JP2009290471A - Image evaluation method and device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a technique of achieving appropriate image evaluation based on frequency characteristics of an image formed by an image forming apparatus. <P>SOLUTION: Test pattern data with a single gray pixel value is formed in an image recording medium as a test pattern image, and the test pattern image is obtained as image data to be evaluated. The image data to be evaluated is subjected to two-dimensional frequency analysis to calculate a frequency strength, and then evaluation databased on the frequency strength in a predetermined angle direction, which is selected from the calculated frequency strength, is displayed. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention relates to a technique for evaluating an image formed on an image recording medium by an image forming apparatus.

  In image recording apparatuses such as printers and copiers, it is known that defects such as streaks and unevenness occur in the formed image due to uneven conveyance and uneven ink discharge. For this reason, the operator performs a test print and visually determines the presence or absence of an image defect.

  Various methods for automatically evaluating the quality of an image are also being studied. For example, a striped test document is read, the change in density of the read image is detected, the frequency spectrum is calculated from the detection result, the frequency corresponding to the peak value of the frequency spectrum, and the natural vibration of each component of the copying machine There is a method of comparing the numbers and specifying the component having the natural frequency corresponding to the peak value (see Patent Document 1).

  In the technique of Patent Document 1, by analyzing the frequency of the read test document having a striped pattern, it is possible to identify a frequency having a remarkable feature and to identify a component having the same natural frequency as that frequency. As a result, it is possible to identify the component that causes the unevenness of the image.

JP-A-4-335363 (paragraph numbers 0004-0006)

  When the operator visually determines the presence / absence of a defect, there is a variation in evaluation among operators, so that it is difficult to maintain stable quality and the burden on the operator is large. Further, in the technique of Patent Document 1, it is possible to identify a component that causes a defect in an image by performing frequency analysis on the read test document, but there are various analysis results. Since the frequency characteristics of the factors are mixed, it is difficult to accurately identify the factors that degrade the image quality. Further, since the test document has a striped pattern, only the frequency characteristics in the main scanning direction or the sub-scanning direction can be analyzed.

  In view of such a problem, an object of the present invention is to provide a technique for accurately realizing image evaluation based on a frequency characteristic of an image formed by an image forming apparatus.

  The image evaluation method of the present invention includes an image forming step of forming test pattern data having a single grayscale pixel value as a test pattern image on an image recording medium, and an image acquiring step of acquiring the test pattern image as evaluation image data. And a frequency analysis step of calculating a frequency intensity by performing a two-dimensional frequency analysis on the image data to be evaluated, and a step of displaying evaluation data based on the frequency intensity in a specific angular direction out of the frequency intensity.

  With such a configuration, it is possible to display evaluation data based only on the frequency intensity in the angular direction in which the frequency characteristics are conspicuous among the frequency intensities resulting from the frequency analysis of the image to be evaluated. As a result, the evaluator can evaluate that the displayed evaluation data satisfies the predetermined condition and that there is no problem with the formed image. If the evaluation data does not satisfy the condition, the cause can be accurately determined. It is possible to grasp the frequency characteristics.

  Normally, unevenness in the test pattern image formed on the image recording medium appears in the main scanning direction or the sub-scanning direction. In the frequency analysis result of the image data to be evaluated acquired from such a test pattern image, the feature appears remarkably on the x axis or the y axis in the frequency space. Therefore, it is possible to know the characteristics of image unevenness by paying attention only to the frequency intensity on the x-axis or y-axis in the frequency space. However, when the image recording medium is placed on a flatbed scanner or the like, the angle direction in which the characteristic appears remarkably in the frequency space may be slightly inclined from the x axis or the y axis.

  Therefore, in one preferred embodiment of the image evaluation method of the present invention, the specific angular direction is an angular direction in which one frequency component in the frequency space is 0, and the display step is performed in the frequency space. Evaluation data based on frequency intensity included in a region having a predetermined width in a direction orthogonal to a specific angular direction is displayed. In this configuration, since the evaluation data based on the frequency intensity included in the region having a predetermined width in the direction orthogonal to the specific angular direction as well as the specific angular direction is displayed, without performing additional calculations, The influence of tilt can be absorbed.

  In addition, when the image recording medium on which the test pattern image is formed is placed on an image reading apparatus such as a flat bed scanner, the angular direction in which the frequency characteristic is most noticeable is the axial direction of the frequency space. It does not match. In such a case, it is necessary to obtain an angular direction in which the frequency characteristic appears most remarkably.

