Patent Description:
"Moiré (or moire)" is an interference fringe that visually occurs when a plurality of periodic patterns or structures are superimposed. Moreover, in physics, moiré can be said to be a beat phenomenon of two spatial frequencies.

Although moiré can be leveraged for useful purposes, when an unintended moiré occurs, the design of images may be impaired, which may lead to deterioration of the quality of printed materials. Accordingly, means for removing undesirable moiré have been proposed.

For example, Patent Document <NUM> discloses a technique in which "a first halftone processing means for generating first halftone image data from image data input using a threshold value, a first filter processing means for smoothing the first halftone image data using a first filter having a size corresponding to a period of the threshold value, a second filter processing means for smoothing the input image data using a second filter having a characteristic corresponding to the first filter, and an evaluation means for evaluating moiré generated in the first halftone image data based on a difference between the first halftone image data smoothed by the first filter processing means and the image data smoothed by the second filter processing means" are used as means for removing moiré.

[Patent Document <NUM>] <CIT> Further prior art documents are <CIT> showing an image processing apparatus and non-transitory computer readable medium, <CIT> showing an image processing method and device and terminal equipment and <CIT> showing an image forming apparatus and image processor.

Patent Literature <NUM> discloses a method of detecting moiré by taking the difference between two smoothing filters having different degrees, and performing a moiré removal process.

However, in the method described in Patent Literature <NUM>, since all of the regions where the difference between the smoothing filters is large are converted from an AM screen to an FM screen, the regions of the image where no moiré occurs are processed together with the regions where moiré does occur. However, when a moiré removal process is performed on regions of an image where no moiré has occurred, the texture of the object in the image may be impaired, the sharpness of the image may be decreased, and the printing quality may be deteriorated. For this reason, when the means described in Patent Document <NUM> is applied, since the moiré removal processing is also performed on regions of the image where no moiré has occurred, in addition to the processing load increasing, deterioration due to excessive performing of moiré prevention processing occurs.

Accordingly, it is desirable to have a means for predicting in advance those regions in which moiré will occur, and for performing moiré suppression processing only on the predicted regions.

Accordingly, an object of the embodiments of the present disclosure is to provide a moiré occurrence prediction means that predicts a region in which moiréwill occur and implements a moiré suppression process only on a pattern including said region, thereby mitigating print deterioration, suppressing the occurrence of moiré, and enabling high quality printing.

In order to solve the above problem, there are provided a moiré occurrence prediction device, a moiré occurrence prediction system, and a moiré occurrence prediction method according to the respective claims.

According to the present disclosure, it is possible to provide a moiré occurrence prediction means that predicts a region in which moiré will occur and implements a moiré suppression process only on a pattern including said region, thereby mitigating print deterioration, suppressing the occurrence of moiré, and enabling high quality printing.

It should be noted that the present invention is not limited by these embodiments. In addition, in the description of the drawings, the same components are denoted by the same reference numerals.

As described above, in the case that a periodic pattern such as a stripe pattern or a mesh pattern exists in an image to be printed, when the image is converted into a halftone dot image for printing and the pattern is represented by halftone dots, the period of the pattern and the period of the halftone dots interfere with each other, and a texture called moiré that does not exist in the original image may appear. In particular, when a moiré having a large period occurs and is viewed, the viewer may feel discomfort, which can cause a deterioration in quality.

For example, in the case of catalog printing in which the required characteristics of the print quality are strict, when moiré occurs, many pages of the catalog must be reprinted, resulting in a large loss.

Also, in the case of comic printing, if an area using a screen tone is present, moiré is likely to occur in the screen tone area, which may cause a large amount of printing loss.

Conventionally, with regard to such moiré, visual inspection of the image and correction of the moiré were performed manually, but inspection and correction of many images and pages requires the effort of workers, leading to a large workload.

On the other hand, in order to prevent moiré, for example, a method of removing the high-frequency components of an original image in advance is common, but in the case of catalogs, this impairs the texture of clothes, and in the case of comics, the sharpness of the printing is impaired. As a result, the deterioration in printing quality and the high cost of manual operation become problematic.

Accordingly, as described above, according to the present disclosure, it is possible to provide a moiré occurrence prediction means that predicts a region in which moiré will occur and implements a moiré suppression process only on a pattern including said region, thereby mitigating print deterioration, suppressing the occurrence of moiré, and enabling high quality printing.

Referring first to <FIG>, a computer system <NUM> for implementing the embodiments of the present disclosure will be described. The mechanisms and devices of the various embodiments disclosed herein may be applied to any suitable computing system. The main components of the computer system <NUM> include one or more processors <NUM>, a memory <NUM>, a terminal interface <NUM>, a storage interface <NUM>, an I/O (Input/Output) device interface <NUM>, and a network interface <NUM>. These components may be interconnected via a memory bus <NUM>, an I/O bus <NUM>, a bus interface unit <NUM>, and an I/O bus interface unit <NUM>.

The computer system <NUM> may include one or more general purpose programmable central processing units (CPUs), 302A and 302B, herein collectively referred to as the processor <NUM>. In some embodiments, the computer system <NUM> may contain multiple processors, and in other embodiments, the computer system <NUM> may be a single CPU system. Each processor <NUM> executes instructions stored in the memory <NUM> and may include an on-board cache.

In some embodiments, the memory <NUM> may include a random access semiconductor memory, storage device, or storage medium (either volatile or non-volatile) for storing data and programs. The memory <NUM> may store all or a part of the programs, modules, and data structures that perform the functions described herein. For example, the memory <NUM> may store a moiré prediction application <NUM>. In some embodiments, the moiré prediction application <NUM> may include instructions or statements that execute the functions described below on the processor <NUM>.

In some embodiments, the moiré prediction application <NUM> may be implemented in hardware via semiconductor devices, chips, logic gates, circuits, circuit cards, and/or other physical hardware devices in lieu of, or in addition to processor-based systems. In some embodiments, the moiré prediction application <NUM> may include data other than instructions or statements. In some embodiments, a camera, sensor, or other data input device (not shown) may be provided to communicate directly with the bus interface unit <NUM>, the processor <NUM>, or other hardware of the computer system <NUM>.

The computer system <NUM> may include a bus interface unit <NUM> for communicating between the processor <NUM>, the memory <NUM>, a display system <NUM>, and the I/O bus interface unit <NUM>. The I/O bus interface unit <NUM> may be coupled with the I/O bus <NUM> for transferring data to and from the various I/O units. The I/O bus interface unit <NUM> may communicate with a plurality of I/O interface units <NUM>, <NUM>, <NUM>, and <NUM>, also known as I/O processors (IOPs) or I/O adapters (IOAs), via the I/O bus <NUM>.

The display system <NUM> may include a display controller, a display memory, or both. The display controller may provide video, audio, or both types of data to the display device <NUM>. Further, the computer system <NUM> may also include a device, such as one or more sensors, configured to collect data and provide the data to the processor <NUM>.

For example, the computer system <NUM> may include biometric sensors that collect heart rate data, stress level data, and the like, environmental sensors that collect humidity data, temperature data, pressure data, and the like, and motion sensors that collect acceleration data, movement data, and the like. Other types of sensors may be used. The display system <NUM> may be connected to a display device <NUM>, such as a single display screen, television, tablet, or portable device.

