Planning support device, information processing method, and program

Abstract

The planning assistance device comprises: an image generation unit that predicts, on the basis of an image to be printed, a plate-making condition of a printing plate, and a printing condition, ink dots of each color on a printed matter obtained by printing the image to be printed on the basis of the plate-making condition and the printing condition; generating a prediction image of each color represented by the ink dots; and an image synthesis unit that synthesizes a synthesized prediction image on the basis of the plurality of color prediction images.

Description

Technical Field

[0001] This invention relates to a planning and formulation auxiliary device, an information processing method, and a program. Background Technology

[0002] For example, Patent Document 1 proposes a printing system that allows for easy control of plate-making and printing conditions for obtaining high-quality prints in flexographic printing.

[0003] Existing technical documents

[0004] Patent documents

[0005] Patent Document 1: Japanese Patent Application Publication No. 2016-049768 Summary of the Invention

[0006] The problem the invention aims to solve

[0007] However, in the printing system described in Patent Document 1, it is necessary to actually print the original image through the printing unit and determine the characteristics of the original image from the evaluation area of ​​the original image. Therefore, in terms of repeatedly performing actual printing until the desired plate-making and printing conditions are determined, there is no change compared to conventional condition adjustments.

[0008] The present invention was made in view of the above-mentioned problems, and its object is to provide a planning and formulation auxiliary device that can obtain appropriate color prediction images and composite prediction images even without a printing process, an information processing method and a program performed by the device.

[0009] Solution for solving the problem

[0010] [1]

[0011] A planning and formulation auxiliary device, comprising:

[0012] An image generation unit, based on a printable object image, plate-making conditions, and printing conditions, predicts ink dots of each color on a printed material obtained by printing the printable object image based on the plate-making conditions and printing conditions, and generates predicted images of each color represented by the ink dots; and

[0013] The image synthesis unit synthesizes a composite prediction image based on multiple color prediction images.

[0014] [2]

[0015] According to the planning and development of auxiliary devices described in [1],

[0016] It also includes a prediction unit that, based on the plate-making conditions and the printing conditions, outputs correction information for correcting each ink dot contained in the printed object image.

[0017] The image generation unit generates color prediction images represented by the corrected ink dots by correcting the ink dots in the printed object image based on the correction information.

[0018] [3]

[0019] According to the planning and development of auxiliary devices described in [2],

[0020] The correction information is at least one of the following: whether it can be formed, size, and shape.

[0021] [4]

[0022] The auxiliary device is designed and formulated according to any one of [1] to [3].

[0023] A closed region formed by the connection of multiple adjacent ink dots is identified as a void ink dot.

[0024] The image generation unit corrects the gap ink dots contained in the printed object image based on the plate-making conditions and printing conditions of the printing plate to generate corrected gap ink dots, thereby generating the predicted images of each color.

[0025] [5]

[0026] According to the planning and development of auxiliary devices described in [4],

[0027] The correction information used to correct the void ink dots based on the plate-making conditions and the printing conditions is at least one of the following: whether it can be formed, its size, and its shape.

[0028] [6]

[0029] The auxiliary device is designed and formulated according to any one of [1] to [5].

[0030] It has a display control unit that displays the synthetic prediction image and the printed object image in a way that allows for comparison.

[0031] [7]

[0032] The auxiliary device is designed and formulated according to any one of [1] to [6].

[0033] It has a display control unit that displays and controls the plate-making conditions and / or printing conditions related to the synthetic prediction image.

[0034] [8]

[0035] The auxiliary device is designed and formulated according to any one of [1] to [7].

[0036] It also has a condition generation unit that changes the plate-making conditions and / or the printing conditions if the information related to the ink dots in the predicted color images does not meet the predefined conditions.

[0037] [9]

[0038] The auxiliary device is designed and formulated according to any one of [1] to [8].

[0039] It includes a correction unit that handles corrections to each color prediction image based on user input.

[0040] The image generation unit generates corrected color prediction images based on the corrections.

[0041] The image synthesis unit synthesizes a corrected and synthesized prediction image based on the corrected color prediction images.

[0042]

[10]

[0043] An information processing method involves planning and implementing auxiliary devices to perform the following steps:

[0044] The image generation step, based on the image of the printing object, the plate-making conditions of the printing plate, and the printing conditions, predicts the ink dots of each color on the printed material obtained by printing the image of the printing object based on the plate-making conditions and the printing conditions, and generates predicted images of each color represented by the ink dots; and

[0045] The image synthesis step involves synthesizing a composite prediction image based on multiple color prediction images.

[0046]

[11]

[0047] A program that enables a planning and development support device to perform the following steps:

[0048] The image generation step, based on the image of the printing object, the plate-making conditions of the printing plate, and the printing conditions, predicts the ink dots of each color on the printed material obtained by printing the image of the printing object based on the plate-making conditions and the printing conditions, and generates predicted images of each color represented by the ink dots; and

[0049] The image synthesis step involves synthesizing a composite prediction image based on multiple color prediction images.

[0050] The effects of the invention

[0051] According to the present invention, a planning and formulation aid device, an information processing method and a program for obtaining appropriate color prediction images and composite prediction images even without a printing process can be provided. Attached Figure Description

[0052] Figure 1 This is a diagram illustrating the system structure of this embodiment.

[0053] Figure 2A This is a schematic diagram of the auxiliary device for planning and formulating this embodiment.

[0054] Figure 2B This is a schematic diagram illustrating an example of the data structure of printed data.

[0055] Figure 2C This is a schematic diagram illustrating an example of a predictive model.

[0056] Figure 2D This diagram illustrates the concept of transforming ink dots in a split image into corrected ink dots in each color prediction image.

[0057] Figure 2E This diagram illustrates the concept of transforming gap ink dots in a split image into corrected gap ink dots in each color prediction image.

[0058] Figure 2F This is an example of representing a page-separated image using ink dots.

[0059] Figure 2G This is an example of how to represent each color prediction image by correcting ink dots.

[0060] Figure 2H This is a diagram showing a specific example of printing data related to ink dots.

[0061] Figure 2I This is a diagram showing a specific example of printing data related to pore ink dots.

[0062] Figure 2J This is a graph showing the results of using the pre-defined model to output information related to correcting ink dots and correcting void ink dots.

[0063] Figure 3A It is a structural diagram showing the relationship between the calibration curve and the printing curve.

[0064] Figure 3B It is a schematic diagram showing how the tonal expression occurs in the case of a smooth decline.

[0065] Figure 3C This is a schematic diagram showing how tonal representations with hard edges can be observed.

[0066] Figure 3DThis is a schematic diagram showing how the tonal representation is inverted.

[0067] Figure 3E This is a schematic diagram showing the overlap of ink dots and correction ink dots in the split image.

[0068] Figure 4A This is a schematic diagram showing a typical process up to obtaining a synthetic predicted image based on the image of the printed object.

[0069] Figure 4B This is an example of a screen displayed on a display device.

[0070] Figure 5A This is a flowchart illustrating an example of the processing performed by the planning and formulation device of this embodiment.

[0071] Figure 5B This is a flowchart illustrating an example of the processing performed by the planning and formulation device of this embodiment. Detailed Implementation

[0072] Hereinafter, embodiments of the present invention (hereinafter referred to as "this embodiment") will be described in detail, but the present invention is not limited thereto, and various modifications can be made without departing from its spirit.

[0073] 1. Planning and developing auxiliary devices

[0074] The planning and formulation assistance device of this embodiment includes: an image generation unit that predicts ink dots of each color on a printed matter obtained by printing the image of the printing object based on the plate-making conditions and the printing conditions, and generates a color prediction image represented by the ink dots; and an image synthesis unit that synthesizes a composite prediction image based on a plurality of the color prediction images.

