Planning assistance device, information processing method, and program

Abstract

A planning assistance device that functions on the basis of an image to be printed as well as a printing plate plate-making condition and a printing condition, and that comprises: an image generation unit that predicts dots of each color in a printed article obtained by printing the image to be printed on the basis of the plate-making condition and the printing condition, and generates by-color prediction images represented by the dots; and an image combination unit that creates a composite prediction image on the basis of the plurality of by-color prediction images.

Description

Formulation support device, information processing method, and program

[0001] The present invention relates to a formulation support device, an information processing method, and a program.

[0002] For example, Patent Document 1 proposes a printing system that makes it easy to grasp the plate-making conditions and printing conditions required to obtain high-quality printed matter in flexographic printing.

[0003] Japanese Patent Application Laid-Open No. 2016-049768

[0004] However, the printing system described in Patent Document 1 requires that the original image be actually printed by the printing unit and that the characteristics of the original image be measured from the evaluation area of ​​the original image. Therefore, this is no different from conventional condition adjustment in that actual printing is performed multiple times until the desired platemaking conditions and printing conditions are determined.

[0005] The present invention has been made in consideration of the above-mentioned problems, and has as its object to provide a formulation support device that can obtain appropriate color predicted images and a composite predicted image without going through a printing process, an information processing method using the device, and a program.

[0006] [1] A planning support device comprising: an image generation unit that predicts dots for each color in a printed product obtained by printing the image to be printed based on an image to be printed, platemaking conditions for a printing plate, and printing conditions, and generates each color predicted image represented by the dots, and an image synthesis unit that synthesizes a synthesized predicted image based on a plurality of the color predicted images. [2] The planning support device described in [1], further comprising: a prediction unit that outputs correction information for correcting each dot included in the image to be printed based on the platemaking conditions and the printing conditions, wherein the image generation unit corrects the dots of the image to be printed based on the correction information, thereby generating each color predicted image represented by the corrected dots. [3] The planning support device described in [2], wherein the correction information is at least one of formability, size, and shape. [4] The planning support device according to any one of [1] to [3], wherein a closed area formed by contact between a plurality of adjacent dots is recognized as a void dot, and the image generation unit corrects the void dots included in the image to be printed based on the platemaking conditions and printing conditions of the printing plate, and generates corrected void dots to generate each of the color predicted images. [5] The planning support device according to [4], wherein correction information for correcting the void dots based on the platemaking conditions and the printing conditions is at least one of whether or not they can be formed, their size, and their shape. [6] The planning support device according to any one of [1] to [5], wherein the planning support device has a display control unit that displays the composite predicted image and the image to be printed so that they can be compared. [7] The planning support device according to any one of [1] to [6], wherein the planning support device has a display control unit that displays the platemaking conditions and / or the printing conditions related to the composite predicted image. [8] The formulation support device according to any one of [1] to [7], further comprising a condition generation unit that changes the platemaking conditions and / or the printing conditions when information about the dots in each of the color prediction images does not satisfy a predetermined condition.[9] The planning support device according to any one of [1] to [8], further comprising: a correction unit that accepts corrections to the color predicted images in response to a user operation; the image generation unit generates corrected color predicted images based on the corrections; and the image synthesis unit synthesizes a corrected composite predicted image based on the corrected color predicted images.

[10] An information processing method, wherein the planning support device executes: an image generation step that predicts dots for each color in a printed material obtained by printing the image to be printed based on the platemaking conditions and the printing conditions, based on an image to be printed, platemaking conditions for a printing plate, and printing conditions, and generates color predicted images represented by the dots; and an image synthesis step that synthesizes a composite predicted image based on a plurality of the color predicted images.

[11] A program that causes a formulation support device to execute an image generation step of predicting dots for each color in a printed matter obtained by printing an image to be printed based on an image to be printed, platemaking conditions for a printing plate, and printing conditions, and generating each color predicted image expressed by the dots, and an image synthesis step of synthesizing a synthesized predicted image based on a plurality of each color predicted image.

[0007] According to the present invention, it is possible to provide a formulation support device that can obtain appropriate color predicted images and a composite predicted image without going through a printing process, an information processing method using the device, and a program.

[0008] 1 is a diagram illustrating a system configuration of the present embodiment; FIG. 2 is a schematic diagram illustrating a configuration of a planning support device of the present embodiment; FIG. 3 is a schematic diagram illustrating an example of the data structure of print data; FIG. 4 is a schematic diagram illustrating an example of a prediction model; FIG. 5 is a diagram illustrating the concept of converting dots in a separated image into correction dots in each color predicted image; FIG. 6 is a diagram illustrating the concept of converting gap dots in a separated image into correction gap dots in each color predicted image; FIG. 7 is an example showing a separated image represented by dots; FIG. 8 is an example showing each color predicted image represented by correction dots; FIG. 9 is a diagram illustrating a specific example of print data related to dots; FIG. 10 is a diagram illustrating a specific example of print data related to gap dots; FIG. 11 is a diagram illustrating the result of outputting information regarding correction dots and correction gap dots using a prediction model; FIG. 12 is a configuration diagram illustrating the relationship between a correction curve and a print curve; FIG. 13 is a schematic diagram illustrating a mode of gradation expression in the case of clean fading; FIG. 14 is a schematic diagram illustrating a mode of gradation expression where hard edges are observed; FIG. 15 is a schematic diagram illustrating a mode of gradation expression where tones are inverted; FIG. 16 is a schematic diagram illustrating overlapping of dots in a separated image and correction dots; FIG. 17 is a schematic diagram illustrating typical steps until a composite predicted image is obtained from an image to be printed; FIG. 18 is a diagram illustrating an example of a screen displayed on a display device; and FIG. 19 is a flowchart illustrating an example of processing executed by the planning device of the present embodiment. FIG. 10 is a flowchart illustrating an example of processing executed by the formulation device of the present embodiment.

[0009] The following describes in detail an embodiment of the present invention (hereinafter referred to as "the present embodiment"); however, the present invention is not limited to this embodiment, and various modifications are possible without departing from the spirit of the present invention.

[0010] 1. Planning Support Device The planning support device of this embodiment has an image generation unit that predicts dots for each color in a printed product obtained by printing an image to be printed based on an image to be printed, platemaking conditions for a printing plate, and printing conditions, and generates each color predicted image represented by the dots, and an image synthesis unit that synthesizes a synthesized predicted image based on a plurality of the each color predicted images.

[0011] In addition, the formulation support device of this embodiment may have an image generation unit that generates each color predicted image expressed by corrected dots by correcting the dots contained in a plate separation image that expresses the image to be printed by dots for each color based on the platemaking conditions and printing conditions of the printing plate, and an image synthesis unit that synthesizes a synthesized predicted image based on multiple each color predicted image.

[0012] The present invention is applicable to offset printing, gravure printing, and flexographic printing. In the following, the present embodiment will be described using flexographic printing as an example, but the present embodiment is not limited to this and can be similarly applied to offset printing and gravure printing.

