JP5018878B2 - Resolution conversion program, resolution conversion method, and resolution conversion apparatus - Google Patents

Description

  According to the present invention, in a computer apparatus having storage means for storing and storing image data in which pixel values are associated with each pixel in a memory, the resolution of the input binary halftone dot image data is the resolution of the output apparatus. In particular, a resolution conversion program, a resolution conversion method, and a resolution conversion apparatus that perform resolution conversion to be converted into a halftone dot shape by reducing moire and eliminating regularity in a multivalued stage even when resolution conversion is performed. The present invention relates to a resolution conversion program, a resolution conversion method, and a resolution conversion apparatus that can be retained.

  For example, when a newspaper image is created by a newspaper production system, a print image having a predetermined development resolution is created using a RIP (Raster Image Processor) device, and each printing press is determined from the development resolution of the print image in the paper sending system portion. The resolution is converted to the output resolution.

  However, when the resolution of the binary image is converted, moire (interference fringes) is generated. In newspaper printing, “halftone dots” are used as a representative technique for expressing gradations in binary image images. A halftone dot is a technique for expressing area gradation by accumulation of dots. When resolution conversion of halftone dots is performed, a halftone dot period and a fraction at the time of coordinate conversion that occurs at the time of enlargement / reduction interfere with each other, causing moire.

  In order to solve this problem, for example, as disclosed in Patent Document 1, a quantization error generated by simple coordinate transformation is reduced, and a coordinate position that does not become an integer value is expressed as a multi-value gradation pixel. A method of reducing the density change by converting the resolution has been devised. According to this method, it is possible to guarantee density by converting a binary image into a multi-value image and converting the resolution while reducing moire.

JP 2003-179749 A

  However, in the prior art represented by the above-mentioned Patent Document 1, the problem of regularity of positions still remains. That is, when resolution conversion is performed with multiple values, the appearance probability of a halftone (white and black intermediate level values generated by interpolation) changes depending on the coordinate position. This still caused moiré.

  Therefore, in order to remove the periodicity of halftone dots in the binary gradation stage and suppress the occurrence of moire, there is a descreening technique that obtains multilevel gradation by breaking the halftone dot shape by blurring (hereinafter referred to as smoothing). Although it has been devised, there remains a problem that when the descreening is performed, the halftone dot shape cannot be maintained, and the halftone dot of the original image is not preserved.

  The present invention has been made to solve the above problems (problems), and the present invention reduces moiré even if resolution conversion is performed from the developed resolution of a print image to the output resolution of each printing press. Another object of the present invention is to provide a resolution conversion program, a resolution conversion method, and a resolution conversion apparatus capable of maintaining a halftone dot shape while eliminating regularity in a multi-value stage.

  In order to solve the above-described problems and achieve the object, the present invention is inputted in a computer apparatus provided with a storage means for storing and storing image data in which pixel values are associated with each pixel. A resolution conversion program for causing the computer device to perform resolution conversion for converting the resolution of binary halftone image data to the resolution of an output device, wherein the binary halftone image data is converted into multivalued image data. A multi-value conversion procedure stored in the storage means, and a complementary pixel for randomly determining a complementary position of a complementary pixel for resolution conversion of multi-value image data converted by the multi-value conversion procedure under a certain condition Determining a pixel value of a complementary pixel determined by the position determining procedure and the complementary pixel position determining procedure based on a pixel value of a pixel in a predetermined vicinity of the complementary position of the complementary pixel; A complementary pixel value determination procedure for updating the multi-value image data stored in the storage means, and a binary halftone image for outputting the multi-value image data updated by the complementary pixel value determination procedure to the output device A binary conversion procedure for converting the data into data and storing the data in the storage means is executed by the computer device.

  Further, the present invention is the above invention, wherein the binary conversion procedure includes a pixel range determination procedure for determining a pixel range to be processed in the multi-valued image data, and a processing target determined by the pixel range determination procedure. A pixel value totaling procedure for calculating the total value by summing the pixel values of the pixels in the pixel range, and dividing the total value calculated by the pixel value totaling procedure by the maximum value that can be taken by the pixel value. A division procedure for calculating a remainder, and a pixel value corresponding to the number of quotients from a pixel having a large pixel value belonging to the pixel range to be processed is replaced with the maximum value, and the pixel value is the maximum value A pixel value replacement procedure for replacing the pixel value of the pixel having the next largest pixel value with the remainder after the pixel replaced with the pixel value and replacing the pixel value of the pixel whose pixel value has not yet been replaced with 0, According to the pixel value replacement procedure Pixel values of all pixels is equal to or to be stored in the storage means and converted into binary halftone dot image data for outputting the multi-value image data is substituted into the output device.

  Further, according to the present invention, in the multi-value image data converted by the multi-value conversion procedure according to the above-described invention, the pixel values of pixels having pixel values higher than the peripheral pixels are distributed to the peripheral pixels and the pixel values are dispersed. And causing the computer device to further execute a value distribution procedure, wherein the pixel value distribution procedure distributes pixel values of pixels whose pixel values of the multi-value image data converted by the multi-value conversion procedure are higher than the peripheral pixels to the peripheral pixels. Then, the pixel value is distributed, and the complementary pixel position determining procedure randomly determines the complementary position of the complementary pixel of the multi-valued image data in which the pixel value is distributed by the pixel value distributing procedure under a certain condition. It is characterized by that.