  Therefore, in one preferred embodiment of the image evaluation method of the present invention, an angular direction distribution is calculated by integrating the frequency intensity for each angle, and an angle at which the angular direction distribution is maximized is determined as the specific angle. An angle determination step for determining the direction is provided. With this configuration, it is possible to determine the angular direction in which the frequency characteristic appears remarkably from the angular direction distribution.

  In addition, the test pattern image formed on the image recording medium may include frequency characteristics due to various factors. However, not all frequency characteristics are necessary for image evaluation. In particular, when calculating the above-described angular direction distribution, if the calculation is based on the frequency characteristics of all frequencies, the accuracy may be reduced due to the frequency characteristics of unexpected frequencies.

  Therefore, in a preferred embodiment of the image evaluation method of the present invention, a radial direction distribution is calculated by integrating the frequency intensity for each radial distance, and a radial distance threshold is calculated based on the radial direction distribution. The angle determination step calculates the angular direction distribution using the frequency intensity corresponding to the determined radial distance threshold. With this configuration, the radial distance threshold is determined based on the radial direction distribution, and the angular direction distribution is determined from the frequency intensity of the frequency that satisfies the determined radial distance threshold. Thereby, it is possible to avoid mixing frequency characteristics of unnecessary frequencies into the angular direction distribution, and it is possible to calculate the angular direction distribution with high accuracy.

  Further, the image evaluation apparatus of the present invention includes an image forming unit that forms test pattern data having a single grayscale pixel value as a test pattern image on an image recording medium, and an image that acquires the test pattern image as evaluation image data. An acquisition unit; a frequency analysis unit that performs two-dimensional frequency analysis on the image data to be evaluated to calculate a frequency intensity; and a display unit that displays evaluation data based on a frequency intensity in a specific angular direction out of the frequency intensity. ing. Naturally, such an image evaluation apparatus can also obtain the same effects as those of the above-described image evaluation method, and can also have the above-described additional configuration.

[First Embodiment]
Hereinafter, a first embodiment of an image evaluation method and an image evaluation apparatus according to the present invention will be described with reference to the drawings.

  A photographic printing apparatus (an example of an image evaluation apparatus) shown in FIG. 1 includes a dark box-structured housing 10, and an image forming unit Ex that forms an image on an image recording medium P inside the housing 10 and an image The image recording medium P on which is formed is discharged from the discharge portion 11 at the end of the housing 10, is received by the transfer belt 12, is transferred in the horizontal direction, and can be collected in any order on a plurality of trays 13 </ b> A. 13 is provided.

  In addition, an operating unit A that functions as an operation terminal for an operator to acquire image data and perform operations necessary for print processing is installed outside the housing 10. In the operating part A, image data is obtained from an image processing device 21 composed of a general-purpose computer installed on the operation table 20, a display 22 installed on the upper surface of the image processing device 21, and a frame image of the photographic film F after development processing. A film scanner 23 for acquiring image data, two keyboards 24, one mouse 25, and a flatbed scanner 27 for acquiring image data from the image recording medium P. Image data is acquired on the front surface of the image processing apparatus 21 from various semiconductor-type storage media (not shown), or magnetic or optical storage media (not shown) such as CD-ROM, DVD, and MO. A plurality of media drives 26 are provided.

  Note that various methods such as an ink jet method and a laser exposure method can be used as the image forming unit Ex.

  As shown in FIG. 2, in the image processing device 21, as each functional element related to the image evaluation device of the present invention, an image acquisition unit 31 that acquires a test pattern image as evaluation image data by causing a flatbed scanner 27 to function. It is determined whether or not a pattern other than the test pattern exists in the acquired evaluated image data, and when it is determined that the pattern is present, a protrusion processing unit 32 that removes the pattern other than the test pattern, and frequency analysis of the evaluated image data The frequency analysis unit 33 that calculates the frequency intensity, and the display unit 36 that visualizes and displays the frequency intensity in the specific angle direction among the frequency intensities calculated by the frequency analysis unit 33 are provided.

  The image acquisition unit 31 digitizes the test pattern image formed on the image recording medium P by the flatbed scanner 27 and acquires it as image data to be evaluated. The acquired evaluated image data is stored in the memory 40.

  When the test pattern image data is configured as a part of other image data, the protrusion processing unit 32 determines whether there is other image data in the acquired evaluation image data, and the other image data When data exists, other image data is removed by a method described later.