[0075] Alternatively, the planning and formulation assistance device of this embodiment may include: an image generation unit that, based on the plate-making conditions and printing conditions of the printing plate, corrects the ink dots contained in the plate-separation image representing the image of the printed object through ink dots of each color, thereby generating a color prediction image represented by the corrected ink dots; and an image synthesis unit that synthesizes a composite prediction image based on multiple color prediction images.

[0076] This invention can be applied to offset printing, gravure printing, and flexographic printing. The following description uses flexographic printing as an example to illustrate this embodiment, but this embodiment is not limited to this and can also be applied to offset printing and gravure printing.

[0077] Figure 4AThis diagram illustrates a typical process up to obtaining a synthesized predicted image based on a printable object image. (See attached diagram.) Figure 4A As shown, in conventional flexographic printing processes, before mass production printing, the design of digital images (hereinafter also referred to as "printing object images") and color management related to quality standards based on printing certification systems that comply with international standard ISO 12647 (such as Fogra's PSO certification in Germany, Idealliance's G7 certification in the United States, Japan's JapanColor certification, etc.) are repeatedly performed during the manufacture and printing of flexographic printing plates. This is done to search for optimal color density conditions, such as ink density and opacity, in order to obtain plate-making / printing conditions that achieve the target color density value. Then, in order to obtain plate-making / printing conditions that achieve tonal stability, the manufacture and printing of flexographic printing plates are repeated to search for tonal conditions, such as ink dots, that can be printed stably.

[0078] In addition, such as Figure 2D As shown, a "dot" is a halftone dot used to represent the varying shades of ink in a printed image. Dots are also called halftone dots. For example, when observing a print made in flexographic printing using C (cyan) under a microscope, several small printed dots can be observed (see reference). Figure 2F This point is called an ink dot, and the collection of ink dots represents the printed matter. Generally speaking, the smaller the ink dot and the lower the number per unit area, the lighter the color appears; the larger the ink dot and the higher the number per unit area, the darker the color appears. Printed matter with varying shades is reproduced by combining the size and number of ink dots per unit area. In addition, when reproducing the tonal gradations of photographs, patterns (monochrome, color), etc., through printing, shades can also be represented by the ratio of dot size, number per unit area, to the white background of the printing paper.

[0079] In addition, information related to ink dots can include "line count," which indicates how many ink dots exist in a 1-inch width; "roundness," "slant," which indicates the shape of the ink dot; "size," which is used to represent the density; and "angle," which is related to the generation of moiré fringes.

[0080] In addition, such as Figure 2E As shown, a "gap dot" refers to a closed area formed by multiple adjacent ink dots. Information related to gap dots can include the "line count," which indicates how many gap dots exist in a 1-inch width; the "length-to-width ratio" when fitting an ellipse to a gap dot; and the "size," which represents the density.

[0081] In conventional flexographic printing processes, the search for color density and tonal conditions described above does not use digital images of the printing object, but rather test patterns suitable for evaluating each condition. Flexographic printing plates with test patterns formed under various plate-making conditions are created, and these plates are printed under various printing conditions, thereby searching for appropriate plate-making and printing conditions. This allows for the search of plate-making and printing conditions suitable for the ink, plate-making equipment, or printing equipment used.

[0082] Next, as Figure 4A As shown, a vector image in digital design, for example, has color layer information for each color of ink such as cyan, magenta, yellow, and black. Here, a vector image in digital design refers to an image form that numerates multiple points and represents two-dimensional information such as images and characters using curves derived from numerical formulas.

[0083] The pixel representation of a digital image differs from the representation achieved through the embossing of a printed plate. Therefore, as... Figure 4A As shown, a vector image containing information for each color layer is subjected to RIP processing, and the color density and ink dot size are determined according to each color to create a raster image for each color. In other words, in this process, the state in the digital image that represents two-dimensional information such as images and characters through curves derived from numerical formulas is transformed to match the form represented by the raised and recessed ink dots of a printing plate, for constructing an image through pixels. Furthermore, in this embodiment, a vector image is described as the printing object image, but in other embodiments, the aforementioned raster image can also be used as the printing object image.

[0084] like Figure 4A As shown, each color raster image is created based on each color of the ink used, such as cyan, magenta, yellow, and black. Therefore, even when the vector images are the same, each color raster image is generated according to the ink group used. For example, when using four colors of ink, raster images of each of the four colors are created; when using seven colors of ink, raster images of each of the seven colors are created. Furthermore, in this embodiment, the information related to the raster image of each color is also referred to as a "plate image".

[0085] Each color's raster image becomes the data used to create a flexographic printing plate for that color. In other words, during the plate-making process, the relief and indentation of the flexographic printing plate are formed, enabling the printing of ink dots represented by each color's raster image.

[0086] Then, flexographic printing plates for each ink color are created based on the raster image of each color. These flexographic printing plates are used for multicolor printing to obtain the printed material.Figure 4A At this point, flexographic printing plates for each ink color are also created based on the raster images for each color. Multicolor printing tests are conducted using these flexographic plates to determine the conditions under which reproducible printing is possible. For example, even when flexographic printing plates are created based on the raster images for each color, depending on factors such as pressing pressure and ink characteristics, it is not always possible to reproduce ink dots on the recording medium that are exactly the same as those in the raster images for each color. Specifically, sometimes fewer ink dots are actually transferred to the recording medium than the number of ink dots in each color's raster image. In such cases, the raster images for each color are modified to increase the overall size of the ink dots or to change the arrangement of the ink dots. Then, multicolor printing tests are conducted again to search for conditions under which reproducible printing is possible.

[0087] Through the above steps, the flexographic printing plate and its printing conditions for mass production are determined in the traditional flexographic printing process. The determination of plate-making and printing conditions largely depends on the skill of the operator, and multiple tests are required before finalizing the conditions. Therefore, the entire process can sometimes take more than a year.

[0088] One reason for requiring such testing is that during printing, the ink dots deform as the printing plate is pressed against the printing substrate. For example, when printing with a printing plate having raised or recessed areas corresponding to the ink dots, the raised or recessed areas deform depending on the printing conditions, resulting in ink dots of different sizes, shapes, etc., than the raised or recessed areas of the printing plate. This phenomenon is particularly likely to occur when the printing plate is made of resin such as rubber.

[0089] Therefore, the planning and formulation auxiliary device of this embodiment corrects the ink dots of the plate image (raster image) that represents the printed object image by ink dots of each color in the above process based on the plate-making conditions and printing conditions of the printing plate. In this way, it predicts in advance the color prediction images that are represented by the corrected ink dots when the printing plate made with the plate-making conditions is printed according to the printing conditions. Based on the color prediction images, they are synthesized to obtain a composite prediction image.

[0090] Alternatively, if the relationship between the information related to the ink dots in the plate-making image and the information related to the corrected ink dots in each color prediction image does not meet the specified target index, the plate-making conditions and / or printing conditions can be changed to repeat the same process, thereby searching for the optimal solution for each color prediction image for each color.

[0091] Furthermore, if the reproduction accuracy of the synthesized predicted image and the printed object image is high, meaning it is determined that the printed object image can be printed with good reproducibility, then any one of the plate-making conditions, printing conditions, and the sub-images for each color obtained through the search is output as recommendation information. On the other hand, if the reproduction accuracy of the synthesized predicted image and the printed object image is low, meaning it is determined that the printed object image has not been reproduced, then the plate-making conditions and / or printing conditions can be changed to regenerate new sub-images for each color, and the same process can be repeated to search for the optimal solution for each color's predicted image.

[0092] According to the planning and development of the auxiliary device in this embodiment, even without the printing process, it is possible to obtain the target color prediction image or composite prediction image. In addition, as an information processing method, compared with the method of directly physically calculating the deformation phenomenon of the unevenness on the printing substrate based on the shape or hardness of the printing plate, as well as the amount of ink or the pressure, and calculating the size or dimension of the ink dot based on the amount of deformation, the processing load in the information processing device can be significantly reduced.

[0093] The structure of the planning and formulation auxiliary device for this embodiment will be described in detail below.

