel corresponds to either a highlight portion or a shadow portion is smaller than the error usage rate when the density value corresponds to the halftone portion. A program characterized by:

(Configuration 10)
The quantization execution process is
maximum value determination processing for determining whether the density value of the pixel, which is the input value for the quantization, is equal to a maximum value in a range that the density value can take;
a minimum value determination process of determining whether the density value of the pixel that is the input value is equal to a minimum value in a range that the density value can take;
quantization value acquisition processing for acquiring a quantization value that is a result of the quantization,
If the input value and the maximum value are determined to be equal in the maximum value determination process, the quantization value acquisition process outputs the density value as the quantization value if it is larger than the threshold value. Get the power value,
If the input value and the minimum value are determined to be equal in the minimum value determination process, the quantization value acquisition process outputs the density value as the quantization value when the density value is smaller than the threshold value. The program according to configuration 9, characterized in that the program acquires an exponent value.

(Configuration 11)
The usage rate determination process is characterized in that, regardless of the density value of the pixel, the noise usage rate is set to a value greater than or equal to a minimum noise usage rate that is preset to a value greater than 0. The program according to configuration 9 or 10.

(Configuration 12)
The usage rate determination process includes:
Using a preset first highlight reference value and a second highlight reference value larger than the first highlight reference value as a reference indicating the density range in the highlight part,
Using a preset first shadow reference value and a second shadow reference value larger than the first shadow reference value as a reference indicating the density range in the shadow part,
A first halftone reference value that is larger than the second highlight reference value and smaller than the first shadow reference value, and the first halftone reference value as a reference indicating the density range in the center of the halftone portion. using a second halftone reference value that is larger than the first shadow reference value and smaller than the first shadow reference value;
If the density value of the pixel is less than or equal to the first highlight reference value or greater than or equal to the second shadow reference value, the noise usage rate is set to 1;
If the density value of the pixel is greater than or equal to the first halftone reference value and less than or equal to the second halftone reference value, setting the noise usage rate to the lowest noise usage rate;
When the density value of the pixel is greater than or equal to the first highlight reference value and less than or equal to the first halftone reference value, the noise usage rate is set to a value greater than or equal to the minimum noise usage rate and less than or equal to 1; Set to a value that is gradually decreased from 1 according to the difference between the density value of the pixel and the first highlight reference value,
When the density value of the pixel is greater than or equal to the second halftone reference value and less than or equal to the second shadow reference value, the noise usage rate is set to a value that is greater than or equal to the minimum noise usage rate and less than or equal to 1; set to a value that is gradually increased from the lowest noise usage rate according to the difference between the density value of and the second halftone reference value;
If the density value of the pixel is greater than or equal to the second highlight reference value and less than or equal to the first shadow reference value, the error usage rate is set to 1;
When the density value of the pixel is less than or equal to the second highlight reference value, the error usage rate is set to a value of 0 or more and 1 or less, depending on the difference between the second highlight reference value and the density value. Set the value gradually decreasing from 1,
When the density value of the pixel is greater than or equal to the first shadow reference value, the error usage rate is a value of 0 or more and 1 or less, depending on the difference between the density value of the pixel and the first shadow reference value. The program according to configuration 11, wherein the program is set to a value gradually decreased from 1.

(Configuration 13)
The usage rate determination process includes:
further using a third highlight reference value of a value greater than 0 and less than or equal to the first highlight reference value as a reference indicating the density range in the highlight part;
further using a third shadow reference value that is greater than or equal to the second shadow reference value and smaller than the maximum value of the range that the density value can take as a reference for indicating the density range in the shadow part;
If the density value of the pixel is less than or equal to the third highlight reference value, setting the error usage rate to 0;
When the density value of the pixel is greater than or equal to the third highlight reference value and less than or equal to the second highlight reference value, the error usage rate is calculated as the difference between the density value and the third highlight reference value. Set to a value divided by the difference between the second highlight reference value and the third highlight reference value,
When the density value of the pixel is greater than or equal to the first shadow reference value and less than or equal to the third shadow reference value, the error usage rate is calculated as the difference between the third shadow reference value and the density value in the third shadow. Set to a value divided by the difference between the reference value and the first shadow reference value,
13. The program according to configuration 12, wherein the error usage rate is set to 0 when the density value of the pixel is equal to or higher than the third shadow reference value.