[0013] Figure 4A is a schematic diagram showing a typical process for obtaining a composite predicted image from an image to be printed. As shown in Figure 4A, in a conventional flexographic printing process, before mass printing, a digital image design called a vector image (hereinafter also referred to as the "image to be printed") is created. Based on color management, which leads to quality standards based on a printing certification system compliant with the international standard ISO 12647 (e.g., PSO certification by Fogra in Germany, G7 certification by Idealliance in the United States, and JapanColor certification in Japan), flexographic printing plates are repeatedly manufactured and printed to find platemaking and printing conditions that will achieve the target color density. Color density conditions that result in good ink density and opacity are then explored. Finally, to find platemaking and printing conditions that will achieve gradation, flexographic printing plates are repeatedly manufactured and printed to find gradation conditions that will result in stable printing of dots, etc.

[0014] As shown in FIG. 2D, "dots" refer to a mesh of dots used to express the shading of a printed material. Dots are also called halftone dots. For example, when a print made with cyan (C) in flexographic printing is viewed under a microscope, numerous small dots are visible (see FIG. 2F). These dots are called dots, and a print is expressed as a collection of dots. Generally, the smaller the dots and the fewer the number per unit area, the lighter the color; the larger the dots and the higher the number per unit area, the darker the color. Shaded prints are reproduced by combining the size of the dots and the number per unit area. Furthermore, when reproducing the gradation of a photograph or picture (monochrome or color) in printing, the shading can be expressed by the ratio of the dot size, the number per unit area, and the white area of ​​the paper on which the print is made.

[0015] Information about dots includes the "number of lines" which indicates how many dots there are per inch, the "circularity" and "tilt" which indicate the shape of the dot, the "size" which is used to express shades, and the "angle" which is related to the occurrence of moire.

[0016] Furthermore, a "void dot" refers to a closed area formed by the contact of multiple adjacent dots, as shown in Figure 2E. Information about the gap dots includes the "line count" which indicates how many gap dots there are per inch, the "length ratio" when the gap dot is approximated as an ellipse, and the "size" which is used to express shades.

[0017] In the conventional flexographic printing process, the search process for the color density and gradation conditions uses a test pattern suitable for evaluating each condition, rather than the digital image to be printed. Flexographic printing plates with test patterns formed under various platemaking conditions are created, and the resulting flexographic printing plates are printed under various printing conditions to search for appropriate platemaking and printing conditions. This allows the search for platemaking and printing conditions suitable for the ink, platemaking equipment, and printing equipment to be used.

[0018] 4A, a vector image of a digital design has color layer information for each ink color, such as cyan, magenta, yellow, and black. Here, a vector image of a digital design refers to an image format in which multiple points are converted into numbers and two-dimensional information such as images and text is expressed using curves derived from mathematical formulas.

[0019] The pixel representation of a digital image differs from the representation expressed by the relief of a printing plate. Therefore, as shown in Figure 4A, RIP processing is performed on a vector image containing information for each color layer to determine the color density and dot size for each color and create a raster image for each color. In other words, during this process, the digital image, which expresses two-dimensional information such as images and text using curves derived from mathematical formulas, is converted to a pixel representation of the image that matches the relief dot representation of the printing plate. Note that, although this embodiment describes a vector image as the image to be printed, in other embodiments, the above-mentioned raster image can also be used as the image to be printed.

[0020] As shown in FIG. 4A , a raster image for each color is created for each ink color used, such as cyan, magenta, yellow, and black. Therefore, even if the vector image is the same, a raster image for each color is generated depending on the ink set used. For example, if four ink colors are used, raster images for each of the four colors are created, and if seven ink colors are used, raster images for each of the seven colors are created. Note that, in this embodiment, information about the raster images for each color corresponding to each color is also referred to as a "separated image."

[0021] The raster image for each color becomes the data for creating a flexographic printing plate for each color. That is, in the plate-making process, the projections and recesses of the flexographic printing plate are formed so that the dots represented by the raster image for each color can be printed.

[0022] Then, flexographic printing plates for each ink color are prepared based on the raster image for each color, and multi-color printing is performed using these flexographic printing plates to obtain a printed product ( Figure 4A ). In this process, flexographic printing plates for each ink color are also prepared based on the raster image for each color, and multi-color printing tests are performed using these flexographic printing plates to determine conditions for printing with good reproducibility. For example, even if flexographic printing plates are prepared based on the raster image for each color, it is not possible to express exactly the same dots on the recording medium as in the raster image for each color due to factors such as pressing force and ink characteristics. Specifically, fewer dots than those in the raster image for each color may actually be transferred onto the recording medium. In such cases, the raster image for each color is modified by enlarging the dots overall or changing the dot arrangement. Then, a multi-color printing test is performed again to find conditions for printing with good reproducibility.

[0023] In conventional flexographic printing processes, the above steps determine the flexographic printing plates and printing conditions that are suitable for mass production. The determination of plate-making and printing conditions is largely dependent on skilled workers, and multiple tests are required before the conditions are determined, so the entire process can take more than a year.

[0024] One of the reasons for the need for such testing is that the dots are deformed when the printing plate is pressed against the printing substrate during printing. For example, if printing is performed using a printing plate with concave and convex shapes corresponding to the dots, the concave and convex shapes will be deformed depending on the printing conditions, and the size and shape of the printed dots will differ somewhat from the concave and convex shapes of the printing plate. This phenomenon can occur particularly when the printing plate is made of resin such as rubber.

[0025] Therefore, in the above process, the formulation support device of this embodiment corrects the dots of the plate-separated image (raster image) that represents the image to be printed using dots for each color based on the platemaking conditions and printing conditions of the printing plate, thereby predicting in advance each color predicted image represented by the corrected dots that will be obtained when printing is performed under printing conditions using a printing plate made under specified platemaking conditions, and then synthesizes these color predicted images to obtain a composite predicted image.

[0026] Also, at this time, if the relationship between the information about the dots in the plate-separated image and the information about the corrected dots in each color prediction image does not satisfy a predetermined target index, the plate-making conditions and / or printing conditions may be changed, and the same process may be repeated to search for the optimal solution for each color prediction image for each color.

[0027] Furthermore, the reproducibility of the composite predicted image and the image to be printed is evaluated, and if the reproducibility is high, that is, if it is determined that the image to be printed can be printed with good reproducibility, any of the platemaking conditions, printing conditions, and color-specific separation images obtained by the search are output as recommended information. On the other hand, if the reproducibility of the composite predicted image and the image to be printed is low, that is, if it is determined that the image to be printed has not been reproduced, the platemaking conditions and / or printing conditions may be changed, new color-specific separation images may be generated, and the same process may be repeated to search for an optimal solution for each color-specific prediction image.

[0028] According to the formulation support device of this embodiment, it is possible to obtain the desired color predicted images or composite predicted images without going through a printing process, and it is also possible to clearly reduce the processing load on the information processing device as compared to an information processing method in which the deformation phenomenon of the unevenness on the printing substrate is directly calculated physically from the shape and hardness of the printing plate, the amount of ink, and the pressing force, and the dot size and dimension are calculated from the amount of deformation.

[0029] The configuration of the formulation support device of this embodiment will be described in detail below.

[0030] 1.1. Hardware Configuration Figure 1 shows an example of the present system. This system is a planning support system that displays recommended information for raster images for each color. The planning support device 100 of this embodiment may be connected to a platemaking device 200 and a printing device 300 via a network. In this embodiment, the platemaking device 200 may be a device for manufacturing flexographic printing plates, and the printing device 300 may be a printing press that executes a flexographic printing method. Conventionally known devices may be used as the platemaking device 200 and the printing device 300.