  The frequency analysis unit 33 performs two-dimensional frequency analysis on the image data to be evaluated, and calculates the frequency intensity. In the present embodiment, two-dimensional Fourier transform is used as the two-dimensional frequency analysis, but other frequency analysis methods may be used.

  The display unit 36 graphs and displays the frequency intensity at a specific angle among the frequency intensities calculated by the frequency analysis unit 33.

  Next, a detailed processing flow of image evaluation according to the present invention will be described based on the flowchart of FIG. Prior to the following processing, it is assumed that the test pattern image data stored on the memory 40 is formed as a test pattern image on the image recording medium P by the image forming unit Ex. Further, the test pattern image data in the present invention is composed of pixels having a single grayscale pixel value, but one image data may be composed of only the test pattern image data, or the test pattern image data May be configured as a part of another image. The latter case is preferable because various test pattern images can be formed in one test print and various tests can be performed efficiently. In the latter case, at the time of reading an image, which will be described later, the range of the test pattern image used in the present invention is specified to obtain the evaluated image data. From the read image, the range of the test pattern image used in the present invention. For example, additional processing is not required.

  First, the user places the image recording medium P on which the above-described test pattern image is formed on the flatbed scanner 27, and then operates the image processing device 21 to instruct execution of the image evaluation process. The flatbed scanner 27 sends image data to be evaluated obtained by digitizing an image to the image processing device 21.

  The image acquisition unit 31 acquires image data to be evaluated from the flatbed scanner 27 and stores it in the memory 40 (# 01). At this time, it is preferable to exclude peripheral pixels with low accuracy from the acquired image data to be evaluated because improvement in image evaluation accuracy can be expected. For example, when the acquired image data to be evaluated has a width W and a height H, it is defined by (0.1 W, 0.1 H) − (0.9 W, 0.9 H) with 10% of the periphery removed. The rectangular area to be evaluated is set as image data to be evaluated. Further, when digitized as color image data by the flatbed scanner 27, the color image data is converted into grayscale image data based on the luminance component of the color image data by a known method. Hereinafter, the evaluated image data refers to data converted into grayscale image data.

  In some cases, the image recording medium P includes an unnecessary pixel (hereinafter referred to as a protruding portion) when the image recording medium P is placed on the flat bed scanner 27 while being tilted. Therefore, the protrusion processing unit 32 acquires the evaluated image data from the memory 40 and performs the protrusion processing. Specifically, first, it is determined whether all the pixel values of the image data to be evaluated are within a predetermined gray value range (# 02). The predetermined gray value range is determined based on the pixel value of the test pattern image data. If the above condition is not satisfied (No branch at # 03), a change is made by replacing the pixel value of the protruding portion with a predetermined gray value described later (# 04) by the following processing, and the condition is satisfied. If this is the case (Yes branch at # 03), the process of changing the pixel value of the protruding portion is skipped.

  First, a light / dark histogram H [] is created from the image data to be evaluated, and a light / dark value d giving a peak value of the light / dark histogram H [] is obtained. The calculation of the gray value d does not simply determine the gray value that gives the maximum value of the gray histogram H, but the sum of the gray histograms H with respect to the gray values i−1, i, i + 1, that is, H [i−1] + H [i]. The gray value i that maximizes + H [i + 1] is set as a gray value d that gives a peak value. Thus, by obtaining the light and shade value d, the correct light and shade value d can be obtained even when, for example, an overhanging portion having a white pixel exists.

Next, an integrated value of the light / dark histogram H [] for a predetermined light / dark value range centered on the light / dark value d is obtained. That is, when the predetermined gray value range is [d−r, d + r], the integrated value s is obtained by the equation (1). Then, the ratio of the integrated value s to the number of pixels of the image data to be evaluated is compared with a predetermined threshold value. If the ratio is equal to or greater than the predetermined threshold value, a pixel having a pixel value outside the predetermined gray value range [dr, d + r]. Are determined as pixels constituting the protruding portion. In the present embodiment, the pixel value of the protruding portion is replaced with the average gray value d ′ in the predetermined density value range obtained by Expression (2).

  On the other hand, if the above-described ratio is equal to or less than the predetermined threshold, there are many protruding portions, and sufficient evaluation cannot be performed, so the operator is notified that the image is to be acquired again.