[0094] 1.1 Hardware Structure

[0095] Figure 1 An example of this system is shown. This system is a planning and formulation assistance system that displays recommendation information for raster images of each color. The planning and formulation assistance device 100 of this embodiment can also be connected to the plate-making device 200 and the printing device 300 via a network. In this embodiment, the plate-making device 200 can be a flexographic printing plate manufacturing device, and the printing device 300 can be a printing press that performs a flexographic printing method. Conventionally known devices can be used as these plate-making devices 200 and printing devices 300.

[0096] Figure 2A This diagram shows a schematic structural diagram of the planning and formulation assistance device 100 according to this embodiment. The planning and formulation assistance device in this embodiment can also be a general-purpose computer such as a personal computer or a server. Figure 2A As shown, the planning and design auxiliary device 100 includes, for example, a processor 110, a communication interface 120, an input / output interface 130, a memory 140, a storage device 150, and one or more buses 160 for interconnecting these components.

[0097] The processor 110 is not limited. For example, the various processes, functions or methods disclosed in each embodiment can be implemented by one or more central processing units (CPUs), microprocessors (including MPUs, graphics processing units (GPUs), processor cores, multiprocessors, application-specific integrated circuits (ASICs), application-specific integrated circuits (FPGAs), integrated circuit (IC) chips, LSs), or dedicated circuits.

[0098] The communication interface 120 transmits and receives various types of data with other devices via a network. This communication can be performed via either wired or wireless means, and any communication protocol can be used as long as mutual communication is possible. For example, the communication interface 120 can be installed as hardware such as a network adapter, various communication software, or a combination thereof.

[0099] The input / output interface 130 includes input devices for inputting various operations and output devices for outputting processing results. For example, the input / output interface 130 may also include information input devices such as a keyboard, mouse, and touch panel, and information output devices such as a display device 131. Furthermore, the planning and design auxiliary device 100 can accept specified inputs and execute specified outputs by connecting to the external input / output interface 130.

[0100] For example, the planning and design auxiliary device 100 can also output recommended conditions for plate making and printing to the plate making device 200 and the printing device 300.

[0101] Memory 140 temporarily stores programs loaded from storage device 150 and provides a working area for processor 110. Various data generated during program execution by processor 110 are also temporarily stored in memory 140. Memory 140 can be a high-speed random access memory such as DRAM, SRAM, DDR RAM, or other random access solid-state storage devices, or a combination thereof.

[0102] Storage device 150 stores programs, functional units, and various data. For example, storage device 150 can store printed data 151.

[0103] Printing data 151 records data obtained from printing based on the plate-making image under specified plate-making and printing conditions. Specifically, it can also be as follows: Figure 2B As shown, a correspondence is established and recorded for the printing ID that uniquely identifies the printing conditions, information related to the printed plate image, information related to the plate-making conditions of the flexographic printing plate used for printing the digital image, information related to the printing conditions of the printing plate, and information related to the printed image. The printing data 151 can be used as learning data for various models used in this embodiment, or as data for constructing algorithms.

[0104] Alternatively, storage device 150 may be one or more disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid-state storage devices, or a combination thereof. Other examples of storage device 150 include one or more storage devices disposed remotely from processor 110.

[0105] 1.2. Software Structure

[0106] Processor 110 executes the code contained in the program stored in storage device 150, or the processing, functions, or methods implemented by instructions. For example... Figure 2A As shown, the processor 110 of this embodiment can also function as a RIP processing unit 111, a learning unit 112, a prediction unit 1121, an image generation unit 113, an image synthesis unit 114, an evaluation unit 115, a condition generation unit 116, a correction unit 117, and a display control unit 118. Each functional unit will be described below.

[0107] The RIP processing unit 111 generates a partition image represented by ink dots of each color based on the printed object image. More specifically, the RIP processing unit 111 can perform RIP (Raster Image Processor) processing, which transforms a printed object image in vector form or the like, created on a computer, into a partition image in raster form that can be interpreted by a printing press or printer. The resulting partition image is an image obtained by color decomposing the printed object image based on ink dots of ink colors such as cyan, magenta, yellow, and black, and is as follows: Figure 4A The image obtained by transforming continuous tonal image data into a dot pattern, as shown.

[0108] Furthermore, when the vector image is a photograph acquired by a camera device equipped with a CMOS sensor or the like, the printing object image becomes an image that has been pre-acquired by the CMOS sensor and has undergone CMYK color segmentation. In this case, since the printing object image is image information in a state with CMYK color layer information, it can be directly used as a color-decomposed image. Thus, when the printing object image is image information in a state with CMYK color layer information, color decomposition is not required, and the RIP processing unit 111 can obtain the plate separation image simply by performing the process of converting the image data of the continuous tones of each color image into ink dot patterns.

[0109] The RIP processing unit 111 can perform color decomposition processing based on information related to the ink used. Regarding the colors of the RGB pixels of the printing object image, which is a digital image, the data is processed in advance by converting it to CMYK on a vector image. Furthermore, the colors of the pigments, such as the ink used, must also be considered. Therefore, even if the printing object image is simply decomposed into RGB, the color of the ink used will not be immediately apparent. In particular, in digital images such as those on displays, the three colors RGB and brightness are typically used primarily for representation, but in the case of ink, four colors—cyan, magenta, yellow, and black—can usually be used, or lighter shades such as light cyan, light magenta, and light yellow can be added to create a seven-color ink, or special colors such as orange, green, and purple can be used to create a multi-color structure. Therefore, the RIP processing unit 111 can also perform color decomposition processing on the printing object image based on the pre-acquired information related to the ink colors, obtaining a separate image for each color of the ink used.

[0110] Furthermore, the RIP processing unit 111 can utilize the rasterization process on a typical computer workstation. The RIP processing unit 111 can also perform the process of transforming continuous-tone image data into an ink dot pattern based on data that establishes a correspondence between digital color density and the ink dot structure of the printing plate. For example, the RIP processing unit 111 can predict and evaluate the color density for each pixel of one or more pixels in a printing object image of each color, and obtain a plate image representing the printing object image through ink dots of each color by transforming the pixel into the ink dot structure of the printing plate in a rasterization process that reflects the color density of that pixel.

[0111] The learning department 112 creates a predictive model for correcting each ink dot contained in a plate-separation image when printing is performed under specified printing conditions using a flexographic printing plate manufactured under specified plate-making conditions.

[0112] Here, the "predictive model" can also refer to information used to transform the ink dots contained in the plate-making image based on specified plate-making and printing conditions. For example, such as... Figure 2D , Figure 2E As shown, it is possible to derive a predictive model for correcting ink dots by considering whether ink dots can be formed when printing each ink dot contained in a plate-making image under specified printing conditions using a flexographic printing plate manufactured under specified plate-making conditions, the degree of change in the size of the ink dots, and the deformation of the shape of the ink dots.

[0113] In addition, the prediction model can be related to void ink dots, or it can be derived from whether void ink dots can be formed when printing each void ink dot contained in the plate image under specified printing conditions using a flexographic printing plate manufactured under specified plate-making conditions, the degree of change in the size of void ink dots, the deformation of the shape of void ink dots, etc.

[0114] The prediction model in this embodiment can also be as follows: Figure 2C As shown, when information related to each ink dot or gap ink dot contained in the plate image, plate-making conditions, and printing conditions are input, the model can output information related to whether correction ink dots or correction gap ink dots can be formed, their size, and shape.

[0115] As Figure 2B A portion of the printed data shown is in Figure 2H The example shown establishes corresponding printing data by using the ID of an ink dot to uniquely identify a printing plate, the plate-making conditions, the printing conditions of the printed matter using the plate-making conditions, and information related to the printed ink dot as one of the printing qualities. Furthermore, in Figure 2I The example shown is a specific example of establishing corresponding printing data, which includes the ID used to uniquely identify a gap ink dot in a plate image, the plate-making conditions of the printing plate, the printing conditions of the printed matter using the printing plate made under the plate-making conditions, and information related to the printed gap ink dot as one of the printing qualities. Figure 2H and Figure 2I The printed data shown can also be used as learning data.