[0031] 2A shows a schematic configuration diagram of a planning support device 100 according to this embodiment. The planning support device according to this embodiment may be a general-purpose computer such as a personal computer or a server. As shown in FIG. 2A, the planning support device 100 includes, for example, a processor 110, a communication interface 120, an input / output interface 130, a memory 140, a storage 150, and one or more buses 160 for interconnecting these components.

[0032] The processor 110 may implement each process, function, or method disclosed in each embodiment using, for example and without limitation, one or more central processing units (CPUs), microprocessors (including MPUs, graphic processing units (GPUs), processor cores, multiprocessors, application specific integrated circuits (ASICs), application specific integrated circuits (FPGAs), etc., integrated circuit (IC) chips, LSs) or dedicated circuits.

[0033] The communication interface 120 transmits and receives various data to and from other devices via a network. The communication may be performed either wired or wirelessly, and any communication protocol may be used as long as mutual communication is possible. For example, the communication interface 120 may be implemented as hardware such as a network adapter, various communication software, or a combination of these.

[0034] The input / output interface 130 includes an input device for inputting various operations and an output device for outputting processing results, etc. For example, the input / output interface 130 may include information input devices such as a keyboard, a mouse, and a touch panel, and an information output device such as a display device 131. Note that the formulation support device 100 may accept a predetermined input or execute a predetermined output by connecting an external input / output interface 130.

[0035] For example, the planning support device 100 may output recommended conditions for platemaking and printing to the platemaking device 200 and the printing device 300 .

[0036] The memory 140 temporarily stores programs loaded from the storage 150 and provides a working area for the processor 110. The memory 140 also temporarily stores various data generated while the processor 110 is executing the programs. The memory 140 may be, for example, a high-speed random access memory such as a DRAM, an SRAM, a DDR RAM, or other random access solid-state storage device, or a combination of these.

[0037] The storage 150 stores programs, various functional units, and various data. The storage 150 may store print data 151, for example.

[0038] The print data 151 records the date of printing under specified platemaking conditions and printing conditions based on the plate separation image. Specifically, as shown in FIG. 2B , a print ID that can uniquely identify the printing conditions, information about the plate separation image to be printed, information about the platemaking conditions of the flexographic printing plate used to print the digital image, information about the printing conditions of the printing plate, and information about the printed image may be recorded in association with each other. The print data 151 can be used as learning data for various models used in this embodiment or data for constructing algorithms.

[0039] Storage 150 may also be, for example, one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or non-volatile memory such as other non-volatile solid-state storage devices, or a combination thereof. Another example of storage 150 may be one or more storage devices located remotely from processor 110.

[0040] 1.2 Software Configuration The processor 110 executes processes, functions, or methods realized by code or instructions included in a program stored in the storage 150. As shown in Fig. 2A , the processor 110 of this embodiment may 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.

[0041] The RIP processing unit 111 generates a separated image represented by dots of each color based on the image to be printed. More specifically, the RIP processing unit 111 may execute RIP (Raster Image Processor) processing to convert the image to be printed, such as a vector format created on a computer, into a separated image in a raster format that can be interpreted by a printing machine or printer. The separated image obtained in this way is an image obtained by color-separating the image to be printed using dots of ink colors such as cyan, magenta, yellow, and black, and is obtained by converting continuous tone image data into a dot pattern, as shown in FIG. 4A .

[0042] Furthermore, if the vector image is a photograph captured by an imaging device with a CMOS sensor or the like, the image to be printed will already have CMYK color information captured by the CMOS sensor. In this case, the image to be printed will be image information with CMYK color layer information, and can be used as a color-separated image as is. In this way, if the image to be printed is image information with CMYK color layer information, color separation is not necessary, and the RIP processing unit 111 may simply convert the continuous tone image data of each color image into a dot pattern to obtain a plate-separated image.

[0043] The RIP processor 111 may perform color separation processing based on information about the inks to be used. The pixel colors, such as RGB, of the digital image to be printed are converted to CMYK in advance as a vector image beforehand, and the data is handled in that state. Furthermore, the colors of the ink pigments and other color materials to be used must also be taken into consideration. Therefore, simply separating the image to be printed into RGB does not immediately indicate the ink colors to be used. In particular, digital images on displays and the like are typically expressed primarily using the three RGB colors and light and dark tones. However, in the case of ink, it is common to express it using four colors: cyan, magenta, yellow, and black, or to add light colors such as light cyan, light magenta, and light yellow to create a seven-color ink, or to use spot colors such as orange, green, and violet to create a more multi-color configuration. Therefore, the RIP processor 111 may perform color separation processing on the image to be printed based on previously acquired information about the ink colors, and obtain plate-separated images for each type of ink color to be used.

[0044] The RIP processor 111 may also utilize a rasterization step on a general computer workstation. The RIP processor 111 may convert continuous tone image data into a dot pattern based on data associating digital color densities with dot configurations on printing plates. For example, the RIP processor 111 may predict and evaluate the color density for one or more pixels of the image to be printed for each color, and then perform a rasterization step to convert the image to a dot configuration on the printing plate in a manner that reflects the color densities, thereby obtaining a plate-separated image in which the image to be printed is represented by dots for each color.

[0045] The learning unit 112 creates a prediction model for correcting each dot contained in a plate-separated image when it is assumed that printing is performed under specified printing conditions using a flexographic printing plate manufactured under specified plate-making conditions.

[0046] Here, the "prediction model" may be information for converting each dot included in the plate-separated image based on predetermined plate-making conditions and printing conditions. For example, as shown in Figures 2D and 2E, when each dot included in the plate-separated image is printed under predetermined printing conditions using a flexographic printing plate manufactured under predetermined plate-making conditions, a prediction model for deriving corrected dots, such as whether or not the dots can be formed, the degree of change in dot size, and distortion of dot shape, can be used.

[0047] The predictive model may also be related to gap dots, and may be a predictive model for deriving the resulting gap dots, such as whether gap dots can be formed when each gap dot contained in a plate separation image is printed under specified printing conditions using a flexographic printing plate manufactured under specified platemaking conditions, the degree of change in the size of the gap dots, and distortion of the shape of the gap dots.

[0048] The predictive model of this embodiment may be a model that can output information about correction dots or correction gap dots, such as whether or not they can be formed, their size, and their shape, when information about each dot or gap dot contained in the plate-separated image, plate-making conditions, and printing conditions are input, as shown in Figure 2C.

[0049] As part of the printing data shown in Figure 2B, Figure 2H shows a specific example of printing data that associates an ID for uniquely identifying one dot in the separated image, the platemaking conditions for the printing plate, the printing conditions for a printed product using a printing plate manufactured under the platemaking conditions, and information about the printed dot, which is one of the print qualities. Figure 2I also shows a specific example of printing data that associates an ID for uniquely identifying one void dot in the separated image, the platemaking conditions for the printing plate, the printing conditions for a printed product using a printing plate manufactured under the platemaking conditions, and information about the printed void dot, which is one of the print qualities. The printing data shown in Figures 2H and 2I may also be used as learning data.

[0050] 2D shows four examples of print results due to space limitations, but in this embodiment, data for 3,000 examples of dots and gap dots was used. Note that the print data or learning data used is not limited to this, and a much larger number of data may be used.

