JP6366487B2 - Image processing apparatus, image processing method, and program - Google Patents

Description

  The present invention relates to a method for converting image data into halftone image data.

  An image forming apparatus such as a printer that prints image data represented by a digital signal input via a computer or a digital camera on a recording medium using a recording material is widely used. In digital image data, generally, the pixel value of each pixel constituting the image data is expressed in multiple gradations such as 8 bits or 16 bits per color. On the other hand, when outputting with an image forming apparatus, since the image is formed by turning dots on and off by the recording material, the number of gradations that the image forming apparatus can output is often smaller than the number of gradations of the image data. Therefore, in general, halftone processing is performed on digital image data in order to print an image with the image forming apparatus. The halftone image data obtained by the halftone process represents a dot pattern output on the recording medium by the image forming apparatus, and represents the gradation of the image data in a pseudo manner.

  One type of halftone processing is called dither processing using a threshold matrix. The threshold matrix is a matrix in which thresholds are arranged corresponding to each pixel in the image data. In the dither processing, the pixel value of each pixel is quantized by comparing the pixel value of the pixel with a corresponding threshold value for each pixel constituting the image data, and the number of gradations of the image data is reduced. The threshold value matrix is repeatedly arranged in a tile shape with respect to the entire image data, and is set so that the threshold value corresponds to all the pixels.

  The threshold matrix used for dither processing is classified into two types. One is an FM (frequency modulation) threshold matrix in which large and small threshold values are distributed. The other is an AM (Amplitude modulation) system threshold matrix arranged so that the threshold increases in order from a certain position.

  When image data is dithered using an FM threshold matrix, the obtained halftone image data is a dot pattern with dispersed dots. The FM threshold matrix tends to have a relatively large matrix size in order to improve dot dispersibility. On the other hand, when the image data is dithered using the AM threshold matrix, the obtained halftone image data is a dot pattern in which a cluster of dots in which a plurality of dots are concentrated is periodically arranged. In the dither processing, the gradation is represented in a pseudo manner by the number (area) of dots per unit area, but a good halftone image can be obtained when flat image data is input to any threshold matrix. Generally, the arrangement of threshold values is determined.

  On the other hand, as a result of comparing the pixel value of each pixel of the input image data with the corresponding threshold value, the halftone image data representing the desired dot pattern may not be obtained. This occurs due to interference between the characteristics represented by the input image data and the characteristics of the threshold matrix. For example, features that are not visually recognized in the input image data in some areas are excessively emphasized, or the edge portions and thin lines of the object to be reproduced in the halftone image data are interrupted.

  Therefore, in Patent Document 1, an ideal output total value for each region is generated according to the average value of the pixel values of a plurality of pixels included in the region corresponding to the threshold matrix. A method is disclosed in which output values are assigned in order from the pixel at the center of gravity within the region determined by the pixel value until the ideal output total value is reached.

JP 2007-194904 A

  According to the technique disclosed in Patent Document 1, since processing is performed for each region corresponding to a threshold matrix, when applied to an FM threshold matrix, a trade-off between dot dispersibility and realization cost according to the matrix size. Off occurs.

  In addition, since the total output value in the area is determined from the average value, there is no interference between the input image data and the threshold matrix. It becomes a number of dots. Since these dots are generated in order from the position of the center of gravity, the characteristics in the input image data cannot be maintained, resulting in blurred halftone image data.

  Therefore, an object of the present invention is to realize an image processing apparatus that converts digital image data into high-quality halftone image data.

  In order to solve the above problems, an image processing apparatus according to the present invention provides a threshold group corresponding to a processing target area for each pixel value representing the small area in a plurality of small areas constituting the processing target area in input image data. A target value calculation means for calculating a target value to be output in the processing target region by calculating a quantization total value in the processing target and mixing in accordance with a ratio of the small region occupying the processing target region, and the processing target Distribution order determining means for determining a distribution order of output values in the region, and assigning the target value to each pixel included in the processing target region according to the distribution order, thereby outputting an output value of each pixel included in the processing target region Output value determining means for determining

  According to the present invention, it is possible to realize an image processing apparatus that converts digital image data into high-quality halftone image data.