The I/O interface unit is capable of communicating with a variety of storage and I/O devices. For example, the terminal interface unit <NUM> supports the attachment of a user I/O device <NUM>, which may include user output devices such as a video display device, a speaker, a television or the like, and user input devices such as a keyboard, mouse, keypad, touchpad, trackball, buttons, light pens, or other pointing devices or the like. A user may use the user interface to operate the user input device to input input data and instructions to the user I/O device <NUM> and the computer system <NUM> and receive output data from the computer system <NUM>. The user interface may be presented via the user I/O device <NUM>, such as displayed on a display device, played via a speaker, or printed via a printer.

The storage interface <NUM> supports the attachment of one or more disk drives or direct access storage devices <NUM> (which are typically magnetic disk drive storage devices, but may be arrays of disk drives or other storage devices configured to appear as a single disk drive). In some embodiments, the storage device <NUM> may be implemented as any secondary storage device. The contents of the memory <NUM> are stored in the storage device <NUM> and may be read from the storage device <NUM> as needed. The I/O device interface <NUM> may provide an interface to other I/O devices such as printers, fax machines, and the like. The network interface <NUM> may provide a communication path so that computer system <NUM> and other devices can communicate with each other. The communication path may be, for example, the network <NUM>.

In some embodiments, the computer system <NUM> may be a multi-user mainframe computer system, a single user system, or a server computer or the like that has no direct user interface and receives requests from other computer systems (clients). In other embodiments, the computer system <NUM> may be a desktop computer, a portable computer, a notebook computer, a tablet computer, a pocket computer, a telephone, a smart phone, or any other suitable electronic device.

Next, with reference to <FIG>, a moiré occurrence prediction system according to the embodiments of the present disclosure will be described.

<FIG> is a diagram illustrating an example of a configuration of the occurrence prediction system <NUM> according to the embodiments of the present disclosure. As illustrated in <FIG>, the occurrence prediction system <NUM> includes a client terminal <NUM>, a printing unit <NUM>, and a moiré occurrence prediction device <NUM>. The client terminal <NUM>, the printing unit <NUM>, and the moiré occurrence prediction device <NUM> are connected to each other via, for example, a communication network <NUM>. The communication network <NUM> may be, for example, the Internet or a Local Area Network (LAN).

The client terminal <NUM> is a terminal that transmits an input image to be subjected to a moiré occurrence prediction process, which will be described later, to the moiré occurrence prediction device via the communication network <NUM>. The client terminal <NUM> may be a terminal used by an individual or a terminal shared by an organization such as a private company. The client terminal <NUM> may be any device such as a desktop personal computer, a notebook personal computer, a tablet, or a smartphone.

The user of the client terminal <NUM> may input an input image, printing conditions for the input image, and the like via, for example, a graphical user interface (GUI) of the client terminal <NUM>. It should be noted that the GUI of the client terminal <NUM> will be described later (see <FIG>).

The printing unit <NUM> is a printing unit for printing the image data generated by the moiré occurrence prediction device <NUM> after any moiré are suppressed. It should be noted that the printing unit <NUM> may print an image output from the moiré suppression unit <NUM> of the moiré occurrence prediction device <NUM>, or may print an image received from the client terminal.

The moiré occurrence prediction device <NUM> is a device for performing the processing according to the moiré occurrence prediction method according to the embodiments of the present disclosure. As illustrated in <FIG>, the moiré occurrence prediction device <NUM> includes a communication unit <NUM>, a preprocessing unit <NUM>, a periodic structure inclusion determination unit <NUM>, a moiré prediction unit <NUM>, a region extraction unit <NUM>, a moiré suppression unit <NUM>, and a storage unit <NUM>. The moiré occurrence prediction device <NUM> can provide a moiré occurrence prediction means that mitigates printing deterioration, suppresses the occurrence of moiré, and enables high-quality printing by processing an input image received from the client terminal <NUM>, for example, using the functional units described above, thereby predicting a degree of risk of moiré occurrence for each of a plurality of regions that form the input image, and performing moiré suppression only on regions with a high degree of risk, for example.

The communication unit <NUM> is a functional unit for transmitting and receiving various types of information communicated between the moiré occurrence prediction device <NUM>, the client terminal <NUM>, and the printing unit <NUM>. For example, the communication unit <NUM> may receive an input image received from the client terminal <NUM> or transmit an image generated by the moiré prediction unit <NUM> or the moiré suppression unit <NUM> to the client terminal <NUM> or the printing unit <NUM>.

The pre-processing unit <NUM> is a functional unit for performing pre-processing on an image to be subjected to the moiré occurrence prediction process by the moiré occurrence prediction device. For example, the pre-processing unit <NUM> may perform optional pre-processing such as performing RIP (Raster Image Processor) processing on an original image in a vector data format received from the client terminal <NUM>, generating a halftone dot image in the raster data format, or dividing the input image into a plurality of processing blocks.

The periodic structure inclusion determination unit <NUM> is a functional unit for determining, in the original image, a periodic structure region that includes a periodic structure that induces the occurrence of a moiré. As will be described later, the periodic structure inclusion determination unit <NUM> may, with respect to the original image, perform a color conversion process to convert to a predetermined color space; perform a contour component extraction process for extracting a contour component, perform a smoothing process, normalize a maximum and a minimum value of pixels, and determine, by performing the predetermined frequency analysis process, a periodic structure region that includes a periodic structure that induces occurrence of a moiré based on either a frequency peak, a frequency peak intensity, or an intensity distribution of the original image.

It should be noted that the details of the processing of the periodic structure inclusion determination unit <NUM> will be described later with reference to <FIG>.

The moiré prediction unit <NUM> is a functional unit for determining a degree of risk of moiré occurrence in each periodic structure region by performing a predetermined frequency analysis process with respect to each of the periodic structure regions determined by the periodic structure inclusion determination unit <NUM>, generating a moiré occurrence notification that indicates the degree of risk for each periodic structure region, and outputting the moiré occurrence notification. More particularly, the moiré prediction unit <NUM> may, with respect to the original image, perform a color conversion process to convert to a predetermined color space, perform a contour component extraction process for extracting a contour component, and perform a smoothing process, and may, with respect to the halftone dot image, perform a color conversion process to convert to a predetermined color space, perform a resolution conversion process for aligning to a resolution of the original image, perform a smoothing process, subsequently generate a difference extraction image that indicates a pixel difference between the original image and the halftone dot image, determine, by performing the predetermined frequency analysis process with respect to the difference extraction image, a degree of risk of moiré occurrence based on either a frequency peak, a frequency peak intensity, or an intensity distribution of the difference extraction image, and generate a moiré occurrence notification that indicates the degree of risk for each processing block.

In an embodiment, the moiré occurrence notification may be transmitted to the client terminal <NUM>, and in other embodiments, the region extraction unit <NUM> and the moiré suppression unit <NUM> described later may perform a moiré suppression process based on the moiré occurrence notification.

The details of the processing of the moiré prediction unit <NUM> will be described later with reference to <FIG>.

The region extraction unit <NUM> is a functional unit that, with respect to the original image, performs a color conversion process for converting to a predetermined color space and generates an element similarity map that indicates similar regions having similar pixel values.