[0051] In Figures 2H and 2I, the abbreviations mean the following. Note that Midpoint, Slope at midpoint, Minimum Dot, highlight shape, shadow shape, and Keep 0% to are incorporated into this embodiment by reference to US11,575,806 B2. MC: Refers to the type of microcell. Note that a microcell refers to a portion where ink transferability is improved by creating unevenness on the surface with a fine pattern that does not affect the design. Ratio @ 50%: Refers to Midpoint. DGC type: Refers to Slope at midpoint. min.dot: Refers to Minimum Dot. h: Refers to highlight shape. s: Refers to shadow shape. k: Refers to Keep 0% to.

[0052] 2H and 2I, the presence or absence of dots or voids was marked with a circle if 50% or more of the dots in a test pattern print containing multiple dots of the same shape were formed on the print, and marked with an X if less than 50% of the dots were formed on the print. Furthermore, the size Δ, shape, and tilt were measured for each dot in a test pattern print containing multiple dots of the same shape, and the average values ​​were calculated and recorded.

[0053] The prediction model shown in Figure 2C was created using the 3,000 examples of data shown in Figures 2H and 2I as training data. Specifically, this training data was used to construct prediction models using three methods: Partial Least Squares (PLS), Support Vector Regression (SVR), and Random Forests (RF). Hyperparameters that maximize generalization performance were selected and used to construct each prediction model. Specifically, the decision coefficient R2, calculated based on the predicted values ​​obtained by performing 5-fold cross-validation (described below), was used as an index of the model's generalization performance. The R2 was calculated for models obtained using all possible combinations of values ​​for each hyperparameter, and the hyperparameter with the maximum value was adopted.

[0054] Furthermore, during learning, some data may be converted into dummy variables before being used for machine learning. For example, for PET and OPP, which indicate the materials of the printing substrate, PET may be set to 0 and OPP may be set to 1 as dummy variables. In this way, data classified by category rather than by numerical value may be quantified, allowing the model to handle categorical data.

[0055] The prediction accuracy of each prediction model was evaluated using the coefficient of determination R2. Specifically, in 5-fold cross-validation, the training data was divided into five, four of which were used as model construction data, and a regression model was constructed using each of the above models. The remaining training data was used as validation data to evaluate the predictive performance of the constructed regression model. There were five ways to select the model construction data, and by predicting the respective validation data with each model, prediction results were obtained by regarding all of the training data as "external data not used in model construction." This resulted in five R2 values, the average of which was used as the R2CV value. The final model was constructed using all of the training data and the determined hyperparameters.

[0056] Using the predictive model obtained as described above, the dots and gap dots of the plate-separated image were input with the specified platemaking conditions and printing conditions, and the size, shape, and whether or not they could be formed of the corrected dots and corrected gap dots obtained at that time were determined. The output results are shown in Figure 2J.

[0057] As described above, it has been demonstrated that it is possible to construct a prediction model that can output information about correction dots or correction gap dots that are estimated to be formed in a printed matter, based on information about dots or gap dots in a plate-separated image, platemaking condition information indicating the platemaking conditions for the printing plate, and printing condition information indicating the printing conditions for obtaining a printed matter using a printing plate manufactured under the platemaking conditions. Furthermore, it has been demonstrated that the prediction model has a predetermined accuracy and can output information about appropriate correction dots or correction gap dots without going through a printing process.

[0058] Furthermore, it was also shown that in this prediction, it is not necessary to directly predict the deformation phenomenon of the unevenness on the printing substrate, but by taking into account platemaking condition information that affects the shape and hardness of the printing plate, and printing condition information that affects the ink amount and pressing strength, etc., a judgment can be made that takes into account the deformation phenomenon of the unevenness on the printing substrate, and this makes it possible to predict information related to correction dots or correction void dots.

[0059] Note that the specific examples shown in FIGS. 2H to 2J are examples for illustrating the technical significance of the formulation support device of this embodiment, and this embodiment is not limited to the specific examples of FIGS. 2H to 2J.

[0060] For example, the predictive model may be, but is not limited to, multiple regression analysis, ridge regression, partial least squares regression, multilayer perceptron, neural networks such as CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network), support vector machines using any kernel function such as a Gaussian kernel, random forests modeled as regression trees, models using hidden Markov models, statistical models, probabilistic models, and various other models. Also, a model that combines various models to perform a comprehensive judgment may be employed. Furthermore, in the case of qualitative variables, various discriminant analysis methods such as logistic regression, random forests, and neural networks may be used.

[0061] The information on plate-making conditions, information on printing conditions, and information on print quality will be further described below. Note that, although one embodiment of the information on plate-making conditions, information on printing conditions, and information on print quality is shown, the information on plate-making conditions, information on printing conditions, and information on print quality is not limited to these.

[0062] First, the method for making a flexographic printing plate in the platemaking apparatus 200 is not particularly limited, but examples include a method in which a printing blank is irradiated with radiation to engrave a pattern. Also, back exposure and front exposure may be performed before or after engraving, or development may be performed after engraving. The method for making a flexographic printing plate may vary depending on the type of printing blank.

[0063] The radiation used to form a pattern on the printing plate can be appropriately selected depending on the configuration of the printing base plate. Examples of such radiation include, but are not limited to, laser light, ultraviolet light, infrared light, visible light, X-rays, and electron beams. For example, the printing base plate may be irradiated with laser light to engrave a pattern. Furthermore, if the printing base plate has an infrared ablation layer, the pattern may be irradiated with infrared light to engrave a pattern. In this case, the infrared ablation layer serves as a mask image during exposure and curing. When the unexposed portions of the photosensitive resin composition layer are washed out after exposure, the infrared ablation layer may also be removed at the same time.

[0064] Such platemaking condition information for flexographic printing plates is not particularly limited as long as it includes the conditions for manufacturing a flexographic printing plate from a flexographic printing plate master, but examples include the type of master, the type of microcell, information on radiation, a correction curve, the number of image lines, the type of screen, and the screen angle.

[0065] Although not particularly limited, the type of master, the type of microcell, and information regarding radiation are platemaking conditions that are mainly related to color density and hiding power, while the correction curve, image line count, screen type, and screen angle may be platemaking conditions that are mainly related to gradation expression.

[0066] Examples of information related to radiation include radiation intensity. Depending on the radiation intensity, the shape of the engraving may change, which may affect the color density and hiding power of the print obtained using the flexographic printing plate. It is preferable to set appropriate information related to radiation taking into consideration platemaking conditions such as the type of master plate and the type of microcell, as well as printing conditions such as the contact pressure between the anilox roll and the plate cylinder and the contact pressure between the printing substrate and the plate cylinder.

[0067] The correction curve is a curve that shows the relationship between the pixel area rate per unit area (hereinafter, "data dot %) and ink density in the raster image data of a flexographic printing plate based on a print curve. Figure 3A shows the relationship between the correction curve and the print curve. Because flexographic printing plates are elastic, the contact area between the printing substrate and the plate cylinder increases depending on the printing pressure. Therefore, the actual ink density tends to be higher than the ink density estimated from the area of ​​the outermost surface of the flexographic printing plate. Therefore, from the perspective of producing a flexographic printing plate that will produce a desired image when the contact area between the printing substrate and the plate cylinder increases due to printing pressure, it is preferable to create a correction curve that takes into account the increase in contact area based on the print curve, which is the relationship between the actual ink density and data dot %,.