  Further, according to the present invention, in the multi-value image data converted by the multi-value conversion procedure according to the above-described invention, the pixel values of pixels having pixel values higher than the peripheral pixels are distributed to the peripheral pixels and the pixel values are dispersed. Further causing the computer device to execute a value distribution procedure, wherein the pixel value distribution procedure is for pixels whose pixel values of the multi-valued image data for which the pixel values of the complementary pixels are determined by the complementary pixel value determination procedure are higher than the peripheral pixels Distributing pixel values to the surrounding pixels to distribute the pixel values, and the binary conversion procedure is a binary for outputting multi-valued image data in which pixel values are distributed by the pixel value distribution procedure to the output device. The image data is converted into halftone dot image data and stored in the storage means.

  According to another aspect of the present invention, there is provided a computer apparatus comprising storage means for storing image data in which pixel values are associated with each pixel in memory, and outputting the resolution of input binary halftone image data as an output device. A resolution conversion method for converting the binary halftone dot image data into multi-value image data and storing it in the storage means; and the multi-value conversion step. A complementary pixel position determining step for randomly determining a complementary pixel complementary position for resolution conversion of the multi-valued image data under a certain condition, and a pixel value of the complementary pixel determined by the complementary pixel position determining step Is determined based on the pixel value of a pixel in the vicinity of the complementary position of the complementary pixel and updates the multi-valued image data stored in the storage means; and the complementary pixel value A binary conversion step of converting the multi-valued image data updated in a predetermined step into binary halftone dot image data for output to the output device and storing the binary halftone image data in the storage means. .

  Also, in the present invention according to the above invention, the binary conversion step includes a pixel range determination step for determining a pixel range to be processed in the multi-valued image data, and a processing target determined by the pixel range determination step. A pixel value summing step of summing pixel values of pixels in the pixel range of the pixel value to calculate a sum value, and dividing the sum value calculated by the pixel value summing step by a maximum value that can be taken by the pixel value. A division step for calculating a remainder, and a pixel value corresponding to the number of quotients from a pixel having a large pixel value belonging to the pixel range to be processed is replaced with the maximum value, and the pixel value is the maximum value A pixel value replacement step of substituting the pixel value of the pixel having the next largest pixel value with the remainder after replacing the pixel value with the remainder and then replacing the pixel value of the pixel whose pixel value has not yet been replaced with 0, According to the pixel value replacement step Pixel values of all pixels is equal to or to be stored in the storage means and converted into binary halftone dot image data for outputting the multi-value image data is substituted into the output device.

  In addition, the present invention includes storage means for storing and storing image data in which pixel values are associated with each pixel in a memory, and converting the resolution of the input binary halftone image data to the resolution of the output device. A resolution conversion device for converting, wherein the binary halftone image data is converted into multi-value image data and stored in the storage means, and the multi-value image converted by the multi-value conversion means Complement pixel position determining means for randomly determining complementary positions of complementary pixels for data resolution conversion under a certain condition, and the pixel values of the complementary pixels determined by the complementary pixel position determining means A complementary pixel value determining unit that updates the multi-valued image data that is determined based on a pixel value of a pixel in the vicinity of a pixel complementary position and that is stored in the storage unit, and updated by the complementary pixel value determining unit in front And having a binary conversion means for storing the multivalued image data in the storage means and converts into binary halftone dot image data for outputting to the output device.

  Further, in the present invention according to the above invention, the binary conversion unit includes a pixel range determination unit that determines a pixel range to be processed in the multi-value image data, and a processing target that is determined by the pixel range determination unit. A pixel value summing unit that sums the pixel values of the pixels in the pixel range to calculate a sum, and a quotient obtained by dividing the sum calculated by the pixel value summing unit by a maximum value that the pixel value can take. A division means for calculating a remainder; and a pixel value corresponding to the number of quotients from a pixel having a large pixel value belonging to the pixel range to be processed is replaced with the maximum value, and the pixel value is the maximum value Pixel value replacement means for replacing the pixel value of the pixel having the next largest pixel value with the remainder after the pixel replaced with, and replacing the pixel value of the pixel whose pixel value has not yet been replaced with 0 , By the pixel value replacement means Pixel values of all pixels is equal to or to be stored in the storage means and converted into binary halftone dot image data for outputting the multi-value image data is substituted into the output device.

  According to the present invention, since the regularity of halftone dots is eliminated while leaving the halftone dot shape of the image, the effect of reducing the moire of the image is achieved. Also, since the regularity of halftone dots is eliminated, moiré is reduced in various combinations of resolutions before and after resolution conversion, and moiré is generated only by resolution conversion for multiple output devices with various resolutions. There is an effect that an image output with less can be achieved.

  In addition, according to the present invention, the isolated points as errors that occur when converting from a multi-valued image to a binary image are reduced by concentrating the isolated points to pixels with higher pixel values, thereby reducing image output noise. There is an effect that stable image output with improved output image quality becomes possible.

  Further, according to the present invention, there is an effect that moire is reduced by distributing high pixel values to surrounding pixels.