It should be noted that the details of the processing of the region extraction unit <NUM> will be described later with reference to <FIG>.

The moiré suppression unit <NUM> is a functional unit for performing a predetermined moiré suppression process for each similar region in the halftone dot image. As the moiré suppression process performed by the moiré suppression unit <NUM>, a means for adjusting the size of the tone so that the individual tone densities of the halftone dot image match the original image densities at the same positions, or a means of converting the tone shape of a halftone dot image to a clean tone can be considered, but the moiré suppression unit <NUM> according to the embodiments of the present disclosure is not limited hereto, and may perform any moiré suppression processing.

The storage unit <NUM> is a storage region for storing various types of information used by the above-described functional units. For example, as illustrated in <FIG>, the storage unit <NUM> may store a source image <NUM> in a vector data format and a halftone dot image <NUM> in a raster data format that is generated by performing a Raster Image Process (RIP) process on the source image received from the client terminal <NUM>. The storage unit <NUM> may be, for example, a storage device such as a hard disk drive or a solid state drive, or may be a cloud-type storage area.

According to the moiré occurrence prediction device <NUM> configured as described above, it is possible to provide a moiré occurrence prediction means that predicts a region in which moiré will occur and implements a moiré suppression process only on a pattern including said region, thereby mitigating print deterioration, suppressing the occurrence of moiré, and enabling high quality printing.

Next, with reference to <FIG>, the overall flow of a printing process including the moiré occurrence prediction process according to the embodiments of the present disclosure will be described.

<FIG> is a diagram illustrating the overall flow of a printing process <NUM> including the moiré occurrence prediction process according to the embodiments of the present disclosure. With the exception of the moiré occurrence prediction processes <NUM> and <NUM>, the printing process <NUM> illustrated in <FIG> is substantially similar to what is known as offset printing. Accordingly, in the present disclosure, a detailed description of existing printing processes will be omitted, and the moiré occurrence prediction process according to the embodiments of the present disclosure will be primarily described.

In step <NUM>, first, data submission is performed. Here, a user who wishes to request printing may input an original image to be printed, printing conditions, and the like using the client terminal illustrated in <FIG> for example.

Next, in step <NUM>, the original image input in step <NUM> is corrected for printing. For example, an original image in a vector data format may be converted into a halftone dot image in a raster data format by a RIP process, or the resolution and size may be adjusted.

Next, in step <NUM>, the arrangement of letters, diagrams, photographs, and the like in the image to be printed are adjusted based on, for example, the printing conditions input in step <NUM>.

Next, in step <NUM>, the moiré occurrence prediction process according to the embodiments of the present disclosure is performed. Here, the moiré occurrence prediction process is performed individually for each part of the image to be printed.

It should be noted that the details of the moiré occurrence prediction process will be described later with reference to <FIG> and the like.

Next, in step <NUM>, a Direct Digital Color Proof (DDCP) of the images to be printed is generated, and digital data that can be directly output to the printer is prepared.

Next, in step <NUM>, the digital data prepared in step <NUM> is transferred to the execution stage of printing (what is known as "proof presentation" or "print ready plates") after confirmation is completed.

Next, in step <NUM>, the moiré occurrence prediction process according to the embodiments of the present disclosure is performed. The moiré occurrence prediction process performed in step <NUM> is substantially the same as the moiré occurrence prediction process of step <NUM> described above, but the moiré occurrence prediction process performed in step <NUM> is different in that the moiré occurrence prediction process is performed on the entire page to be printed.

Next, in step <NUM>, a machine plate for printing is manufactured by baking a printing film (a plate-making film) created based on the digital data prepared in step <NUM> onto a plate material.

Next, in step <NUM>, the printer performs printing using the machine plate produced in step <NUM>.

As described above, the moiré occurrence prediction process according to the embodiments of the present disclosure is a process performed during an existing general offset printing process. Further, in the example illustrated in <FIG>, the moiré occurrence prediction process according to the embodiments of the present disclosure is executed twice, after the end of the typesetting process in step <NUM> and after the end of the proof presentation/print-ready plate preparation step <NUM>, but the present disclosure is not limited thereto, and a configuration in which the moiré occurrence prediction process is performed only once, or a configuration in which the moiré occurrence prediction process is performed three times or more is also possible. However, from the viewpoint of maintaining the overall efficiency of the printing process while accurately predicting and suppressing moiré, it is desirable to perform the moiré occurrence prediction process twice as illustrated in <FIG>.

Next, with reference to <FIG>, an example GUI for inputting input images according to the embodiments of the present disclosure will be described.

<FIG> is a diagram illustrating an example GUI <NUM> for inputting input images according to the embodiments of the present disclosure. The GUI <NUM> is an interface for inputting an input image and print conditions of the input image to be subjected to the moiré occurrence prediction analysis according to the embodiments of the present disclosure.

The input images and print conditions entered through the GUI <NUM> are transmitted to the moiré occurrence prediction device. The GUI <NUM> may be, for example, a web interface or an interface on an application provided to a client terminal by the moiré occurrence prediction device via a communication network.

First, the user of the GUI <NUM> enters user data such as the section/department <NUM> and the employee number <NUM>. In embodiments, the user of the GUI <NUM> may prepare a preset for each section and department in advance, and select a desired preset from among the stored presets. In addition, in embodiments, a job for printing and moiré occurrence prediction requested by a user via the GUI <NUM> is associated with an employee number of the user, and only the result of the job corresponding to the predetermined employee number can be displayed in the result list for the job. As a result, only the person who has requested the job can confirm the result of the job, and thus security can be improved, for example.

Next, the user may input the presets <NUM> registered in advance for each of the settings, the halftone dot type <NUM>, the RIP processing machine designation <NUM> that designates the storage location of the RIP process algorithm or machine, the conditions <NUM> of the profile conversion (generating halftone dots under the designated conditions in the case that color conversion is applied to the machine plate), the printer <NUM> for designating the settings of the resulting auto-printout destination, and the usage plate <NUM> for designating the plates to be used in printing (for example, deleting the unnecessary plates in the case of two-color printing).

The content set by the halftone dot type <NUM> may include, for example, the settings of the halftone dot shapes (square dots, chain dots, and the like), the halftone dot angle and the number of lines for each ink, the resolution (2400dpi/4000dpi) of the <NUM>-bit data to be written, and the auto-overprint (nukinose).

In addition, a configuration is also possible in which recommended settings are prepared in advance, and the recommended settings are automatically selected when the user selects a specific setting.

Next, the user may press the initiate registration button <NUM> to input the input image and the printing conditions of the input image to be subjected to the printing and the moiré occurrence prediction according to the embodiments of the present disclosure. In the case that there are a plurality of input images, the user may set the plurality of input images and the printing conditions corresponding to the respective input images all at once.

Here, the input image includes at least an image in a vector data format (hereinafter, referred to as an original image), but may also include a halftone dot image for printing. For example, in embodiments, the original image and a halftone dot image corresponding to the original image that has been subjected to a RIP process may be created by the client-terminal and input together via the GUI <NUM>. In addition, in other embodiments, after the original image is input via the GUI <NUM>, a halftone dot image corresponding to the original image that has been subjected to a RIP process may be created by the moiré occurrence predicting device. In the present disclosure, when it is not necessary to distinguish between the original image and the halftone dot image, these images are collectively referred to as an "input image.