[0068] For example, standardized print curves are defined in ISO 12647-2 and other standards. To align a print curve obtained under specific printing conditions with such a standardized curve, corrections such as plate dot size can be made. A correction curve is used for this correction. For example, flexographic printing is a printing process for printing on all kinds of flexible and rigid substrates, such as plastic, paper, carton, film, and metal, which have different thicknesses and surface characteristics. In addition to the printing substrate, the ink and plate also have a significant influence. Therefore, print curves vary significantly depending on the printing conditions.

[0069] Here, the print curve refers to a curve plotting the relationship between the actual area ratio calculated using the Murray-Davies formula in Tone Value Increment (TVI) and the data dot %, as shown in Figure 3A. The Tone Value Increment (TVI) is a numerical value that represents the difference between Tone Value and Tone, and is also called dot gain. In the Murray-Davies formula, when the colorimetric value of the data dot % for which the actual area ratio is to be calculated is X for a certain monochromatic tone, the actual area ratio for the target data dot % is expressed by the following formula. The data dot % is also called the data area ratio. The colorimetric value may also be the ink density. Actual area ratio = {(Xw - X) / (Xw - Xs)} x 100 Xw: Colorimetric value for an area ratio of 0% (paper white) Xs: Colorimetric value for an area ratio of 100% (solid)

[0070] A screen is a control algorithm that determines the size and number of dots formed on the surface of a printing plate. In dot printing, ink is printed in small dots, and gradation, such as the expression of light and shade, can be achieved by controlling the number, size, and shape of the dots. Setting the screen to achieve appropriate gradation, in conjunction with the setting of the correction curve described above, can produce a printing plate capable of appropriate gradation. On the other hand, even if the manufacturing method of the printing plate is the same except for the screen, if the screen setting is inappropriate, gradation expression, especially in light areas, can be insufficient, resulting in hard edges as shown in Figure 3C or tone inversion as shown in Figure 3D. On the other hand, if the screen setting is appropriate, fine gradation expression with clean fading as shown in Figure 3B is possible.

[0071] Furthermore, the screen image parameters are information about a raster image corresponding to a screen, and are, for example, information about a raster image that represents the image of a printed matter obtained when a certain screen is used. The screen image parameters may include parameters such as the number, size, and shape of dots in each gradation region obtained by applying the screen. The storage 150 of the formulation support device 100 of this embodiment may store the type of screen and the screen image parameters such as the number, size, and shape of dots in each gradation region obtained by applying the screen, in association with each other.

[0072] The flexographic printing method is not particularly limited, but for example, ink is supplied from an anilox roll to a flexographic printing plate (plate cylinder), and the ink is transferred from the flexographic printing plate (plate cylinder) to which the ink has been supplied to a printing substrate.

[0073] The printing condition information for such a flexographic printing method is not particularly limited, and examples thereof include ink conditions such as viscosity, solids concentration, and pigment concentration; anilox roll information such as cell shape, line count, and capacity; printing press information such as the printing model, printing speed, contact pressure between the anilox roll and the plate cylinder, contact pressure between the printing substrate and the plate cylinder, drying speed, and sleeve circumference; cushion tape conditions such as the type, hardness, and thickness of the cushion tape used to secure the flexographic printing plate to the plate cylinder; printing substrate conditions such as the type of printing substrate, and surface free energy. Among these, the printing condition information preferably includes at least one of printing press conditions, anilox conditions, ink conditions, printing substrate conditions, and cushion tape conditions. The anilox conditions may be conditions related to the anilox roll, such as the type of anilox roll and the contact pressure between the anilox roll and the plate cylinder.

[0074] Print quality is information that represents the quality of a printed matter from any viewpoint, and includes print quality at a predetermined gradation. The predetermined gradation is not particularly limited, but examples include shadow areas, halftone areas, and highlight areas. Here, based on the effective area ratio calculated using the Murray / Davies formula in a printed matter, for example, a region where the effective area ratio is 100% may be defined as a solid area, a region where the effective area ratio is 80% or more but less than 100% may be defined as a shadow area, a region where the effective area ratio is 20% or more but less than 80% may be defined as a halftone area, and a region where the effective area ratio is 0% or more but less than 20% may be defined as a highlight area. Here, the target value for print quality may be set based on standards and specifications described in existing literature.

[0075] Furthermore, the index of print quality in each gradation may be different for shadow, halftone, and highlight areas. For example, in highlight areas, relatively small dots are formed sparsely, so the possibility of dot formation, size, and shape may be an index of print quality. On the other hand, in shadow areas, relatively large dots are formed densely, so gap dots may occur, so the possibility of gap dot formation, size, and shape may be an index of print quality. Furthermore, in halftone areas located between highlight and shadow areas, the possibility of dot formation, size, and shape of each of the dots and gap dots may be an index of print quality. In this way, print quality tends to be further improved by evaluating highlight areas not only by ink density but also by evaluating the size, number, and shape of dots on the printed matter in highlight areas.

[0076] Specifically, it is difficult to distinguish between Figures 3B to 3D using only the ink density indicator. Furthermore, it is not easy to achieve both solid ink density and highlight quality. For example, increasing the ink density tends to result in smeared fading, while decreasing the ink density tends to produce prints with clean fading. In conventional printing environments, a person would look at the print and distinguish between Figures 3B, 3C, and 3D to find the optimal conditions. However, the design support device of this embodiment takes into account not only ink density but also the number, size, and shape of dots, making it possible to find conditions that result in clean fading, as shown in Figure 3B.

[0077] Incidentally, even in the shadow and halftone areas, the print quality information may include a print curve or at least one of the number, size, and shape of dots in the printed matter in a predetermined gradation area.

[0078] The target values ​​for the number, size, or shape of dots may be set based on the smallest dot of a certain color in a raster image for printing tests. Because dots that are too small cannot be formed reliably, there may be technical limitations on the minimum printable dot size. Therefore, the image generation unit 113 (described later) may determine whether dots can be formed based on the platemaking condition information and printing condition information, and if dots can be formed, may repeatedly estimate the size of the dots that can be formed, thereby performing optimization processing to search for conditions under which the smallest dots can be formed. A model may be used to determine whether dots can be formed and to estimate the size of the dots that can be formed.

[0079] The number, size, or shape of dots on a printed material can be confirmed by magnifying and observing the printed material using a microscope or the like. This allows for the creation of data that corresponds information about the dots on the printed material to the printing condition information and platemaking condition information for the printed material. Among this data, the printing plate has irregularities corresponding to the dots. By using data in which dots are observed and data in which dots are not observed under a microscope as learning data, a model can be created that determines whether dots can be formed based on the platemaking condition information and the printing condition information. For example, when determining based on the platemaking condition information and the printing condition information, it may be determined that dots can be formed if the size of the dots to be formed is not zero.

[0080] In addition, the printing plate has bumps and grooves corresponding to the dots in the data, and by using data in which dots are observed under a microscope on the printed material as learning data, a model can be created to estimate the size of the dots that can be formed.

[0081] Indicators of print quality, particularly print quality in halftone areas, may also include evaluations of color density such as ink density, predicted opacity, predicted ink usage, and predicted readable character point size. Here, "opacity" refers to the uniformity of the ink transferred onto the printing substrate.

[0082] Here, the predicted ink usage amount may be the amount of ink required to obtain a predetermined ink density or a predetermined hiding power. By taking the ink usage amount into consideration, platemaking conditions and printing conditions that can achieve both print quality and economy can be obtained.