  FIG. 4A is an example of the evaluated image T having the protruding portion X. When the frequency intensity of the evaluated image T is imaged, FIG. 5A is obtained. As is apparent from the figure, the frequency characteristic shows the frequency characteristic of the protruding portion X. On the other hand, when the pixel value of the protruding portion X is changed by the above processing, the evaluated image T shown in FIG. 4B is obtained, and when the frequency intensity of the evaluated image T is imaged, FIG. 5B is obtained. It can be seen that the frequency characteristics are reduced.

  Thus, by changing the pixel value of the protruding portion, the frequency characteristics of the protruding portion can be reduced, and the frequency characteristics of the image data to be evaluated can be accurately analyzed in the subsequent processing.

  Next, the frequency analysis unit 33 acquires the evaluated image data from the memory 40, and calculates the frequency intensity by two-dimensional Fourier transform (# 05). The present invention performs Fourier transformation (FT) on image data. Since the image data is discrete data, two-dimensional discrete Fourier transformation (DFT) is used. Further, a discrete fast Fourier transformation (DFFT) algorithm capable of high-speed computation is used for actual computation.

  The display unit 36 visualizes the frequency intensity in the specific angle direction of the frequency intensity calculated by the frequency analysis unit 33 and displays it on the display 22 as evaluation data (# 10).

Usually, uneven printing that occurs in an image is caused by uneven transportation, uneven ink ejection, the natural frequency of components of the image forming apparatus, and the like. When the evaluated image data having such unevenness is subjected to frequency analysis, it has a strong frequency intensity in the x-axis or y-axis direction in the frequency space. Therefore, in the present embodiment, an angle direction in which one frequency is 0 is used as the specific angle direction. For example, the evaluated image data in FIG. 6 has unevenness in the sub-scanning direction (y-axis direction in the evaluated image data), and FIG. 7 shows the frequency intensity of the evaluated image data. In this case, the specific angle is an angular direction in which the frequency in the x-axis direction is zero. That is, if the frequency in the x-axis direction is f ₁ , the frequency in the y-axis direction is f ₂ , and the frequency intensity of the frequency f _{1 in} the x-axis direction and the frequency f _{2 in} the y-axis direction is S (f ₁ , f ₂ ), The frequency intensity that becomes S (0, f ₂ ) is visualized. On the other hand, when unevenness occurs in the main scanning direction (x-axis direction in the image data to be evaluated), the frequency intensity in the angular direction where the frequency in the y-axis direction becomes 0, that is, S (f ₁ , 0). The frequency intensity is visualized.

When the image recording medium P on which the test pattern image is formed is placed on the flat bed scanner 27, there is a possibility that the image recording medium P is slightly inclined. In this case, the calculated frequency intensity is obtained by rotating the frequency intensity of FIG. 7 as shown in FIG. 9, and even if the above-described visualization is performed on the frequency intensity inclined in this way. The correct result cannot be obtained. In such a case, not only the frequency intensity on the line where one frequency is 0, but also the frequency intensity in a region having a predetermined width on both sides thereof is visualized. For example, when unevenness occurs in the sub-scanning direction, S (f ₂ ) is calculated by the expression (3) for each frequency f _{2 in} the sub-operation direction, and this S (f ₂ ) is visualized. To do. Visualizing in this way is preferable because the influence of tilt can be reduced.

  Various visualization methods are conceivable. For example, as shown in FIG. 8, a graph in which the horizontal axis represents the radial distance and the vertical axis represents the frequency intensity can be used. The operator can grasp the frequency characteristic of unevenness based on this graph.

  As described above, according to the present embodiment, since the frequency intensity only in a specific angle direction is used among the frequency intensities, the characteristics of the frequency intensity can be remarkably expressed, and the operator can accurately describe the evaluation image data. The frequency characteristics can be grasped.

[Second Embodiment]
Hereinafter, a second embodiment of the image evaluation method and the image evaluation apparatus of the present invention will be described with reference to the drawings. FIG. 10 is a functional block diagram showing each functional unit in the present embodiment, and functional units similar to those in the first embodiment are denoted by the same reference numerals. In the present embodiment, the first embodiment is provided with an angle determination unit 35 that calculates the angular direction distribution by integrating the frequency intensity for each angle of the frequency intensity, and determines an angle at which the angular direction distribution is maximized. Is different.

The angle determination unit 35 calculates an angular direction distribution by accumulating the frequency intensities calculated by the frequency analysis unit 33 for each angle, and determines an angle based on the calculated angular direction distribution. The angle is a value represented by tan ⁻¹ (f ₂ / f ₁ ).

