JP2016092744A - Image conversion processing method, program for performing them, and image conversion processing apparatus - Google Patents

Abstract

PROBLEM TO BE SOLVED: To create a hybrid screening image suitable for a printing process in high quality.SOLUTION: An image conversion processing apparatus creates a hybrid screening image as an output binary image by converting an input multi-valued image into a first binary image formed by black and white pixels; detecting a number of first black pixels included in the first binary image; detecting a conversion error generated at the time of creating the first binary image; converting the first binary image into a second binary image based on a number of first black pixels and the conversion error; detecting a number of second black pixels included in the second binary image; converting the second binary image into an FM screening image by converting a kind of a pixel in a labyrinth pattern image depending a number of differences based on the difference between the detected second black pixel number and a black pixel number of a prepared labyrinth pattern image; detecting a predetermined pixel pattern formed by a plurality of adjacent pixels from the created FM screening image; and performing a predetermined pixel fusing processing to the detected predetermined pixel pattern.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an image conversion processing method, a program for executing the image conversion method, and an image conversion processing device, and more particularly, to a method, program, and device for converting a grayscale image that is a multi-valued image into a hybrid screening image.

  In the field of lithographic printing, a grayscale image, which is an input multi-valued image, is expressed using a screening technique that represents a binary image with gradation values “0” and “1”. Has been done. In such lithographic printing, the density is expressed by whether or not the ink adheres to the paper surface, so the uniformity of the ink deposit and the dot gain characteristic have a great influence on the quality of the printed matter.

  Conventional screening methods are roughly classified into a halftone dot AM (Amplitude Modulation) method and an FM (Frequency Modulation) screening method.

  The halftone dot AM method is a method that has been used for a long time. The halftone dots are regularly arranged based on the multi-value gradation values of the gray scale image, and the dot size of each halftone dot representing the light and shade information. Is a method of expressing the gradation in a pseudo manner by modulating the image according to the multi-value gradation value of the gray scale image.

  On the other hand, the FM screening method is a method of expressing gradation by making the dot size constant and modulating the dot arrangement density. Conventionally, when creating an image using the FM screening method, for example, a dither method or an error diffusion process is used. In the dither method and the error diffusion process, the gray scale image is reproduced using two chromatic pixels of “black” and “white” using the illusion of human eyes.

  However, when an image (halftone dot image) is created by the conventional halftone dot AM technique, halftone dots are arranged on a regularly arranged grid, so that an image having no rough graininess can be expressed. This has the advantage of being less susceptible to the printing environment. However, on the other hand, there is a drawback that interference fringes such as moire and rosetta patterns occur. Also, by shifting the screen angles of the CMYK4 plates, the generation of interference fringes can be suppressed, but the generated interference fringes cannot be completely suppressed, and moiré occurs in the pattern portion of the striped pattern. To do.

  In addition, when an image (FM screening image) is created by an FM screening technique using a dither method or error diffusion processing, since the arrangement positions of the dots are random and have no regularity, moire, rosette patterns, etc. There is an advantage that interference fringes hardly occur. Furthermore, since the degree of freedom of dot arrangement is higher than that of halftone dots, there is an advantage that an image with higher resolution and higher image quality can be obtained.

  However, since each arranged dot is small, it is difficult to transfer the ink from the plate to the paper with a rotary press, and there is a drawback that a visually rough feeling is generated on the printed matter on the highlight side.

  Therefore, recently, with the penetration of CTP (Computer To Plate) and digital workflow, the halftone dot AM method and the FM screening method in the newspaper printing field that requires high image quality and high added value, such as commercial printing and mass printing. Hybrid screening technology that combines the advantages of both and enables high-precision and high-resolution printing has been put into practical use and has begun to spread.

  For example, in Patent Documents 1 and 2, the center of a halftone screen is used as a generating point, the generating point position is changed by a random number, Voronoi division is performed using the generating point as a nucleus, and the input image is divided into a plurality of unit regions. A method for generating a screening image from which the periodicity of halftone dots is eliminated is described.

  Patent Documents 3 and 4 describe a method of creating a hybrid screening image from a multivalued image. In the method described in Patent Document 3, first, a binary image is created by performing simple binarization processing on a multi-value image based on a DBS (Direct Binary Search) method, and the created binary A multi-valued image is restored by two-dimensional filter processing on the image. Then, an error between the restored multi-value image and the original multi-value image is calculated, and by determining the pixels of the binary image so that the calculated error becomes a luminance value pattern satisfying a predetermined condition, the multi-value image A hybrid screening image is created from

  In the method described in Patent Document 4, first, binarization processing is performed to obtain the number of black pixels, which is the size of a dot representing the density information of a conventional halftone dot, using a halftone cell. An FM screening image having no periodic unevenness is created by a pixel arrangement similar to a chess board shape that is evenly distributed in space. Then, a hybrid screening image is created by fusing isolated black pixels and white pixels in the created FM screening image based on a series of pixel fusion processes.

JP 2005-341089 A Japanese Patent No. 4599511 JP 2004-304543 A JP 2014-165904 A

  However, in the methods described in Patent Documents 1 and 2, the non-periodic cluster shape representing dots becomes larger as the density value increases, and a pattern like a turtle shell is generated, giving an unnatural impression to the entire image. There was a problem that. In addition, the dot shape of the negative image and the dot shape of the inverted positive image are different, and there is a possibility that the continuity of gradation like a halftone dot is lost.

  In addition, the method described in Patent Document 3 has attracted attention as a new hybrid screening process. In this method, an original multivalued image in which the luminance of each pixel is expressed in multiple values is converted into a binary luminance of each pixel. It is necessary to create an initial binary image by a halftoning process for converting to a binary image expressed by Furthermore, a two-dimensional convolution operation with a filter coefficient must be performed for all binarized pixels. Therefore, there has been a problem that it takes a long time for processing.

  Furthermore, in the method described in Patent Document 4, first, two pixel arrangement layers are randomly selected, and further, the order in which the pixels are arranged for each of the two pixels in each pixel arrangement layer is determined, and the generated random number By performing the placement process, an FM screening image is created. Specifically, for example, when the number of pixels to be arranged is 8, since the number of arranged pixels is 0 to 7, random numbers 0 to 7 are generated, and the arranged pixels corresponding to the generated random numbers are arranged in pixels. Arrangement processing to arrange in the layer is performed.

  When creating an FM screening image in this way, the pixels are determined after the arrangement order is randomly determined depending on whether the total value of the horizontal and vertical coordinates of the arrangement pixels obtained by different random numbers is even or odd. Perform placement processing. Therefore, there is a problem that an unnatural turtle shell pattern is generated in an image having a density of around 50%.

  In addition, there is a problem in that the image quality such as sharpness in the created hybrid screening image cannot be adjusted according to printing conditions or user requirements.

  Therefore, the present invention has been made in view of the above-described problems in the prior art, and an input multi-valued image can be produced with high image quality and high addition without generating interference fringes such as moire and rosetta patterns. It is an object of the present invention to provide an image conversion processing method that has value and can be converted into a hybrid screening image suitable for a printing process, a program for executing the method, and an image conversion processing apparatus.

  In order to achieve the above object, the present invention is an image conversion processing method for converting a multi-valued image having multi-valued gradation values into a hybrid screening image, wherein the multi-valued image is a first comprising black pixels and white pixels. And binarization for detecting a first black pixel number included in the first binary image and detecting a conversion error generated when the first binary image is generated Converting the first binary image into a second binary image based on the first black pixel count and the conversion error, and the second black image included in the second binary image. Based on the error distribution step for detecting the number of pixels, and the difference between the detected number of the second black pixels and the number of black pixels of a predetermined binary image made up of black pixels and white pixels prepared in advance, One pixel in the predetermined binary image is converted into the other by the number of pixels Thus, an FM screening image creating step for converting the second binary image into an FM screening image, and a predetermined pixel pattern formed by a plurality of adjacent pixels is detected from the created FM screening image, A pixel fusion step of performing a predetermined pixel fusion process on the detected predetermined pixel pattern to create a hybrid screening image.