The format of the inputted images may be any format, such as a PDF, RAW, JPEG, or the like.

In addition, the printing conditions here may include, for example, the number of colors, the designation of colors, the presence or absence of a characteristic color, a halftone dot type (halftone dot angle, the number of lines), and the like. These printing conditions may be input as a specific numerical value, may be input as a range, or may be selected from among templates prepared in advance. In some embodiments, each of these pre-prepared templates may correspond to a different printing line and include printing conditions for that printing line.

The above-described information regarding the settings, the input image, and the printing conditions are transmitted to the moiré occurrence prediction device via the communication network. Thereafter, the input image is analyzed by the moiré occurrence prediction process to be described later, and the moiré suppression process is performed only on regions where a moiré is predicted to occur. In this way, it is possible to mitigate print deterioration, suppress generation of moiré, and enable high-quality printing.

Next, with reference to <FIG>, a moiré occurrence prediction process according to the embodiments of the present disclosure will be described.

<FIG> is a diagram illustrating an example of a moiré occurrence prediction process <NUM> according to the embodiments of the present disclosure. The moiré occurrence prediction process <NUM> illustrated in <FIG> is, for example, a process performed by the moiré occurrence prediction device <NUM> illustrated in <FIG>, predicts a region where moiré will occur, and performs moiré suppression processing only on patterns that include this region.

It should be noted that the moiré occurrence prediction process <NUM> is a process corresponding to the moiré occurrence prediction processes <NUM> and <NUM> described with reference to <FIG>.

First, in Step <NUM>, the moiré occurrence prediction device inputs an input image to be subjected to printing and moiré occurrence prediction processing. Here, the moiré prediction device may receive an input image and printing conditions set via the GUI <NUM> described with reference to <FIG>, for example.

The file format of the input images received here may be any file format such as a JPEG, GIF, TIFF, BMP, PNG, or the like. The input image may be from <NUM>,000x10,<NUM> pixels to <NUM>,<NUM>,000x1,<NUM>,<NUM> pixels, for example, and may be large image data.

At this time, the moiré occurrence prediction device may input, as the input image, both the vector data format image and the RIP processed halftone dot image corresponding to the original image, but when only the vector data format original image is transmitted from the client terminal, the moiré occurrence prediction device may perform RIP processing on the original image to generate a raster data format halftone dot image.

Further, after the input image is input, the pre-processing unit <NUM> of the moiré occurrence prediction device may divide the received input image into processing regions that are partitions of a fixed size. The size of a processing block is preferably, for example, a power of <NUM>, but is not particularly limited. In addition, it is preferable that the processing blocks have the same size in the vertical and horizontal directions. For example, 128x128, 256x256, 512x512, 1024x1024, 2048x2048 or the like may be used as examples of the size of the processing block.

It should be noted that the actual size of the processing blocks may be equal to or larger than <NUM> and equal to or smaller than <NUM>.

In addition, the pre-processing unit may convert the resolution of the input images and set the resolution to be greater than or equal to 600dpi and less than or equal to 2400dpi.

In the subsequent processing (for example, the process <NUM> for extracting the structure-including regions and the moiré prediction process <NUM>), the determination of the presence or absence of a periodic structure that induces moiré and the determination of the moiré detection are performed for each processing block. More specifically, it is possible to determine whether or not there is a periodic structure that will induce moiré by determining, from the two-dimensional data obtained by a discrete Fourier transform, whether or not a characteristic peak exists. The reason for dividing into processing blocks is that, rather than performing determination in a state in which various periodic structures are included, it is desirable to perform block division because it is easier to observe particular pattern information when divided into small regions and analyzed.

Next, in step <NUM>, the periodic structure inclusion determination unit of the moiré occurrence prediction device determines a periodic structure region that includes a periodic structure that will induce the occurrence of moiré with respect to the original image in the vector data format.

It should be noted that the details of the periodic structure inclusion determination process will be described later with reference to <FIG>.

Next, in step <NUM>, the moiré prediction unit of the moiré occurrence prediction device performs a predetermined frequency analysis process on each periodic structure region determined by the periodic structure inclusion determination unit, thereby determining the degree of risk of a moiré occurring in each periodic structure region, and generating a moiré occurrence notification that indicates the degree of risk for each periodic structure region.

It should be noted that the details of the moiré prediction process will be described later with reference to <FIG>.

Next, in step <NUM>, with respect to the original image, the region extraction unit of the moiré occurrence prediction device extracts similar regions which have the same pattern as the region targeted for the moiré prediction process in step <NUM> by performing a color conversion process to convert the original image into a predetermined color space, and generating an element similarity map that indicates similar regions having similar pixel values.

It should be noted that a filter process of the suppression processing regions will be described later with reference to <FIG>.

Next, in step <NUM>, the moiré suppression unit of the moiré occurrence prediction device performs a predetermined moiré suppression process on the similar regions extracted in step <NUM>. As the moiré suppression processing performed by the moiré prediction unit, a method of adjusting the size of the tone so that the individual tone densities of the halftone dot image match the original image densities at the same positions, or a method of converting the tone shape of the halftone dot image to a clean tone can be considered, but the moiré prediction process according to the embodiments of the present disclosure is not limited thereto, and any moiré suppression process may be performed.

Next, in step <NUM>, the image in which the moiré is suppressed, which has been processed by the moiré suppression unit of the moiré occurrence prediction device, is output to the client terminal or the printing unit.

According to the moiré occurrence prediction process described above, by predicting a region in which moiré will occur and performing the moiré suppression process only on a pattern including this region, it is possible to suppress print deterioration, suppress the occurrence of moiré, and perform high-quality printing.

Next, with reference to <FIG>, a periodic structure inclusion determination process according to the embodiments of the present disclosure will be described.

<FIG> is a diagram illustrating an example of a flow of the periodic structure inclusion determination process <NUM> according to the embodiments of the present disclosure. The periodic structure inclusion determination process <NUM> illustrated in <FIG> is a process performed by the periodic structure inclusion determination unit <NUM> illustrated in <FIG>, and is a process for determining a periodic structure region in the original image that includes a periodic structure that induces the occurrence of moiré.

In general, moiré are generated by interference between the periodic structures included in the original image and the pitch and angle of the halftone dots. Accordingly, as described above, by performing the periodic structure inclusion determination process <NUM> according to the embodiments of the present disclosure on each of the processing blocks that constitute the original image, it is possible to determine periodic structure regions in the original image that include periodic structures that induce the occurrence of moiré. Further, as will be described later, by performing a predetermined frequency analysis process on the periodic structure regions determined in this way, it is possible to determine the degree of risk of moiré occurrence in each periodic structure region and generate a moiré occurrence notification that indicates the degree of risk for each periodic structure region.

It should be noted that the processing blocks determined not to include periodic structures are not subjected to the moiré prediction process, the moiré suppression process, or the like, which will be described later. As described above, by performing the moiré prediction process, the moiré suppression process, and the like, which will be described later, only for those blocks that include a periodic structure that induces the occurrence of moiré, the processing speed can be increased as compared with conventional moiré suppression means, and the quality deterioration caused by performing the moiré suppression process on the extra regions that do not include moiré can be suppressed.