[0083] Here, the predicted readable character point size refers to the predicted value of the smallest character point size that can be read by a human when recorded under specified platemaking and printing conditions. Here, character point is a unit that expresses the size of characters and figures. There is no particular limitation on the unit standard for character points, but one example is DTP points. The lower the predicted readable character point size, the more smoothly and neatly characters other than the minimum character point size can be printed, and the more improved the print quality.

[0084] Furthermore, the index of print quality, particularly print quality in highlight areas, is not particularly limited, but may include, for example, a print curve or the number, size, or shape of dots in a predetermined gradation area. Here, the predetermined gradation area may be a highlight area. Furthermore, the shape may be circularity, length ratio, or the like. In particular, the index of print quality in highlight areas may be considered as a print quality index related to gradation.

[0085] Furthermore, the print quality may include the print quality of a solid area. Indicators of print quality of a solid area are not particularly limited, and may include, for example, ink density, opacity, and ink usage. The print quality of a solid area may be considered as a quality indicator of color density and opacity. By taking the ink usage into consideration, platemaking conditions and printing conditions that can achieve both print quality and economy can be obtained.

[0086] The print quality may include the print quality of characters and thin lines. Indicators of the print quality of characters and thin lines are not particularly limited, and may include, for example, readability and opacity.

[0087] The image generation unit 113 described later may use the above-mentioned prediction model to correct the dots contained in the plate-separated image, which represents the image to be printed using dots for each color, based on the plate-making conditions and printing conditions of the printing plate, thereby generating each color predicted image represented by corrected dots.

[0088] Alternatively, the prediction unit 1121 may output correction information for correcting each dot contained in the image to be printed based on the platemaking conditions and printing conditions, and the image generation unit 113 may correct the dots of the image to be printed based on the correction information, thereby generating each color predicted image represented by the corrected dots.

[0089] The correction information is information for correcting each dot and / or void dot contained in the image to be printed, and may include at least one of whether to form, size, and shape.

[0090] The prediction unit 1121 may output the correction information using a prediction model.

[0091] Based on the image to be printed, the platemaking conditions of the printing plates, and the printing conditions, the image generation unit 113 predicts dots for each color in a printed product obtained by printing the image to be printed under the platemaking conditions and printing conditions, and generates each color predicted image represented by dots. For example, the image generation unit 113 corrects dots contained in a plate-separated image representing the image to be printed using dots for each color based on the platemaking conditions and printing conditions of the printing plates, thereby generating each color predicted image represented by corrected dots. At this time, the image generation unit 113 may generate each color predicted image represented by corrected dots by correcting the dots of the plate-separated image based on a prediction model, or may generate each color predicted image represented by corrected dots by correcting the dots of the image to be printed using correction information output by the correction unit 1121. This allows areas that require detailed representation of shading using dots, such as halftone areas and highlight areas, to be more appropriately reproduced in each color predicted image.

[0092] For example, the image generation unit 113 may calculate the brightness of each pixel in the divided image and recognize a series of pixels whose brightness is not 0 as one dot. Alternatively, the image generation unit 113 may perform object detection processing on the divided image using a known method such as the R-CNN method or the Yolo method to recognize dots. Alternatively, the image generation unit 113 may extract dots that have an area smaller than the area of ​​a circle whose diameter is the reciprocal of the number of lines per inch (for example, when the "number of lines per inch" is 175 LPI (lines per inch)), the image generation unit 113 may extract the area of ​​a circle whose diameter is 1 inch / 175 ≒ 0.145 mm (approximately 0.066 mm 2 ) may be used as a threshold value, and dots having an area smaller than the threshold value may be recognized as dots.) At this time, the dot area may be calculated by multiplying it by a margin rate as necessary. The image generating unit 113 may record each dot extracted from the separated image in this way by associating it with the position, such as coordinates, of the dot within the separated image, the size and shape of the dot, and the like. Also, an ID may be assigned to uniquely identify the dot.

[0093] The image generation unit 113 then performs parallel processing on each of the dots contained in the plate-separated image, correcting them from the perspective of at least one of whether or not the dot can be formed, the dot size, and the dot shape, thereby obtaining corrected dots (see FIG. 2D). By converting the dots contained in the plate-separated image into corrected dots in this way, it is possible to obtain predicted images of each color when actually printed under specified plate-making and printing conditions.

[0094] FIG. 2F shows an example of a separated image, and FIG. 2G shows an example of each color predicted image including corrected dots obtained by performing correction on each of the dots included in the separated image through parallel processing.

[0095] Furthermore, the image generating unit 113 may generate each color predicted image including corrected gap dots for the gap dots contained in the plate-separated image based on the plate-making conditions and printing conditions of the printing plate, thereby making it possible to more appropriately reproduce in each color predicted image the expression of shadow areas where adjacent dots are in contact and cannot be recognized as dots.

[0096] For example, the image generation unit 113 may calculate the brightness of each pixel in the separated image and recognize a series of pixels with a brightness of 0 as a single gap dot. Alternatively, the image generation unit 113 may perform object detection processing on the separated image using a known method such as the R-CNN method or the Yolo algorithm to recognize gap dots. The image generation unit 113 may also extract a closed area with an area smaller than the area of ​​a circle whose diameter is the reciprocal of the line count as a gap dot. In this case, the image generation unit 113 may calculate the gap dot area by multiplying it by a margin factor as necessary. The image generation unit 113 may record each gap dot extracted in the separated image in association with its position (e.g., coordinates) within the separated image, the size and shape of the dot, and so on. An ID may also be assigned to each gap dot to uniquely identify it.

[0097] The image generation unit 113 then performs parallel processing on each of the gap dots contained in the plate-separated image, correcting them from the perspective of at least one of whether they can be formed, their size, and their shape, to obtain corrected gap dots (see FIG. 2E). By converting the dots contained in the plate-separated image into corrected dots in this way, it is possible to obtain predicted images of each color when actually printed under specified plate-making and printing conditions.

[0098] The image synthesis unit 114 synthesizes a synthesized predicted image based on the multiple color predicted images. Specifically, the image synthesis unit 114 overlaps the multiple color predicted images obtained as described above, and instead of actually printing, aggregates images of the printed matter of each color that would be obtained if printed (each color predicted image), and outputs the aggregated image as a synthesized predicted image. This allows the print result to be confirmed as an image without actually printing.

[0099] The evaluation unit 115 may determine whether the relationship between the information about the dots in the separated image and the information about the corrected dots in each color predicted image satisfies the target index.

[0100] For example, the evaluation unit 115 may determine whether the difference between the number of dots in the separated image and the number of corrected dots in each color predicted image satisfies the target index. As shown in FIG. 2D , when the dots are small, correction dots may not be formed depending on the platemaking conditions and printing conditions, which may result in a difference between the number of dots in the separated image and the number of corrected dots in each color predicted image. When this difference is small, the separated image and each color predicted image can be considered to be closer to each other. In particular, when this difference is small, the color predicted images can better reproduce the separated image, such as in highlight gradations represented by small-diameter dots.

[0101] The method for calculating the difference in the number of dots is not particularly limited, but an example is a method of counting the number of corrected dots that are determined to be unformable in the correction process for dots in the divided image by the image generation unit 113. Note that in this case, the target dots may be dots with an area smaller than the area of ​​a circle whose diameter is the reciprocal of the number of lines.