  The present invention also provides a program for causing a computer apparatus to execute an image conversion processing method for converting a multi-value image having multi-value gradation values into a hybrid screening image, the multi-value image being a black pixel and a white pixel. And a first black pixel number included in the first binary image is detected to detect a conversion error that occurs when the first binary image is created. And converting the first binary image into a second binary image based on the first black pixel number and the conversion error, and being included in the second binary image. Based on an error distribution step for detecting a second number of black pixels, and a difference between the detected number of second black pixels and the number of black pixels of a predetermined binary image composed of a black pixel and a white pixel prepared in advance. , One in the predetermined binary image by the number of the differences. The second pixel image is converted to the other pixel by converting the second binary image into an FM screening image, and the created FM screening image is formed of a plurality of adjacent pixels. A computer-implemented image conversion processing method comprising: a pixel fusion step of detecting a predetermined pixel pattern, performing a predetermined pixel fusion process on the detected predetermined pixel pattern, and generating a hybrid screening image It is characterized by that.

  Furthermore, the present invention is an image conversion processing device for converting a multi-value image having multi-value gradation values into a hybrid screening image, wherein the multi-value image is converted into a first binary image comprising black pixels and white pixels. A binarization processing unit that detects a conversion error that occurs during the creation of the first binary image by detecting the number of first black pixels included in the first binary image, Based on the first number of black pixels and the conversion error, the first binary image is converted into a second binary image, and the second number of black pixels included in the second binary image is detected. Based on the difference between the detected error distribution processing unit and the detected number of the second black pixels and the number of black pixels of a predetermined binary image made up of a black pixel and a white pixel prepared in advance. By converting one pixel in a predetermined binary image to the other pixel, An FM screening image creation processing unit for converting the binary image of the image into an FM screening image, and a predetermined pixel pattern formed by a plurality of adjacent pixels is detected from the created FM screening image, and the detected predetermined image is detected And a pixel fusion processing unit that performs a predetermined pixel fusion process on the pixel pattern and creates a hybrid screening image.

  As described above, according to the present invention, the input multi-valued image has high image quality and high added value without generating interference fringes such as moire and rosette patterns, and is suitable for the printing process. It can be converted into an image.

It is a block diagram which shows one Embodiment of the image conversion processing apparatus which concerns on this invention. It is the schematic for demonstrating the binarization process which converts a gray scale image into a binary image. It is the schematic for demonstrating an error dispersion | distribution process. It is the schematic for demonstrating the effect of an error dispersion | distribution process. It is the schematic for demonstrating the production process of FM screening image. It is the schematic for demonstrating a labyrinth pattern image. It is the schematic for demonstrating the produced FM screening image. It is the schematic for demonstrating a 1st pixel fusion process. It is the schematic for demonstrating the 2nd pixel fusion process. It is the schematic for demonstrating the 3rd pixel fusion process. It is the schematic for demonstrating the hybrid screening image produced by the pixel fusion process. It is the schematic for demonstrating the hybrid screening image by this Embodiment. It is a flowchart for demonstrating the flow of each process in an image conversion processing apparatus. It is a flowchart for demonstrating the flow of a binarization process and an error dispersion | distribution process. It is a flowchart for demonstrating the flow of FM screening image creation processing. It is a flowchart for demonstrating the flow of a black pixel increase process or a black pixel decrease process. It is a flowchart for demonstrating the flow of a two-dimensional pixel fusion process. It is a flowchart for demonstrating the flow of a one-dimensional pixel fusion process.

  Next, an embodiment for carrying out the present invention will be described in detail with reference to the drawings.

  FIG. 1 shows an embodiment of an image conversion processing device according to the present invention. This image conversion processing device 1 is roughly divided into a memory 2, a binarization processing unit 3, an error variance processing unit 4, an FM screening image. A creation processing unit 5 and a pixel fusion processing unit 6 are provided.

  The image conversion processing apparatus 1 receives input multi-valued image data 10 that is converted from a grayscale image having multi-valued gradation values output from an external input device such as a scanner. The input multivalued image data 10 is based on a grayscale image of each color of CMYK obtained by color conversion from a color image that is an original image.

  Next, the image conversion processing apparatus 1 performs various processes on the input multi-valued image data 10, and thereafter, the black pixel whose gradation value is “1” and the gradation value is “0”. The output binary image data 20 expressed in binary with a white pixel that is “is output.

  The memory 2 stores input multi-valued image data 10 input from the outside, and various processes are performed by the binarization processing unit 3, the error variance processing unit 4, the FM screening image creation processing unit 5, and the pixel fusion processing unit 6. Is a storage medium for storing image data subjected to the above, information necessary for each process, and the like.

  The binarization processing unit 3 reads the input multi-value image data 10 stored in the memory 2 and converts the multi-value gradation value of the input multi-value image (input gray scale image) based on the input multi-value image data 10 to a black pixel. And a binary image (first binary image) consisting only of binary gradation values of white pixels, and the number of black pixels included in the first binary image is detected. The converted first binary image and the first black pixel number information indicating the detected number of black pixels are stored in the memory 2.

  The error variance processing unit 4 reads out the first binary image and the first black pixel number information stored in the memory 2. Then, the error variance processing unit 4 performs the image conversion by the binarization processing unit 3 on the first binary image converted by the binarization processing unit 3 based on the first black pixel number information. Error dispersion processing is performed to suppress the tone jump phenomenon that occurs, and a second binary image is created. In addition, the error variance processing unit 4 detects the number of black pixels included in the created second binary image. The generated second binary image and the second black pixel number information indicating the detected number of black pixels are stored in the memory 2.

  The FM screening image creation processing unit 5 reads the second binary image and the second black pixel number information stored in the memory 2 and creates an FM screening image using the second black pixel number information. The created FM screening image is stored in the memory 2. Further, the FM screening image creation processing unit 5 has a required arrangement pixel number counter (not shown) used when creating an FM screening image. This required pixel number counter will be described later.

  The pixel fusion processing unit 6 reads the FM screening image stored in the memory 2 and detects a predetermined pixel pattern from the FM screening image. Then, the pixel fusion processing unit 6 performs pixel fusion processing on the detected predetermined pixel pattern to create a hybrid screening image. The created hybrid screening image is stored in the memory 2.

  Note that the above-described binarization processing unit 3, error variance processing unit 4, FM screening image creation processing unit 5, and pixel fusion processing unit 6 do not necessarily have to be configured by hardware. Program).

  Next, the hybrid screening image creation process in the image conversion processing apparatus 1 having the above configuration will be described for each process in each unit.

  First, the binarization processing by the binarization processing unit 3 will be described with reference to FIG. In the binarization processing, first, a halftone cell (halftone cell) block is included in the input multilevel image data 10 based on the resolution information of the input multilevel image data 10 as in the case of creating halftone dots. It is set as a conversion block which is a detection unit for detecting black pixels to be detected. Then, the input multi-valued image data 10 is converted into a first binary image composed of black pixels and white pixels for each conversion block.

  Next, based on the gradation value of each pixel included in the conversion block, an average gradation value of the input multi-valued image data 10 in the conversion block is calculated, and the corresponding first value is calculated using the calculated average gradation value. A density value indicating the ratio between the number of black pixels and the number of all pixels in one binary image conversion block is acquired. Based on the density value of the conversion block, the number of black pixels corresponding to the average gradation value in the conversion block in the grayscale image that is a multi-valued image is detected.

  That is, when the transform block size is set to “Lx” pixels and “Ly” pixels in the horizontal and vertical directions and the grayscale image gradation value is P, the black of the first binary image obtained by the conversion The number of pixels s (first black pixel number information) can be obtained based on Expression (1).

  Here, in the binarization process, the grayscale values of the pixels included in the conversion block of the grayscale image are averaged to obtain the number of black pixels in the first binary image, and therefore expressed by the size of the halftone cell block. The maximum number of gradations that can be made can be determined. Further, the number of gradations that can be expressed changes depending on the number of pixels included in the halftone cell block, and the change in gradation is expressed by the number of pixels.

  Therefore, when the size of the halftone cell block is large, the converted first binary image can increase the number of gradations that can be expressed, but it becomes a grainy image and the image quality deteriorates. On the other hand, when the size of the halftone cell block is small, the converted first binary image has a small number of gradations that can be expressed, but becomes a fine-grained image, and the image quality can be improved.