  Exemplary embodiments of a resolution conversion program, a resolution conversion method, and a resolution conversion apparatus according to the present invention will be described below in detail with reference to the accompanying drawings. In the following embodiment, after converting an image into binary halftone dot image data having a predetermined resolution, the binary halftone dot image data obtained by converting the predetermined resolution to the resolution of each of a plurality of output devices such as a printing press is converted into the binary dot image data. A case where the present invention is applied to a newspaper production system to be sent to an output device will be shown. However, the present invention is not limited to this, and the output device may be a device other than a printing machine, for example, an image display device, and may be applied not only to a newspaper production system but also to other image printing systems or image display systems.

  Note that each processing function of the resolution conversion apparatus shown in the following embodiments includes a central processing unit (CPU) (or a microcomputer such as a micro processing unit (MPU) and a micro controller unit (MCU)) and the CPU (or MPU). Or a microcomputer that is analyzed and executed by a microcomputer such as an MCU) or hardware as wired logic.

  In the following embodiment, it is assumed that the multi-value image data has 256 gradations in which pixel values are expressed by numerical values from 0 to 255. Binary halftone dot image data corresponding to the 256-level multi-valued image data has a minimum pixel value of 0 and a maximum pixel value of 255. Note that the gradation of the multi-valued image data is not limited to 256 gradations.

  First, an outline of the embodiment will be described. FIG. 1 is a diagram for explaining the outline of the embodiment. As shown in the figure, the image captured by the image data expansion device is expanded into image data having a resolution of, for example, 909 dpi (dot per inch). The 909 dpi image data is transferred to the image data transmission device.

  The image data transmission device that has received the 909 dpi image data converts the resolution of the image data in accordance with a plurality of connected output devices, and transmits the image data to each output device. For example, as shown in the figure, if image data is sent to a 909 dpi output device having a resolution of 909 dpi, there is no need for resolution conversion, and the image data of 909 dpi is sent as it is.

  On the other hand, if image data is sent to a 1200 dpi output device with 1200 dpi resolution, the resolution will be converted from 909 dpi to 1200 dpi, and 1200 dpi image data will be sent. On the other hand, if image data is transmitted, resolution conversion is performed from 909 dpi to 600 dpi, and 600 dpi image data is transmitted.

  As described above, when the image data sending device performs resolution conversion according to the embodiment in accordance with an output device having various resolutions, and sends the resolution-converted image data to each output device, the image data developing device It is possible to output a moire-free image having a halftone dot shape to a plurality of output devices having different resolutions simply by developing the image data into one type of resolution image data.

  Here, the moire is as follows. FIG. 2 is a diagram for explaining moire. As shown in the figure, when the input binary halftone dot image data is applied to the output resolution, there are cases where a halftone portion appears when converted to multi-value image data. This half-tone part, when further converted into binary halftone dot image data, is the part that is converted into one of the binary value of the smallest possible pixel value and the largest possible pixel value. Will appear. This is recognized by human eyes as interference fringes called moire. The embodiment aims to reduce this moire.

  The cause of moiré occurs due to image enlargement / reduction, conversion from multi-value image data to binary halftone image data, interference with a halftone dot period, and the like.

  Next, the configuration of the resolution conversion apparatus according to the embodiment will be described. FIG. 3 is a functional block diagram illustrating the configuration of the resolution conversion apparatus according to the embodiment. As shown in the figure, a resolution conversion apparatus 100 includes a binary halftone dot image data input unit 101, a storage unit 102 such as a memory as an image data storage unit, a binary multi-value conversion processing unit 103, a smoothing A processing unit 104, a complementary position random number determination resolution conversion processing unit 105, a rearrangement processing unit 106, and a binary halftone image data output unit 107.

  The binary halftone dot image data input unit 101 develops and stores binary halftone dot image data received by the resolution conversion apparatus 100 from the outside in the storage unit 102. The binary multi-value conversion processing unit 103 performs binary multi-value conversion processing for converting binary halftone dot image data stored in the storage unit 102 into multi-value image data by a simple method. Details of the binary multi-value conversion process will be described later.

  The smoothing processing unit 104 performs a smoothing process on the multivalued image data stored in the storage unit 102. Details of the smoothing process will be described later. The complementary position random number determination resolution conversion processing unit 105 determines the pixel complementary position and its pixel value for the smoothed multi-valued image data stored in the storage unit 102 and determines the resolution of the multi-valued image data. Perform the conversion. Details of the complementary position random number determination resolution conversion processing will be described later.

  The rearrangement processing unit 106 performs rearrangement processing on the arrangement of the pixel values of the multivalued image data subjected to resolution conversion by the complementary position random number determination resolution conversion processing unit 105. Details of the rearrangement process will be described later. The binary halftone image data output unit 107 converts the multivalued image data in which the pixel values are rearranged by the rearrangement processing unit 106 into binary halftone image data by the simple binary method, and outputs the binary halftone image data to the outside.

  Since the above-described resolution conversion apparatus 100 includes the smoothing processing unit 104 that suppresses the occurrence of moire and the complementary position random number determination resolution conversion processing unit 105 that retains the halftone dot shape in multiple stages, the cause of deterioration of the output image is pursued. Can be performed for each functional unit, and verification / development efficiency can be improved.