(Configuration 14)
An image processing device that quantizes a density value indicating the color density of each pixel in an image, the image processing device comprising:
a pixel selection processing unit that sequentially selects pixels to be quantized;
Accumulates the noise usage rate that indicates the degree to which dither matrix noise, which is noise specified by a preset dither matrix, influences the quantization process, and the quantization error, which is the error that occurs in the quantization of surrounding pixels. a processing unit that determines an error usage rate indicating the degree to which the accumulated error caused by the noise affects the quantization process, and the noise usage rate according to the density value of the pixel sequentially selected by the pixel selection processing unit. and a usage rate determination processing unit that determines the error usage rate;
The quantization for the pixel selected by the pixel selection processing section is determined from the noise usage rate and the error usage rate corresponding to each of the pixel selected by the pixel selection processing section and the surrounding pixels. an applied usage rate determination processing unit that determines an applied noise usage rate and an applied error usage rate to be applied at the time;
A processing unit that performs the quantization on the density values of the pixels sequentially selected by the pixel selection processing unit, and uses dither matrix noise according to the applied noise usage rate corresponding to the pixel, and a quantization execution processing unit that performs the quantization using the cumulative error according to the applied error usage rate corresponding to the pixel;
A processing unit that calculates an error-corrected input value, which is a density value after correction based on the cumulative error, for the pixels sequentially selected by the pixel selection processing unit, and calculates the applied error corresponding to the pixel. an error-corrected input value calculation processing unit that calculates, as the error-corrected input value, a value obtained by adding the product of the usage rate and the cumulative error to the density value of the pixel;
A processing unit that calculates a noise-corrected threshold that is a threshold that reflects the dither matrix noise as a threshold used in the quantization for the pixels sequentially selected by the pixel selection processing unit, and corresponds to the pixel. a noise-corrected threshold calculation processing unit that calculates, as the noise-corrected threshold, a value obtained by adding the product of the applied noise usage rate and the dither matrix noise to a preset initial threshold;
The quantization execution processing unit performs the quantization by comparing the noise-corrected threshold and the error-corrected input value,
The usage rate determination processing unit, in determining the noise usage rate and the error usage rate,
The noise usage rate when the density value of the pixel is a density value corresponding to either a highlight part or a shadow part is a density value corresponding to a halftone part between the highlight part and the shadow part. Make the value larger than the above noise usage rate in case,
The error usage rate when the density value of the pixel corresponds to either a highlight portion or a shadow portion is smaller than the error usage rate when the density value corresponds to the halftone portion. An image processing device characterized by converting the image into a value.

(Configuration 15)
An image processing method that quantizes a density value indicating the color density of each pixel in an image, the method comprising:
a pixel selection process step of sequentially selecting pixels to be quantized;
Accumulates the noise usage rate that indicates the degree to which dither matrix noise, which is noise specified by a preset dither matrix, influences the quantization process, and the quantization error, which is the error that occurs in the quantization of surrounding pixels. The noise usage rate is determined according to the density value of the pixels sequentially selected in the pixel selection processing step. and a usage rate determination processing step for determining the error usage rate;
The quantization for the pixel selected in the pixel selection processing step is determined from the noise usage rate and the error usage rate corresponding to each of the pixel selected in the pixel selection processing step and the surrounding pixels. an applied usage rate determination processing step for determining an applied noise usage rate and an applied error usage rate to be applied at the time;
A processing step for performing the quantization on the density values of the pixels sequentially selected in the pixel selection processing step, in which dither matrix noise is used according to the applied noise usage rate corresponding to the pixel, and a quantization execution processing step of performing the quantization using the cumulative error according to the applied error usage rate corresponding to the pixel;
A processing step of calculating an error-corrected input value, which is a density value after correction based on the cumulative error, for the pixels sequentially selected in the pixel selection processing step, and the applied error corresponding to the pixel. an error-corrected input value calculation step of calculating, as the error-corrected input value, a value obtained by adding the product of the usage rate and the cumulative error to the density value of the pixel;
This is a processing step of calculating a noise-corrected threshold, which is a threshold that reflects the dither matrix noise, as a threshold used in the quantization, for the pixels sequentially selected in the pixel selection processing step, and corresponds to the pixel. a noise-corrected threshold calculation step of calculating, as the noise-corrected threshold, a value obtained by adding the product of the applied noise usage rate and the dither matrix noise to a preset initial threshold;
The quantization execution processing step performs the quantization by comparing the noise-corrected threshold and the error-corrected input value;
The usage rate determination processing step includes determining the noise usage rate and the error usage rate,
The noise usage rate when the density value of the pixel is a density value corresponding to either a highlight part or a shadow part is a density value corresponding to a halftone part between the highlight part and the shadow part. Make the value larger than the above noise usage rate in case,
The error usage rate when the density value of the pixel corresponds to either a highlight portion or a shadow portion is smaller than the error usage rate when the density value corresponds to the halftone portion. An image processing method characterized by converting the image into a value.