[0116] Figure 2D Due to layout constraints, only four examples of printing results are shown, but this embodiment uses data from 3000 examples of ink dots and voids. Furthermore, the printing data or training data used are not limited to this and can be much larger.

[0117] In addition, Figure 2H and Figure 2I In this document, the abbreviations refer to the following meanings. Furthermore, the terms Midpoint, Slope at Midpoint, Minimum Dot, Highlight Shape, Shadow Shape, and Keep 0% to are incorporated herein by reference to US11,575,806 B2.

[0118] •MC: Refers to the type of microcell. Furthermore, a microcell refers to a portion that improves ink transferability by creating a raised or recessed surface through the addition of fine patterns that do not affect the overall pattern.

[0119] • Ratio @ 50%: refers to the midpoint.

[0120] • DGC type: refers to the slope at the midpoint.

[0121] min.dot: refers to the smallest ink dot.

[0122] •h: refers to the highlight shape.

[0123] •s: refers to the shadow shape.

[0124] ·k: refers to Keep 0% to.

[0125] In addition, Figure 2H as well as Figure 2I In the process, regarding the presence or absence of ink dots or voids, on test pattern printouts containing multiple ink dots of the same shape, the case where the printout can form more than 50% of the ink dots is marked as 0, and the case where the printout can only form less than 50% of the ink dots is marked as ×. Furthermore, regarding size Δ, shape, and tilt, these values ​​are measured for each ink dot within the test pattern printout containing multiple ink dots of the same shape, their average value is calculated, and recorded.

[0126] Will Figure 2H as well as Figure 2I The 3000 examples provided were used as training data to create... Figure 2C The prediction models shown are as follows. Specifically, using the training data, prediction models were constructed using three methods: PLS (Partial Least Squares), SVR (Support Vector Regression), and RF (Random Forest). Furthermore, in the construction of each prediction model, hyperparameters that maximize generalization performance were selected and used. Specifically, as a metric for the model's generalization performance, the decision count R² was calculated based on the predicted values ​​obtained from 5-fold cross-validation (described later). For models obtained using all possible combinations of values ​​for each hyperparameter, the aforementioned R² was calculated, and the hyperparameter that maximized this value was selected.

[0127] Alternatively, during the learning process, some data can be transformed into simulation variables before being provided to machine learning. For example, for PET and OPP, which represent the materials of printing substrates, PET can be set to 0 and OPP to 1 as simulation variables. This allows non-numerical data categorized by type to be quantified, enabling the model to handle categorical data.

[0128] The prediction accuracy of each prediction model is evaluated using the coefficient of determination (R²). Specifically, in five-fold cross-validation, the training data is split into five parts, with four parts used as model-building data. Regression models are constructed using the aforementioned models. The remaining part of the training data is used as validation data to evaluate the predictive performance of the constructed regression models. There are five methods for selecting model-building data. Each model is used to predict on each validation data point, thus treating all data in the training data as "without external data for model building." This yields five R² values, and their average is used as the R²CV value. Furthermore, the final model is constructed using all training data and the determined hyperparameters.

[0129] Figure 2J The diagram illustrates how a prediction model, obtained as described above, is used to determine the size, shape, and whether the corrected ink dots and corrected gap ink dots are formed based on specified plate-making and printing conditions for the ink dots and gap ink dots in the plate-separation image.

[0130] As shown above, a predictive model can be constructed that outputs information related to the estimated correction ink dots or correction voids formed on the printed material, based on information related to ink dots or voids in the plate-making image, plate-making condition information representing the plate-making conditions of the printing plate, and printing condition information representing the printing conditions used to obtain the printed material from the printing plate manufactured under the plate-making conditions. Furthermore, it has been demonstrated that this predictive model possesses the specified accuracy and can output information related to appropriate correction ink dots or correction voids even without a printing process.

[0131] In addition, the following situation is also shown: In this prediction, it is not necessary to directly predict the deformation phenomenon of unevenness on the printing substrate. By taking into account the plate-making condition information that affects the shape and hardness of the printing plate, as well as the printing condition information that affects the ink volume and pressing strength, the determination of the deformation phenomenon of unevenness on the printing substrate is adopted. Thus, it is possible to predict information related to correcting ink dots or correcting gap ink dots.

[0132] also, Figure 2H~Figure 2J The specific example shown is one example used to illustrate the technical significance of the planning and formulation auxiliary device of this embodiment, and this embodiment is not limited to... Figure 2H~Figure 2J specific example.

[0133] For example, as a predictive model, there are no particular limitations; various other models can be used, including multiple regression analysis, ridge regression, partial least squares regression, multilayer perceptrons, neural networks such as CNNs (Convolutional Neural Networks) and RNNs (Recurrent Neural Networks), support vector machines using arbitrary kernel functions such as Gaussian kernels, random forests modeled as regression trees, models utilizing hidden Markov models, statistical models, probabilistic models, and many others. Furthermore, models that combine various models for comprehensive judgment can also be used. Moreover, in the case of qualitative variables, various discriminant analysis methods such as logistic regression, random forests, and neural networks can be employed.

[0134] The following provides further information regarding plate-making conditions, printing conditions, and print quality. Furthermore, while one approach to providing information regarding plate-making conditions, printing conditions, and print quality is shown, such information is not limited thereto.

[0135] First, the method for making the flexographic printing plate in the plate-making apparatus 200 is not particularly limited; for example, a method of engraving a pattern by irradiating a printing master plate with radiation can be cited. Furthermore, back exposure or surface exposure can be performed before or after engraving, and development can be performed after engraving. The method for making the flexographic printing plate may vary depending on the type of printing master plate.

[0136] Furthermore, the radiation used in pattern formation on the printing plate can be appropriately selected based on the structure of the original printing plate. There are no particular limitations on such radiation; examples include laser beams, ultraviolet rays, infrared rays, visible light, X-rays, and electron beams. For instance, a laser beam can be used to irradiate the original printing plate to engrave the pattern. Additionally, if the original printing plate has an infrared ablation layer, infrared radiation can be used to engrave the pattern. In this case, the infrared ablation layer acts as a mask image during exposure and curing. This infrared ablation layer can also be removed simultaneously when cleaning the unexposed portions of the photosensitive resin composition layer after exposure.

[0137] Information regarding the conditions for making such a flexographic printing plate is not particularly limited as long as it includes the conditions for making the flexographic printing plate from the flexographic printing master. For example, it can include the type of master, the type of micro-unit, information related to radiation, correction curves, image line count, type of screen, screen angle, etc.

[0138] Although there are no specific limitations, it is possible for the following tonal conditions to be considered: the type of original, the type of micro-unit, and information related to radiation are the main tonal conditions related to color concentration and opacity; the correction curve, image line count, type of screen, and screen angle are the main tonal conditions related to tonal representation.

[0139] Information related to radiation can include, for example, the intensity of the radiation. Depending on the intensity of the radiation, the engraved relief shape may change, and the color density and opacity of the printed material obtained through the flexographic printing plate may also change. Preferably, in addition to considering plate-making conditions such as the type of original plate and the type of micro-units, printing conditions such as the contact pressure between the anilox roller and the plate cylinder, and the contact pressure between the printing substrate and the plate cylinder, are considered when setting appropriate radiation-related information.

[0140] The calibration curve is a curve based on the printing curve that shows the relationship between the pixel area ratio per unit area (hereinafter referred to as "data ink dots %)" and the ink concentration in the raster image data of the flexographic printing plate. Figure 3A The relationship between the correction curve and the printing curve is shown. Flexographic printing plates are elastic, therefore the contact area between the printing substrate and the plate cylinder increases with printing pressure. Consequently, there is a tendency for the actual ink concentration to be greater than the ink concentration estimated based on the surface area of ​​the flexographic printing plate. Therefore, from the viewpoint of producing a flexographic printing plate in a manner that yields the desired image when the contact area between the printing substrate and the plate cylinder increases with printing pressure, it is preferable to produce a correction curve that takes into account the increase in contact area, based on the printing curve which represents the relationship between the actual ink concentration and the percentage of ink dots.