  Next, resolution conversion processing performed by the resolution conversion apparatus 100 shown in FIG. 3 will be described. FIG. 4 is a flowchart showing the resolution conversion processing procedure. As shown in the figure, first, the binary multilevel conversion processing unit 103 receives a binary halftone image data input notification from the binary halftone image data input unit 101, and performs a binary multilevel conversion process. (Step S101). Subsequently, the smoothing processing unit 104 performs a smoothing process on the multi-valued image data converted in step S101 (step S102). Details of the smoothing process will be described later.

  Subsequently, the complementary position random number determination resolution conversion processing unit 105 performs a complementary position random number determination resolution conversion process on the multi-valued image data smoothed in step S102 (step S103). Details of this complementary position random number determination resolution conversion processing will be described later. Subsequently, the rearrangement processing unit 106 performs rearrangement processing on the multi-valued image data that has undergone the complementary position random number determination resolution conversion processing in step S103 (step S104). Details of the rearrangement process will be described later.

  Note that the processing order of step S102 and step S103 may be reversed, and smoothing processing may be performed after performing complementary position random number determination resolution conversion processing. In this case, also in the functional block diagram shown in FIG. 3, the multi-value image data converted by the binary multi-value conversion processing unit 103 is first subjected to the complementary position random number determination resolution conversion processing by the complementary position random number determination resolution conversion processing unit 105. Thereafter, smoothing processing is performed by the smoothing processing unit 104. Then, after the smoothing process, a rearrangement process is performed.

  FIG. 5 is a diagram for explaining the outline of the binary multi-value conversion process shown in step S101 of FIG. As shown in FIG. 5A, the shaded portion of the set of 5 × 5 pixels is a pixel to which the maximum pixel value (for example, 1) is given in binary representation, and the other pixels are binary. A pixel to which a minimum pixel value (for example, 0) is given in the expression. When this is converted into, for example, a 256-level multi-value expression, as shown in FIG. 5B, a pixel value 255 is given to the shaded portion, and a pixel value 0 is given to the other portions. It becomes.

Next, the smoothing process shown in step S102 of FIG. 4 will be described. FIG. 6 is a flowchart showing the smoothing processing procedure. In the following, (x, y) (where x and y are natural numbers) are coordinates indicating the pixel position of the image to be smoothed as shown in FIG. The lower left of the image is the origin 0, the horizontal axis where the value of x increases toward the right is the x axis, and the vertical axis where the value of y increases toward the top is the y axis. The pixel located at (x, y) is the pixel located at x + 1th in the x-axis direction and y + 1th in the y-axis direction. x can take a value from 0 to x ₁ , and y can take a value from 0 to y ₁ .

  As shown in the figure, the smoothing processing unit 104 first substitutes 0 for variables x and y stored in a predetermined storage area (step S201). Note that x represents the pixel position on the straight line when the image to be processed is crossed by a straight line parallel to the x-axis of the image (hereinafter referred to as the x-axis parallel straight line). Y represents the pixel position on the straight line when the image to be processed is crossed by a straight line parallel to the vertical axis of the image (hereinafter referred to as the y-axis parallel straight line).

  Next, in the case where the pixel value at the pixel position (x, y) is expressed as I (x, y), the smoothing processing unit 104 sets I (x + 1, y) −I ( It is determined whether or not x, y) <α holds (step S202). When it is determined that I (x + 1, y) −I (x, y) <α holds (Yes in step S202), the process proceeds to step S203, and I (x + 1, y) −I (x, y) <α is satisfied. If it is not determined that it is true (No at Step S202), the process proceeds to Step S205. Here, α is a threshold value for the amount of change in I (x, y).

  In step S203, the smoothing processing unit 104 substitutes I (x, y) −β for I (x, y), with β being a predetermined positive number, and I (x + 1, y) for I (x + 1, y). ) Substitute + β. Subsequently, the smoothing processing unit 104 substitutes x + 1 for x (step S204). When step S204 ends, the process proceeds to step S206. On the other hand, in step S205, the smoothing processing unit 104 substitutes I (x, y) + β for I (x, y) and substitutes I (x + 1, y) −β for I (x + 1, y). When step S205 ends, the process proceeds to step S204. Here, β is a distribution amount (movement amount) of the pixel value.

Subsequently, the smoothing processing unit 104 determines whether or not x> x ₁ . That is, it is determined whether or not the end of the x axis of the image to be processed has been reached. When it is determined that x> x ₁ is satisfied (Yes at Step S206), the process proceeds to Step S207, and when it is not determined that x> x ₁ is satisfied (No at Step S206), the process proceeds to Step S202.

Subsequently, in step S207, the smoothing processing unit 104 substitutes y + 1 for y. Subsequently, the smoothing processing unit 104 determines whether or not y> y ₁ is satisfied. That is, it is determined whether or not the end of the y-axis of the image to be processed has been reached. When it is determined that y> y ₁ is satisfied (Yes at Step S208), the smoothing process ends and the process returns to the resolution conversion process. When it is not determined that y> y ₁ is satisfied (No at Step S208), Step The process proceeds to S202.

