Print quality evaluation device and print quality evaluation method

Abstract

To provide a print quality evaluation device and a print quality evaluation method capable of objectively and correctly evaluating the quality of print applied to a print object.SOLUTION: A print quality evaluation device EA for evaluating the quality of print applied to a print object comprises: image acquisition means 21 for acquiring an image obtained by capturing a print image printed on the print object; luminance value acquisition means 22 for acquiring a luminance value of the image from the image acquired by the image acquisition means 21; skewness calculation means 23 which applies the luminance value acquired by the luminance value acquisition means 22 to skewness being a roughness parameter and calculates the skewness from the luminance value; and evaluation means 24 which evaluates the quality of print on the basis of the skewness calculated by the skewness calculation means 23.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a print quality evaluation device and a print quality evaluation method. [Background technology]

[0002] For example, when printing on a print object at high speed by reducing the number of printing passes or lowering the print resolution, print quality may be reduced due to ink contours bleeding, etc. For this reason, a print quality evaluation method for evaluating the print quality of a print applied to a print object is known (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2020-90612 Summary of the Invention [Problem to be solved by the invention]

[0004] The evaluation method described in Patent Document 1 involves visually evaluating the print quality of the print applied to the printing object, which has the disadvantage that the evaluation varies depending on the evaluator's subjectivity, making it impossible to objectively and accurately evaluate the print quality.

[0005] An object of the present invention is to provide a print quality evaluation device and a print quality evaluation method that can objectively and accurately evaluate the print quality of a print applied to a printing object. [Means for solving the problem]

[0006] A print quality evaluation device according to one aspect of the present invention is a print quality evaluation device that evaluates the print quality of printing applied to a printing object, and is equipped with an image acquisition means that acquires a captured image of a print image printed on the printing object, a brightness value acquisition means that acquires a brightness value of the captured image from the captured image acquired by the image acquisition means, a skewness calculation means that applies the brightness value acquired by the brightness value acquisition means to skewness, which is a roughness parameter, and calculates the skewness from the brightness value, and an evaluation means that evaluates the print quality based on the skewness calculated by the skewness calculation means.

[0007] In a print quality evaluation device according to one aspect of the present invention, the evaluation means may evaluate the print quality using a trained model that has been machine-learned using as training data the ink density of a print image printed on a learning print object, the skewness of the brightness values ​​of a training image obtained by capturing the print image, and an evaluation result of the print quality of the print applied to the learning print object.

[0008] A print quality evaluation method according to one aspect of the present invention is a print quality evaluation method for evaluating the print quality of a print applied to a print object, and includes an image acquisition step for acquiring an image of a print image printed on the print object, a brightness value acquisition step for acquiring a brightness value of the image acquired in the image acquisition step from the image, a skewness calculation step for applying the brightness value acquired in the brightness value acquisition step to skewness, which is a roughness parameter, and calculating the skewness from the brightness value, and an evaluation step for evaluating the print quality based on the skewness calculated in the skewness calculation step. [Effects of the Invention]

[0009] According to one aspect of the present invention, skewness, which is a roughness parameter, is calculated from the brightness value of an image captured of a printed image printed on a printing object, and print quality is evaluated based on skewness.This means that the evaluation does not change depending on the evaluator's subjectivity, and print quality can be evaluated objectively and accurately. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is an explanatory diagram of a print quality evaluation system according to an embodiment. [Figure 2] FIG. 1 is an explanatory diagram of a print quality evaluation method according to an embodiment. [Figure 3] FIG. 10 is a diagram showing skewness and evaluation results. [Figure 4] An explanatory diagram of a method for generating a trained model for evaluating print quality. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. [Device configuration] In Figure 1, the print quality evaluation device EA is a device that evaluates the print quality of printing performed on a printing medium such as printing paper or printing film, and is configured with a computer such as a personal computer or server. Note that print quality refers to the actual print resolution and color development relative to the required resolution and color development, and the print quality is judged to be good when the printed image on the printing medium has the color development (including color tone and bleeding) expected by the user. Note that poor color development can be caused by ink pigment segregation, the use of multiple inks, differences in ink wettability, etc. The print quality evaluation device EA comprises a storage means 10, a processing means 20, and an operation means 30, and together with a printing means 40, an imaging means 50, and an output means 60 constitute a print quality evaluation system EA1.