First, in step <NUM>, the periodic structure inclusion determination unit inputs the original image. Here, the periodic structure inclusion determination unit may acquire the original image received from the client terminal using the communication unit and preprocessed by the preprocessing unit from the storage unit.

Next, in step <NUM>, the periodic structure inclusion determination unit performs a color conversion process for converting the original image into an arbitrary color space. In this color conversion process, an original image composed of the colors of RGB may be converted into a LUV color space or a LAB space.

Among the components of the converted color, a luminance component with high visibility can be used. In addition, since there is a difference in luminosity between a* and b*, weighting corresponding to the difference may be performed. Furthermore, only one of a* and b* may be used.

Next, in step <NUM>, the periodic structure inclusion determination unit generates a mask from which the contour component of the original image is extracted (that is, removed or eliminated).

More specifically, when determining the presence or absence of a periodic structure in the original image, in the case that a boundary where the brightness abruptly changes, such as the vicinity of the outline of the object, exists within the processing block, the high-frequency components resulting from parts of the boundary may become noise, and the S/N ratio (Signal/Noise Ratio) at the time of the determination may decrease. Therefore, it is desirable to perform frequency analysis after pre-processing to remove boundaries where sudden luminance changes exist.

Accordingly, the periodic structure inclusion determination unit generates a mask from which the contour components are extracted from the original image after the color-conversion, thereby avoiding a low S/N ratio and improving the accuracy of the periodic structure inclusion determination.

Next, in step <NUM>, the periodic structure inclusion determination unit performs a process of normalizing the maximum value and the minimum value of the pixels in the original image.

More specifically, in the frequency analysis which will be described later, it is possible to determine the periodic structures that will induce the occurrence of moiré by determining from the two-dimensional data obtained by a discrete Fourier transform whether or not a specific peak is included. When the presence or absence of the periodic structures in the original image is determined, it is possible to further increase the accuracy of the periodic structure inclusion determination by removing the aforementioned contour components and normalizing the density information (color/brightness information) of the structure. As an example, a smoothing process can be performed on the original image after color conversion, the difference before and after smoothing can be extracted, and normalization can be performed so that the maximum and minimum values of the pixel values become specific values. However, the present disclosure is not limited hereto, and any normalization means may be used.

Next, in step <NUM>, the periodic structure inclusion determination unit performs a frequency analysis process on the original image subjected to the color conversion process, the contour mask generation process, and the normalization process described above. As the frequency analysis process at this time, for example, a Fast Fourier Transform (FFT) can be mentioned as a representative algorithm. FFT is an algorithm that speeds up Discrete Fourier Transforms (DFTs), which are Fourier transforms for discrete data.

The power spectrum image generated by performing FFT on the original image has a frequency of zero at its center position, and has a distribution such that the frequency components in each direction increase in frequency away from the center.

The characteristics of the frequency distribution of an image including a periodic structure that induces the occurrence of moiré are that high-intensity frequency components are concentrated in a specific region, that the maximum peak intensity is high, and that high-intensity frequency components are also present in high-frequency regions.

On the other hand, in the frequency distribution of an image that does not include a periodic structure, frequency components having a high intensity are dispersed in low frequency regions.

Accordingly, in step <NUM>, in order to determine the presence or absence of periodic structures that induce the occurrence of moiré, the periodic structure inclusion determination unit quantitatively calculates the peak position, the peak intensity, and the intensity distribution when the frequency distribution is divided into arbitrary angle increments as feature values. After that, the obtained feature values are evaluated, and the degree of risk of moiré occurrence can be determined for each block.

It should be noted that examples of the method of evaluating the feature values include a method of evaluating each feature value by setting a threshold value, a method of evaluating the feature value based on likelihood after a regression analysis, a method of classifying the presence or absence of moiré by cluster analysis, or a combination of the methods described above.

Next, in step <NUM>, the information of the periodic structure regions that include periodic structures determined by the above-described processing are output to the moiré prediction unit.

Next, with reference to <FIG>, a frequency analysis according to the embodiments of the present disclosure will be described.

<FIG> is a diagram illustrating an example of frequency analysis according to the embodiments of the present disclosure. As described above, in the moiré occurrence prediction process according to the embodiments of the present disclosure, by performing a frequency analysis process on the input image (the original image and/or the halftone dot image), it is possible to determine the periodic structures that induce the occurrence of moiré in the input image, and to predict the degree of risk of moiré occurrence.

<FIG> is a diagram illustrating frequency-analyzed images <NUM> and <NUM> obtained by performing the frequency analysis process on each of the input images <NUM> and <NUM>. As described above, since moiré is a periodic structure of light and dark, examples of the characteristics of the frequency distribution of an image including a periodic structure that induces the occurrence of moiré are that high-intensity frequency components are concentrated in a specific region, that the maximum peak intensity is high, and that high-intensity frequency components are also present in high-frequency regions.

On the other hand, in a frequency distribution of an image that does not include a periodic structure, frequency components having a high intensity are dispersed in low frequency regions. Accordingly, by performing the frequency analysis process according to the embodiments of the present disclosure on the input image, it is possible to determine whether or not there is a periodic structure in the input image that induces moiré.

As an example, by performing the above-described frequency analysis process on the input image <NUM>, a frequency-analyzed image <NUM> having spectral components with high peak intensities which are spaced apart at regular intervals is obtained. From the periodic distribution of the spectral components appearing in the frequency-analyzed image <NUM> and the intensity of the peaks, the input image <NUM> is determined as having a periodic structure that induces moiré.

On the other hand, as another example, by performing the above-described frequency analysis process on the input image <NUM>, a frequency-analyzed image <NUM> having a frequency distribution in which the spectral components having high intensity are dispersed in the low frequency region is obtained. From the distribution of the spectral components of the frequency-analyzed image <NUM>, the input image <NUM> is determined as having no periodic structure that induces moiré.

Accordingly, as described below, by performing the frequency analysis process according to the embodiments of the present disclosure on an arbitrary input image, it is possible to determine a region including periodic structures that induce moiré. In addition, by performing the moiré suppression process only on the regions that include periodic structures that induce moiré, it is possible to mitigate print deterioration, suppress the occurrence of moiré, and provide high-quality printed materials.

Next, with reference to <FIG>, a moiré prediction process according to the embodiment of the present disclosure will be described.

<FIG> is a diagram illustrating an example of a flow of a moiré prediction process <NUM> according to the embodiments of the present disclosure. The moiré prediction process <NUM> illustrated in <FIG> is a process performed by the moiré prediction unit <NUM> illustrated in <FIG>, and is a process for predicting moiré for each processing block of the periodic structure region determined to include a periodic structure that induces moiré, and determining the degree of risk of moiré occurrence.

The halftone dot image and the original image obtained from the RIP process have different resolutions and depiction methods, but the components other than moiré, such as color/brightness/pattern information, are the same. Accordingly, in the moiré prediction process <NUM>, the moiré components can be extracted by aligning and comparing the resolutions and depictions of both images. After that, by performing frequency analysis on the moiré component, it is possible to predict the occurrence of moiré for each processing block in the periodic structure regions, and to determine the degree of risk of moiré occurrence.