  The first binary image converted using the binarization processing in the present embodiment has the same conversion image quality as that obtained when blur processing using an average filter is performed on a grayscale image. It is necessary to make the size as small as possible. The inventor has verified through an evaluation experiment that better conversion image quality can be obtained by setting the size of the conversion block to about “4 × 4” pixels to “6 × 6” pixels. In the example shown in FIG. 2, the correspondence relationship between the gradation value of the multi-valued image and the number of black pixels of the first binary image to be converted when the size of the conversion block is “6 × 6” is shown. Show.

  Next, error variance processing by the error variance processing unit 4 will be described with reference to FIG.

  As described above, when binarization processing is performed by setting a halftone cell block as a conversion block, the number of gradations that can be expressed changes according to the size of the conversion block. Here, when the size of the conversion block is increased, the size of the halftone dot increases to such an extent that it can be seen by human eyes, so that the image quality of the entire converted binary image deteriorates.

  On the other hand, in order to prevent such deterioration in image quality, it is necessary to reduce the size of the conversion block. However, if the size of the conversion block is reduced, the number of gradations that can be expressed for each conversion block decreases. Therefore, in the converted binary image, a “tone jump phenomenon” in which a boundary occurs between halftones (between converted blocks) occurs.

  By the way, in the binarization process described above, the number of black pixels s acquired based on the mathematical expression (1) is the number of black pixels in the binary image corresponding to the multi-value gradation value pixels of the conversion block. It includes an integer part that can be represented and a part after the decimal point that cannot be represented as a black pixel. At this time, the portion below the decimal point that cannot be expressed as a black pixel becomes a conversion error when the grayscale image is converted into a binary image, which causes a tone jump phenomenon.

  Therefore, in the present embodiment, error dispersion processing is performed on the first binary image in order to suppress the influence of image quality blur due to the binarization processing and to obtain a finer image quality. Thereby, it is possible to prevent a tone jump phenomenon that occurs when the gradation expression is reduced by setting the conversion block size to be small as described above.

  In the error distribution processing, as shown in FIG. 3, the error (tone value conversion error) ε for each conversion block generated in the binarization processing is distributed at a predetermined ratio with respect to adjacent surrounding conversion blocks. For example, for a conversion block having a high correlation with the conversion block of interest, the gradation value conversion error ε is distributed at a higher rate than a conversion block with a low correlation. In the conversion block to be converted, the number of black pixels s ″ (second black pixel) using the gradation value conversion error ε distributed from the surrounding conversion blocks and considering the conversion error based on Equation (2). Number information) and a second binary image obtained by performing error dispersion processing on the first binary image.

In Equation (2), the value “0” is used as the initial value (ε _0,0 ) of the gradation value conversion error ε. This is because the tone value conversion error ε distributed from the adjacent surrounding conversion blocks is set to the value “0” in the conversion block that first performs the error distribution processing.

  FIG. 4 shows the effect when the error variance processing is performed, and FIG. 4A shows the first binary image (without error variance processing) when the halftone block size is set small. (B) shows a second binary image (with error dispersion processing).

  As described above, by performing the error dispersion processing, it is possible to create a binary image in which gradation expression is smooth and moire or the like does not occur without causing a tone jump phenomenon such as a pseudo contour.

  Next, FM screening image creation processing by the FM screening image creation processing unit 5 will be described with reference to FIGS.

  In the FM screening image creation processing, the second black pixel number information (black pixel number s ″) detected by the error dispersion processing by the error dispersion processing unit 4 and the same size as the second binary image prepared in advance. Based on the labyrinth pattern image, an FM screening image is created from the second binary image created by the error variance processing unit 4.

  Here, the labyrinth pattern image is an image composed only of straight lines in the horizontal direction and the vertical direction by continuous black pixels and white pixels in the image, for example, as shown in FIG. In addition, the labyrinth pattern image in this example can be created by using a conventionally used predetermined algorithm so that the number of black pixels and the number of white pixels in the image are the same.

In this FM screening image creation process, first, the number of black pixels BM of the portion corresponding to the conversion block in the labyrinth pattern image having the same size as the second binary image is acquired. Next, the difference pixel number Bs is calculated from the obtained black pixel number BM of the labyrinth pattern image and the black pixel number s ″ in the corresponding conversion block of the second binary image. It can be calculated based on Equation (3).
Number of black pixels in the second binary image s "
Number of black pixels in labyrinth pattern image BM = (Lx × Ly) / 2 (3)
Difference pixel number Bs = s ″ −BM

  Next, based on the difference pixel number Bs thus calculated, the white pixel or the black pixel in the labyrinth pattern image is converted into a black pixel or a white pixel by the value of the difference pixel number Bs.

  When the difference pixel number Bs is a positive number, black pixels corresponding to the difference pixel number Bs are added by converting white pixels into black pixels (black pixel increase processing). On the other hand, when the difference pixel number Bs is a negative number, the black pixels corresponding to the difference pixel number Bs are deleted by converting the black pixels into white pixels (black pixel reduction process).

  Specifically, first, based on the difference pixel number Bs, it is determined which one of black pixel increase processing and black pixel decrease processing is to be performed, and pixel arrangement conditions are set. When the difference pixel number Bs is a positive number, black pixel increase processing is set as the pixel arrangement condition, and when the difference pixel number Bs is negative, black pixel reduction processing is set as the pixel arrangement condition.

  Next, in order to determine the black pixel to be increased or decreased in the conversion block, random numbers corresponding to the horizontal and vertical coordinates are generated, and the type of pixel (black) located at the coordinates corresponding to the random number value is generated. Pixel or white pixel).

  Next, when the pixel type matches the arrangement condition, the type of the pixel is converted. For example, when the pixel arrangement condition is black pixel increase processing and the pixel corresponding to the random number value is a white pixel, the white pixel is converted into a black pixel. When the pixel arrangement condition is black pixel reduction processing and the pixel corresponding to the random number value is a black pixel, the black pixel is converted into a white pixel. Then, such black pixel increase processing or black pixel decrease processing is repeated for the value of the difference pixel number Bs.

  If the type of pixel corresponding to the random number value does not match the pixel arrangement condition, the pixel conversion process is not performed, the random number is generated again, and the above process is repeated. Here, the case where the pixel arrangement condition is not met is a case where the pixel arrangement condition is a black pixel increasing process, a pixel corresponding to a random number value is a black pixel, and a pixel arrangement condition is a black pixel reducing process. In some cases, the pixel corresponding to the random number value is a white pixel.

  Thus, in the FM screening image creation processing, for example, from the number of black pixels acquired based on the multi-value original image shown in FIG. 5A and the number of black pixels of the labyrinth pattern image shown in FIG. Based on the obtained difference pixel number Bs, black pixel increase processing or black pixel decrease processing is performed on the labyrinth pattern image of FIG. Thereby, the FM screening image shown in FIG. 5C can be created. This FM screening image is different from the FM screening image created by the conventional method.

  FIG. 7 is a gradation image created using the FM screening image creation processing according to the present embodiment. In this image, it can be confirmed that gradations in all areas from the highlight area to the shadow area are expressed continuously and smoothly.

  Next, pixel fusion processing by the pixel fusion processing unit 6 will be described with reference to FIGS. The FM screening image created by the above-described FM screening image creation processing unit 5 has distribution characteristics that maintain a balance between a global uniform distribution and a local random distribution by a curve composed of a single pixel and black / white pixels. realizable. Therefore, there is a feature that worm-like moire does not occur in the FM screening image created by the conventional error diffusion processing.

  However, as shown in FIG. 5 (c), in this FM screening image, isolated pixels and fine lines become dots that are the minimum information unit.

  Therefore, in this embodiment, in order to realize screening suitable for the printing process while maintaining the advantages of the FM screening image, pixel fusion for converting the dot size, which is the minimum information unit, into a size that is easy to print. Process.

  In the pixel fusion processing, first, raster scanning is performed on the FM screening image created by the FM screening image creation processing unit 5. Then, a predetermined pixel pattern in which black pixels and white pixels are distributed at one-pixel intervals is detected from a pixel pattern formed by a plurality of adjacent pixels, and two-dimensional pixel fusion is performed on the detected predetermined pixel pattern. Process.