[0012] The storage means 10 is configured with a memory, a hard disk, etc., and stores various programs for controlling the print quality evaluation device EA and the print quality evaluation system EA1. In this embodiment, the storage unit 10 also stores a threshold value for the skewness Rsk used to evaluate print quality.

[0013] The processing means 20 is configured by a processor such as a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit), and includes an image acquisition means 21, a brightness value acquisition means 22, a skewness calculation means 23, and an evaluation means 24.

[0014] The image acquisition means 21 acquires a captured image of a print image printed on a printing object from the imaging means 50. Examples of the print image include an image, a character, a pattern, an identifier, and a test print pattern.

[0015] The brightness value acquisition means 22 acquires the brightness value of the captured image acquired by the image acquisition means 21 from the captured image.

[0016] The skewness calculation means 23 applies the brightness value acquired by the brightness value acquisition means 22 to the skewness Rsk, which is a roughness parameter, and calculates the skewness Rsk from the brightness value. The skewness Rsk is calculated by the following formula 1, where the roughness curve is Z(x), the reference length of the roughness curve is l, and the square root height of the roughness curve at the reference length is Rq.

[0017]

number

[0018] The evaluation means 24 evaluates the print quality based on the skewness Rsk calculated by the skewness calculation means 23.

[0019] The operation means 30 is composed of a keyboard, an operation panel, a touch panel, a mouse, various switches, a microphone for voice input operation, etc., and is capable of inputting operation signals from various operations into the print quality evaluation device EA and the print quality evaluation system EA1. For example, the ink density of printing on the printing object is set via the operation means 30, and the operation signal is sent to the processing means 20 and the printing means 40 together with the set value of the ink density.

[0020] The printing means 40 is configured with an inkjet printer, laser printer, thermal printer, dot printer, offset printing machine, letterpress printing machine, intaglio printing machine, screen printing machine, or the like, and is capable of sending and receiving various signals to and from the processing means 20. The printing means 40 is also configured so that the number of printing passes and printing resolution can be changed via the operation means 30.

[0021] The imaging means 50 is composed of a camera, a camera, a microscope with an imaging function, an imaging sensor, etc., and is capable of transmitting and receiving captured images and various signals to and from the processing means 20.

[0022] The output means 60 is composed of display devices such as displays or panels, and notification devices such as indicator lights or speakers, and is configured to output the evaluation results from the print quality evaluation device EA on a screen or by lighting or sound, etc.

[0023] [Print quality evaluation method] A print quality evaluation method carried out in the following procedure shown in FIG. 2 will be described using the print quality evaluation system EA1 equipped with the above print quality evaluation device EA as an example. First, a user of the print quality evaluation system EA1 (hereinafter simply referred to as the "user") sets the ink density of the print to be applied to the print target via the operation unit 30, and then inputs a signal to start automatic operation via the operation unit 30. Next, when the user or a transport unit (not shown), such as an articulated robot or a belt conveyor, places the print target in a predetermined position, the processing unit 20 drives the printing unit 40 to print on the print target with the set ink density (step ST11). The processing unit 20 then drives the imaging unit 50 to capture an image of the print image printed on the print target (step ST12). At this time, it is possible to avoid capturing images of print images printed with a number of passes or resolution that is likely to result in a good print quality evaluation, and instead capture images of print images printed with a number of passes or resolution below a predetermined value that is likely to result in a poor print quality evaluation. The predetermined values ​​for the number of passes and resolution may be set depending on the print target and the printing unit 40.

[0024] Next, the image acquisition means 21 acquires a captured image of the print image from the imaging means 50 (step ST13). Then, the brightness value acquisition means 22 acquires the brightness value of each pixel of the captured image from the captured image acquired in step ST13 (step ST14). In this embodiment, the brightness value acquisition means 22 performs grayscale processing on the captured image and then acquires the brightness value of each pixel. Next, the skewness calculation means 23 calculates the skewness Rsk from the brightness value acquired in step ST14 (step ST15).

[0025] Thereafter, the evaluation means 24 evaluates the print quality based on the skewness Rsk calculated by the skewness calculation means 23, and outputs the evaluation result to the output means 60 (step ST16). In this embodiment, the evaluation means 24 evaluates the print quality by comparing the calculated skewness Rsk with a threshold value stored in the storage means 10. The evaluation means 24 also classifies the print quality into two levels and determines whether the print quality is good (pass) or poor (fail). Next, the output means 60 outputs the print quality evaluation result and notifies the user by displaying it on a display device, turning on an alarm device, or emitting a sound from the alarm device (step ST17).