Block diagram showing hardware configuration of image forming system The block diagram which shows the structure of the image process part 106 The figure explaining the structural example of input image data The figure which shows an example of FM type | system | group threshold value matrix The block diagram which shows the structure of the target value calculation part 202 The figure which shows the operation | movement in the image process part 106 Diagram explaining halftone processing Diagram explaining binary dither processing The block diagram which shows the structure of the target value calculation part 202 The figure which shows the example of division of the processing object area Diagram explaining ternary dither processing The figure which shows an example of AM type | system | group threshold value matrix The block diagram which shows the structure of the target value calculation part 202 The figure explaining the histogram analysis part 2026 The figure explaining the position of the processing target area The figure explaining the quantization total value holding | maintenance part 2028 Diagram explaining dither processing

  Preferred embodiments of the present invention will be described below with reference to the accompanying drawings. Note that the configurations shown in the following embodiments are merely examples, and the present invention is not limited to the illustrated configurations.

<First Embodiment>
(Configuration of image processing system)
FIG. 1 shows a hardware configuration of an image forming system including an image processing apparatus applicable to the first embodiment. As an example, the image processing apparatus according to the present embodiment will be described using an image processing controller built in a printer that forms an image using a recording material on a recording medium. The image forming system according to the first embodiment includes a CPU 100, a RAM 101, a ROM 102, an operation unit 103, a display unit 104, an external storage device 105, an image processing unit 106, an image forming unit 107, an I / F (interface) unit 108, and a bus. 109.

  The CPU 100 controls the operation of the entire image forming system using input data and computer programs stored in a RAM 101 and a ROM 102 described later. Although the case where the CPU 100 controls the entire apparatus will be described as an example here, the entire apparatus may be controlled by a plurality of hardware sharing the processing.

  The RAM 101 has a storage area for temporarily storing computer programs and data read from the external storage device 105 and data received from the outside via the I / F unit 108. The RAM 101 is used as a storage area used for the CPU 100 to execute various processes and as a storage area used when the image processing unit 106 performs image processing. That is, the RAM 101 can provide various storage areas as appropriate. The ROM 102 stores setting parameters, a boot program, and the like for setting each part in the image forming system.

  The operation unit 103 includes a keyboard, a mouse, and the like, and accepts an operator's instruction through an operation by the operator. As a result, the operator can input various instructions to the CPU 100.

  The display unit 104 is configured by a CRT, a liquid crystal screen, or the like, and can display a processing result by the CPU 100 as an image or a character. When the display unit 104 is a touch panel that can detect a touch operation, the display unit 104 may function as part of the operation unit 103.

  The external storage device 105 is a mass information storage device represented by a hard disk drive. The external storage device 105 stores an OS (operating system) and computer programs and data for causing the CPU 100 to execute processing. In addition, temporary data generated by the processing of each unit (image data to be input / output, a threshold matrix used in the image processing unit, and the like) are held. Computer programs and data stored in the external storage device 105 are appropriately stored in the RAM 101 under the control of the CPU 100 and are processed by the CPU 100.

  The image processing unit 106 is implemented as a processor that can execute a computer program or a dedicated image processing circuit, and performs each image processing to convert image data input as a print target into image data that can be output by the image forming unit 107. Run. For example, when an instruction to execute image processing is received from the CPU 100, input image data of N gradations (N: natural number) stored in the external storage device 105 is processed, and M gradations (M: natural number, N> M). Output image data.

  The image forming unit 107 forms an image using a recording material on a recording medium based on the output image data received from the image processing unit 106. The image forming unit 107 may be an ink jet system that forms an image by ejecting ink from a nozzle onto a recording medium. Alternatively, the image forming unit 107 exposes a charged image carrier, develops it with toner, and transfers the toner image onto the recording medium. Thus, an electrophotographic method for forming an image may be used.

  The I / F unit 108 functions as an interface for connecting the image forming system and external devices in the present embodiment. Further, the I / F unit 108 also functions as an interface for connecting to the Internet in order to exchange data with a communication device using infrared communication, wireless LAN, or the like. Each of the above units is connected to the bus 109 and exchanges data via the bus 109.

(Configuration of the image processing unit 106)
The image processing unit 106 in this embodiment will be described. The image processing unit 106 performs halftone processing for converting the input image data into halftone image data that represents a smaller number of gradations than the number of gradations represented by the input image data. FIG. 2 is a block diagram illustrating a configuration of the image processing unit 106. The image processing unit 106 in the present embodiment is realized as a dedicated image processing circuit constituting the block diagram shown in FIG. The image processing unit 106 includes a pixel value acquisition unit 200, a threshold acquisition unit 201, a target value calculation unit 202, a distribution order determination unit 203, and an output value determination unit 204.