  Specifically, first, a first pixel pattern including four pixels of “2 pixels × 2 pixels” shown in FIG. 8A is detected from the FM screening image. Specifically, the first pixel pattern is a pattern in which the upper left and lower right pixels are black pixels, and the upper right and lower left pixels are white pixels.

  When the first pixel pattern is detected, a first pixel fusion process is performed on the first pixel pattern. In the first pixel fusion process, as shown in FIG. 8B, an exchange process is performed in which the lower right black pixel in the first pixel pattern is replaced with the adjacent upper right or lower left white pixel. At this time, the replacement probability of the lower right black pixel and the upper right or lower left white pixel is both 50%.

  That is, in the first pixel fusion process, an exchange process (hereinafter referred to as “first pixel fusion process 100a”) in which the lower right black pixel and the upper right white pixel shown in FIG. ), And an exchange process (hereinafter referred to as “first pixel fusion process 100b”) for exchanging the lower right black pixel and the lower left white pixel shown in FIG. 8D. I do.

  Next, in the two-dimensional pixel fusion process, the second pixel pattern shown in FIG. 9A is detected. Specifically, the second pixel pattern is a pattern in which the upper left and lower right pixels are white pixels, and the upper right and lower left pixels are black pixels.

  When the second pixel pattern is detected, a second pixel fusion process is performed on the second pixel pattern. In the second pixel fusion process, as shown in FIG. 9B, an exchange process is performed in which the lower right white pixel and the adjacent upper right or lower left black pixel in the second pixel pattern are replaced. At this time, the exchange probability between the lower right white pixel and the upper right or lower left black pixel is set to 50%.

  That is, in the second pixel fusion process, an exchange process (hereinafter referred to as “second pixel fusion process 200a”) that replaces the lower right white pixel and the upper right black pixel shown in FIG. 9C with a probability of 50%. ), And an exchange process (hereinafter referred to as “second pixel fusion process 200b”) for exchanging the lower right white pixel and the lower left black pixel shown in FIG. 9D. I do.

  Finally, in the two-dimensional pixel fusion process, the first and second pixel fusion processes are alternately performed until the first and second pixel patterns cannot be detected from the FM screening image.

  As described above, in the two-dimensional pixel fusion processing, the black pixels and the white pixels are exchanged so that the black pixels in the pixel pattern in which the black pixels and the white pixels are distributed at intervals of one pixel are continuously distributed. A predetermined screening image is created.

  Next, in pixel pixel fusion processing, raster scanning is performed on the screening image converted by the above-described two-dimensional pixel fusion processing. Then, a predetermined pixel pattern in which black pixels and white pixels are distributed at one-pixel intervals is detected from a pixel pattern formed by a plurality of pixels adjacent in the angular direction, and one-dimensional with respect to the detected predetermined pixel pattern Perform pixel fusion processing.

  In the one-dimensional pixel fusion processing, first, a third pixel pattern including four pixels that are adjacent to each other in the angular direction from the screening image and in which each pixel is arranged as “white / black / white / black” is detected.

  When a third pixel pattern is detected, a third pixel fusion process is performed on the third pixel pattern. In the third pixel fusion process, the third pixel pattern arranged as “white / black / white / black” is converted into a pixel pattern arranged as “white / white / black / black”.

  Here, the third pixel pattern is detected at 45 ° intervals in a 360 ° space of the image, as shown in FIG. Specifically, in the third pixel fusion process, when a third pixel pattern is detected in the 0 ° direction, as shown in FIG. 10B, “white” is displayed in the 0 ° direction from the left to the right. A third pixel pattern aligned with “black / white / black” (hereinafter referred to as “third pixel pattern (0 °)”) is converted into a pixel pattern aligned with “white / white / black / black” ( Third pixel fusion process (0 °).

  When the third pixel pattern is detected in the 45 ° direction, as shown in FIG. 10C, the third pixel pattern aligned with “white / black / white / black” from the lower left to the upper right in the 45 ° direction. A pixel pattern (hereinafter referred to as “third pixel pattern (45 °)”) is converted into a pixel pattern aligned with “white / white / black / black” (third pixel fusion processing (45 °)).

  When the third pixel pattern is detected in the 90 ° direction, as shown in FIG. 10D, the third pixel pattern aligned with “white / black / white / black” from the bottom to the top in the 90 ° direction. A pixel pattern (hereinafter referred to as “third pixel pattern (90 °)”) is converted into a pixel pattern aligned with “white / white / black / black” (third pixel fusion processing (90 °)).

  When the third pixel pattern is detected in the 135 ° direction, as shown in FIG. 10E, the third pixel pattern in which “white / black / white / black” is arranged in the 135 ° direction from the lower right to the upper left is arranged. The pixel pattern (hereinafter referred to as “third pixel pattern (135 °)”) is converted into a pixel pattern aligned with “white / white / black / black” (third pixel fusion processing (135 °)). .

  When the third pixel pattern is detected in the 180 ° direction, as shown in FIG. 10F, the third pixel pattern aligned with “white / black / white / black” from the right to the left in the 180 ° direction. A pixel pattern (hereinafter referred to as “third pixel pattern (180 °)”) is converted into a pixel pattern aligned with “white / white / black / black” (third pixel fusion processing (180 °)).

  When the third pixel pattern is detected in the 225 ° direction, as shown in FIG. 10G, the third pixel pattern aligned with “white / black / white / black” from the upper right to the lower left in the 225 ° direction. A pixel pattern (hereinafter referred to as “third pixel pattern (225 °)”) is converted into a pixel pattern aligned with “white / white / black / black” (third pixel fusion processing (225 °)).

  When the third pixel pattern is detected in the 270 ° direction, as shown in FIG. 10H, the third pixel pattern aligned with “white / black / white / black” from the top to the bottom in the 270 ° direction. A pixel pattern (hereinafter referred to as “third pixel pattern (270 °)”) is converted into a pixel pattern aligned with “white / white / black / black” (third pixel fusion processing (270 °)).

  When the third pixel pattern is detected in the 315 ° direction, as shown in FIG. 10 (i), the third pixel pattern aligned with “white / black / white / black” from the upper left to the lower right in the 315 ° direction. The pixel pattern (hereinafter referred to as “third pixel pattern (315 °)”) is converted into a pixel pattern aligned with “white / white / black / black” (third pixel fusion processing (315 °)). .

  Since the 360 ° direction is the same as the 0 ° direction, the third pixel pattern is not detected.

  Thus, in the third pixel fusion processing, the third pixel pattern is sequentially detected at 45 ° intervals with respect to the 360 ° space of the image, and the above-described conversion is performed until the third pixel pattern cannot be detected from the screening image. The process is repeated.

  And the image in which the 1st-3rd pixel fusion process was performed by the pixel fusion process in this way turns into the hybrid screening image which converted the FM screening image.

  FIG. 11 shows a hybrid screening image created by performing pixel fusion processing. FIG. 11A is an FM screening image (FM screening image input to the pixel fusion processing unit 6) created by the FM screening image creation processing. FIG. 11B is a screening image obtained by performing two-dimensional pixel fusion processing (first and second pixel fusion processing) on the FM screening image shown in FIG. Further, FIG. 11C is a hybrid screening image created by performing one-dimensional pixel fusion processing (third pixel fusion processing) on the screening image shown in FIG. 11B.

  By performing the pixel fusion process in this way, the number of isolated pixels can be reduced and converted into large dots formed by a plurality of pixels.

  FIG. 12 shows a hybrid screening image created by the hybrid screening image creation processing according to the present embodiment. 12A is a conventional halftone dot image, and FIG. 12B is a conventional FM screening image created using error diffusion processing. FIG. 12C is a hybrid screening image created by performing each process according to the present embodiment.

  Each of these images was created using the same original image. When comparing each image, the hybrid screening image shown in FIG. 11 (c) shows the advantages of the conventional halftone image and FM screening image. It is a combination of both, and its effectiveness can be confirmed.

  Next, the flow of each process in the image conversion processing apparatus 1 will be described with reference to the flowcharts shown in FIGS.