[0026] If printing is performed so that the area of ​​the ink-free base and the area of ​​the ink-covered portion of the printed image are equal, and the ink in the ink-covered portion maintains its dot shape, the base and the ink-covered portion will be clearly distinguishable. In this case, the histogram of brightness values ​​will deviate from a normal distribution and exhibit a bimodal distribution with two peaks, and the skewness Rsk value will move away from zero. Therefore, if |Rsk| is greater than the threshold, the print quality will be evaluated as good.

[0027] On the other hand, even if printing is performed so that the area of ​​the ink-free base is equal to the area of ​​the ink-covered area, if the ink bleeds, a gradation caused by the ink will form on the base, the boundary with the ink-covered area will become faint, and the base and the ink-covered area will no longer be clearly distinguishable. This condition tends to be more pronounced when printing at high speeds with fewer passes. In this case, the histogram of brightness values ​​will approach a normal distribution with a single peak, and the skewness Rsk value will approach zero. For this reason, if |Rsk| ≒ 0, the print quality will be evaluated as poor.

[0028] Furthermore, if the skewness Rsk is excessively large, for example, the ink density may be too high or the ink may be too poorly adhered despite the appropriate ink density, or the ink density may be too low or the ink may not be adhered despite the appropriate ink density, resulting in an excessively large area of ​​the base where ink is not adhered or an excessively large area of ​​the ink-adhered portion. For this reason, if |Rsk|>>0 (|Skewness Rsk| is sufficiently greater than zero), the printability is evaluated as poor.

[0029] In this embodiment, if the absolute value of skewness Rsk is equal to or greater than a first threshold value greater than zero and equal to or less than a second threshold value greater than the first threshold value, the printability is evaluated as good; otherwise, the printability is evaluated as poor. Specifically, the first threshold value is set to 0.1, the second threshold value is set to 0.5, and if 0.1≦|Rsk|≦0.5, the printability is evaluated as good, and if |Rsk|<0.1 or |Rsk|>0.5, the printability is evaluated as poor.

[0030] [Evaluation example] A polyvinyl chloride film sample was used as the printing object, and the print quality was evaluated. First, the same color photograph was printed on the sample using an inkjet printer with different numbers of passes. In this case, it is preferable to adjust the ink density so that the area of ​​the ink-free base and the area of ​​the ink-covered portion of the printed color photograph are equivalent. In this evaluation example, the ink density was set to CY120.

[0031] Next, the color photographs printed on the samples were visually inspected for print quality, and then a portion of each color photograph was imaged at 100x magnification using a digital optical microscope. Each image was then grayscale processed, and the brightness values ​​of each pixel in the image were obtained from the image data as line profiles in the vertical, horizontal, or any other direction. The brightness skew Rsk was then calculated for each line profile using Equation 1, and the skew Rsk values ​​obtained from each line profile were averaged to obtain the brightness skew Rsk for the entire image. The number of printing passes performed on the samples, the brightness skew Rsk, and the results of the visual inspection of the print quality are shown in Table 1 and Figure 3.

[0032] [Table 1]

[0033] As shown in Table 1 and Figure 3, the 24-pass and 48-pass prints, which were evaluated as having good print quality (0.1 ≦ |Rsk| ≦ 0.5), were confirmed to have good print quality even by visual inspection, while the 12-pass prints, which were evaluated as having poor print quality (|Rsk| > 0.5), were confirmed to have poor print quality even by visual inspection.

[0034] According to the above-described embodiment, the roughness parameter skewness Rsk is calculated from the brightness value of an image captured of a print image printed on a printing object, and print quality is evaluated based on the skewness Rsk. This prevents the evaluation from being affected by the evaluator's subjective judgment, and allows for objective and accurate evaluation of print quality.

[0035] Furthermore, for example, it is possible to evaluate the print quality when high-speed printing is performed on the print object by reducing the number of passes or lowering the resolution, and therefore it is possible to determine whether high-speed printing is possible.