  Next, the flow of processing of this embodiment will be described based on the flowchart of FIG. In addition, the same code | symbol is attached | subjected to the process similar to 1st Embodiment, and detailed description is abbreviate | omitted.

The angle determination unit 35 calculates the angular direction distribution by acquiring the frequency intensity calculated by the frequency analysis unit 33 and integrating the frequency intensities for each angle (# 08). Specific processing for calculating the angular direction distribution is as follows. First, the angle determination unit 35 secures a one-dimensional array D ₁ [] representing an angular direction distribution on the memory 40 and initializes it with zero. Next, the angle determination unit 35 acquires one value S (f ₁ , f ₂ ) of the frequency intensity, and calculates an angle θ = tan ⁻¹ (f ₂ / f ₁ ). In this embodiment, θ is converted from radians to degrees, then rounded to an integer, and θ is used as the array number of D _1. However, the array number of D ₁ may be calculated from the decimal value θ by a predetermined calculation. Absent. Then, D ₁ [θ] + = as _{S (f 1, f 2)} , and updates the D _1. This process is performed for all frequency intensities.

Next, the angle determination unit 35 determines a specific angle based on the angular direction distribution calculated by the above processing (# 09). Specifically, the maximum angle is extracted from the angular direction distribution and determined as a specific angle. In general, the frequency intensity in the two-dimensional frequency analysis is point-symmetric with respect to a point where both frequencies are 0, so there are two specific angles, and the angle difference between them is 180 °. Therefore, when there are three or more local maximum points, the angles θ and θ + 180 that maximize D ₁ [θ] + D ₁ [θ + 180] are determined as specific angles.

  FIG. 12 shows an example in which the angular direction distribution calculated by the above processing is graphed using the frequency intensity calculated from the evaluated image data in FIG. As is clear from the figure, the frequency intensity of the image data to be evaluated having unevenness in the y-axis direction has a strong frequency intensity distributed in the y-axis direction, that is, in the angle directions of 90 ° and 270 °.

  The display unit 36 visualizes the frequency intensity in the specific angular direction determined by the angle specifying unit 35 among the frequency intensities calculated by the frequency analyzing unit 33 and displays it on the display 22 (# 10).

  Thus, according to the present embodiment, the angular direction distribution is calculated from the frequency intensity calculated from the evaluated image data, and a specific angle can be determined based on the angular direction distribution. Even when the formed image recording medium P is inclined and placed on the flatbed scanner 27 or the like, and the image data to be evaluated includes an inclination, the angle direction to be observed accurately can be specified.

[Third Embodiment]
Hereinafter, a third embodiment of the image evaluation method and the image evaluation apparatus of the present invention will be described with reference to the drawings. FIG. 13 is a functional block diagram showing each functional unit in the present embodiment, and functional units similar to those in the second embodiment are denoted by the same reference numerals. The present embodiment includes a radial distance threshold determination unit 34 that calculates a radial direction distribution by integrating frequency intensity for each radial distance of frequency intensity and determines a radial distance threshold based on the radial direction distribution. The angle determination unit 35 is different from the second embodiment in that the angle is determined based on a frequency intensity having a radial distance within the radial distance threshold range determined by the radial distance threshold determination unit 34.

The radial distance threshold value determination unit 34 calculates a radial direction distribution by integrating the frequency intensity calculated by the frequency analysis unit 33 for each radial distance, and sets the radial distance threshold value based on the calculated radial direction distribution. decide. The radial distance is a value represented by (f ₁ ² + f ₂ ² ) ^1/2 .

  Next, the flow of processing of this embodiment will be described based on the flowchart of FIG. In addition, the same code | symbol is attached | subjected to the process similar to 2nd Embodiment, and detailed description is abbreviate | omitted.

The radial distance threshold value determination unit 34 calculates the radial direction distribution by acquiring the frequency intensity calculated by the frequency analysis unit 33 and integrating the frequency intensity for each radial distance (# 06). Specific processing for calculating the radial direction distribution is as follows. First, the radial distance threshold value determination unit 34 secures a one-dimensional array D ₂ [] representing the radial direction distribution on the memory 40 and initializes it with zero. Next, the radial distance threshold value determination unit 34 acquires one value S (f ₁ , f ₂ ) of the frequency intensity and calculates a radial distance L = (f ₁ ² + f ₂ ² ) ^1/2 . In this embodiment, L is rounded to an integer and L is used as the array number of D _2. However, the array number of D ₂ may be calculated from the decimal value L by a predetermined calculation. Then, D ₂ [L] + = as _{S (f 1, f 2)} , and updates the D _2. This process is performed for all frequency intensities.