As described above, the smoothing process performed for all x satisfying 0 ≦ x ≦ x ₁ while fixing y is performed for all y satisfying 0 ≦ y ≦ y _1. The smoothing process is sequentially performed until the smoothing of all the images to be processed by 104 is completed.

  The outline of the smoothing process is as follows. FIG. 8 is a diagram for explaining an outline of the smoothing process. As shown in the figure, the pixel value of the pixel whose pixel value was 255 before the smoothing process is distributed to a pixel adjacent to the pixel value by a certain amount, thereby reducing the protrusion of the pixel value. As a result, an effect of “blurring” can be obtained, an image with a gentle change in pixel value can be obtained, and moire can be reduced.

  The smoothing process is not limited to the method described above, and may be a method using a Gaussian filter or a filter smoothing method using a 3 × 3 filter matrix.

  In addition, since the smoothing process performs smoothing on multi-valued image data, it is not necessary to consider the smoothing of the moire period compared to the case of smoothing on binary halftone image data. There are advantages.

Next, the complementary position random number determination resolution conversion process shown in step S103 of FIG. 4 will be described. FIG. 9 is a flowchart showing a complementary position random number determination resolution conversion processing procedure. As shown in the figure, first, as shown in FIG. 10A, the complementary position random number determination resolution conversion processing unit 105 has pixel values I ₀₀ , I ₀₁ , A complementary position P based on the average distance of the pixels of I ₁₀ and I ₁₁ is calculated (step S301). In the example shown in FIG. 10A, the intersection of square diagonal lines composed of pixels with pixel values I ₀₀ , I ₀₁ , I ₁₀ , and I ₁₁ is the complementary position P based on the distance average. Note that I ₀₀ , I ₀₁ , I ₁₀ , and I ₁₁ are P pixel position (x, y), image horizontal width (x-axis direction) m / magnification rate, and image vertical width (y-axis direction) enlargement If the reduction ratio is n, each is defined as follows. In the following, [•] is a Gaussian symbol and represents an integer not exceeding “•”.

  Next, as illustrated in FIG. 10B, the complementary position random number determination resolution conversion processing unit 105 extracts random numbers from, for example, a range from 0 to 127, and based on the extracted random numbers, the complementary position based on the average distance is extracted. P is moved to P ′ determined based on the following equation (step S302). Here, “rand (P, R)” in the following equation is a function that randomly determines one pixel present in a circle having a radius R centered at the complementary position P based on the extracted random number. The unit of R is a unit of various lengths or the number of dots.

  In equation (2), R can be arbitrarily set, so that R can be used as an image quality adjustment parameter.

  Here, when Δx and Δy are defined as shown in FIG. 10-3, Δx and Δy are obtained by the following equations.

  Subsequently, the complementary position random number determination resolution conversion processing unit 105 calculates the pixel value I ′ of P ′ based on the following equation based on Δx and Δy. This process is a process called “bilinear conversion”. Instead of this “bilinear conversion”, “nearest neighbor method”, “bicubic conversion”, “average pixel method”, or the like may be used.

  As described above, the complementary position of the pixel for resolution conversion accompanying the enlargement / reduction of the image is randomly determined, and the pixel value of the determined complementary position P ′ is calculated. As the pixels are complemented in this way, enlargement / reduction and resolution conversion are performed.

  In addition, by performing the smoothing process and the complementary position random number determination resolution conversion process described above, it becomes possible to eliminate the regularity of the distribution of pixels and pixel values (regularity of halftone dots), thereby reducing moire. be able to. In particular, by the complementary position random number determination resolution conversion process, it is possible to eliminate the regularity of the halftone dots while maintaining the halftone dot shape.

  Next, the rearrangement process shown in step S104 of FIG. 4 will be described. FIG. 11 is a flowchart showing the rearrangement processing procedure. As shown in the figure, first, the rearrangement processing unit 106 selects a 3 × 3 pixel area to be rearranged (step S301). In the area selected here, for example, as shown in FIG. 12, the pixels are prioritized. The reason why priority is given in this way is to cope with the case where the same pixel value exists in the region of 3 × 3 pixels to be rearranged.

  Next, the rearrangement processing unit 106 stores the pixel values of all the pixels in the region selected in step S301 in the storage unit 102 serving as an image buffer (step S302). Subsequently, all the pixel values in the image buffer are summed, and the sum is divided by 255 (that is, the maximum value that the pixel value can take) (step S303).

  Subsequently, the rearrangement processing unit 106 sequentially replaces pixels having a large pixel value by “255” in the region selected in step S301 by the number of division quotients in step S303 (step S304). ). Subsequently, the pixel value of the pixel having the next highest pixel value after the pixel value replaced with “255” is replaced with the remainder of the division in step S303 (step S305).

  Subsequently, the rearrangement processing unit 106 replaces the pixel value of the pixel whose pixel value has not been updated in step S304 and step S305 with 0 (step S306). Subsequently, the rearrangement processing unit 106 determines whether or not the pixel values of all the pixels of the processing target image have been replaced (step S307). When it is determined that the pixel values of all the pixels of the processing target image have been replaced (Yes in step S307), the rearrangement process is terminated and the process returns to the resolution conversion process. On the other hand, when it is not determined that the pixel values of all the pixels of the processing target image have been replaced (No at Step S307), the process proceeds to Step S301. Next, when step S301 is executed, the rearrangement processing unit 106 selects the next 3 × 3 area to be rearranged.