14 Image processing device

Claims (15)

A program that causes a computer to function as an image processing device that quantizes the pixel value of each pixel in an input image, the program comprising:
pixel selection means for selecting the pixel of interest to be quantized;
At least, for each pixel located in the target area including the pixel of interest, a first usage rate indicating the degree to which the two-dimensional matrix influences the quantization according to the pixel value, and a peripheral area of the pixel of interest. usage rate determining means for determining a second usage rate indicating the degree to which the cumulative error, which is the cumulative value of quantization error values generated in the quantization for the pixels, influences the quantization;
determining a first application usage rate to be applied to the quantization of the pixel of interest based on the first usage rate of each pixel located in the target area; and determining the second usage rate of each pixel located in the target area. applied usage rate determining means for determining a second applied usage rate to be applied to the quantization of the pixel of interest based on the quantization rate;
applied error value acquisition means for acquiring an applied error value to be applied to the quantization of the pixel of interest based on the cumulative error corresponding to the pixel of interest and the second applied usage rate;
Cumulative pixel value obtaining means for obtaining a cumulative pixel value based on the applied error value and the pixel value of the pixel of interest;
Threshold acquisition means for acquiring a quantization threshold to be applied to the quantization of the pixel of interest based on the two-dimensional matrix and the first applied usage rate;
A program that functions as a quantization value acquisition means for acquiring a quantization value and a quantization error value based on the cumulative pixel value and the quantization threshold. The first usage rate is higher when the first pixel value is different from the intermediate value than when the second pixel value is closer to the intermediate value than the first pixel value,
2. The program according to claim 1, wherein the second usage rate is lower when the pixel value is the first pixel value than when the second pixel value is the second pixel value. 3. The program according to claim 1, wherein the target area includes at least 3×3 pixels centered on the pixel of interest. The first applied usage rate is an average value of the first usage rates of each pixel in the target area,
3. The program according to claim 1, wherein the second applied usage rate is an average value of the second usage rates of each pixel in the target area. The first applied usage rate is a median value of the first usage rates of each pixel in the target area,
3. The program according to claim 1, wherein the second applied usage rate is a median value of the second usage rates of each pixel in the target area. The first applied usage rate is the value that occurs most frequently among the first usage rates of each pixel in the target area,
3. The program according to claim 1, wherein the second applied usage rate is a value that occurs most frequently among the second usage rates of each pixel in the target area. pixel of interest selection means for selecting a pixel of interest to be quantized from each pixel in the input image;
At least, for each pixel located in the target area including the pixel of interest, a first usage rate indicating the degree to which the two-dimensional matrix influences the quantization according to the pixel value, and a peripheral area of the pixel of interest. usage rate determining means for determining a second usage rate indicating the degree to which the cumulative error, which is the cumulative value of quantization error values generated in the quantization for the pixels, influences the quantization;
determining a first application usage rate to be applied to the quantization of the pixel of interest based on the first usage rate of each pixel located in the target area; and determining the second usage rate of each pixel located in the target area. applied usage rate determining means for determining a second applied usage rate to be applied to the quantization of the pixel of interest based on the quantization rate;
applied error value acquisition means for acquiring an applied error value to be applied to the quantization of the pixel of interest based on the cumulative error corresponding to the pixel of interest and the second applied usage rate;
Cumulative pixel value obtaining means for obtaining a cumulative pixel value based on the applied error value and the pixel value of the pixel of interest;
Threshold acquisition means for acquiring a quantization threshold to be applied to the quantization of the pixel of interest based on the two-dimensional matrix and the first applied usage rate;
An image processing device comprising: quantization value acquisition means for acquiring a quantization value and a quantization error value based on the cumulative pixel value and the quantization threshold. An image processing method in an image processing device that quantizes the pixel value of each pixel in an input image, the method comprising:
Selecting the pixel of interest to be quantized,
At least, for each pixel located in the target area including the pixel of interest, a first usage rate indicating the degree to which the two-dimensional matrix influences the quantization according to the pixel value; determining a second usage rate indicating the degree to which the cumulative error, which is the cumulative value of quantization error values generated in the quantization for the pixel, influences the quantization;