  Next, the radial distance threshold value determination unit 34 determines a radial distance threshold value based on the radial direction distribution calculated by the above processing (# 07). Specifically, the radial direction distribution is integrated from the shorter distance, and the radial distance at which the ratio of the integrated value to the total integrated value is a predetermined ratio is determined as the radial distance threshold.

Next, the angle determination unit 35 determines a specific angle based on the radial distance threshold value determined by the radial distance threshold value determination unit 34. Specifically, the angle determination unit 35 calculates only the frequency intensity S (f ₁ , f ₂ ) that satisfies (f ₁ ² + f ₂ ² ) ^1/2 ≦ radial distance threshold when calculating the angular direction distribution. Use.

Note that the method for determining the radial distance threshold is not limited to the above-described method, and other methods may be used. In the above-described method, the radial distance threshold determination unit 34 determines one radial distance threshold. However, the two radial distance thresholds L ₁ and L ₂ are determined, and the angle determination unit 35 performs L _1. The angular direction distribution may be calculated using only the frequency intensity S (f ₁ , f ₂ ) satisfying ≦ (f ₁ ² + f ₂ ² ) ^1/2 ≦ L ₂ .

  Thus, according to this embodiment, the radial direction distribution is calculated from the frequency intensity calculated from the evaluated image data, the radial distance threshold is determined based on the radial direction distribution, and the radial distance threshold is further determined. Since the angular direction distribution is determined based on this, only the frequency characteristics excluding unnecessary frequencies can be displayed.

Overview of a photo printing apparatus according to an embodiment of the present invention 1 is a functional block diagram showing functional elements constructed in a controller of a photo printing apparatus according to a first embodiment of the present invention. The flowchart showing the flow of the process in 1st Embodiment of this invention. Examples of images to be evaluated Example of imaging the frequency intensity of the image to be evaluated Example of image data to be evaluated in the embodiment of the present invention Example of imaging frequency intensity of image data to be evaluated in FIG. Example of visualizing frequency intensity in an embodiment of the present invention Example of imaging frequency intensity of image data to be evaluated acquired at an incline The functional block diagram which shows the functional element constructed | assembled in the controller of the photo printing apparatus in 2nd Embodiment of this invention The flowchart showing the flow of processing in the second embodiment of the present invention. Example of graph of angular distribution of frequency intensity The functional block diagram which shows the functional element constructed | assembled in the controller of the photo printing apparatus in 3rd Embodiment of this invention The flowchart showing the flow of the process in 3rd Embodiment of this invention.

Explanation of symbols

21: Image processing device 22: Display 27: Flatbed scanner 31: Image acquisition unit 32: Projection processing unit 33: Frequency analysis unit 34: Radial distance threshold determination unit 35: Specific angle determination unit 36: Display unit 40: Memory

Claims (5)

An image forming step of forming test pattern data having a single grayscale pixel value as a test pattern image on an image recording medium;
An image acquisition step of acquiring the test pattern image as image data to be evaluated;
A frequency analysis step of performing a two-dimensional frequency analysis on the evaluated image data to calculate a frequency intensity;
Displaying the evaluation data based on the frequency intensity in a specific angular direction out of the frequency intensities. The specific angular direction is an angular direction in which one frequency component in the frequency space is zero,
The image evaluation method according to claim 1, wherein the display step displays evaluation data based on a frequency intensity included in a region having a predetermined width in a direction orthogonal to the specific angular direction in the frequency space.   2. An angle determination step of calculating an angular direction distribution by integrating the frequency intensity for each angle and determining an angle at which the angular direction distribution becomes a maximum as the specific angular direction. The image evaluation method described. A radial distance threshold determining step of calculating a radial direction distribution by integrating the frequency intensity for each radial distance, and determining a radial distance threshold based on the radial direction distribution;
The image evaluation method according to claim 3, wherein the angle determination step calculates the angular direction distribution using a frequency intensity corresponding to the determined radial distance threshold. An image forming unit for forming test pattern data having a single grayscale pixel value as a test pattern image on an image recording medium;
An image acquisition unit for acquiring the test pattern image as evaluated image data;
A frequency analysis unit that performs two-dimensional frequency analysis of the image data to be evaluated and calculates a frequency intensity;
An image evaluation apparatus comprising: a display unit that displays evaluation data based on a frequency intensity in a specific angular direction among the frequency intensity.