  This rearrangement process suppresses pixel variation compared to the conventional error diffusion method, preserves the net shape of the original image as much as possible, and generates gradations (image values) frequently generated by smoothing processing and complementary position random number determination resolution conversion processing. ) Is prevented from becoming an isolated pixel at the time of conversion from multivalue to binary.

  Next, how the distribution of pixel values of the original image before the rearrangement process changes due to the rearrangement process will be described. FIG. 13 is a diagram illustrating a distribution of pixel values of the original image before the rearrangement process. FIG. 14 is a diagram illustrating the rearrangement process and the distribution of pixel values after the rearrangement process.

FIG. 13 (a ₀ ) shows the distribution of pixel values of the original image before the rearrangement process. When the rearrangement process is performed on the original image, in FIG. 14, (a ₁ ) → (a ₂ ) → (a ₃ ) →... → (a _n−2 ) → (a _n−1 ) → The distribution of pixel values changes as in (a _n ).

For example, in FIG. 14A ₁ , first, the 3 × 3 area at the upper left of the processing target image is selected as the 3 × 3 area to be processed, and the pixel values “147” and “8” of the pixels in this area are selected. , “3”, “51”, “0”, “4”, “0”, “0”, “0” are “213”, “0”, “0”, “0” by the relocation processing, respectively. , “0”, “0”, “0”, “0”, “0”. However, the display order of pixel values follows the priority order shown in FIG. The same applies thereafter.

  This replacement process is as follows. In other words, 147 + 8 + 3 + 51 + 0 + 4 + 0 + 0 + 0 = 213. When 213 is divided by the maximum value 255 that the pixel value can take, the quotient is 0 and the remainder is 213. For this reason, there is no pixel whose pixel value is replaced with 255. Since the maximum pixel value in this region is 147, 147 is replaced with 213. The pixel values of the remaining pixels are replaced with 0.

Next, in FIG. 14A ₂ , a 3 × 3 area that is to the right of one column in the upper left 3 × 3 area of the processing target image is selected as a 3 × 3 area to be processed, and pixels in this area Pixel values “255”, “213”, “0”, “103”, “0”, “0”, “0”, “0”, “0” are respectively changed to “255”, “0” by the rearrangement process. Replaced by 255 ”,“ 0 ”,“ 61 ”,“ 0 ”,“ 0 ”,“ 0 ”,“ 0 ”,“ 0 ”.

  This replacement process is as follows. In other words, 255 + 213 + 0 + 103 + 0 + 0 + 0 + 0 + 0 = 571. When this 571 is divided by the maximum value 255 that the pixel value can take, the quotient is 2 and the remainder is 61. Therefore, the pixel value of the pixel value “255” and the pixel value “213” having the largest pixel value is replaced with 255. Since the pixel having the next largest pixel value in this area is the pixel having the pixel value “103”, 103 is replaced with 61. The pixel values of the remaining pixels are replaced with 0.

The same processing, by shifting the 3 × 3 area of the processing target to the left and down one by one row, and finally, as shown in FIG. 14 (a _n), the pixel values of all the pixels is replaced, pixel Diffusion is suppressed, and the pixel values are aggregated. In this state where pixel diffusion is suppressed, compared to the conventional moiré reduction processing by error diffusion method, pixel diffusion is prevented and the halftone dot shape is maintained, so that a high-quality output image with less moiré can be obtained. Is possible.

  In addition, with reference to FIG. 15-1 to FIG. 15-5, the outline of the processing result of the original image by the resolution conversion process shown in the above embodiment is shown. FIG. 15A is a diagram illustrating an outline of an original image before resolution conversion processing according to the embodiment. FIG. 15B is a diagram illustrating an outline of the image after the multi-value conversion process. FIG. 15C is a diagram illustrating an outline of the image after the smoothing process. FIG. 15D is a diagram illustrating an outline of the image after the complementary position random number determination resolution change conversion process. FIG. 15-5 is a diagram illustrating an outline of the image after the rearrangement process.

  In the image after the multi-value conversion processing of the current image shown in FIG. 15A, as shown in FIG. 15B, regularity remains in the pixel value distribution of the pixels, and moire occurs. Further, when smoothing processing is performed on the image after the multi-value conversion processing shown in FIG. 15-2, as shown in FIG. 15-3, “edges” with sharp pixel value changes are caused by “blurring”. Reduced. Further, when the complementary position random number determination resolution conversion process is performed on the smoothed image shown in FIG. 15-3, the regularity of the halftone dot shape is reduced and the moire is reduced as shown in FIG. 15-4. Is done.

  However, in this state, the halftone dot shape is lost, an error occurs when converting from multi-value to binary, and many isolated pixels are generated, resulting in image noise. Therefore, when the rearrangement process is performed on the image after the complementary position random number determination resolution conversion process of FIG. 15-4, as shown in FIG. 15-5, it occurs at the time of conversion from multivalue to binary. It is possible to aggregate errors into pixels with higher pixel values and reduce the occurrence of the errors. In this way, a high-quality image output can be obtained.