[0141] As an example, standardized printing profiles are defined in standards such as ISO 12647-2. To ensure that the printing profile obtained under specified printing conditions conforms to these standardized profiles, corrections can be made to the ink dot size of the printing plate. This correction uses a correction profile. For instance, flexographic printing is a printing process used for printing on all kinds of soft and hard substrates, such as plastics, paper, cartons, films, and metals, which have different thicknesses and surface characteristics. Furthermore, not only the printing substrate but also the ink or printing plate has a significant impact. Therefore, the printing profile varies greatly depending on the printing conditions.

[0142] Here, the printed curve refers to, for example... Figure 3AThe diagram illustrates the relationship between the actual area ratio calculated using the Tone Value Increment (TVI) via the Murray-Davies formula and the data ink dot percentage. The Tone Value Increment (TVI) is a numerical value representing the difference between the Tone Value and the Tone, also known as ink dot gain. In the Murray-Davies formula, for a given monochrome tone, with the measured colorimetric value of the data ink dot percentage of the object being calculated set to X, the actual area ratio relative to the object's data ink dot percentage is expressed by the following formula. The data ink dot percentage is also called the data area ratio. Alternatively, the measured colorimetric value can also be ink density.

[0143] Actual area ratio = {(Xw-X) / (Xw-Xs)} × 100

[0144] Xw: Colorimetric value for 0% area (white paper)

[0145] Xs: Colorimetric value for 100% area (pure fill or solid color)

[0146] The screen printing process uses a control algorithm to determine the size and number of ink dots formed on the surface of the printing plate. In printing using ink dots, ink is printed as small dots, and by controlling the number, size, and shape of these dots, tonal gradations such as density and lightness can be achieved. By setting the screen printing in a way that allows for appropriate tonal representation, along with the aforementioned correction curve, a printing plate capable of appropriate tonal representation can be obtained. On the other hand, even if the printing plate manufacturing method is the same except for the screen printing, if the screen printing settings are inappropriate, tonal representation, especially in thinner areas, may become insufficient, potentially resulting in... Figure 3C Hard edges as shown, such as Figure 3D As shown, color reversal, etc. On the other hand, with proper screen settings, it is possible to perform, for example... Figure 3B The subtle tonal gradations that fade smoothly as shown.

[0147] Furthermore, screen printing image parameters are information related to the raster image corresponding to the same screen. For example, they are parameters that represent the printed material obtained using a certain screen as information related to the raster image. Screen printing image parameters may include parameters such as the number, size, and shape of ink dots in each tonal region obtained by applying the screen. Alternatively, the storage device 150 of the planning and formulation assistance device 100 in this embodiment can establish a correspondence between the type of screen and the screen printing image parameters such as the number, size, and shape of ink dots in each tonal region obtained by applying the screen, and record them.

[0148] In addition, there are no particular limitations to flexographic printing. For example, ink is supplied from an anilox roller to a flexographic printing plate (plate cylinder), and ink is transferred from the ink-supplied flexographic printing plate (plate cylinder) to the printing substrate.

[0149] The printing condition information for this flexographic printing method is not particularly limited, but can include, for example: ink conditions such as viscosity, solid content concentration, and pigment concentration; information about the anilox roller such as unit shape, line count, and capacity; printing press information such as printing press type, printing speed, contact pressure between the anilox roller and the plate cylinder, contact pressure between the printing substrate and the plate cylinder, drying speed, and sleeve circumference; buffer conditions such as the type, hardness, and thickness of the buffer strip used to fix the flexographic printing plate on the plate cylinder; and printing substrate conditions such as the type of printing substrate and surface free energy. Preferably, the printing condition information includes at least one of the following: printing press conditions, anilox conditions, ink conditions, printing substrate conditions, and buffer strip conditions. Furthermore, the anilox conditions can be conditions related to the anilox roller, such as the type of anilox roller and the contact pressure between the anilox roller and the plate cylinder.

[0150] Print quality refers to information representing the quality of a printed matter from any viewpoint, including print quality within a predefined tonal range. This predefined tonal range is not particularly limited; examples include shadows, halftones, and highlights. Alternatively, based on the actual area ratio calculated using the Murray-Davis formula, areas with a actual area ratio of 100% can be designated as pure fills or solid colors, areas with a ratio of 80% or more but less than 100% as shadows, areas with a ratio of 20% or more but less than 80% as halftones, and areas with a ratio of 0% or more but less than 20% as highlights. Target values ​​for print quality can also be set based on benchmarks and standards documented in existing literature.

[0151] Furthermore, the indicators of print quality at different tonal levels can differ in shadow areas, halftone areas, and highlight areas. For example, in highlight areas, smaller ink dots are sparsely formed, so the formation, size, and shape of these dots can also be indicators of print quality. On the other hand, in shadow areas, larger ink dots are densely formed, potentially creating gaps in the ink. Therefore, the formation, size, and shape of these gaps can also be indicators of print quality. Additionally, in the halftone areas between highlight and shadow areas, the formation, size, and shape of both ink dots and gaps can also be indicators of print quality. Thus, by evaluating highlight areas not only through ink density but also by the size, number, and shape of ink dots within the highlighted areas, there is a tendency to further improve print quality.

[0152] Specifically, it is difficult to conduct [the analysis] solely based on ink concentration. Figure 3B~Figure 3DThe judgment is complex. Furthermore, balancing ink density and high gloss quality in pure fill or solid color areas is not easy. For example, there is a tendency to obtain prints where the fading becomes messy when ink density is increased, and smooth when ink density is low. In the past, the printing process relied on manual observation of the printed material to make this judgment. Figure 3B , 3C While 3D analysis can identify the optimal conditions, the planning and design assistance device in this embodiment considers not only ink concentration but also the number, size, and shape of ink dots, thereby enabling the identification of optimal conditions. Figure 3B The conditions for a smooth decline, as shown.

[0153] In addition, in the shadow and halftone areas, print quality information may also include at least one of the number, size, and shape of ink dots in the specified tonal areas, or the print curve.

[0154] Furthermore, the target values ​​for the number, size, or shape of ink dots can be set based on the smallest ink dot of a certain color in the raster image used for printing tests. Since it is impossible to consistently form excessively small ink dots, there may be technical limitations to the minimum printable ink dot size. Therefore, the image generation unit 113, described later, can also determine whether ink dots can be formed based on plate-making condition information and printing condition information. If ink dots can be formed, the size of the ink dots that can be formed is repeatedly estimated, and optimization processing is performed to search for the conditions that can form the smallest ink dot. Models can also be used separately for determining whether ink dots can be formed and for estimating the size of the ink dots that can be formed.

[0155] The number, size, or shape of ink dots on a printed material can be confirmed by observing the material under magnification using a microscope or similar device. This allows for the creation of data that correlates information related to the ink dots with information about the printing conditions and plate-making conditions of the printed material. In this data, the printing plate has raised or recessed areas corresponding to the ink dots. By setting data on ink dots observed under a microscope and data on ink dots not observed as learning data, a model can be created to determine whether ink dots can be formed based on plate-making and printing condition information. For example, when making a judgment based on plate-making and printing condition information, if the size of the formed ink dot is not zero, it can be determined that it can be formed.

[0156] In addition, the printing plate has reliefs corresponding to ink dots in the data. By setting the data of ink dots observed in the microscopic observation of the printed matter as learning data, it is possible to create a model that estimates the size of ink dots that can be formed.

[0157] In addition, indicators of print quality, especially halftone print quality, can also include evaluations related to color density, such as ink density, predicted hiding power, predicted ink usage, and predicted number of readable character dots. Here, "hiding power" refers to the uniformity of ink transferred onto the printing substrate.