[0102] The evaluation unit 115 may also determine whether the difference between the area occupied by the dots in the separated image and the area occupied by the correction dots in each color predicted image satisfies the target index. As shown in FIG. 2D , depending on the platemaking conditions and printing conditions, the correction dots may be formed larger, which may result in a difference between the area occupied by the dots in the separated image and the area occupied by the correction dots in each color predicted image. If this difference is small, the separated image and each color predicted image can be considered to be closer to each other.

[0103] There are no particular limitations on the method for calculating the difference in area occupied by dots, but one example is a method in which, during the correction process of dots in a divided image by the image generation unit 113, the area of ​​corrected dots that are determined to be unable to be formed is counted, and the difference in area between corrected dots that are determined to be able to be formed and the original dot is counted, and the sum of these is calculated.

[0104] Specifically, as shown in Figure 3E, which is a schematic diagram illustrating the overlap between dots in the separated image and correction dots, the area of ​​the region where the correction dots and the original dots do not overlap can be counted and the resulting value can be added up for each dot. The difference in the area occupied by the dots obtained in this way reflects not only the difference in dot size but also the dot shape and distortion. When this difference is small, each color predicted image can better reproduce the separated image in highlight and halftone areas. Note that in this case, the target dots may be dots with an area smaller than the area of ​​a circle whose diameter is the reciprocal of the line frequency.

[0105] From the same viewpoint as above, evaluation may be performed on gap dots instead of dots. Specifically, the evaluation unit 115 may determine whether the difference between the number of gap dots in the separated image and the corrected number of gap dots in each color predicted image satisfies the target index. The evaluation unit 115 may also determine whether the difference between the total area of ​​gap dots in the separated image and the total area of ​​corrected gap dots in each color predicted image satisfies the target index.

[0106] The evaluation unit 115 determines whether the difference in the number of dots, the difference in the dot area, the difference in the number of shadows, the difference in the shadow area, etc., obtained as described above, satisfy predetermined target indices. If the predetermined target indices are satisfied, the image generation unit 113 may correct the dots included in the plate-separated image based on the changed platemaking conditions and printing conditions, and output each color prediction image expressed by the corrected dots.

[0107] The evaluation unit 115 may also evaluate the degree of reproducibility between the composite predicted image and the image to be printed. The method for evaluating the degree of reproducibility between images is not particularly limited as long as it is a conventionally known method, but examples include a method using an average of pixel values ​​and a method using a histogram of pixel values.

[0108] The condition generation unit 116 changes the platemaking conditions and / or printing conditions used by the image generation unit 113 when information about dots in each color prediction image does not satisfy predetermined conditions, such as the target index, or when the reproducibility does not satisfy the target index. The image generation unit 113 may then correct the dots contained in the separated images based on the changed platemaking conditions and printing conditions to obtain new color prediction images represented by corrected dots. In this embodiment, changing the platemaking conditions and / or printing conditions based on each color prediction image represented by the corrected dots allows the conditions to be changed based on an image that is closest to the actual printed matter, thereby generating appropriate platemaking conditions and / or printing conditions. As a result, it is possible to conserve computational resources for condition generation. Furthermore, it is possible to reduce the number of test prints required to adjust the platemaking conditions and / or printing conditions, thereby conserving resources related to test prints.

[0109] Here, the target index may be, for example, an upper threshold value for the difference in the number of dots, the difference in the dot area, the difference in the number of voids in the shadow part, the difference in the shadow area, or the like, or a predetermined threshold value for the degree of reproducibility.

[0110] Furthermore, the condition generating unit 116 may generate color predicted images (hereinafter also referred to as "new color predicted images") that are different from the above-described color predicted images according to the reproducibility. For example, when a color histogram is used as the reproducibility, the condition generating unit 116 may generate color predicted images according to the reproducibility so as to improve the reproducibility.

[0111] Furthermore, when the condition generating unit 116 generates new color predicted images and / or new printing indicators for inks of each color, the image generating unit 113, the image combining unit 114, and the evaluating unit 115 may re-execute the processes based on the new color predicted images and / or new printing indicators for inks of each color. Then, the generation of color predicted images and the evaluation of the reproducibility may be continued until the reproducibility satisfies the target indicator.

[0112] In the optimization process executed by the evaluation unit 115 and the condition generation unit 116 to satisfy the target index, known algorithms such as steepest descent method, genetic algorithm, Bayesian optimization, etc. may be used.

[0113] The correction unit 117 accepts correction information for each color predicted image in response to a user operation. At this time, the image generation unit 113 may generate each corrected color predicted image based on the correction information, and the image synthesis unit 114 may synthesize a synthesized predicted image based on the multiple corrected color predicted images. For example, in the example shown in FIG. 4B , when the user moves the print curve (OP1) or the parameter slider (OP2), the correction unit 118 may accept correction information in conjunction with this.

[0114] This allows each corrected color predicted image to be generated in response to a user operation. There are no particular restrictions on the parameters that the user can control when generating corrected raster image information, as long as they relate to each color predicted image.

[0115] The display control unit 118 may control the display so that the composite predicted image composited by the image composition unit 114 and the image to be printed can be compared. The composite predicted image may include a composite predicted image obtained based on each color predicted image generated by the RIP processing unit 111, a composite predicted image obtained based on each color predicted image generated by the condition generation unit 116, and a composite predicted image obtained based on each corrected color predicted image generated by the correction unit 118.

[0116] This allows the vector image and the composite predicted image to be displayed in comparison on the display device 131, allowing the finished print to be visually confirmed and the finish to be adjusted.

[0117] 4B shows an example of a screen that the display control unit 118 controls to display on the display device 131. In the example shown in FIG. 4B, the display control unit 118 controls the display of a vector image and a composite predicted image. The display control unit 118 may also control the display of raster images for each color that make up the composite predicted image.

[0118] 4B , the display of parameters related to printing indicators, platemaking conditions, and printing conditions is controlled for each ink color, such as cyan, magenta, and yellow. For example, when the user moves the print curve (OP1) or moves a parameter slider (OP2), the correction unit 118 generates correction information in conjunction with the movement, and controls the display of corrected color prediction images in place of the color prediction images that were already displayed, and also controls the display of the corrected composite prediction image in place of the composite prediction image that was already displayed.

[0119] The display control unit 118 may also control the display of the platemaking conditions and / or printing conditions related to the composite predicted image.

[0120] 1.3 Operational Processing Next, a description will be given of the operation of the formulation support device 100. Figures 5A and 5B are flowcharts showing an example of processing performed by the formulation support device 100. Note that the processing performed by the formulation support device 100 is not limited to Figures 5A and 5B.

[0121] First, the embodiment shown in FIG. 5A will be described.

[0122] In step S1, the RIP processor 111 separates the image to be printed into color-separated images represented by dots for each color. At this time, the RIP processor 111 may output color-separated images corresponding to the colors based on information about the types of ink colors.

[0123] When appropriate platemaking conditions and printing conditions have been found by the above-described search process using the test pattern, in step S2 the learning unit 112 outputs correction information based on the platemaking conditions and printing conditions. The learning unit 112 may also predict information about the printing index of each color of ink when it is assumed that the flexographic printing plate manufactured under the platemaking conditions is printed under those printing conditions. Alternatively, in step S2, the learning unit 112 may predict the printing index of each color of ink based on the image to be printed or the plate-separated image.