  In the present embodiment, digital image data (hereinafter, input image data) input to the image processing unit 106 is 8-bit data indicating any value from 0 to 255 for each pixel. The image processing unit 106 converts 8-bit input image data per pixel into 1-bit binary halftone image data (output image data) having a value of either 0 or 1 for each pixel. In the halftone image data, a pixel with a pixel value (output value) of 0 represents dot off, and a pixel with a pixel value (output value) of 1 represents dot on. Such halftone image data reproduces the input image data in a pseudo manner with the number of gradations smaller than the number of gradations represented by the input image data.

  The pixel value acquisition unit 200 acquires each pixel value of a pixel included in a unit area (hereinafter, a process target area) that performs processing on input image data. FIG. 3 shows the input image data 300. In the present embodiment, a region of 4 pixels × 4 pixels indicated by a thick frame in FIG. If the processing target region is the region 301, the pixel value acquisition unit 200 acquires each pixel value of 16 pixels included in the region 301.

  The threshold acquisition unit 201 reads out from the RAM 101 or the external storage device 105 a threshold group used for dither processing corresponding to the processing target area. In the present embodiment, it is assumed that the FM threshold matrix shown in FIG. 4 is used. The threshold value matrix is repeatedly arranged in a tile shape with respect to the entire image data, and is set so that the threshold value corresponds to all the pixels. The threshold acquisition unit 201 acquires a threshold group corresponding to the processing target area and indicated by a thick frame having the same size (4 pixels × 4 pixels) as the processing target area.

  The target value calculation unit 202 calculates a quantized total value for each pixel value included in the processing target area based on a threshold group corresponding to the processing target area, and determines the quantum value according to the ratio of the pixel value to the processing target area. The output target value of the processing target region is calculated by mixing the totalized values. FIG. 5 is a block diagram showing a more detailed configuration of the target value calculation unit 202 in the present embodiment. The target value calculation unit 202 includes a dither processing unit 2020, a quantized total value calculation unit 2021, and an output target value mixing unit 2022. In this embodiment, it is a process for obtaining binary halftone image data, and since the quantized total value in the processing region is equivalent to the number of on dots, the quantized total value is called the number of dots.

  The distribution order determination unit 203 determines the dot distribution order in the processing target region based on the pixel value of the processing target region acquired from the pixel value acquisition unit 200 and the threshold acquired from the threshold acquisition unit 201. In this embodiment, a difference value between a pixel value corresponding to a position and a threshold value is calculated as an evaluation value, and the dot distribution order is determined by sorting the calculated evaluation value of each pixel.

  The output value determining unit 204 assigns each pixel as an output value according to the distribution order determined by the distribution order determining unit until the target value (total output value) calculated by the target value calculating unit 202 is reached. The output value of each pixel is determined.

(Operation of the image processing unit 106)
FIG. 6 shows a processing flowchart in the image processing unit 106. FIG. 7 shows a process result process obtained by the following process.

  In step S <b> 601, the pixel value acquisition unit 200 acquires pixel values of pixels included in the processing target area from the input image data from the RAM 101 or the external storage device 105. For example, it is assumed that the pixel value of each pixel is acquired using the region 700 as a processing target region. When one pixel value is acquired, in step S602, the pixel value acquisition unit 200 determines whether the pixel values of all the pixels included in the processing target region have been acquired. When the pixel values of all the pixels included in the processing target area have not been acquired, the process returns to step S601, and the acquisition of pixel values is repeated. When the pixel values of all the pixels included in the processing area are acquired, the process proceeds to the next step.

  In step S <b> 603, the threshold acquisition unit 201 acquires a threshold matrix of threshold matrices corresponding to the processing target area from the RAM 101 or the external storage device 105. Here, each threshold value of the threshold value group 401 is acquired. In step S604, the threshold acquisition unit 201 determines whether all the thresholds corresponding to the processing target area have been acquired. If all the threshold values have not been acquired, the process returns to step S603 to sequentially acquire the threshold values. When all the threshold values corresponding to the processing target area are acquired, the process proceeds to the next step. In addition, since the process of step S601 and step S602 and the process of step S603 and step S604 are processes which do not have mutual dependency, even when processing sequentially, whichever may be performed first, it is parallel May be executed.