[0158] Here, the predicted ink usage can be the amount of ink required to achieve a specified ink concentration and a specified opacity. By taking ink usage into account, plate-making and printing conditions that balance print quality and economy can be obtained.

[0159] Here, the predicted readable character point count refers to the predicted minimum number of character points that a human can read when recording under specified plate-making and printing conditions. Here, a character point is a unit representing the size of a character or graphic. There is no particular limitation on the unit reference for character points; for example, DTP points can be used. As the predicted readable character point count decreases, characters other than the smallest character point can be printed smoothly and evenly, further improving print quality.

[0160] Furthermore, the indicators for print quality, particularly in highlights, are not specifically limited. For example, they can include the printing curve, or the number, size, or shape of ink dots in a defined tonal range. Here, the defined tonal range can also be the highlight. Additionally, the shape can be roundness, aspect ratio, etc. In particular, the indicators for print quality in highlights can also be considered as print quality indicators related to tonal characteristics.

[0161] Furthermore, print quality can also include the print quality of pure fill or solid color areas. There are no particularly limited indicators for print quality in pure fill or solid color areas; for example, it can include ink density, opacity, and ink usage. Print quality in pure fill or solid color areas can also be considered in terms of color density and opacity. By taking ink usage into account, plate-making and printing conditions that balance print quality and economy can be obtained.

[0162] Print quality can also include the print quality of characters / fine lines. There are no particular limitations on the indicators of print quality of characters / fine lines; for example, it can include readability and opacity.

[0163] The image generation unit 113 described later can also use the above-described prediction model to correct the ink dots contained in the plate image representing the printed object image through ink dots of each color, based on the plate-making conditions and printing conditions of the printing plate, thereby generating a color prediction image represented by the corrected ink dots.

[0164] Alternatively, the prediction unit 1121 may output correction information for correcting each ink dot contained in the printed object image based on the plate-making conditions and printing conditions, and the image generation unit 113 may generate a color prediction image represented by the corrected ink dots by correcting the ink dots in the printed object image based on the correction information.

[0165] Correction information is information used to correct individual ink dots and / or void ink dots contained in a printed object image, and may also include at least one of the following: whether it can be formed, its size, and its shape.

[0166] Furthermore, the prediction unit 1121 can use a prediction model to output correction information.

[0167] The image generation unit 113 predicts the ink dots of each color on the printed matter obtained by printing the printing object image based on the plate-making conditions and printing conditions, and generates predicted images of each color represented by the ink dots. For example, the image generation unit 113 corrects the ink dots contained in the partition image representing the printing object image based on the plate-making conditions and printing conditions, thereby generating predicted images of each color represented by corrected ink dots. At this time, the image generation unit 113 can generate predicted images of each color represented by corrected ink dots by correcting the ink dots in the partition image based on the prediction model, or it can generate predicted images of each color represented by corrected ink dots by correcting the ink dots in the printing object image using the correction information output by the correction unit 1121. As a result, it is possible to more appropriately reproduce the parts that require detailed representation based on the density of ink dots, such as halftones and highlights, in each predicted color image.

[0168] For example, the image generation unit 113 can also calculate the brightness of each pixel in the partitioned image and identify the connection of pixels with non-zero brightness as an ink dot. Alternatively, the image generation unit 113 can use known methods such as R-CNN or YOLO to perform object detection processing on the partitioned image to identify ink dots. Furthermore, the image generation unit 113 can also extract ink dots whose area is smaller than the area of ​​a circle whose diameter is the reciprocal of the line count (e.g., when the line count is 175 LPI (lines per inch), the image generation unit 113 can extract ink dots with an area of ​​approximately 0.145 mm (about 0.066 mm²) of a circle with a diameter of 1 inch / 175 ≈ 0.145 mm. 2(A threshold is used to identify ink dots with an area smaller than the threshold). Alternatively, the area of ​​the ink dot can be calculated by multiplying by a margin as needed. The image generation unit 113 can also establish and record a correspondence between each ink dot extracted from the partition image and its coordinates, position, size, shape, etc., within the partition image. In addition, an ID can be attached to uniquely identify the ink dot.

[0169] Then, the image generation unit 113 performs parallel processing on each ink dot contained in the partitioned image, correcting it from at least one of the following perspectives: whether an ink dot can be formed, the size of the ink dot, and the shape of the ink dot. This allows it to obtain corrected ink dots (see reference). Figure 2D In this way, by transforming the ink dots contained in the plate-making image into correction ink dots, it is possible to obtain the predicted images of each color under the actual conditions of plate-making and printing.

[0170] exist Figure 2F An example of a split-page image is shown in the image. Figure 2G The image shows an example of a color prediction image of corrected ink dots obtained by correcting individual ink dots contained in a page image through parallel processing.

[0171] Furthermore, the image generation unit 113 can also generate color prediction images containing corrections for the gap ink dots contained in the plate separation image, based on the plate-making conditions and printing conditions of the printing plate. This allows for a more appropriate reproduction of the shadow areas where adjacent ink dots meet but cannot be identified as ink dots in each color prediction image.

[0172] For example, the image generation unit 113 can calculate the brightness of each pixel in the partitioned image and identify the connection of pixels with a brightness of 0 as a gap ink dot. Alternatively, the image generation unit 113 can perform object detection processing on the partitioned image using known methods such as R-CNN or YOLO to identify gap ink dots. Furthermore, the image generation unit 113 can extract closed regions with an area smaller than the area of ​​a circle whose diameter is the reciprocal of the line count, treating them as gap ink dots. In this case, the area of ​​the gap ink dot can be calculated by multiplying by a margin as needed. The image generation unit 113 can also establish and record a correspondence between each gap ink dot extracted in the partitioned image and its coordinates, position, size, shape, etc., within the partitioned image. Additionally, an ID can be attached to uniquely identify the gap ink dot.

[0173] Then, the image generation unit 113 performs parallel processing on each gap ink dot contained in the partitioned image, correcting it from the viewpoints of at least one of the following: whether it can be formed, its size, and its shape, thereby obtaining corrected gap ink dots (see reference). Figure 2EIn this way, by transforming the ink dots contained in the plate-making image into correction ink dots, it is possible to obtain the predicted images of each color under the actual conditions of plate-making and printing.

[0174] The image compositing unit 114 synthesizes a composite prediction image based on multiple color prediction images. Specifically, the image compositing unit 114 combines multiple color prediction images obtained as described above, and instead of actual printing, aggregates the images of the printed matter of each color (color prediction images) obtained during printing and outputs them as a composite prediction image. Thus, even if printing is not actually performed, the printing result can be confirmed in the form of an image.

[0175] The evaluation unit 115 can also determine whether the relationship between information related to ink dots in the partitioned image and information related to corrected ink dots in each color prediction image meets the target index.

[0176] For example, the evaluation unit 115 can also determine whether the difference between the number of ink dots in the partitioned image and the corrected number of ink dots in each color prediction image meets the target index. Figure 2D As shown, when the ink dots are small, correction ink dots may not form depending on the plate-making and printing conditions. Therefore, the number of ink dots in the plate separation image may differ from the number of correction ink dots in each color prediction image. A small difference allows the plate separation image to be considered more closely approximated to each color prediction image. In particular, a small difference results in each color prediction image becoming a better reproduction of the plate separation image, particularly in the gradation of highlights represented by ink dots with small diameters.

[0177] There are no particular limitations on the method for calculating the difference in the number of ink dots. For example, one method could be to count the number of correction ink dots that are determined to be impossible to form during the ink dot correction processing within the split image performed by the image generation unit 113. Furthermore, the ink dot being considered could also be an ink dot with an area smaller than the area of ​​a circle whose diameter is the reciprocal of the line count.