[0124] In step S3, the image generation unit 113 corrects the dots included in the plate-separated image based on the plate-making conditions and printing conditions, and generates each color prediction image including the corrected dots. At this time, each color prediction image may be generated for each color ink based on the plate-separated image and the prediction model. Each color prediction image is a prediction image when a specific color ink is printed.

[0125] In step S4, the evaluation unit 115 evaluates whether the relationship between the information about the dots in the separated image and the information about the corrected dots in each color predicted image satisfies the target index.

[0126] If the evaluation unit 115 evaluates that the target index is satisfied, then in step S5, the image synthesis unit 114 synthesizes a synthesized predicted image based on the multiple color predicted images. The synthesized predicted image is a predicted image when all inks are printed, and is an expected image of the printed matter obtained when printing is actually performed.

[0127] Furthermore, in step S6, the evaluation unit 115 executes an evaluation step of evaluating the degree of reproducibility of the composite predicted image and the vector image, and if in step S7 the evaluation unit 115 determines that the degree of reproducibility satisfies a predetermined target index, in step S8 the display control unit 119 controls the display of the composite predicted image and the image to be printed in comparison. The comparison method is not particularly limited, and for example, the composite predicted image and the vector image may be displayed side by side, as shown in FIG.

[0128] On the other hand, if the evaluation unit 115 determines in step S4 that the target index is not satisfied, the condition generation unit 116 may change the platemaking conditions and printing conditions in step S9 and execute step S2 again. Also, if the evaluation unit 115 determines in step S7 that the reproducibility does not satisfy the predetermined target index, the condition generation unit 116 may change the platemaking conditions and printing conditions in step S9 and execute step S2 again. These processes may be continued until the target index is satisfied.

[0129] Next, the aspect shown in Fig. 5B will be described. The process shown in Fig. 5B can be inserted after step S8 in Fig. 5A, for example.

[0130] In step S11, the correction unit 118 receives correction information for each color predicted image in response to a user operation. At this time, the user may generate correction information by operating the screen shown in FIG. 4B based on a comparison between the composite predicted image whose display has been controlled in step S8 and the image to be printed.

[0131] Then, in step S12, the image generation unit 113 generates each color prediction image for each color ink based on the correction information, and 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 control the display by comparing the composite prediction image with the vector image.

[0132] By performing steps S12 to S14, the composite predicted image or each color predicted image may change in response to, for example, the user dragging the print curve in the screen operation shown in FIG. 4B.

[0133] 2. Information Processing Method In the information processing method of this embodiment, the planning support device executes an image generation step in which, based on an image to be printed, platemaking conditions for a printing plate, and printing conditions, dots for each color in a printed product obtained by printing the image to be printed based on the platemaking conditions and the printing conditions, and generates each color predicted image represented by the dots, and an image synthesis step in which a synthesized predicted image is synthesized based on a plurality of the each color predicted images.

[0134] In addition, the program of this embodiment may cause the planning support device to execute an image generation step of generating each color predicted image expressed by corrected dots by correcting the dots contained in a plate separation image, in which the image to be printed is expressed by dots for each color, based on the platemaking conditions and printing conditions of the printing plate, and an image synthesis step of synthesizing a composite predicted image based on multiple each color predicted image.

[0135] Note that the specific aspects of the method of this embodiment have been described above in the operational processing, so detailed description thereof will be omitted here.

[0136] 3. Program The program of this embodiment includes an image generation unit in a formulation support device that predicts dots for each color in a printed product obtained by printing an image to be printed based on an image to be printed, platemaking conditions for a printing plate, and printing conditions, and generates each color predicted image represented by the dots, and an image synthesis unit that synthesizes a synthesized predicted image based on a plurality of each color predicted image.

[0137] In addition, the program of this embodiment may cause the planning support device to execute an image generation step of generating each color predicted image expressed by corrected dots by correcting the dots contained in a plate separation image, in which the image to be printed is expressed by dots for each color, based on the platemaking conditions and printing conditions of the printing plate, and an image synthesis step of synthesizing a synthesized predicted image based on multiple each color predicted image.

[0138] The program may be recorded on a readable recording medium. Note that the specific aspects of the processing executed by the program of this embodiment have been described in the above operational processing, and therefore detailed description thereof will be omitted here.

[0139] The present invention has industrial applicability as an apparatus for assisting in the formulation of platemaking conditions and printing conditions in flexographic printing.

[0140] 100...Planning support 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...Modification unit, 118...Display control unit, 120...Communication interface, 130...Input / output interface, 131...Display device, 140...Memory, 150...Storage, 151...Print data, 160...Bus, 200...Prepress device, 300...Printing device

Claims

1. A planning assistance device having: an image generation unit that predicts dots for each color in a printed matter obtained by printing the image to be printed based on an image to be printed, platemaking conditions for a printing plate, and printing conditions, and generates each color predicted image represented by the dots; and an image synthesis unit that synthesizes a synthetic predicted image based on a plurality of each color predicted image.

2. The planning support device of claim 1, further comprising a prediction unit that outputs correction information for correcting each dot contained in the image to be printed based on the platemaking conditions and the printing conditions, and the image generation unit generates each color predicted image represented by the corrected dots by correcting the dots of the image to be printed based on the correction information.

3. The formulation support device according to claim 2, wherein the correction information is at least one of formability, size, and shape.

4. The formulation support device of claim 1, wherein a closed area formed by the contact of multiple adjacent dots is recognized as a void dot, and the image generation unit corrects the void dots contained in the image to be printed based on the platemaking conditions and printing conditions of the printing plate, and generates corrected void dots to generate each of the color prediction images.

5. The formulation support device according to claim 4, wherein the correction information for correcting the void dots based on the platemaking conditions and the printing conditions is at least one of whether or not they can be formed, their size, and their shape.

6. The formulation assistance device according to claim 1, further comprising a display control unit that displays the composite predicted image and the image to be printed in a comparative manner.

7. The planning support device according to claim 1, further comprising a display control unit that controls the display of the platemaking conditions and / or the printing conditions related to the composite predicted image.

8. The formulation support device of claim 1, further comprising a condition generation unit that changes the platemaking conditions and / or the printing conditions when information regarding the dots in each of the color predicted images does not satisfy predetermined conditions.

9. The formulation support device of claim 1, further comprising a correction unit that receives correction information for each of the color predicted images in response to a user operation, wherein the image generation unit generates each of the corrected color predicted images based on the correction information, and the image synthesis unit synthesizes a corrected synthesized predicted image based on each of the corrected color predicted images.

10. An information processing method in which a formulation support device executes an image generation step of predicting dots for each color in a printed matter obtained by printing the image to be printed based on the image making conditions and printing conditions of a printing plate, and generating each color predicted image represented by the dots, based on the image to be printed, and an image synthesis step of synthesizing a synthetic predicted image based on a plurality of each of the color predicted images.

11. A program that causes a formulation support device to execute an image generation step of predicting dots for each color in a printed matter obtained by printing the image to be printed based on the platemaking conditions and printing conditions of a printing plate, and generating each color predicted image represented by the dots, and an image synthesis step of synthesizing a synthetic predicted image based on a plurality of each of the color predicted images.

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