  First, processing of the entire image conversion processing apparatus 1 that converts input multi-valued image data 10 input from the outside into output binary image data 20 and outputs it will be described with reference to FIG.

  Prior to each processing, when input multivalued image data 10 that is a grayscale image is input to the image conversion processing device 1 from the outside, the image conversion processing device 1 stores the input multivalued image data 10 in the memory 2. To do.

  The binarization processing unit 3 reads the input multivalued image data 10 stored in the memory 2 and sets a conversion block for the input multivalued image data 10 based on the resolution information regarding the input multivalued image data 10. And the binarization process which detects the 1st black pixel number s while converting the image for every conversion block into a 1st binary image is performed (step S1). The binarization processing unit 3 temporarily stores in the memory 2 the first binary image and the first black pixel number information s obtained by the binarization process.

  Next, the error variance processing unit 4 reads the first binary image and the first black pixel number information s stored in the memory 2, and based on these images and information, converts the first binary image to the first binary image. 2 and an error dispersion process for detecting the second black pixel number s ″ (step S2). The error dispersion processor 4 obtains the second binary value obtained by the error dispersion process. The image and the second black pixel number information s ″ are temporarily stored in the memory 2.

  Next, the FM screening image creation processing unit 5 reads the second black pixel number information s ″ stored in the memory 2 and sets the black pixel number for each conversion block indicated by the second black pixel number information s ″. Based on the labyrinth pattern image having the same size as the conversion block, an FM screening conversion process is performed to convert the second binary image into an FM screening image (step S3). The FM screening image creation processing unit 5 temporarily stores the FM screening image created by the FM screening conversion process in the memory 2.

  Next, the pixel fusion processing unit 6 reads out the FM screening image stored in the memory 2, and performs a two-dimensional pixel fusion process (first and second pixel fusion processing) on the FM screening image (Step 1). S4). The pixel fusion processing unit 6 temporarily stores the screening image created by the two-dimensional pixel fusion processing in the memory 2.

  Next, the pixel fusion processing unit 6 reads out the screening image created by the two-dimensional pixel fusion processing from the memory 2, and performs one-dimensional pixel fusion processing (third pixel fusion processing) on the screening image ( Step S5). Thereby, the input multivalued image data 10 is converted into a hybrid screening image. The pixel fusion processing unit 6 stores the hybrid screening image created by the pixel fusion processing in the memory 2.

  The hybrid screening image stored in the memory 2 is output as output binary image data 20.

  In the example shown in FIG. 13, the processing of step S1, step S2, and step S3 has been described as being performed sequentially. However, the present invention is not limited to this. May be performed.

  Next, the flow of binarization processing and error dispersion processing in step S1 and step S2 of FIG. 13 will be described with reference to FIG.

  First, in step S <b> 11, the binarization processing unit 3 calculates the number of black pixels of the first binary image based on the gradation value of each pixel included in the set conversion block using Equation (1) described above. A certain first black pixel number s is calculated.

  Next, in step S <b> 12, the binarization processing unit 3 performs an error dispersion process by adding dispersion errors from transform blocks located around the transform block to be processed. Finally, the binarization processing unit 3 calculates the second black pixel number s ″, which is the second black pixel number information, by using Equation (2), and the dispersion error ε with respect to the surrounding conversion blocks. Is calculated (step S13).

  Next, the flow of the FM screening conversion process in step S3 of FIG. 13 will be described with reference to FIG.

  First, in step S <b> 21, the FM screening image creation processing unit 5 reads the second binary image from the memory 2, and converts the second binary image based on the resolution information regarding the second binary image. Set.

  Next, the FM screening image creation processing unit 5 acquires the number of black pixels BM in the labyrinth pattern image having the same size as the conversion block corresponding to the set conversion block (step S22).

  Then, the FM screening image creation processing unit 5 performs a raster scan for each conversion block on the second binary image (step S23), and calculates the number of black pixels s ″ in each conversion block for each conversion block. (Step S24) The second black pixel number information calculated in step S13 of FIG. 14 may be used as the black pixel number s ″.

  Next, in step S25, the FM screening image creation processing unit 5 sets the black pixel number BM of the labyrinth pattern image acquired in step S22 and the black pixel number s ″ of the second binary image acquired in step S24. Based on equation (3), the difference pixel number Bs is calculated.

  In step S <b> 26, the FM screening image creation processing unit 5 determines whether or not to increase black pixels in the labyrinth pattern image based on the difference pixel number Bs. If it is determined that the difference pixel number Bs is a positive number and black pixels are to be increased, the process proceeds to step S27, and the FM screening image creation processing unit 5 performs black pixel increase processing as arrangement processing. On the other hand, if the difference pixel number Bs is a negative number and it is determined that the black pixels are to be reduced, the process proceeds to step S28, and the FM screening image creation processing unit 5 performs the black pixel reduction process as the arrangement process. The details of the black pixel increasing process and the black pixel decreasing process will be described later.

  Finally, the FM screening image creation processing unit 5 determines whether or not the black pixel arrangement processing for the labyrinth pattern image has been completed (step S29). When it is determined that the arrangement process has been completed (step S29; Yes), the series of processes ends.

  On the other hand, when it is determined that the black pixel arrangement processing has not been completed (step S29; No), the processing returns to step S25, and the FM screening image creation processing unit 5 arranges the black pixels in all the conversion blocks. Until the processing is completed, the processing from step S25 to step S28 is repeated.

  In this way, by creating FM screening images from input multi-valued images in units of transform blocks and creating FM screening images for all transform blocks, the input multi-valued images can be converted into FM screening images.

  Next, the flow of arrangement processing in step S27 and step S28 in FIG. 15 will be described with reference to FIG.

  First, in step S31, the FM screening image creation processing unit 5 determines one of the black pixel increasing process and the black pixel decreasing process as the arrangement process based on the determination in step S26 of FIG. Set. When the determined arrangement process is the black pixel increase process, the FM screening image creation processing unit 5 sets the pixel arrangement condition to “increase in black pixels”. On the other hand, when the determined arrangement process is the black pixel reduction process, the FM screening image creation processing unit 5 sets the pixel arrangement condition to “black pixel reduction”.

  Next, the FM screening image creation processing unit 5 sets the absolute value of the difference pixel number Bs calculated in step S25 of FIG. 15 in the required arrangement pixel number counter (step S32). The value of the required arrangement pixel number counter is the maximum number of black pixels arranged (converted) in the arrangement process.

  In step S33, the FM screening image creation processing unit 5 generates a uniform random number and determines arrangement pixel coordinates (m, n) for arranging (converting) black pixels.

  Next, in step S34, the FM screening image creation processing unit 5 determines whether or not the pixel type at the arrangement pixel coordinates determined in step S33 satisfies the pixel arrangement condition set in step S31.

  When it is determined that the type of pixel at the arrangement pixel coordinate (m, n) satisfies the pixel arrangement condition (step S34; Yes), the process proceeds to step S35, and the FM screening image creation processing unit 5 The pixel type at the coordinates (m, n) is converted. At this time, if the arrangement process is a black pixel increasing process, the type of pixel in the arrangement pixel coordinate (m, n) is “white pixel”, so the white pixel is converted to a black pixel, increase. When the arrangement process is a black pixel reduction process, the type of pixel at the arrangement pixel coordinate (m, n) is “black pixel”. Therefore, the black pixel is converted into a white pixel, and the black pixel is reduced. Let Then, the FM screening image creation processing unit 5 decrements the value of the required arrangement pixel number counter by “1” (step S36).

  On the other hand, if it is determined in step S34 that the type of pixel at the arrangement pixel coordinate (m, n) does not satisfy the pixel arrangement condition (step S34; No), the process returns to step S33. The FM screening image creation processing unit 5 discards the arrangement pixel coordinates (m, n) determined in step S33 and newly generates a uniform random number to determine the arrangement pixel coordinates (m, n).

  In step S <b> 37, the FM screening image creation processing unit 5 determines whether or not the value of the required arrangement pixel number counter is “0”. When it is determined that the value of the required arrangement pixel number counter is “0” (step S37; Yes), the FM screening image creation processing unit 5 determines that the processing for all the black pixels to be arranged (converted) has been completed. Then, a series of processing ends.