  In step S605, the target value calculation unit 202 calculates the number of on dots for each pixel value in the processing target area. The threshold group used for calculating the number of dots is a threshold group corresponding to the processing target area. For example, when the number of dots is calculated for the pixel A of the processing target area 700, the threshold value and the dither processing of the threshold value group 401 corresponding to the processing target area are performed on the value 144, which is the pixel value of the pixel A. carry out. The dither processing is performed by the dither processing unit 2020, and as shown in FIG. 17, the threshold values in the threshold group 401 are compared with the pixel value of the pixel A, and quantization is performed to obtain a quantization result as shown in FIG. It is processing to do. The number of dots of the pixel A is obtained by integrating the quantization result obtained by the dither processing by the quantization total value calculation unit 2021. The number of dots calculated for the pixel 700 is equivalent to the number of on-dots when the entire processing target area is a flat area having the pixel value 144 of the pixel A. A value obtained by performing this process on all the pixels in the processing target area 700 and calculating the number of dots of each pixel is indicated by reference numeral 701.

  In step S606, the target value calculation unit 202 calculates the output target value of the processing target area by averaging the number of dots for each pixel value calculated in step S605. In the present embodiment, the number of dots calculated for each pixel contributes to (1 pixel) / (number of pixels in the processing target region) for the output target value of the processing target region, and the contribution rate in each pixel is equal. Accordingly, the output target value is obtained by averaging the number of dots calculated in each pixel in the output target value mixing unit 2022. For example, 7 (rounded off after the decimal point), which is the average value of the number of dots 701 of each pixel in the processing target area, is calculated as the output target value.

  In step S607, the distribution order determination unit 203 calculates, as an evaluation value, a value obtained by subtracting the corresponding threshold value from the pixel value for each pixel in the processing target region. In the case of the present embodiment, since 16 pixels are included in the processing target area, 16 evaluation values corresponding to 16 pixels are calculated. An evaluation value group 702 is calculated based on the region 700 and the threshold value group 401. Furthermore, the distribution order determination unit 203 sets the distribution order as a result of sorting the calculated evaluation values in descending order. That is, dots are distributed in the order of pixels having the largest evaluation value in the processing target area. In the case of the processing target area 700, the distribution order 703 is determined based on the evaluation value group 702.

  In step S608, the output value determination unit 204 determines an output value 1 representing on dots in the distribution order calculated in step S607 until the output target value of the processing target area obtained in step S606 is reached. Further, the pixel that does not have an output value of 1 is determined to have an output value of 0 representing off dots. The output value of each pixel in the processing target area 700 is determined as shown in the output result 704.

  In step S609, it is determined whether or not the processing has been completed for all unit areas. If the processing has not been completed, the above processing is repeated.

  With the above processing, the target value calculation unit calculates the number of dots by performing dither processing for each threshold value group and each pixel corresponding to the processing target region, so that interference between the input image data and the threshold value matrix can be reduced. In normal dither processing, the output value is determined by comparing one pixel with the corresponding threshold value. Therefore, if the threshold value matrix causes interference with the input image data, the desired halftone image data is did not become. On the other hand, according to the present embodiment, the result of dithering using a plurality of threshold values for one pixel value is employed. For this reason, for example, even if the threshold corresponding to the pixel is a threshold that causes interference, the influence of the processing result due to the threshold that causes the interference can be reduced by dithering with all the threshold groups in the processing region. Thereby, desired halftone image data in which interference between the input image data and the threshold matrix is suppressed can be obtained.