First, in step 531a and step 531b, the original image and the halftone dot image are input. Here, the moiré prediction unit may acquire, from the storage unit, the original image subjected to the periodic structure inclusion determination process and the halftone dot image corresponding to the original image.

Next, in step 532a and step 532b, the moiré prediction unit performs a color conversion process for converting the original image and the halftone dot image into a predetermined color space. More specifically, the original image and the halftone dot image are image data separated as Cyan, Magenta, Yellow, Black (CMYK), for instance, but in some cases, moiré manifests as the result of the overlapping of a plurality of plates. Accordingly, since there are moiré that cannot be extracted even when the images of respective plates are compared with each other, it is desirable to combine the information of the plates and perform color conversion in order to detect the moiré that occur due to the overlapping of a plurality of plates. Examples of the color-conversion include a Lab and a LUV space.

It should be noted that the subsequent processing is performed on the obtained brightness image (for example, L) and color image (for example, a and b). In addition, the color conversion result of the original image generated by the above-described periodic structure inclusion determination may be used.

Next, in step <NUM>, the moiré prediction unit generates a mask that extracts (that is, removes and eliminates) the contour component of the original image.

When performing the resolution conversion which will be described later on the halftone dot image, there is a possibility that the halftone dot image may, with respect to the original image, have a misalignment of the outline of the pattern, or the outline may become thicker or thinner. Accordingly, when the difference extraction process to be described later is performed, if the halftone dot image and the original image are compared and the density difference is obtained between the images, there is a possibility that an accurate difference cannot be extracted. Accordingly, in a finely represented region in an image, although there is no sense of discomfort due to the optical illusion effect when viewed macroscopically, when the pixel value data between the halftone dot image and the original image are compared, there is a tendency for large errors to occur.

Accordingly, in view of the above, in order to extract a more high-quality difference, the contour component is extracted with respect to the original image, and a contour mask for excluding the contour region from the analysis target is created.

It should be noted that the contour mask generated by the above-described periodic structure inclusion determination may be used.

Next, in step 534a, the moiré prediction unit performs a smoothing process on the original image. This smoothing process is a process of performing a filtering process on the original image and smoothing the pixel of interest while including information about the neighboring pixel values. Examples of the smoothing filter include a moving average filter having a uniform weight with respect to neighboring pixels, and a Gaussian filter having a larger weight with respect to the pixel of interest, and either of them may be used.

It should be noted that the degree of smoothing depends on the size of the smoothing filter, and it is desirable to reduce the size of the filter as the number of lines increases. When the number of lines is small, the halftone dots are large, so it is necessary to perform strong smoothing in order to eliminate the halftone dots, and when the number of lines is large, strong smoothing may cause a new moiré.

Next, in step 534b, the moiré prediction unit performs a resolution conversion process for aligning the resolution of the halftone dot image with the resolution of the original image and a smoothing process on the halftone dot image.

In the state prior to the resolution conversion process, the halftone dot image is <NUM>-bit data, and the resolution is high. On the other hand, the original image is depicted in <NUM>-bit grayscale, and the resolution is lower than that of the halftone dot image (for example, the halftone dot image may be 2400dpi, and the original image may be 600dpi, or the like). Accordingly, in order to extract a higher-quality difference, it is desirable to perform resolution conversion on the halftone dot image and smoothing to align the depictions.

Typical methods of resolution conversion include the nearest neighbor method, the bicubic method, and the bilinear method. However, when the halftone dot image is compressed with the nearest neighbor method, the halftone dot image becomes too coarse, and when the halftone dot image is compressed by the bicubic method, the smoothing when interpolation is performed is too strong, which may cause a problem in the subsequent analysis. For this reason, resolution compression by the bilinear method is desirable.

It should be noted that the smoothing process performed on the halftone dot image in step 534b is substantially the same as the smoothing process performed on the original image in step 534a, and therefore will not be described here. In addition, with regard to the order of processing, it is desirable to perform resolution conversion and smoothing after color conversion for the halftone dot image. This is because the strength component of moiré resulting from overlapping plates is weakened.

Next, in step <NUM>, the moiré prediction unit generates a difference extraction image representing the pixel difference between the halftone dot image subjected to the color conversion process, the smoothing process, and/or the resolution conversion process described above and the original image, and multiplies the difference extraction image by the contour mask described above to extract the moiré component.

Next, in step <NUM>, the moiré prediction unit performs a frequency analysis process on the image data subjected to the above processing. Since the frequency analysis process is substantially the same as the frequency analysis process in the structure inclusion determination process described with reference to <FIG>, for example, a detailed description thereof will be omitted here.

In the frequency analysis process, the peak position of the frequency component, the peak intensity, and the intensity distribution when the frequency distribution is divided into arbitrary angular increments are quantitatively calculated as feature values. Accordingly, the evaluation is performed based on the feature values obtained in this manner, and the degree of risk of moiré occurrence can be determined for each block.

In addition, in order to mitigate the influence of high-frequency noise including Rosetta patterns on the determination and in order to conform with human visual sensitivity, it is desirable to improve the accuracy through a low-pass filter. The low-pass filter is preferably a Gaussian filter or a Welch filter whose strength decreases with increasing distance from the direct flow component. The size of the low-pass filter is set to a size similar to the visual sensitivity of humans, according to the grid size.

It should be noted that, in order to reflect the information on the peak position with a dark color, in the extraction of the feature values, it is desirable to improve the evaluation accuracy by extracting the feature values separately for the low frequency and high frequency regions using the two types of masks described later with reference to <FIG>. The boundary between the low-frequency and high-frequency regions may be a fixed value or may be a function of the number of lines. The function may be a monotonic function.

The feature values obtained by performing the frequency analysis process are evaluated, and the degree risk of moiré occurring in each processing block is determined. Here, the degree of risk of moiré occurrence is a measure indicating a possibility of occurrence of a moiré, and may be expressed in any number of stages. For example, in some embodiments, the degree of risk of moiré occurrence may be expressed in three stages such as: <NUM>: No possibility of moiré occurrence, <NUM>: Possibility of moiré occurrence, <NUM>: High possibility of moiré occurrence. In addition, in some embodiments, each of the processing blocks may be colored with a predetermined pseudo-color in accordance with the possibility that moiré will occur in that block (gray: no possibility of moiré occurrence, yellow: possibility of moiré occurrence, red: high possibility of moiré occurrence). In yet another embodiment, each of the processing blocks may represent the probability of moiré occurrence in that block as a percentage.

It should be noted that examples of the evaluation method described above include a method of evaluating each feature value by setting a threshold value, a method of evaluating the feature value based on likelihood after a regression analysis, a method of classifying the presence or absence of moiré by cluster analysis, or a combination of the methods described above. In addition, the threshold for determination may be set in two steps. For example, when a relaxed condition of "moiré advisory" and a strict condition of "moiré warning" are set, the image correction process can be judged based on the moiré advisory, and the printing process can be judged based on the moiré warning.

Next, in step <NUM>, the moiré prediction unit may generate and output a moiré occurrence notification that indicates the degree of risk of moiré occurrence for each processing block. As described above, the moiré occurrence notification may be, for example, an image indicating the degree of risk of moiré occurrence for each processing block (see <FIG>).