  On the other hand, when it is determined that the value of the required pixel number counter is not “0” (step S37; No), the process returns to step S33. Then, the FM screening image creation processing unit 5 newly generates a uniform random number to determine the arrangement pixel coordinates (m, n), and steps S33 to S33 until the value of the required arrangement pixel number counter becomes “0”. The process of step S36 is repeated.

  In this way, by converting the pixels in the labyrinth pattern image by the value of the difference pixel number Bs, an FM screening image can be created for each conversion block.

  Next, the flow of the two-dimensional pixel fusion processing (first and second pixel fusion processing) in step S4 of FIG. 13 will be described with reference to FIG.

  First, in step S41, the pixel fusion processing unit 6 reads an FM screening image that is a processing target image from the memory 2 and performs a raster scan. Next, the pixel fusion processing unit 6 determines whether or not the first pixel pattern is detected from the “2 pixels × 2 pixels” pixel pattern obtained in step S41 (step S42).

  If it is determined that the first pixel pattern has been detected (step S42; Yes), the pixel fusion processing unit 6 generates a random number in step S43. In this example, a uniform random number in which all real numbers appear with the same probability in the interval from “0” to “1” is used as the random number.

  Next, in step S44, the pixel fusion processing unit 6 determines whether or not the value of the random number generated in step S43 is less than “0.5”. When the random value is 0.5 or more (step S44; No), the pixel fusion processing unit 6 performs the first operation on the first pixel pattern detected in step S42 as shown in FIG. 8C. The pixel fusion processing 100a is performed (step S45).

  If the random number value is less than 0.5 (step S44; Yes), the pixel fusion processing unit 6 applies the first pixel pattern detected in step S42 as shown in FIG. 8D. A first pixel fusion process 100b is performed (step S46).

  On the other hand, in step S42, when the pixel fusion processing unit 6 determines that the first pixel pattern is not detected (step S42; No), the process proceeds to step S47.

  In step S47, the pixel fusion processing unit 6 determines whether or not the second pixel pattern is detected from the “2 pixels × 2 pixels” pixel pattern obtained in step S41. If it is determined that the second pixel pattern has been detected (step S47; Yes), the pixel fusion processing unit 6 generates a uniform random number having a value from “0” to “1” (step S48).

  Next, the pixel fusion processing unit 6 determines whether or not the value of the random number generated in step S48 is less than “0.5” (step S49). When the random number value is 0.5 or more (step S49; No), the pixel fusion processing unit 6 performs the second operation on the second pixel pattern detected in step S47, as shown in FIG. 9C. The pixel fusion process 200a is performed (step S50).

  If the random number value is less than 0.5 (step S49; Yes), the pixel fusion processing unit 6 applies the second pixel pattern detected in step S47 as shown in FIG. 9D. A second pixel fusion process 200b is performed (step S51).

  On the other hand, when the pixel fusion processing unit 6 determines in step S47 that the second pixel pattern is not detected (step S47; No), the process proceeds to step S52.

  Next, in step S52, the pixel fusion processing unit 6 determines whether scanning for the FM screening image is completed. When the pixel fusion processing unit 6 determines that the scan has ended (step S52; Yes), the process proceeds to step S53. On the other hand, when the pixel fusion processing unit 6 determines that the scan is not completed (step S52; No), the process returns to step S41, and the scan for the FM screening image is continued.

  In step S53, the pixel fusion processing unit 6 determines whether or not the first and second pixel patterns cannot be detected from the processing target image. When the pixel fusion processing unit 6 determines that the first and second pixel patterns can no longer be detected (step S53; Yes), a series of processing ends. On the other hand, when the pixel fusion processing unit 6 determines that the first and second pixel patterns can be detected (step S53; No), the process returns to step S41.

  As described above, the pixel fusion processing unit 6 performs the one-dimensional pixel fusion processing by performing the two-dimensional pixel fusion processing on the FM screening image created by the FM screening image creation processing unit 5. To be screened and stored in the memory 2.

  Next, the flow of the one-dimensional pixel fusion process (third pixel fusion process) in step S5 of FIG. 13 will be described with reference to FIG.

  First, in step S61, the pixel fusion processing unit 6 reads a screening image created by the two-dimensional pixel fusion processing, which is a processing target image, from the memory 2, and performs raster scanning.

  Next, the pixel fusion processing unit 6 determines whether or not a third pixel pattern (0 °) in the 0 ° direction is detected from the pixel pattern obtained in step S61 (step S62). If it is determined that the third pixel pattern (0 °) has been detected (step S62; Yes), the pixel fusion processing unit 6 performs the third pixel pattern (0 °) as shown in FIG. Is subjected to the third pixel fusion processing (0 °) (step S63).

  On the other hand, when the pixel fusion processing unit 6 determines that the third pixel pattern (0 °) is not detected (step S62; No), the process proceeds to step S64.

  Next, in step S64, the pixel fusion processing unit 6 determines whether or not a third pixel pattern (45 °) in the 45 ° direction has been detected. When it is determined that the third pixel pattern (45 °) has been detected (step S64; Yes), the pixel fusion processing unit 6 performs the third pixel pattern (45 °) as shown in FIG. Then, a third pixel fusion process (45 °) is performed (step S65).

  On the other hand, when the pixel fusion processing unit 6 determines that the third pixel pattern (45 °) has not been detected (step S64; No), the process proceeds to step S66.

  Next, in step S66, the pixel fusion processing unit 6 determines whether a third pixel pattern (90 °) in the 90 ° direction is detected. When it is determined that the third pixel pattern (90 °) has been detected (step S66; Yes), the pixel fusion processing unit 6 performs the third pixel pattern (90 °) as shown in FIG. Then, a third pixel fusion process (90 °) is performed (Step S67).

  On the other hand, when the pixel fusion processing unit 6 determines that the third pixel pattern (90 °) is not detected (step S66; No), the process proceeds to step S68.

  Next, in step S68, the pixel fusion processing unit 6 determines whether or not a third pixel pattern (135 °) in the 135 ° direction has been detected. When it is determined that the third pixel pattern (135 °) has been detected (step S68; Yes), the pixel fusion processing unit 6 performs the third pixel pattern (135 °) as shown in FIG. Is subjected to the third pixel fusion process (135 °) (step S69).

  On the other hand, when the pixel fusion processing unit 6 determines that the third pixel pattern (135 °) is not detected (step S68; No), the process proceeds to step S70.

  Next, in step S70, the pixel fusion processing unit 6 determines whether a third pixel pattern (180 °) in the 180 ° direction has been detected. If it is determined that the third pixel pattern (180 °) has been detected (step S70; Yes), the pixel fusion processing unit 6 performs the third pixel pattern (180 °) as shown in FIG. Is subjected to a third pixel fusion process (180 °) (step S71).

  On the other hand, when the pixel fusion processing unit 6 determines that the third pixel pattern (180 °) is not detected (step S70; No), the process proceeds to step S72.

  Next, in step S72, the pixel fusion processing unit 6 determines whether or not a third pixel pattern (225 °) in the 225 ° direction has been detected. When it is determined that the third pixel pattern (225 °) has been detected (step S72; Yes), the pixel fusion processing unit 6 performs the third pixel pattern (225 °) as shown in FIG. Then, a third pixel fusion process (225 °) is performed (step S73).

  On the other hand, when the pixel fusion processing unit 6 determines that the third pixel pattern (225 °) is not detected (step S72; No), the process proceeds to step S74.

  Next, in step S74, the pixel fusion processing unit 6 determines whether or not a third pixel pattern (270 °) in the 270 ° direction has been detected. If it is determined that the third pixel pattern (270 °) has been detected (step S74; Yes), the pixel fusion processing unit 6 performs the third pixel pattern (270 °) as shown in FIG. Then, a third pixel fusion process (270 °) is performed (step S75).

  On the other hand, when the pixel fusion processing unit 6 determines that the third pixel pattern (270 °) is not detected (step S74; No), the process proceeds to step S76.

  Next, in step S76, the pixel fusion processing unit 6 determines whether or not a third pixel pattern (315 °) in the 315 ° direction has been detected. When it is determined that the third pixel pattern (315 °) has been detected (step S76; Yes), the pixel fusion processing unit 6 performs the third pixel pattern (315 °) as shown in FIG. 10 (i). Is subjected to the third pixel fusion processing (315 °) (step S77).