[0178] Furthermore, the evaluation unit 115 can also determine whether the difference between the area occupied by ink dots in the partitioned image and the area occupied by the corrected ink dots in each color prediction image meets the target index. For example... Figure 2D As shown, depending on the plate-making and printing conditions, the correction ink dots may sometimes be larger. Therefore, the area occupied by the ink dots in the plate separation image may differ from the area occupied by the correction ink dots in each color prediction image. If this difference is small, the plate separation image can be considered to be more similar to each color prediction image.

[0179] There are no particular restrictions on the method for calculating the difference in area occupied by the ink dots. For example, the following method can be used: In the ink dot correction process in the split image performed by the image generation unit 113, the area of ​​the correction ink dots that are determined to be unformable is counted. In addition, the difference between the area of ​​the correction ink dots that are determined to be formable and the area of ​​the original ink dots is counted, and their total is calculated.

[0180] Specifically, it can also be as follows: Figure 3E As shown in the schematic diagram illustrating the overlap between ink dots and correction ink dots in the partitioned image, the areas of the regions where the correction ink dots do not overlap with the original ink dots are counted, and this value is accumulated for each ink dot. The difference in the area occupied by the ink dots thus obtained becomes a value that reflects not only the difference in the size of the ink dots but also the shape and deformation of the ink dots. The smaller this difference, the better the color prediction image reproduces the partitioned image in the highlight or halftone areas. Furthermore, at this time, the ink dot being considered can also be an ink dot with an area smaller than the area of ​​a circle whose diameter is the reciprocal of the line count.

[0181] Alternatively, from the same perspective as above, the evaluation can be performed on the gap ink dots instead of the ink dots themselves. Specifically, the evaluation unit 115 can also determine whether the difference between the number of gap ink dots in the partition image and the number of corrected gap ink dots in each color prediction image meets the target index. Furthermore, the evaluation unit 115 can also determine whether the difference between the total area of ​​gap ink dots in the partition image and the total area of ​​corrected gap ink dots in each color prediction image meets the target index.

[0182] The evaluation unit 115 determines whether the differences in the number of ink dots, the area of ​​ink dots, the number of shadows, and the area of ​​shadows, as calculated as described above, meet the prescribed target indicators. Furthermore, if the prescribed target indicators are met, the image generation unit 113 can also correct the ink dots contained in the plate-making image based on the changed plate-making and printing conditions, thereby outputting a color prediction image for each color represented by the corrected ink dots.

[0183] In addition, the evaluation unit 115 can also evaluate the reproducibility between the synthesized predicted image and the printed object image. The method for evaluating the reproducibility between images is not particularly limited as long as it is a conventionally known method; for example, methods using pixel values ​​as an average or using a histogram of pixel values ​​can be cited.

[0184] When the information related to ink dots in each color prediction image does not meet the pre-defined conditions such as the aforementioned target indicators, or when the reproducibility does not meet the target indicators, the condition generation unit 116 changes the plate-making conditions and / or printing conditions used by the image generation unit 113. Furthermore, the image generation unit 113 can also correct the ink dots contained in the partition image based on the changed plate-making and printing conditions, thereby obtaining new color prediction images with corrected ink dot representation. In this embodiment, the plate-making conditions and / or printing conditions are changed based on the color prediction images with corrected ink dot representation, thereby changing the conditions based on an image close to the actual printed material, thus enabling the generation of appropriate plate-making and / or printing conditions. As a result, computational resources used for condition generation can be saved. Furthermore, the number of test prints required to adjust the plate-making and / or printing conditions can be reduced, thus saving resources related to test prints.

[0185] Here, the target index can be, for example, the upper limit threshold of the difference in the number of ink dots, the difference in the area of ​​ink dots, the difference in the number of gaps in the shadow area, and the difference in the shadow area, or a predefined threshold for reproducibility.

[0186] Furthermore, the condition generation unit 116 can also generate color prediction images that are different from the aforementioned color prediction images (hereinafter also referred to as "new color prediction images") based on the reproducibility. For example, when using a color histogram as the reproducibility, the condition generation unit 116 can also generate color prediction images in a way that improves the reproducibility based on that reproducibility.

[0187] Furthermore, when the condition generation unit 116 generates new color prediction images and / or new printing parameters for each color ink, the image generation unit 113, the image synthesis unit 114, and the evaluation unit 115 can also perform processing again based on the new color prediction images and / or the new printing parameters for each color ink. Moreover, the generation of each color prediction image and the evaluation of its reproducibility can be performed until the reproducibility meets the target parameters.

[0188] In the optimization process that ensures the target index is met by the evaluation unit 115 and the condition generation unit 116, well-known algorithms such as steepest descent, genetic algorithm, and Bayesian optimization can also be used to determine the target index.

[0189] The correction unit 117 processes correction information for each color prediction image based on the user's operation. Alternatively, the image generation unit 113 may generate corrected color prediction images based on the correction information, and the image synthesis unit 114 may synthesize a composite prediction image based on multiple corrected color prediction images. For example, in... Figure 4B In the example shown, when the user moves the print curve (OP1) or the slider of the moving parameter (OP2), the correction unit 118 can process the correction information in conjunction with this.

[0190] Therefore, it is possible to generate corrected color prediction images based on user operations. There are no particular restrictions on the parameters that the user can control when generating corrected raster image information, as long as they are parameters related to each color prediction image.

[0191] The display control unit 118 can also control the display of the composite prediction image synthesized by the image compositing unit 114 and the printed object image in a way that allows for comparison. The composite prediction image may include, in addition to the composite prediction image obtained based on each color prediction image generated by the RIP processing unit 111, a composite prediction image obtained based on each color prediction image generated by the condition generation unit 116, and a composite prediction image obtained based on the corrected color prediction images generated by the correction unit 118.

[0192] Therefore, although the vector image and the synthesized predicted image are displayed on the display device 131, the completion of the printed material can be confirmed by visual inspection, thereby enabling the modulation of the completed material.

[0193] Figure 4B This shows an example of a screen where the display control unit 118 performs display control on the display device 131. Figure 4B In the example shown, display control is performed on both the vector image and the synthetic prediction image. Furthermore, the display control unit 118 can also further control the display of the raster image for each color constituting the synthetic prediction image.

[0194] Furthermore, in Figure 4B In the example shown, parameters related to printing indicators, plate-making conditions, and printing conditions are displayed and controlled for each color of ink, such as cyan, magenta, and yellow. For example, when the user moves the print curve (OP1) or moves the parameter slider (OP2), the correction unit 118 generates correction information in conjunction with this, and displays and controls the corrected color prediction images instead of the already displayed color prediction images, and displays and controls the corrected composite prediction images instead of the already displayed composite prediction images.

[0195] In addition, the display control unit 118 can also display and control the plate-making conditions and / or printing conditions related to the composite prediction image.

[0196] 1.3. Motion Processing

[0197] Next, the actions of planning and formulating the auxiliary device 100 will be explained. Figure 5A and Figure 5B This is a flowchart illustrating an example of the processing performed by the planning and formulation auxiliary device 100. Furthermore, the processing performed by the planning and formulation auxiliary device 100 is not limited to...Figure 5A and Figure 5B .

[0198] First of all, Figure 5A The following explanation is provided.

[0199] In step S1, the RIP processing unit 111 divides the printed object image into separate images represented by ink dots of each color. At this time, the RIP processing unit 111 can also output a separate image corresponding to a specific color based on information related to the type of ink color.

[0200] When appropriate plate-making and printing conditions are searched using the test pattern through the aforementioned search process, in step S2, the learning unit 112 outputs correction information based on the plate-making and printing conditions. Additionally, the learning unit 112 can also predict information related to the printing parameters of each color ink when printing on a flexographic printing plate manufactured under the assumed plate-making conditions. Alternatively, in step S2, the learning unit 112 can also predict the printing parameters of each color ink based on the printing target image or the plate separation image.

[0201] In step S3, the image generation unit 113 corrects the ink dots contained in the plate-making image based on the plate-making conditions and printing conditions, and generates color prediction images containing the corrected ink dots. Alternatively, each color prediction image can be generated based on the plate-making image and the prediction model, according to each color of ink. Each color prediction image is a prediction image when a specified color of ink has been printed.