  According to the conventional method, only the image quality of the output image quality is ensured in the resolution conversion by the combination of the fixed resolutions from the first specific resolution to the second specific resolution. In the resolution conversion between the combined resolutions, the image quality of the output image quality can be guaranteed.

  As mentioned above, although the Example of this invention was described, this invention is not limited to this, In the range of the technical idea described in the claim, even if it implements in a various different Example, it is. It ’s good. Moreover, the effect described in the Example is not limited to this.

  In addition, among the processes described in the above embodiment, all or part of the processes described as being automatically performed can be manually performed, or the processes described as being manually performed can be performed. All or a part can be automatically performed by a known method. In addition, the processing procedure, control procedure, specific name, information including various data and parameters shown in the above embodiment can be arbitrarily changed unless otherwise specified.

  Each component of each illustrated device is functionally conceptual and does not necessarily need to be physically configured as illustrated. In other words, the specific form of distribution / integration of each device is not limited to that shown in the figure, and all or a part thereof may be functionally or physically distributed or arbitrarily distributed in arbitrary units according to various loads or usage conditions. Can be integrated and configured.

  Furthermore, each or all of the processing functions performed in each device are entirely or partially a CPU (Central Processing Unit) (or a microcomputer such as an MPU (Micro Processing Unit) or MCU (Micro Controller Unit)) and It may be realized by a program that is analyzed and executed by the CPU (or a microcomputer such as MPU or MCU), or may be realized as hardware by wired logic.

  In the image output system, even if the resolution conversion is performed from the developed resolution of the image image to the output resolution of each output device, the moire of the image is reduced, and the dot shape is eliminated while eliminating the regularity at the multi-value stage. This is useful when you want to output high-quality images with each output device that has various resolutions, especially in newspaper production systems that include images, in a printer with various resolutions, through resolution conversion This is effective when it is desired to obtain a high-quality image print output.

FIG. 1 is a diagram for explaining the outline of the embodiment. FIG. 2 is a diagram for explaining moire. FIG. 3 is a functional block diagram illustrating the configuration of the resolution conversion apparatus according to the embodiment. FIG. 4 is a flowchart showing the resolution conversion processing procedure. FIG. 5 is a diagram for explaining the outline of the binary multi-value conversion process. FIG. 6 is a flowchart showing the smoothing processing procedure. FIG. 7 is a diagram for explaining an image to be smoothed. FIG. 8 is a diagram for explaining an outline of the smoothing process. FIG. 9 is a flowchart showing a complementary position random number determination resolution conversion processing procedure. FIG. 10A is a diagram for explaining the complementary position P calculated based on the average distance. FIG. 10-2 is a diagram for explaining a post-movement position P ′ obtained by randomly moving the complementary position P. FIG. 10C is a diagram for explaining the outline of the calculation of the pixel value at the post-movement complementary position P ′. FIG. 11 is a flowchart showing the rearrangement processing procedure. FIG. 12 is a diagram illustrating the priority order of pixels in a pixel range to be processed in the rearrangement process. FIG. 13 is a diagram illustrating a distribution of pixel values of the original image before the rearrangement process. FIG. 14 is a diagram illustrating the rearrangement process and the distribution of pixel values after the rearrangement process. FIG. 15A is a diagram illustrating an outline of an original image before resolution conversion processing according to the embodiment. FIG. 15B is a diagram illustrating an outline of the image after the multi-value conversion process. FIG. 15C is a diagram illustrating an outline of the image after the smoothing process. FIG. 15D is a diagram illustrating an outline of the image after the complementary position random number determination resolution change process. FIG. 15-5 is a diagram illustrating an outline of the image after the rearrangement process.

DESCRIPTION OF SYMBOLS 100 Resolution converter 101 Binary halftone dot image data input part 102 Storage part 103 Binary multi-value conversion processing part 104 Smoothing process part 105 Complementary position random number determination Resolution conversion process part 106 Relocation processing part 107 Binary halftone dot image data Output section

Claims (5)