determining a first application usage rate to be applied to the quantization of the pixel of interest based on the first usage rate of each pixel located in the target area; and determining the second usage rate of each pixel located in the target area. determining a second applied usage rate to be applied to the quantization of the pixel of interest based on the rate;
obtaining an applied error value to be applied to the quantization of the pixel of interest based on the cumulative error corresponding to the pixel of interest and the second applied usage rate;
obtaining a cumulative pixel value based on the applied error value and the pixel value of the pixel of interest;
obtaining a quantization threshold to be applied to the quantization of the pixel of interest based on the two-dimensional matrix and the first applied usage rate;
An image processing method, comprising: obtaining a quantization value and a quantization error value based on the cumulative pixel value and the quantization threshold. A program that causes a computer to quantize a density value indicating the color density of each pixel in an image, the program comprising:
pixel selection processing that sequentially selects pixels to be quantized;
Accumulates the noise usage rate that indicates the degree to which dither matrix noise, which is noise specified by a preset dither matrix, influences the quantization process, and the quantization error, which is the error that occurs in the quantization of surrounding pixels. The noise usage rate and the error usage rate that indicate the degree to which the accumulated error affected the quantization process are determined according to the density values of the pixels that are sequentially selected in the pixel selection process. a usage rate determination process that determines the error usage rate;
During the quantization for the pixel selected in the pixel selection process, from the noise usage rate and the error usage rate corresponding to each of the pixel selected in the pixel selection process and the surrounding pixels. an applied usage rate determination process that determines an applied noise usage rate and an applied error usage rate to be applied;
This is a process of performing the quantization on the density values of the pixels that are sequentially selected in the pixel selection process, and uses dither matrix noise according to the applied noise usage rate corresponding to the pixel, and causing the computer to execute a quantization execution process of performing the quantization using the cumulative error according to the corresponding applied error usage rate;
The program includes, for the pixels sequentially selected in the pixel selection process,
This is a process of calculating an error-corrected input value, which is a density value after correction based on the cumulative error, and the product of the applied error usage rate corresponding to the pixel and the cumulative error is calculated as the density value of the pixel. an error-corrected input value calculation process of calculating the added value as the error-corrected input value;
This is a process of calculating a noise-corrected threshold that is a threshold that reflects the dither matrix noise as a threshold used in the quantization, and the product of the applied noise usage rate corresponding to the pixel and the dither matrix noise is calculated in advance. further causing the computer to execute a noise-corrected threshold calculation process of calculating a value added to the set initial threshold as the noise-corrected threshold;
The quantization execution process performs the quantization by comparing the noise-corrected threshold and the error-corrected input value,
The usage rate determination process includes, in determining the noise usage rate and the error usage rate,
The noise usage rate when the density value of the pixel is a density value corresponding to either a highlight part or a shadow part is a density value corresponding to a halftone part between the highlight part and the shadow part. Make the value larger than the above noise usage rate in case,
The error usage rate when the density value of the pixel corresponds to either a highlight portion or a shadow portion is smaller than the error usage rate when the density value corresponds to the halftone portion. A program characterized by: The quantization execution process is
maximum value determination processing for determining whether the density value of the pixel, which is the input value for the quantization, is equal to a maximum value in a range that the density value can take;
a minimum value determination process of determining whether the density value of the pixel that is the input value is equal to a minimum value in a range that the density value can take;
quantization value acquisition processing for acquiring a quantization value that is a result of the quantization,
If the input value and the maximum value are determined to be equal in the maximum value determination process, the quantization value acquisition process outputs the density value as the quantization value if it is larger than the threshold value. Get the power value,
If the input value and the minimum value are determined to be equal in the minimum value determination process, the quantization value acquisition process outputs the density value as the quantization value when the density value is smaller than the threshold value. 10. The program according to claim 9, wherein the program acquires an exponent value. The usage rate determination process is characterized in that, regardless of the density value of the pixel, the noise usage rate is set to a value greater than or equal to a minimum noise usage rate that is preset to a value greater than 0. The program according to claim 9 or 10. The usage rate determination process includes:
Using a preset first highlight reference value and a second highlight reference value larger than the first highlight reference value as a reference indicating the density range in the highlight part,
Using a preset first shadow reference value and a second shadow reference value larger than the first shadow reference value as a reference indicating the density range in the shadow part,
A first halftone reference value that is larger than the second highlight reference value and smaller than the first shadow reference value, and the first halftone reference value as a reference indicating the density range in the center of the halftone portion. using a second halftone reference value that is larger than the first shadow reference value and smaller than the first shadow reference value;
If the density value of the pixel is less than or equal to the first highlight reference value or greater than or equal to the second shadow reference value, the noise usage rate is set to 1;
If the density value of the pixel is greater than or equal to the first halftone reference value and less than or equal to the second halftone reference value, setting the noise usage rate to the lowest noise usage rate;
When the density value of the pixel is greater than or equal to the first highlight reference value and less than or equal to the first halftone reference value, the noise usage rate is set to a value greater than or equal to the minimum noise usage rate and less than or equal to 1; Set to a value that is gradually decreased from 1 according to the difference between the density value of the pixel and the first highlight reference value,
When the density value of the pixel is greater than or equal to the second halftone reference value and less than or equal to the second shadow reference value, the noise usage rate is set to a value that is greater than or equal to the minimum noise usage rate and less than or equal to 1; set to a value that is gradually increased from the lowest noise usage rate according to the difference between the density value of and the second halftone reference value;
If the density value of the pixel is greater than or equal to the second highlight reference value and less than or equal to the first shadow reference value, the error usage rate is set to 1;
When the density value of the pixel is less than or equal to the second highlight reference value, the error usage rate is set to a value of 0 or more and 1 or less, depending on the difference between the second highlight reference value and the density value. Set the value gradually decreasing from 1,
When the density value of the pixel is greater than or equal to the first shadow reference value, the error usage rate is a value of 0 or more and 1 or less, depending on the difference between the density value of the pixel and the first shadow reference value. 12. The program according to claim 11, wherein the program is set to a value gradually decreasing from 1. The usage rate determination process includes:
further using a third highlight reference value of a value greater than 0 and less than or equal to the first highlight reference value as a reference indicating the density range in the highlight part;
further using a third shadow reference value that is greater than or equal to the second shadow reference value and smaller than the maximum value of the range that the density value can take as a reference for indicating the density range in the shadow part;
If the density value of the pixel is less than or equal to the third highlight reference value, setting the error usage rate to 0;
When the density value of the pixel is greater than or equal to the third highlight reference value and less than or equal to the second highlight reference value, the error usage rate is calculated as the difference between the density value and the third highlight reference value. Set to a value divided by the difference between the second highlight reference value and the third highlight reference value,
When the density value of the pixel is greater than or equal to the first shadow reference value and less than or equal to the third shadow reference value, the error usage rate is calculated as the difference between the third shadow reference value and the density value in the third shadow. Set to a value divided by the difference between the reference value and the first shadow reference value,
13. The program according to claim 12, wherein the error usage rate is set to 0 when the density value of the pixel is equal to or higher than the third shadow reference value. An image processing device that quantizes a density value indicating the color density of each pixel in an image, the image processing device comprising:
a pixel selection processing unit that sequentially selects pixels to be quantized;
Accumulates the noise usage rate that indicates the degree to which dither matrix noise, which is noise specified by a preset dither matrix, influences the quantization process, and the quantization error, which is the error that occurs in the quantization of surrounding pixels. a processing unit that determines an error usage rate indicating the degree to which the accumulated error caused by the noise affects the quantization process, and the noise usage rate according to the density value of the pixel sequentially selected by the pixel selection processing unit. and a usage rate determination processing unit that determines the error usage rate;
The quantization for the pixel selected by the pixel selection processing section is determined from the noise usage rate and the error usage rate corresponding to each of the pixel selected by the pixel selection processing section and the surrounding pixels. an applied usage rate determination processing unit that determines an applied noise usage rate and an applied error usage rate to be applied at the time;