[0036] As described above, the best configurations, methods, and the like for implementing the present invention have been disclosed in the above description, but the present invention is not limited thereto. That is, although the present invention has been particularly illustrated and described mainly with reference to specific embodiments, those skilled in the art can make various modifications to the above-described embodiments in terms of shape, material, quantity, and other detailed configurations without departing from the scope of the technical idea and purpose of the present invention. Furthermore, the above-disclosed descriptions limiting the shape, material, and the like are provided as examples to facilitate understanding of the present invention and are not intended to limit the present invention. Therefore, descriptions using names of components that are free from some or all of the limitations on shape, material, and the like are included in the present invention.

[0037] For example, the storage means 10 may be built into the print quality evaluation device EA, or may be of a type that is externally attached to the print quality evaluation device EA. When the evaluation means 24 evaluates the print quality of a print object using a trained model based on machine learning, the storage means 10 may store the trained model.

[0038] The image acquisition means 21 may acquire the captured image of the printed image printed on the printing object directly from the imaging means 50, or it may not acquire the captured image directly from the imaging means 50, and may acquire the captured image from an image storage means such as a server or database in which the captured images by the imaging means 50 are stored.

[0039] The brightness value acquisition means 22 may acquire brightness values ​​after performing grayscale processing on the captured image, or may acquire brightness values ​​without performing grayscale processing on the captured image.

[0040] The evaluation means 24 may use a value other than that used in the embodiment as the threshold value of skewness Rsk for evaluating whether print quality is good or bad (pass or fail print quality), or may evaluate print quality into three or more levels depending on the value of skewness Rsk. For example, if |Rsk|≦0.35, print quality may be evaluated as excellent (pass); if 0.35<|Rsk|≦0.5, print quality may be evaluated as good (pass); and if |Rsk|>0.5, print quality may be evaluated as poor (fail); the range of |Rsk|=0 to 0.8 may be divided into four, for example, in increments of 0.2, and print quality may be evaluated in four levels, or the skewness Rsk value may be output directly as the evaluation result. The evaluation means 24 may change the threshold value of the skewness Rsk depending on the ink density.

[0041] The evaluation means 24 may use a trained model that has been machine-learned using the skewness Rsk and ink density as feature quantities, and evaluate the print quality based on the skewness Rsk calculated by the skewness calculation means 23. That is, in the above embodiment, the skewness Rsk calculated in step ST15 is compared with a threshold value in step ST16 to evaluate the print quality, whereas the skewness Rsk and ink density calculated in step ST15 may be input into the trained model in step ST16 to evaluate the print quality.

[0042] The trained model is generated by machine learning using as training data the ink density of the printed image printed on the training printing object, the skewness Rsk of the brightness value of the training image captured from the printed image, and the print quality evaluation results obtained by visually inspecting the printed image printed on the training printing object, as shown in Figure 4.

[0043] First, printing objects of various types and compositions are prepared for learning, and printing is performed on these printing objects for learning (step ST21). Printing may be performed by printing means 40 or by means other than printing means 40, and there are no particular restrictions on the shape, size, color, etc. of the print image printed on the printing object, but it is preferable to adjust the ink concentration so that the area of ​​the base where ink is not attached and the area of ​​the ink-attached portion are approximately the same.

[0044] Next, the quality of the print image printed on the learning printing object is visually confirmed, and the print quality of the learning printing object is evaluated (step ST22). After that, the print image of the learning printing object is captured and used as a teacher image (step ST23). The image may be captured by the imaging means 50 or by a device other than the imaging means 50. Next, the luminance value of each pixel is obtained from the teacher image captured in step ST23 (step ST24), and the skewness Rsk is calculated as a feature from these luminance values ​​(step ST25). Then, machine learning is performed using the ink density of the print image printed on the learning printing object, the skewness Rsk calculated from the teacher image, and the evaluation result of the print quality evaluated in step ST22 as teacher data, and a trained model is generated that inputs the ink density of the print image printed on the printing object and the skewness Rsk of the luminance value of the captured image and outputs the print quality (step ST26). In generating a trained model, the order of performing step ST22 and steps ST23 to ST25 is not particularly limited, and steps ST23 to ST25 may be performed after performing step ST22, or step ST22 may be performed after performing steps ST23 to ST25. Specific machine learning techniques are not particularly limited and may include various known techniques such as neural networks, support vector machines, nearest neighbor methods, random forests, stochastic gradient methods, and kernel approximations.

[0045] The operation means 30 may be configured to be separable from the print quality evaluation device EA, or may be configured to be inseparable. The operation means 30 may or may not be provided in the print quality evaluation device EA or the print quality evaluation system EA1, and if it is not provided, an operation signal from an external input device may be input to the print quality evaluation device EA or the print quality evaluation system EA1.