In a computer apparatus having storage means for storing and storing image data in which pixel values are associated with each pixel in a memory, the resolution of the input binary halftone image data is converted into the resolution of the output apparatus. A resolution conversion program for causing the computer device to perform resolution conversion,
A multi-value conversion procedure for converting the binary halftone dot image data into multi-value image data and storing it in the storage means;
A complementary pixel position determination procedure for randomly determining a complementary pixel complementary position for resolution conversion of the multi-value image data converted by the multi-value conversion procedure under a certain condition in the multi-value image data;
The pixel value of the complementary pixel determined by the complementary pixel position determination procedure is determined based on the pixel value of a pixel in the vicinity of the complementary position of the complementary pixel in the multivalued image data and stored in the storage unit. A complementary pixel value determination procedure for updating the multi-value image data;
A pixel range determination procedure for determining a pixel range to be processed in the multi-valued image data updated by the complementary pixel value determination procedure ;
A pixel value total procedure for calculating the total value by summing the pixel values of the pixels in the pixel range to be processed determined by the pixel range determination procedure;
A division procedure for calculating the quotient and the remainder by dividing the total value calculated by the pixel value summation procedure by the maximum value that can be taken by the pixel value;
The pixel value of a pixel corresponding to the number of quotients from a pixel having a large pixel value belonging to the pixel range to be processed is replaced with the maximum value, and the pixel value is replaced with the maximum value. A pixel value replacement procedure for replacing a pixel value of a pixel having a large pixel value with the remainder and then replacing a pixel value of a pixel whose pixel value has not yet been replaced with 0;
Binary conversion in which the multivalued image data in which the pixel values of all pixels are replaced by the pixel value replacement procedure is converted into binary halftone dot image data for output to the output device and stored in the storage means A resolution conversion program that causes the computer apparatus to execute a procedure. In the multi-value image data converted by the multi-value conversion procedure, the computer device further executes a pixel value distribution procedure for distributing pixel values by distributing pixel values of pixels having pixel values higher than the peripheral pixels to the peripheral pixels. Let
The pixel value distribution procedure distributes pixel values by distributing pixel values of pixels whose pixel values of the multi-value image data converted by the multi-value conversion procedure are higher than the peripheral pixels to the peripheral pixels,
Said complementary pixel position determination procedure, according to claim 1, wherein the determining the complementary position of the complementary pixels of the multivalued image data in which the pixel values are distributed by the pixel value variance procedure, randomly under certain conditions Resolution conversion program described in 1. In the multi-value image data converted by the multi-value conversion procedure, the computer device further executes a pixel value distribution procedure for distributing pixel values by distributing pixel values of pixels having pixel values higher than the peripheral pixels to the peripheral pixels. Let
The pixel value distribution procedure distributes pixel values of pixels whose pixel values of the multivalued image data whose pixel values of the complementary pixels are determined by the complementary pixel value determination procedure to higher than the peripheral pixels are distributed to the peripheral pixels. Distribute
In the binary conversion procedure, the multivalued image data in which pixel values are dispersed by the pixel value dispersion procedure is converted into binary halftone dot image data for output to the output device and stored in the storage means. The resolution conversion program according to claim 1 . In a computer apparatus having storage means for storing and storing image data in which pixel values are associated with each pixel in a memory, the resolution of the input binary halftone image data is converted into the resolution of the output apparatus. A resolution conversion method,
A multi-value conversion step of converting the binary halftone dot image data into multi-value image data and storing it in the storage means;
A complementary pixel position determination step of randomly determining a complementary position of a complementary pixel for resolution conversion of the multi-value image data converted by the multi-value conversion step under a certain condition in the multi-value image data;
The pixel value of the complementary pixel determined in the complementary pixel position determining step is determined based on the pixel value of a pixel in the vicinity of the complementary position of the complementary pixel in the multivalued image data and stored in the storage unit. A complementary pixel value determining step for updating the multi-valued image data;
A pixel range determination step for determining a pixel range to be processed in the multi-valued image data updated by the complementary pixel value determination step ;
A pixel value totaling step of calculating a total value by summing pixel values of pixels in the pixel range to be processed determined by the pixel range determination step;
A division step of calculating a quotient and a remainder by dividing the total value calculated by the pixel value totaling step by a maximum value that can be taken by the pixel value;
The pixel value of a pixel corresponding to the number of quotients from a pixel having a large pixel value belonging to the pixel range to be processed is replaced with the maximum value, and the pixel value is replaced with the maximum value. A pixel value replacement step of replacing a pixel value of a pixel having a large pixel value with the remainder and then replacing a pixel value of a pixel whose pixel value has not yet been replaced with 0
Binary conversion in which the multivalued image data in which the pixel values of all the pixels have been replaced in the pixel value replacing step is converted into binary halftone dot image data for output to the output device and stored in the storage means A resolution conversion method comprising the steps of: A resolution conversion device comprising storage means for storing and storing image data in which pixel values are associated with each pixel in a memory, and converting the resolution of the input binary halftone image data into the resolution of the output device There,
Multi-value conversion means for converting the binary halftone dot image data into multi-value image data and storing it in the storage means;
Complementary pixel position determining means for randomly determining a complementary pixel complementary position for resolution conversion of the multi-valued image data converted by the multi-valued converting means under a predetermined condition in the multi-valued image data;
The pixel value of the complementary pixel determined by the complementary pixel position determining unit is determined based on the pixel value of a pixel in the vicinity of the complementary position of the complementary pixel in the multi-value image data and stored in the storage unit. Complementary pixel value determining means for updating the multi-valued image data;
Pixel range determining means for determining a pixel range to be processed in the multi-valued image data updated by the complementary pixel value determining means ;
Pixel value summing means for summing pixel values of pixels in the pixel range to be processed determined by the pixel range determining means, and calculating a total value;
Dividing means for calculating the quotient and the remainder by dividing the total value calculated by the pixel value summing means by the maximum value that can be taken by the pixel value;
The pixel value of a pixel corresponding to the number of quotients from a pixel having a large pixel value belonging to the pixel range to be processed is replaced with the maximum value, and the pixel value is replaced with the maximum value. A pixel value replacement unit that replaces a pixel value of a pixel having a large pixel value with the remainder and then replaces a pixel value of a pixel whose pixel value has not yet been replaced with 0;
Binary conversion in which the multivalued image data in which the pixel values of all the pixels are replaced by the pixel value replacement means is converted into binary halftone dot image data for output to the output device and stored in the storage means And a resolution conversion apparatus.

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