  In addition, the target value calculation unit calculates the number of dots for each pixel that is a local region in the processing target region, and mixes this according to the area ratio in the processing target region to match the characteristics of the input image. The number of dots can be calculated. The threshold value group corresponding to the processing target region is a part of the threshold value matrix and has an arbitrary distribution. With this characteristic, pseudo gradation is expressed as the entire threshold matrix, and in the case of the FM threshold matrix, it is possible to improve dot dispersion. On the other hand, in the dither processing using a threshold group having a bias in the threshold distribution, nonlinear conversion is performed on the pixel value. That is, if the dither processing result for a certain pixel value X, Y is expressed as D (X), D (Y), the dither processing result D ((X + Y) of the average value (X + Y) / 2 of the pixel values X, Y. ) / 2) and the average value (D (X) + D (Y)) / 2 of the dither processing result of each pixel value do not match. Ideally, the dither processing result D (X) of the pixel value X and the dither processing result D (Y) of the pixel value Y are mixed according to the appearance rate of the pixel values X and Y. Should be the result. Therefore, in this embodiment, since the output target value reflecting the characteristics of the input image data is calculated by mixing the number of dots of each pixel in accordance with the ratio of the area to be processed, the obtained halftone image is obtained. Deterioration in image quality such as blurring is unlikely to occur in data. In other words, according to the present embodiment, it is possible to calculate the output target value reflecting the characteristics of the input image data regardless of the distribution of the threshold value group. Regardless of the case, it is possible to freely set the processing target area.

  Furthermore, when determining the distribution order, the evaluation value is a value obtained by subtracting the corresponding threshold value from each pixel value, and the on-dot pixels are determined in descending order of the evaluation value. Therefore, the obtained halftone image data is input The feature of the image data and the feature of the threshold matrix can be expressed in a balanced manner. If you want to express the characteristics of the input image data more strongly, you can increase the influence of the pixel values in the evaluation value. If you want to express the characteristics of the threshold matrix more strongly, increase the influence of the threshold. Just do it. Further, the distribution order may be determined without depending on the evaluation value according to the attribute information accompanying the input image data.

  With the above processing, it is possible to obtain high-quality halftone image data that matches the features of the input image data, whether it is a flat image or an image that contains many edges and fine lines of the object. .

Second Embodiment
In the first embodiment, an example is shown in which the number of dots is calculated for each pixel value in the processing target area, and the output target value is set by averaging the number of dots. In the second embodiment, the processing target area is divided into a plurality of local areas, the number of dots is calculated in pixel values representing each local area, and the number of dots is calculated as the ratio of the local area to the processing target area. An example in which the output target value is set by mixing accordingly will be described. In addition, the same code | symbol is attached | subjected about the structure similar to 1st Embodiment, and detailed description is abbreviate | omitted.

  The hardware configuration of the image forming system including the image processing apparatus to which the present invention is applicable can be realized by the configuration of FIG. 1 shown in the first embodiment, and the description thereof is omitted.

(Configuration of the image processing unit 106)
The image processing unit 106 in this embodiment will be described. The image processing unit 106 according to this embodiment executes processing for converting 8-bit input image data into ternary halftone image data (output image data) for each pixel. For this reason, the threshold value matrix used in this embodiment has two threshold values per pixel. The configuration of the image processing unit 106 is shown in FIG.

  The target value calculation unit 202 calculates an output target value to be output from the processing target area. FIG. 9 is a block diagram showing a detailed configuration of the target value calculation unit 202 in the present embodiment. The target value calculating unit 202 includes a small region dividing unit 2023, a small region representative value calculating unit 2024, a dither processing unit 2020, a quantized total value calculating unit 2021, and an output target value mixing unit 2022.

  The small region dividing unit 2023 divides each pixel of the processing target region acquired by the pixel value acquisition unit 200 into a plurality of small regions, and outputs the result of dividing the processing target region and the number of pixels (area) of each small region. . For example, the processing target area of 4 pixels × 4 pixels may be divided into four rectangular areas of 2 × 2 pixels as shown in FIG. 10A, or 4 as shown in FIG. It may be divided into four pixel regions. Further, it may be divided into regions as shown in FIG. In these cases, the division method may be changed in accordance with the characteristics of the input image. In an area where there are many fine patterns in the input image, finer division results in better image quality. Dividing the processing target area of 4 pixels × 4 pixels into 16 small areas is equivalent to the example shown in the first embodiment. On the other hand, since the dither processing performed by the dither processing unit 2020 in the subsequent stage is executed by the number of divided small regions, the amount of calculation is reduced when the number of divided small regions is made as small as possible. Further, when the divided small regions are divided so that the number of pixels (area) is the same, the processing of the output target value mixing unit 2022 at the subsequent stage becomes simple.

  The small region representative value calculation unit 2024 calculates a representative value for each small region obtained by the small region dividing unit 2023. Specifically, an average value or a median value of pixel values constituting the small area is set as a representative value.