A processing unit that performs the quantization on the density values of the pixels sequentially selected by the pixel selection processing unit, and uses dither matrix noise according to the applied noise usage rate corresponding to the pixel, and a quantization execution processing unit that performs the quantization using the cumulative error according to the applied error usage rate corresponding to the pixel;
A processing unit that calculates an error-corrected input value, which is a density value after correction based on the cumulative error, for the pixels sequentially selected by the pixel selection processing unit, and calculates the applied error corresponding to the pixel. an error-corrected input value calculation processing unit that calculates, as the error-corrected input value, a value obtained by adding the product of the usage rate and the cumulative error to the density value of the pixel;
A processing unit that calculates a noise-corrected threshold that is a threshold that reflects the dither matrix noise as a threshold used in the quantization for the pixels sequentially selected by the pixel selection processing unit, and corresponds to the pixel. a noise-corrected threshold calculation processing unit that calculates, as the noise-corrected threshold, a value obtained by adding the product of the applied noise usage rate and the dither matrix noise to a preset initial threshold;
The quantization execution processing unit performs the quantization by comparing the noise-corrected threshold and the error-corrected input value,
The usage rate determination processing unit, in determining the noise usage rate and the error usage rate,
The noise usage rate when the density value of the pixel is a density value corresponding to either a highlight part or a shadow part is a density value corresponding to a halftone part between the highlight part and the shadow part. Make the value larger than the above noise usage rate in case,
The error usage rate when the density value of the pixel corresponds to either a highlight portion or a shadow portion is smaller than the error usage rate when the density value corresponds to the halftone portion. An image processing device characterized by converting the image into a value. An image processing method that quantizes a density value indicating the color density of each pixel in an image, the method comprising:
a pixel selection process step of sequentially selecting pixels to be quantized;
Accumulates the noise usage rate that indicates the degree to which dither matrix noise, which is noise specified by a preset dither matrix, influences the quantization process, and the quantization error, which is the error that occurs in the quantization of surrounding pixels. The noise usage rate is determined according to the density value of the pixels sequentially selected in the pixel selection processing step. and a usage rate determination processing step for determining the error usage rate;
The quantization for the pixel selected in the pixel selection processing step is determined from the noise usage rate and the error usage rate corresponding to each of the pixel selected in the pixel selection processing step and the surrounding pixels. an applied usage rate determination processing step for determining an applied noise usage rate and an applied error usage rate to be applied at the time;
A processing step for performing the quantization on the density values of the pixels sequentially selected in the pixel selection processing step, in which dither matrix noise is used according to the applied noise usage rate corresponding to the pixel, and a quantization execution processing step of performing the quantization using the cumulative error according to the applied error usage rate corresponding to the pixel;
A processing step of calculating an error-corrected input value, which is a density value after correction based on the cumulative error, for the pixels sequentially selected in the pixel selection processing step, and the applied error corresponding to the pixel. an error-corrected input value calculation step of calculating, as the error-corrected input value, a value obtained by adding the product of the usage rate and the cumulative error to the density value of the pixel;
This is a processing step of calculating a noise-corrected threshold, which is a threshold that reflects the dither matrix noise, as a threshold used in the quantization, for the pixels sequentially selected in the pixel selection processing step, and corresponds to the pixel. a noise-corrected threshold calculation step of calculating, as the noise-corrected threshold, a value obtained by adding the product of the applied noise usage rate and the dither matrix noise to a preset initial threshold;
The quantization execution processing step performs the quantization by comparing the noise-corrected threshold and the error-corrected input value;
The usage rate determination processing step includes determining the noise usage rate and the error usage rate,
The noise usage rate when the density value of the pixel is a density value corresponding to either a highlight part or a shadow part is a density value corresponding to a halftone part between the highlight part and the shadow part. Make the value larger than the above noise usage rate in case,
The error usage rate when the density value of the pixel corresponds to either a highlight portion or a shadow portion is smaller than the error usage rate when the density value corresponds to the halftone portion. An image processing method characterized by converting the image into a value.

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