In an embodiment, after transmitting the generated moiré occurrence notification to the client terminal, the client terminal that has received the moiré occurrence notice may perform the moiré suppression processing on the client terminal side.

In yet another embodiment, the moiré prediction unit may output the generated moiré occurrence notification to the region extraction unit <NUM>, and then a moiré suppression process may be performed on the moiré occurrence prediction device side. Thereafter, the communication unit may transmit both the moiré occurrence notification and the image subjected to the moiré suppression process to the client terminal.

In yet another embodiment, the moiré prediction unit may extract only the processing blocks that satisfy a predetermined risk criterion (for example, those for which the risk of moiré occurrence is high) and transmit the extracted processing blocks to the client terminal. This makes it possible to reduce the capacity of data to be transmitted.

According to the above-described processing, it is possible to predict the risk of occurrence of moiré for each of the plurality of processing blocks that constitute the original image. In addition, based on this degree of risk, the moiré suppression processing is performed only on the processing blocks for which moiré occurrence is predicted, whereby it is possible to mitigate print deterioration, suppress the occurrence of moiré, and perform high-quality printing.

Next, with reference to <FIG>, an example in which frequency analysis is performed on a difference extraction image according to the embodiments of the present disclosure will be described.

<FIG> is a diagram illustrating an example of a case where frequency analysis is performed on a difference extraction image according to an embodiment of the present disclosure. As described above, in the moiré occurrence prediction process according to the embodiments of the present disclosure, by performing a frequency analysis process on a difference extraction image generated by comparing the original image and the halftone dot image, the peak position of the frequency component, the peak intensity, and the intensity distribution when the frequency distribution is divided into arbitrary angular increments can be quantitatively calculated as feature values, and the risk of moiré occurrence can be predicted based on these feature values.

<FIG> is a diagram illustrating frequency-analyzed images <NUM> and <NUM> obtained by performing a frequency analysis process on each of the difference extraction images <NUM> and <NUM>.

As an example, by performing the above-described frequency analysis process on the difference extraction image <NUM>, a frequency-analyzed image <NUM> that has spectral components with high peak intensities that are spaced apart at regular intervals is obtained.

On the other hand, as another example, by performing the above-described frequency analysis process on the difference extraction image <NUM>, a frequency-analyzed image <NUM> having a frequency distribution in which high-intensity frequency components are dispersed in the low-frequency region is obtained.

<FIG> is a diagram illustrating an example of a case where a moiré is determined using a threshold value after frequency analysis has been performed on a difference extraction image according to the embodiments of the present disclosure. More specifically, <FIG> illustrates an example in which two filters (here, a narrow-band filter and a wide-band filter) are applied to each of the frequency-analyzed images <NUM> and <NUM> shown in <FIG>, and the ratio of the values in the two filters is set as a threshold for determining moiré. By using this threshold, it is possible to distinguish between noise and moiré.

For example, when a narrow-band filter and a wide-band filter are applied to the frequency-analyzed image <NUM>, Enarrow/Ewide, which is the ratio of the sum Enarrow of the spectral components in the narrow-band filter to the sum Ewide of the spectral components in the wide-band filter, is calculated as <NUM>.

On the other hand, when the narrow-band filter and the wide-band filter are applied to the frequency-analyzed images <NUM>, Enarrow/Ewide, which is the ratio of the sum Enarrow of the spectral components in the narrow-band filter to the sum Ewide of the spectral components in the wide-band filter, is calculated as <NUM>.

Assume that <NUM>□Enarrow/Ewide <NUM><NUM> is set as a threshold for determining moiré. That is, when Enarrow/Ewide, which is the ratio of the sum Enarrow of the spectral components in the narrow-band filter to the sum Ewide of the spectral components in the wide-band filter, satisfies <NUM>□Enarrow/Ewide□<NUM>, it is determined that moiré is present in the image, and when Enarrow/Ewide is less than <NUM>, it is determined that the image is noisy.

In this way, after the frequency analysis process is performed on the target image, by applying two filters (here, a narrow-band filter and a wide-band filter) and setting the ratio of the values in the two filters as a threshold value for determining moiré, it is possible to predict whether moiré will occur in the target image based on this threshold value.

Further, in addition to the above thresholds, the absolute values of Enarrow and Ewide may be used as an index of the intensity of the moiré. The threshold at that time may be set by an operator according to the type of image or printing.

Next, with reference to <FIG>, the masks used in the frequency analysis process according to the embodiments of the present disclosure will be described.

<FIG> is a diagram illustrating an example of the masks used in the frequency analysis process according to the embodiments of the present disclosure. As described above, in the frequency analysis process according to the embodiments of the present disclosure, the peak position of the frequency component, the peak intensity, and the intensity distribution when the frequency distribution is divided into arbitrary angular increments can be quantitatively calculated as feature values. In the calculation of these feature values, it is desirable to use a mask in order to reflect the information on the peak position with a dark color. Examples of the mask used here include the low-frequency mask <NUM> and the high-frequency mask <NUM> illustrated in <FIG>. By using two types of masks including the low-frequency mask <NUM> and the high-frequency mask <NUM> shown in <FIG> in the frequency analysis process, the feature values can be calculated individually for each of the low-frequency region and the high-frequency region, and thus the evaluation accuracy of the moiré described above can be increased.

The boundaries between the low-frequency region and the high-frequency region may be fixed values or may be a function of the number of lines. This function may be a monotonic function. It should be noted that in the low-frequency mask <NUM> and the high-frequency mask <NUM> illustrated in <FIG>, the white region indicates effective pixels.

Next, with reference to <FIG>, the filter process of the suppression processing region according to the embodiments of the present disclosure will be described.

<FIG> is a diagram illustrating an example of the flow of a filter process <NUM> with respect to the suppression processing regions according to the embodiments of the present disclosure. The filter process <NUM> of the suppression processing regions illustrated in <FIG> is a process performed by the region extraction unit <NUM> illustrated in <FIG>, and is a process for extracting similar regions having similar elements.

More specifically, at the stage where the above-described moiré prediction process is completed, a moiré occurrence notification indicating the degree of risk of moiré occurrence per arbitrary size processing block unit is obtained. By performing the moiré suppression process on those processing blocks for which moiré occurrence is predicted, this moiré can be removed. However, if the suppression process is performed for each processing block, there is a possibility that the presence or absence of the moiré suppression process may be visually recognized in a block shape. That is, when there are processing blocks with similar depictions or patterns in an image, and the moiré suppression process is applied to only a portion of these processing blocks, a visual difference may occur between those processing blocks for which the moiré suppression process has been performed and those processing blocks for which the moiré suppression process has not been performed, which may give users a sense of discomfort.

Accordingly, in the filter process <NUM> of the suppression processing regions according to the embodiments of the present disclosure, by dividing the original image into similar regions having shared depictions or patterns, and performing the moiré suppression process for each similar region, the problem of visually recognizing the presence or absence of the moiré suppression processing can be solved.

The details of the filter process <NUM> for the reduction processing regions will be described below. Note that the filter process <NUM> of the suppression processing regions is processing performed on the original image that is not divided into the processing blocks.

First, in step <NUM>, the region extraction unit <NUM> inputs the original image. Here, the region extraction unit <NUM> may acquire the original image from the storage unit of the moiré occurrence prediction device.