  On the other hand, when the pixel fusion processing unit 6 determines that the third pixel pattern (315 °) is not detected (step S76; No), the process proceeds to step S78.

  Next, in step S78, the pixel fusion processing unit 6 determines whether scanning for the screening image is completed. If the pixel fusion processing unit 6 determines that the scan has ended (step S78; Yes), the process proceeds to step S79. On the other hand, when the pixel fusion processing unit 6 determines that the scan has not ended (step S78; No), the process returns to step S61, and the pixel fusion processing unit 6 continues scanning the screening image.

  In step S79, the pixel fusion processing unit 6 determines whether or not the third pixel pattern cannot be detected from the processing target image. When the pixel fusion processing unit 6 determines that the third pixel pattern can no longer be detected (step S79; Yes), a series of processing ends. On the other hand, when the pixel fusion processing unit 6 determines that the third pixel pattern can be detected (step S79; No), the process returns to step S61.

  As described above, the pixel fusion processing unit 6 creates a hybrid screening image by performing the one-dimensional pixel fusion processing on the screening image created by performing the two-dimensional pixel fusion processing, and stores it in the memory 2. To do.

  As described above, the image conversion processing apparatus 1 according to the present embodiment can create a hybrid screening image suitable for a print image with dots smaller than halftone dots that are randomly distributed without periodicity. That is, in the image conversion processing device 1 according to the present embodiment, a hybrid screening image having the advantages of the conventional halftone dot and FM screening can be created.

  As described above, the image conversion processing device according to the present embodiment performs binarization processing for converting the input grayscale image, which is a multi-valued image, into the first binary image composed of black pixels and white pixels. Do. Next, the first black pixel number included in the converted first binary image is detected for each conversion block, and an error (conversion error) that occurs when the first black pixel number is detected is taken into consideration. Error variance processing for creating a second binary image is performed. Then, the number of black pixels included in the created second binary image is detected for each change block, and the difference between the detected number of black pixels and the number of black pixels in the labyrinth pattern image prepared in advance is indicated. FM screening image creation processing for creating an FM screening image based on the number of difference pixels is performed. Finally, first to third pixel fusion processing is performed on the created FM screening image, and pixel fusion processing for creating a hybrid screening image is performed.

  As described above, in the image conversion processing device according to the present embodiment, by performing the error dispersion processing in consideration of the conversion error generated in the binarization processing, the tone jump phenomenon that occurs between the conversion blocks, the conversion block Moire generated at the boundary can be reduced. Further, by arranging (converting) pixels corresponding to the number of difference pixels at random with respect to the labyrinth pattern image, it is possible to create an FM screening image that can suppress the occurrence of unevenness while suppressing randomization. Furthermore, by performing the pixel fusion process, isolated pixels can be converted into dots having a size suitable for the printing process, and can be converted into a cluster pattern having no periodicity.

  This makes it possible to improve the image quality, create a hybrid screening image that combines the advantages of being suitable for the printing process of the halftone image and easy to handle, and the high quality of the FM screening image. can do. In addition, image quality such as sharpness can be adjusted according to printing conditions and user requirements.

  In addition, although a part or all of said embodiment can be described also as the following additional remarks, it is not restricted to the following.

(Appendix 1)
An image conversion processing method for converting a multi-value image having multi-value gradation values into a hybrid screening image,
The multi-valued image is converted into a first binary image composed of black pixels and white pixels, and the number of first black pixels included in the first binary image is detected, and the first binary image A binarization step for detecting a conversion error that occurs during creation;
Based on the first number of black pixels and the conversion error, the first binary image is converted into a second binary image, and the second number of black pixels included in the second binary image is determined. An error variance step to detect;
Based on the difference between the detected number of second black pixels and the number of black pixels of a predetermined binary image made up of black pixels and white pixels prepared in advance, the number of the difference in the predetermined binary image An FM screening image creation step of converting the second binary image into an FM screening image by converting one pixel into the other pixel;
A predetermined pixel pattern formed by a plurality of adjacent pixels is detected from the created FM screening image, and a predetermined pixel fusion process is performed on the detected predetermined pixel pattern to create a hybrid screening image. And a pixel fusion step.

(Appendix 2)
The binarization step includes
Based on the resolution information of the multi-valued image, a conversion block corresponding to the size of the halftone cell of the multi-valued image, which is a creation unit of the FM screening image, is set for the multi-valued image,
The image conversion processing method according to attachment 1, wherein the first number of black pixels in the conversion block is detected.

(Appendix 3)
The image conversion processing method according to appendix 2, wherein the error distribution step distributes a conversion error in the predetermined conversion block to surrounding conversion blocks adjacent to the conversion block.

(Appendix 4)
The predetermined binary image is a labyrinth pattern image composed of only straight lines in the horizontal direction and the vertical direction by continuous black pixels and white pixels in the image, and the same number of the black pixels and the white pixels. The image conversion processing method according to 1, 2 or 3.

(Appendix 5)
The FM screening image creation step includes:
Set horizontal and vertical coordinates for all pixels in the predetermined binary image;
The image conversion processing method according to any one of appendices 1 to 4, wherein the FM screening image is created by converting a pixel at a coordinate position determined based on a random number generated corresponding to the coordinate.

(Appendix 6)
The FM screening image creation step includes:
When the difference is a positive number and the pixel at the coordinate position in the predetermined binary image is a white pixel, a black pixel increasing process is performed to convert the white pixel to a black pixel,
When the difference is a negative number and the pixel at the coordinate position in the predetermined binary image is a black pixel, a black pixel reduction process is performed to convert the black pixel into a white pixel,
The image conversion processing method according to appendix 5, wherein any one of the black pixel increasing process and the black pixel decreasing process is performed by the number of the differences.

(Appendix 7)
The pixel fusion step includes:
From the created FM screening image, the predetermined pixel pattern that is formed by a plurality of adjacent pixels and in which the black pixels and the white pixels are distributed at intervals of one pixel is detected.
The image conversion according to any one of appendices 1 to 6, wherein the pixel fusion processing is performed to exchange the black pixels and the white pixels so that the black pixels in the detected predetermined pixel pattern are continuously distributed. Processing method.

(Appendix 8)
The pixel fusion step includes:
A pixel pattern in which black pixels and white pixels are distributed at intervals of one pixel is detected from a pixel pattern formed by four adjacent pixels of “2 pixels × 2 pixels”, and the black pattern in the detected pixel pattern is detected. A two-dimensional pixel fusion process for exchanging the black pixels and the white pixels so that the pixels are continuously distributed;
A pixel pattern in which black pixels and white pixels are distributed at intervals of one pixel is detected from a pixel pattern formed by four pixels adjacent to each other in the angular direction for the image subjected to the two-dimensional pixel fusion processing. The image conversion processing method according to appendix 7, wherein one-dimensional pixel fusion processing is performed in which the black pixels and the white pixels are exchanged so that the black pixels in the pixel pattern are continuously distributed.

(Appendix 9)
The two-dimensional pixel fusion process
When the first pixel pattern in which the upper left and lower right pixels are black pixels and the upper right and lower left pixels are white pixels is detected, the lower right black pixels are replaced with the upper right or lower left white pixels. First pixel fusion processing to
When the second pixel pattern in which the upper left and lower right pixels are white pixels and the upper right and lower left pixels are black pixels is detected, the lower right white pixels and the upper right or lower left black pixels are exchanged. And performing a second pixel fusion process
The image conversion processing method according to appendix 8, wherein the first and second pixel fusion processes are alternately performed until the first and second pixel patterns are no longer detected.

(Appendix 10)
The one-dimensional pixel fusion process is:
When a third pixel pattern composed of four pixels adjacent to each other in the angular direction and arranged in the form of white pixels, black pixels, white pixels, and black pixels is detected, the third pixel pattern is converted into a white pixel and a white pixel. -Perform a third pixel fusion process to convert to a pixel pattern lined up like black pixels / black pixels,
The image conversion processing method according to appendix 8 or 9, wherein the third pixel fusion processing is sequentially performed at 45 ° intervals until the third pixel pattern is not detected.