  The dither processing unit 2020 compares the representative value for each small region with a threshold group corresponding to the processing target region, and performs quantization. In the present embodiment, in order to obtain a ternary halftone processed image, a dither process for obtaining a quantization result as shown in FIG. 11 is performed.

  The quantization total value calculation unit 2021 calculates the total quantization value by taking the sum of the quantization results calculated by the dither processing unit 2020 for each small region.

  The output target value mixing unit 2022 mixes the quantization total value for each small region calculated by the quantization total value calculation unit 2021 according to the number of pixels (area) of each small region in the processing target region, Output target value is calculated. For example, when the processing target area is divided as shown in FIG. 10A or FIG. 10B, the number of pixels (area) of each small area is equal, and thus the contribution to the output target value in each small area The rate is equivalent. In that case, the output target value may be calculated by taking the average of the quantized total values of the small areas. When the processing target area is divided as shown in FIG. 10C, it is necessary to calculate the output target value by mixing the quantized total values according to the area ratio of each small area.

  The distribution order determination unit 203 calculates an evaluation value for each pixel in the processing target region, and determines the order in which the output values are distributed based on the magnitude relationship of the evaluation values. Further, the output determination unit 204 distributes values in order until the output target value of the processing target area is reached. In the first embodiment, an example is shown in which the evaluation value for determining the distribution order is a value obtained by subtracting the corresponding threshold value from the pixel value. In the present embodiment, each pixel has two threshold values. If the evaluation value is calculated for each threshold value and the distribution order is determined, the output value determination unit 204 rotates the order in which the values are distributed. If the output value is increased by one, a ternary halftone image can be obtained.

  As described above, the processing target area is divided into a plurality of small areas according to the characteristics of the input image, so that even if the input image is a flat image or an image containing many fine patterns, the optimal amount of computation can be achieved. A high-quality halftone image can be obtained. In the present embodiment, a method of obtaining a ternary halftone image is shown as an example. However, the application range of the present embodiment is not limited to this, and an N (N: natural number) value halftone image is obtained. It is possible.

<Third Embodiment>
In the third embodiment, a histogram in a processing target region is acquired, and pixel groups having pixel values close to each other are handled together, thereby removing noise on a flat portion while preserving edges with respect to input image data. An example of obtaining halftone image data will be described. In addition, the same code | symbol is attached | subjected about the structure similar to 1st Embodiment, and detailed description is abbreviate | omitted.

  The hardware configuration of the image forming system including the image processing apparatus to which the present invention is applicable can be realized by the configuration of FIG. 1 shown in the first embodiment, and the description thereof is omitted.

(Configuration of the image processing unit 106)
The image processing unit 106 in this embodiment will be described. The image processing unit 106 according to the present embodiment executes a process for converting 8-bit input image data into binary halftone image data (output image data) for each pixel. The threshold matrix used in this embodiment is an AM threshold matrix in which dots as shown in FIG. 12 are intensively arranged, and is repeatedly arranged in tiles with respect to input image data. The configuration of the image processing unit 106 is shown in FIG.

  The target value calculation unit 202 calculates an output target value to be output from the processing target area. FIG. 13 is a block diagram showing a detailed configuration of the target value calculation unit 202 in the present embodiment. The target value calculation unit 202 includes a histogram acquisition unit 2025, a histogram analysis unit 2026, a quantized total value reference unit 2027, a quantized total value holding unit 2028, and an output target value mixing unit 2022.

  The histogram acquisition unit 2025 acquires a pixel value histogram of the processing target area. For example, when the processing target area 700 shown in FIG. 7 is input, a histogram as shown in FIG. 14 is acquired.

  The histogram analysis unit 2026 analyzes the pixel value histogram and divides the processing target area into areas having pixel values close to each other. In this embodiment, at the time of histogram acquisition, the bin width is set to 16 with an 8-bit pixel value, and the pixels that fall within the same bin are regarded as a collection of pixels having pixel values close to each other, and the representative value of each bin. Is calculated. For example, the pixels in the 64-79 bin are a group of pixels indicated by diagonal lines in the processing target area 700. An average value is calculated as a representative value of these pixel groups. Also, the number of pixels in the pixel group is calculated and output to the output target value mixing unit 2022 in the subsequent stage. Note that the bin width may be reduced when acquiring the histogram, and the pixel group may be adaptively determined according to the shape of the histogram when analyzing the histogram.