Next, in step <NUM>, the region extraction unit <NUM> performs a color conversion process for converting the original image into an arbitrary color space. In this color conversion process, an original image composed of the colors of RGB may be converted into LUV color space or a LAB space.

Here, the region extraction unit <NUM> may use, for example, the color conversion result of the original image generated in the periodic structure inclusion determination process <NUM> described above.

Next, the region extraction unit <NUM> divides the original image into similar regions having shared depictions or patterns, and generates an element similarity map that indicates the similar regions that have similar pixel values.

Here, as a method of dividing the original image into similar regions having shared depictions or patterns, calculating Sobel values with respect to the original image and perform a smoothing process can be considered. By means of such processing, an element similarity map having pixel values similar to each depiction or pattern is generated. In addition, with regard to the division of each similar region, a method of dividing the similar regions based on the presence or absence of a fine periodic structure as in the above processing is desirable.

As described above, moiré occurs only when a fine periodic structure is included, and if there are similar regions having the same pattern, since the pixel values are uniformly distributed over the entire surface by the above-described processing, the above-described method is efficient in the subsequent processing and aligns with the intentions of the user.

It should be noted that, as a method of dividing the similar regions, a method based on brightness or chromaticity, or image processing such as semantic segmentation may be used.

By performing contour extraction on the element similarity map obtained in this processing, information on the boundary lines of each similar region is obtained.

Next, in step <NUM>, the region extraction unit <NUM> selects a region to be subjected to the suppression processing based on the boundary line information of the similar regions obtained in step <NUM> and the moiré occurrence notification obtained by the moiré prediction process <NUM> described above. Here, FloodFill (a filling algorithm) may be used as a method for combining information, or other image processing techniques may be used.

According to the above-described filter process <NUM> for the suppression processing regions, the original image can be divided into similar regions having shared depictions or patterns, and therefore, it becomes possible to perform the moiré suppression process in units of similar regions. The similar regions extracted by the filter process <NUM> for the suppression processing region proceed to the moiré suppression process in the moiré occurrence prediction process <NUM> illustrated in <FIG>, and are subjected to the moiré suppression process.

Next, with reference to <FIG>, a moiré occurrence notification according to the embodiments of the present disclosure will be described.

<FIG> is a diagram illustrating an example of a moiré occurrence notification <NUM> according to the embodiments of the present disclosure. As described above, the moiré occurrence notification <NUM> according to the embodiments of the present disclosure is information indicating the degree of risk of moiré occurrence for each processing block. As illustrated in <FIG>, in an embodiment, the moiré occurrence notification <NUM> may be an image that indicates the risk of moiré occurrence for each processing block.

As described above, in the moiré occurrence notification <NUM> illustrated in <FIG>, each of the processing blocks may be colored with a predetermined pseudo color in accordance with the possibility that moiré will occur in that block. For example, in the moiré occurrence notification <NUM>, a processing block <NUM> in which there is no possibility of moiré occurrence may be gray, a processing block <NUM> (advisory area) in which there is a possibility of moiré occurrence may be yellow, and a processing block <NUM> in which there is a high possibility of moiré occurrence may be red.

Next, an example of processing blocks according to the embodiments of the present disclosure will be described with reference to <FIG>.

<FIG> is a diagram illustrating an example of processing blocks according to the embodiments of the present disclosure. As described above, the processing blocks here are small regions obtained by dividing the original image into sections of a fixed size, and are the units on which processing is performed.

As illustrated in <FIG>, the original image <NUM> may be divided into <NUM> (<NUM>×<NUM>) fixed-size processing blocks <NUM>. The size of the processing blocks <NUM> is preferably, for example, a power of <NUM>, but is not particularly limited. In addition, it is preferable that the processing blocks have the same size in the vertical and horizontal directions. For example, 128x128, 256x256, 512x512, 1024x1024, 2048x2048 or the like may be used as examples of the size of the processing blocks. It should be noted that the actual size of the processing blocks may be equal to or larger than <NUM> and equal to or smaller than <NUM>.

As described above, the processing according to the embodiments of the present disclosure (for example, the process <NUM> for extracting the structure-including regions and the moiré prediction process <NUM>) is performed for each processing block. Further, in an embodiment, the processing performed for each processing block may be performed not only for each processing block but also for overlapping regions <NUM> that have the same size as the processing blocks and that overlap a plurality of adjacent processing blocks. For example, as illustrated in <FIG>, the overlapping region <NUM> may be a region shifted by half of a processing block.

Thus, in addition to the processing with respect to the processing blocks, when an overlapping region <NUM> is formed so as to straddle a plurality of adjacent processing blocks, although the amount of processing increases, it is possible to increase the accuracy of predicting moiré in those portions that straddle processing blocks.

By using processing blocks <NUM> that have a fixed size as illustrated in <FIG>, it is possible to increase the calculation speed, and it is possible to reduce the memory of the processing and increase the cache hit rate. In addition, by using overlapping regions <NUM> that straddle a plurality of adjacent processing blocks, it is possible to reduce the difference between the periodic structures that cross processing block boundaries and the periodic structures within processing blocks.

Further, the processing blocks <NUM> illustrated in <FIG> may be divided into smaller sub-blocks <NUM>. These sub-blocks <NUM> may be, for example, half the size of the processing blocks <NUM> or a quarter of the size of the processing blocks <NUM>. In addition, since these sub-blocks <NUM> can reuse the results of the sub-blocks for which calculation has already been performed in the calculation of the Fourier transform of the overlapping regions <NUM>, processing efficiency can be increased.

As described above, by using sub-blocks to provide overlapping regions that overlap processing blocks, it is possible to improve the determination accuracy of periodic structures that induce moiré.

Although the embodiments of the present disclosure have been described above, the scope of the invention is not limited to the above-described embodiments, but is defined by the claims.

As used herein, the terms "unit," "system," and "network" are physical entities. These physical entities can include electrical circuits, associated devices, or the wired/wireless connections between them. These may have specific functions. These combinations having specific functions can exhibit synergistic effects by the combination of their respective functions.

The terms used within the present disclosure and particularly the appended claims (that is, the text of the appended claims) are generally intended as "open-ended" terms (that is, the term "having" should be interpreted as "having at least" and the term "including" should be interpreted as "including, but not limited to," for instance).

In addition, when interpreting terms, configurations, features, aspects, and embodiments, the drawings should be referred to as needed. Matters that can be derived directly and unambiguously from the drawings may be the basis for amendments, equivalent to text.

Claim 1:
A moiré occurrence prediction device comprising:
a communication unit for receiving an input image;
a periodic structure inclusion determination unit for dividing the input image into plural processing regions of a fixed size, and determining, for each of the plural processing regions of the input image, whether or not the respective processing region of the input image is a periodic structure region that includes a periodic structure that induces the occurrence of moiré in the input image;
an obtaining unit for obtaining a halftone dot image corresponding to the input image;
a generation unit for generating a difference extraction image as a difference between the input image and the halftone dot image;
a moiré prediction unit for determining a degree of risk of moiré occurrence in each periodic structure region by performing a predetermined frequency analysis process only with respect to each of the determined periodic structure regions in the difference extraction image, generating a moiré occurrence notification that indicates the degree of risk for each periodic structure region, and outputting the moiré occurrence notification.