(Appendix 11)
The image conversion processing method according to appendix 8, 9 or 10, wherein in the one-dimensional pixel fusion processing, the third pixel fusion processing is performed at intervals of 45 ° from 0 ° to 360 °.

(Appendix 12)
A program for causing a computer apparatus to execute an image conversion processing method for converting a multi-value image having multi-value gradation values into a hybrid screening image,
The multi-valued image is converted into a first binary image composed of black pixels and white pixels, and the number of first black pixels included in the first binary image is detected, and the first binary image A binarization step for detecting a conversion error that occurs during creation;
Based on the first number of black pixels and the conversion error, the first binary image is converted into a second binary image, and the second number of black pixels included in the second binary image is determined. An error variance step to detect;
Based on the difference between the detected number of second black pixels and the number of black pixels of a predetermined binary image made up of black pixels and white pixels prepared in advance, the number of the difference in the predetermined binary image An FM screening image creation step of converting the second binary image into an FM screening image by converting one pixel into the other pixel;
A predetermined pixel pattern formed by a plurality of adjacent pixels is detected from the created FM screening image, and a predetermined pixel fusion process is performed on the detected predetermined pixel pattern to create a hybrid screening image. A program that causes a computer apparatus to execute an image conversion processing method including a pixel fusion step.

(Appendix 13)
An image conversion processing device for converting a multi-value image having multi-value gradation values into a hybrid screening image,
The multi-valued image is converted into a first binary image composed of black pixels and white pixels, and the number of first black pixels included in the first binary image is detected, and the first binary image A binarization processing unit that detects a conversion error that occurs during creation;
Based on the first number of black pixels and the conversion error, the first binary image is converted into a second binary image, and the second number of black pixels included in the second binary image is determined. An error variance processing unit to detect;
Based on the difference between the detected number of second black pixels and the number of black pixels of a predetermined binary image made up of black pixels and white pixels prepared in advance, the number of the difference in the predetermined binary image An FM screening image creation processing unit that converts the second binary image into an FM screening image by converting one pixel into the other pixel;
A predetermined pixel pattern formed by a plurality of adjacent pixels is detected from the created FM screening image, and a predetermined pixel fusion process is performed on the detected predetermined pixel pattern to create a hybrid screening image. An image conversion processing device comprising a pixel fusion processing unit.

DESCRIPTION OF SYMBOLS 1 Image conversion processing apparatus 2 Memory 3 Binarization process part 4 Error dispersion | distribution process part 5 FM screening image creation process part 6 Pixel fusion process part 10 Input multi-value image data 20 Output binary image data

Claims (10)

An image conversion processing method for converting a multi-value image having multi-value gradation values into a hybrid screening image,
The multi-valued image is converted into a first binary image composed of black pixels and white pixels, and the number of first black pixels included in the first binary image is detected, and the first binary image A binarization step for detecting a conversion error that occurs during creation;
Based on the first number of black pixels and the conversion error, the first binary image is converted into a second binary image, and the second number of black pixels included in the second binary image is determined. An error variance step to detect;
Based on the difference between the detected number of second black pixels and the number of black pixels of a predetermined binary image made up of black pixels and white pixels prepared in advance, the number of the difference in the predetermined binary image An FM screening image creation step of converting the second binary image into an FM screening image by converting one pixel into the other pixel;
A predetermined pixel pattern formed by a plurality of adjacent pixels is detected from the created FM screening image, and a predetermined pixel fusion process is performed on the detected predetermined pixel pattern to create a hybrid screening image. An image conversion processing method comprising: a pixel fusion step. The binarization step includes
Based on the resolution information of the multi-valued image, a conversion block corresponding to the size of the halftone cell of the multi-valued image, which is a creation unit of the FM screening image, is set for the multi-valued image,
The image conversion processing method according to claim 1, wherein the first number of black pixels in the conversion block is detected.   The image conversion processing method according to claim 2, wherein the error distribution step distributes a conversion error in the predetermined conversion block to surrounding conversion blocks adjacent to the conversion block.   The predetermined binary image is a labyrinth pattern image composed of only a straight line in a horizontal direction and a vertical direction by continuous black pixels and white pixels in the image, and the same number of the black pixels and the white pixels. The image conversion processing method according to claim 1, 2, or 3. The FM screening image creation step includes:
Set horizontal and vertical coordinates for all pixels in the predetermined binary image;
5. The image conversion process according to claim 1, wherein the FM screening image is created by converting a pixel at a coordinate position determined based on a random number generated corresponding to the coordinate. Method. The FM screening image creation step includes:
When the difference is a positive number and the pixel at the coordinate position in the predetermined binary image is a white pixel, a black pixel increasing process is performed to convert the white pixel to a black pixel,
When the difference is a negative number and the pixel at the coordinate position in the predetermined binary image is a black pixel, a black pixel reduction process is performed to convert the black pixel into a white pixel,
6. The image conversion processing method according to claim 5, wherein one of the black pixel increasing process and the black pixel decreasing process is performed by the number of the differences. The pixel fusion step includes:
From the created FM screening image, the predetermined pixel pattern that is formed by a plurality of adjacent pixels and in which the black pixels and the white pixels are distributed at intervals of one pixel is detected.
7. The pixel fusion process for exchanging the black pixels and the white pixels with each other so that the black pixels in the detected predetermined pixel pattern are continuously distributed. An image conversion processing method according to claim 1. The pixel fusion step includes:
A pixel pattern in which black pixels and white pixels are distributed at intervals of one pixel is detected from a pixel pattern formed by four adjacent pixels of “2 pixels × 2 pixels”, and the black pattern in the detected pixel pattern is detected. A two-dimensional pixel fusion process for exchanging the black pixels and the white pixels so that the pixels are continuously distributed;
A pixel pattern in which black pixels and white pixels are distributed at intervals of one pixel is detected from a pixel pattern formed by four pixels adjacent to each other in the angular direction for the image subjected to the two-dimensional pixel fusion processing. The image conversion process according to claim 7, further comprising: performing one-dimensional pixel fusion processing for exchanging the black pixels and the white pixels so that the black pixels in the pixel pattern are continuously distributed. Method. A program for causing a computer apparatus to execute an image conversion processing method for converting a multi-value image having multi-value gradation values into a hybrid screening image,
The multi-valued image is converted into a first binary image composed of black pixels and white pixels, and the number of first black pixels included in the first binary image is detected, and the first binary image A binarization step for detecting a conversion error that occurs during creation;
Based on the first number of black pixels and the conversion error, the first binary image is converted into a second binary image, and the second number of black pixels included in the second binary image is determined. An error variance step to detect;
Based on the difference between the detected number of second black pixels and the number of black pixels of a predetermined binary image made up of black pixels and white pixels prepared in advance, the number of the difference in the predetermined binary image An FM screening image creation step of converting the second binary image into an FM screening image by converting one pixel into the other pixel;
A predetermined pixel pattern formed by a plurality of adjacent pixels is detected from the created FM screening image, and a predetermined pixel fusion process is performed on the detected predetermined pixel pattern to create a hybrid screening image. A computer program causing a computer apparatus to execute an image conversion processing method including a pixel fusion step. An image conversion processing device for converting a multi-value image having multi-value gradation values into a hybrid screening image,
The multi-valued image is converted into a first binary image composed of black pixels and white pixels, and the number of first black pixels included in the first binary image is detected, and the first binary image A binarization processing unit that detects a conversion error that occurs during creation;
Based on the first number of black pixels and the conversion error, the first binary image is converted into a second binary image, and the second number of black pixels included in the second binary image is determined. An error variance processing unit to detect;
Based on the difference between the detected number of second black pixels and the number of black pixels of a predetermined binary image made up of black pixels and white pixels prepared in advance, the number of the difference in the predetermined binary image An FM screening image creation processing unit that converts the second binary image into an FM screening image by converting one pixel into the other pixel;
A predetermined pixel pattern formed by a plurality of adjacent pixels is detected from the created FM screening image, and a predetermined pixel fusion process is performed on the detected predetermined pixel pattern to create a hybrid screening image. An image conversion processing device comprising: a pixel fusion processing unit.