  Based on the representative value of the pixel group calculated by the histogram analysis unit 2026 and the position of the processing target region, the quantized total value reference unit 2027 stores the quantum for each representative value held in the quantized total value holding unit 2028 in advance. Refer to the totalized value. In this embodiment, the AM threshold matrix shown in FIG. 12 is used, so that the positional relationship between the threshold matrix and the processing target area is limited as shown in FIG. The processing target area is classified into positions A, B, C, D, E, F, G, H, and I according to the positional relationship with the threshold matrix. In accordance with these classifications, the quantized total value of the pixel value and the threshold value group corresponding to the processing target area is calculated and held in the quantized total value holding unit 2028 as shown in FIG.

  The output target value mixing unit 2022 mixes the quantized total value of the representative value of the pixel group according to the ratio of the pixel group in the processing target region, and calculates the output target value of the processing target region.

  By the above processing, it is possible to reduce the interference between the input image data and the threshold matrix and obtain suitable halftone image data that matches the characteristics of the input image data. In addition, noise in the input image data can be reduced by averaging only pixel values that are close to each other in the processing region, while the edges are preserved, so that degradation such as image blur occurs in the halftone image data. There is nothing.

  Further, in order to match the output characteristics of the image forming apparatus, the data after the gamma correction is held in advance for the data of the quantized total value holding unit, and at the same time as referring to the quantized total value. Gamma correction may be realized. In any case, it is preferable that the gamma correction is performed on the quantized total value of each pixel group and then mixed to calculate the output target value of the processing target region.

(Other embodiments)
The present invention supplies a program that realizes one or more functions of the above-described embodiments to a system or apparatus via a network or a storage medium, and one or more processors in the computer of the system or apparatus read and execute the program This process can be realized. It can also be realized by a circuit (for example, ASIC) that realizes one or more functions.

200 pixel value acquisition unit 201 threshold acquisition unit 202 target value calculation unit 203 distribution order determination unit 204 output value determination unit 205 distribution order determination unit 2020 dither processing unit 2021 quantization total value calculation unit 2022 output target value mixing unit 2023 small region division Unit 2024 small region representative value calculation unit 2025 histogram acquisition unit 2026 histogram analysis unit 2027 quantization total value reference unit 2028 quantization total value holding unit

Claims (8)

In a plurality of small regions constituting the processing target region in the input image data, a quantized total value in a threshold value group corresponding to the processing target region is calculated for each pixel value representing the small region, and occupies the processing target region Target value calculation means for calculating a target value to be output in the processing target region by mixing the quantized total value according to the proportion of the small region;
A distribution order determining means for determining a distribution order of output values in the processing target area;
Image processing comprising: an output value determining unit that determines an output value of each pixel included in the processing target region by assigning the target value to each pixel included in the processing target region according to the distribution order. apparatus.   The target value calculation means divides the processing target area into a plurality of small areas, calculates the number of pixels in each small area, and sets a threshold value group corresponding to the processing target area for each pixel value representing the small area. And calculating a target value to be output in the processing target region by mixing the quantization total value according to the number of pixels in the small region occupying the processing target region. The image processing apparatus according to claim 1, wherein:   The image processing apparatus according to claim 2, wherein the plurality of small regions are pixel groups having pixel values close to each other in the processing target region.   The image processing apparatus according to claim 2, wherein the target value calculation unit divides the small region so that the number of pixels is the same.   The target value calculation means performs gamma correction on the quantized total value calculated for each small region, and then mixes the values according to the proportion of the small region in the processing target region. The image processing apparatus according to claim 1, wherein a target value to be output is calculated.   The image processing apparatus according to claim 1, wherein the distribution order determination unit determines a distribution order based on a magnitude relationship between input image data and an evaluation value based on a threshold value.   A computer program that causes a computer to function as the image processing apparatus according to any one of claims 1 to 6 by being read and executed by a computer. In a plurality of small regions constituting the processing target region in the input image data, a quantized total value in a threshold value group corresponding to the processing target region is calculated for each pixel value representing the small region, and occupies the processing target region A target value calculating step for calculating a target value to be output in the processing target region by mixing according to the proportion of the small region;
A distribution order determining step for determining a distribution order of output values in the processing target area;
Image processing comprising: an output value determining step for determining an output value of each pixel included in the processing target region by assigning the target value to each pixel included in the processing target region according to the distribution order Method.

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