[0202] In step S4, the evaluation unit 115 evaluates whether the relationship between the information related to ink dots in the partitioned image and the information related to the corrected ink dots in each color prediction image meets the target index.

[0203] When the evaluation unit 115 evaluates that the target indicators are met, in step S5, the image synthesis unit 114 synthesizes a composite prediction image based on multiple color prediction images. The composite prediction image is a prediction image when all inks have been printed, and is a pre-defined image of the printed material to be obtained during actual printing.

[0204] Furthermore, in step S6, the evaluation unit 115 performs an evaluation step to assess the reproducibility of the synthesized predicted image and the vector image. If, in step S7, the evaluation unit 115 determines that the reproducibility meets the specified target index, then in step S8, the display control unit 119 performs display control by comparing the synthesized predicted image and the printed object image. The comparison method is not particularly limited; for example, it can be as follows... Figure 4B The synthesized predicted image and the vector image are arranged and displayed as shown.

[0205] Alternatively, if the evaluation unit 115 determines in step S4 that the target index is not met, the condition generation unit 116 can change the plate-making and printing conditions in step S9, and then execute step S2 again. Alternatively, if the evaluation unit 115 determines in step S7 that the reproducibility does not meet the specified target index, the condition generation unit 116 can also change the plate-making and printing conditions in step S9, and then execute step S2 again. These processes can continue until the target index is met.

[0206] Next, regarding Figure 5B The following explanation is provided. Figure 5B The process shown, for example, can be inserted into Figure 5A After step S8 in the process.

[0207] In step S11, the correction unit 118 processes correction information for each color prediction image based on the user's operation. Meanwhile, in step S8, the user can also, based on the comparison between the composite prediction image (which has undergone display control) and the printing object image, [correct the image]. Figure 4B The screen operation shown is used to generate correction information.

[0208] Then, in step S12, the image generation unit 113 generates a color prediction image for each color of ink based on the correction information. In step S13, the image synthesis unit 114 synthesizes a composite prediction image based on the multiple color prediction images. In step S14, the display control unit 119 may also perform display control by comparing the composite prediction image with the vector image.

[0209] By performing steps S12 to S14, thereby... Figure 4B In the screen operation shown, for example, the composite prediction image or the prediction images of each color can be changed based on the user dragging the print curve.

[0210] 2. Information Processing Methods

[0211] In the information processing method of this embodiment, the planning and design auxiliary device performs the following steps: an image generation step, based on a printing object image, printing plate making conditions, and printing conditions, predicting ink dots of each color on a printed material obtained by printing the printing object image based on the printing plate making conditions and the printing conditions, and generating a color prediction image represented by the ink dots; and an image synthesis step, synthesizing a composite prediction image based on multiple color prediction images.

[0212] In addition, the procedure of this embodiment can also enable the planning and formulation auxiliary device to perform the following steps: an image generation step, based on the plate-making conditions and printing conditions of the printing plate, correcting the ink dots contained in the plate image representing the printed object image through ink dots of each color, thereby generating a color prediction image represented by the corrected ink dots; and an image synthesis step, synthesizing a composite prediction image based on multiple color prediction images.

[0213] Furthermore, the specific method of this embodiment has been described in the above operation processing, so detailed explanation is omitted here.

[0214] 3. Procedure

[0215] The procedure of this embodiment enables the planning and formulation auxiliary device to include: an image generation unit that, based on a printable object image, plate-making conditions of a printing plate, and printing conditions, predicts ink dots of each color on a printed material obtained by printing the printable object image based on the plate-making conditions and the printing conditions, and generates a color prediction image represented by the ink dots; and an image synthesis unit that synthesizes a composite prediction image based on multiple color prediction images.

[0216] In addition, the procedure of this embodiment can also enable the planning and formulation auxiliary device to perform the following steps: an image generation step, based on the plate-making conditions and printing conditions of the printing plate, correcting the ink dots contained in the plate image representing the printed object image through ink dots of each color, thereby generating a color prediction image represented by the corrected ink dots; and an image synthesis step, synthesizing a composite prediction image based on multiple color prediction images.

[0217] The program can also be a program recorded on a readable recording medium. Furthermore, the specific method of processing performed by the program in this embodiment has already been described in the above-described action processing section, so detailed explanations are omitted here.

[0218] Industrial availability

[0219] This invention is industrially applicable as an apparatus for planning and formulating plate-making and printing conditions in flexographic printing.

[0220] Explanation of reference numerals in the attached figures

[0221] 100… Planning and formulation auxiliary device, 110… Processor, 111… RIP processing unit, 112… Learning unit, 1121… Prediction unit, 113… Image generation unit, 114… Image synthesis unit, 115… Evaluation unit, 116… Condition generation unit, 117… Correction unit, 118… Display control unit, 120… Communication interface, 130… Input / output interface, 131… Display device, 140… Memory, 150… Storage device, 151… Printing data, 160… Bus, 200… Plate-making device, 300… Printing device.

Claims

1. A planning aid, characterized in that having: an image generation section that generates, based on a print target image, a plate making condition of a printing plate, and a printing condition, each color prediction image expressed by dots of each color on a print obtained by printing the print target image based on the plate making condition and the printing condition; and an image synthesis section that synthesizes a synthesis prediction image based on a plurality of the each color prediction image.

2. The planning assistance device according to claim 1, further having a prediction section that outputs correction information for correcting each dot included in the print target image based on the plate making condition and the printing condition, the image generation section generates each color prediction image expressed by the corrected dots by correcting the dots of the print target image based on the correction information.

3. The planning assistance device according to claim 2, wherein the correction information is at least one or more of formability, size, and shape.

4. The planning assistance device according to claim 1, wherein a closed region formed by adjoining a plurality of the dots adjacent to each other is recognized as a void dot, the image generation section generates a corrected void dot by correcting the void dot included in the print target image based on the plate making condition and the printing condition of the printing plate, thereby generating the each color prediction image.

5. The planning assistance device according to claim 4, wherein the correction information for correcting the void dot based on the plate making condition and the printing condition is at least one or more of formability, size, and shape.

6. The planning assistance device according to claim 1, having a display control section that displays the synthesis prediction image and the print target image in a manner that enables comparison.

7. The planning assistance device according to claim 1, having a display control section that performs display control of the plate making condition and / or the printing condition related to the synthesis prediction image.

8. The planning assistance device according to claim 1, further having a condition generation section that changes the plate making condition and / or the printing condition in a case where information related to the dots in the each color prediction image does not satisfy a predetermined condition.

9. The planning assistance device according to claim 1, having a correction section that accepts correction information of the each color prediction image according to an operation of a user, the image generation section generates a corrected each color prediction image based on the correction information, the image synthesis section synthesizes a corrected synthesis prediction image based on the corrected each color prediction image. The planning assistance device performs the following steps: ​ ​ ​ ​ 10. An information processing method characterized by comprising: ​ an image generation step of generating a prediction image of each color represented by dots of ink of each color on a printed matter obtained by printing the print object image based on the plate making conditions and the printing conditions of the printing plate, based on the print object image, the plate making conditions of the printing plate, and the printing conditions, to predict dots of ink of each color on the printed matter obtained by printing the print object image based on the plate making conditions and the printing conditions; and an image synthesis step of synthesizing a synthesized prediction image based on a plurality of the prediction images of each color.

11. A program, characterized by, The planning assistance device is caused to execute the following steps: an image generation step of generating a prediction image of each color represented by dots of ink of each color on a printed matter obtained by printing the print object image based on the plate making conditions and the printing conditions of the printing plate, based on the print object image, the plate making conditions of the printing plate, and the printing conditions, to predict dots of ink of each color on the printed matter obtained by printing the print object image based on the plate making conditions and the printing conditions; and an image synthesis step of synthesizing a synthesized prediction image based on a plurality of the prediction images of each color.

Images