[0046] The printing means 40 may or may not be provided in the print quality evaluation device EA or the print quality evaluation system EA1, and if it is not provided, printing may be performed on the printing object by an external printing device that can communicate with the print quality evaluation device EA or the print quality evaluation system EA1.

[0047] The imaging means 50 may capture the printed image of the printing object in color or in black and white, or may capture a part of the printed image or the entire printed image. The imaging means 50 may or may not be provided in the print quality evaluation device EA or the print quality evaluation system EA1, and if it is not provided, the image acquisition means 21 may acquire an image captured by an external imaging device that can communicate with the print quality evaluation device EA or the print quality evaluation system EA1.

[0048] The output means 60 may or may not be provided in the print quality evaluation device EA or the print quality evaluation system EA1, and if it is not provided, the evaluation results may be output to an external output device that can communicate with the print quality evaluation device EA or the print quality evaluation system EA1.

[0049] The ink density may be set via the operating means 30 and sent to the processing means 20 or the printing means 40, or the ink density value may be attached to the printing object directly by the printing means 40, or converted into a barcode or QR code (registered trademark), etc., which is then read by the imaging means 50, a barcode reader, a QR code reader, etc. and sent to the processing means 20.

[0050] There are no particular limitations on the type, material, composition, etc. of the printing object. For example, the printing object may be a substrate surface of paper, a film, an adhesive sheet, a print-receiving layer such as a coating layer provided on the substrate surface, a substrate surface of a pressure-sensitive adhesive sheet, or a print-receiving layer such as a coating layer provided on the substrate surface, and the material may be resin, metal, wood, ceramic, etc.

[0051] The means and steps of the present invention are not limited in any way as long as they can perform the operations, functions, or steps described for those means and steps, and are in no way limited to the components and steps of a single embodiment shown in the above embodiment. For example, the image acquisition means may be any means capable of acquiring a captured image of a print image printed on a printing object, and is not limited in any way as long as it is within the scope of the common general technical knowledge at the time of filing (the same applies to other means and steps). [Explanation of symbols]

[0052] EA: Print quality evaluation device 10...Memory means 20...Processing means 21...Image acquisition means 22...Luminance value acquisition means 23...Skewness calculation method 24...Evaluation methods 30...Operation means 40...Printing means 50...imaging means 60...Output means

Claims

1. A print quality evaluation device that evaluates the print quality of a print applied to a print object, comprising: an image acquisition means for acquiring a captured image of a print image printed on the printing object; a brightness value acquiring means for acquiring a brightness value of the captured image from the captured image acquired by the image acquiring means; a skewness calculation means for applying the brightness value acquired by the brightness value acquisition means to a skewness that is a roughness parameter, and calculating the skewness from the brightness value; an evaluation unit that evaluates the print quality based on the skewness calculated by the skewness calculation unit, the luminance value acquisition means acquires a plurality of line profiles of the luminance value of the captured image as a line profile in any direction of the captured image over the entire captured image; The print quality evaluation device is characterized in that the skewness calculation means calculates the skewness for each line profile, averages the skewness obtained from each line profile, and calculates the skewness of the entire captured image.

2. The print quality evaluation device described in claim 1, characterized in that the evaluation means evaluates the print quality using a trained model that has been machine-learned using as training data the ink density of the print image printed on the learning print object, the skewness of the brightness values ​​of a training image obtained by capturing the print image, and the evaluation result of the print quality of the print applied to the learning print object.

3. A print quality evaluation method for evaluating the print quality of a print applied to a print object, comprising: an image acquisition step of acquiring a captured image of a print image printed on the printing object; a brightness value acquiring step of acquiring a brightness value of the captured image from the captured image acquired in the image acquiring step; a skewness calculation step of applying the brightness value acquired in the brightness value acquisition step to a skewness that is a roughness parameter, and calculating the skewness from the brightness value; an evaluation step of evaluating the print quality based on the skewness calculated in the skewness calculation step; In the brightness value acquisition step, the brightness value of the captured image is set as a line profile in any direction of the captured image, and a plurality of the line profiles are acquired over the entire captured image; A print quality evaluation method characterized in that in the skewness calculation process, the skewness is calculated for each line profile, and the skewness obtained from each line profile is averaged to calculate the skewness of the entire captured image.

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