JP5163940B2 - Image quality inspection apparatus and image quality inspection method - Google Patents

Description

  The present invention relates to an image quality inspection apparatus and an image quality inspection method for inspecting the image quality of a two-dimensional image.

  In the manufacturing process of the display device, the defect inspection is performed on the panel alone before moving to the assembly process. This is because pixel defects generated in the panel manufacturing process are inspected, and defective parts are repaired before assembly, or a panel that cannot be repaired is not sent to the next process but discarded.

  In conventional visual inspections by inspectors, there are many individual differences and variations, and it is difficult to inspect with high accuracy. In addition, with the recent increase in size and definition of display devices, a wide range can be quickly achieved. Since it is also difficult to inspect, an alternative to an automatic inspection device is also desired in the inspection of mura defects.

  The inspection target is a mura defect that occurs on a display device, an imaging device, or a projection device. Unevenness is caused by various factors such as a change in the thickness of the glass substrate in the manufacturing process, a change in the thickness of the color filter, and an alignment error of the liquid crystal. There are various shapes, orientations, occurrence positions, and sizes (frequencies) such as lump-like unevenness and strip-like unevenness.

  2. Description of the Related Art As a conventional automatic inspection apparatus that uses a display device as an inspection target, an apparatus that captures an inspection target device by combining a photographic lens with a high-pixel monochrome CCD camera at a position facing the inspection target device is known. The camera is installed in a state where the tilt is adjusted with respect to the panel to be inspected, both vertically and horizontally.

  For the captured image, “image correction processing” for performing noise correction of image data, “defect candidate extraction processing” for extracting defect candidates, and calculating feature quantities such as area, volume, and contrast of the extracted defect candidates “ It is output as inspection result data through “feature amount calculation processing” and “determination processing” in which the calculated feature amount is compared with a preset threshold value to determine whether or not it is a defect.

  FIG. 10 is a flowchart showing an inspection procedure by the image quality inspection apparatus using the MTF characteristic with respect to luminance.

  This method expresses the strength of a defect as a ratio between the defect contrast and the recognition limit (JND) contrast of the defect shape, and is expressed as an objective measurement value in the unit of “Sem”. Yes, considering various changes (background brightness, defect shape, etc.) of inspection object.

  Note that the unit of measure “Semu” was proposed in the SEMI standard in 2002.

  First, the MTF characteristic is a sensitivity characteristic represented by a reciprocal of a limit contrast change that can be recognized and sensed by humans in a bright and dark sinusoidal fringe pattern as shown in FIG. The human visual sensitivity characteristic is a characteristic curve having a peak at a certain frequency as shown in FIG. That is, it is shown that human vision is sensitive to certain details.

  Specifically, as shown in FIG. 11C, when a sine wave having a wave number in a certain viewing angle range is displayed, the spatial frequency is

Spatial frequency (cpd) = wave number / viewing angle (degrees) (1)
In addition, the recognition limit (C _JND , JND: just noticeable difference) contrast, when the minimum luminance change that can be sensed is “ΔL” and the background luminance is “L _BG ”,

C _JND = ΔL / L _BG × 100 (2)
It is represented by The relationship between the reciprocals of the equations (1) and (2) is the MTF characteristic “S _MTF ”.

  However, in order to use it for image quality inspection, it is necessary to expand to a two-dimensional MTF characteristic. Regarding the two-dimensional MTF characteristics, a difference in sensitivity depending on the angle has been reported.

  In the “image correction process” of FIG. 17, in step S001, the input image data is subjected to reduction processing and smoothing processing for optical characteristic correction, shading correction, correspondence to defect size, high frequency noise removal, and the like. Etc. are pre-processed.

  In the “defect candidate extraction process”, in step S002, the luminance difference of the defect candidate is enhanced by an enhancement filter such as a Laplacian type, a Sobel type, or a projection. Next, in step S003, the image in which the luminance difference is enhanced is binarized with a threshold value, and a portion that exceeds the threshold value (or a portion that does not satisfy the threshold value) is extracted as a defect candidate and output.

  In the feature amount calculation process, a defect shape obtained by normalizing the defect candidates extracted in the “defect candidate extraction process” is generated in step S004. Normalization is performed, for example, from the maximum brightness inside the defect candidate and the brightness around the defect candidate.

  In step S005, the normalized defect shape is subjected to processing such as two-dimensional Fourier transform to extract a frequency component “Power (u (h, v))” that forms the defect shape. Here, u (h, v) is a spatial frequency.

On the other hand, in step S006, the spatial frequency “(u (h, v))”, background luminance “L _BG ”, defect size “(X, Y)”, angular direction “φ”, and the like are used as parameters. MTF characteristics expanded to dimensions “S _MTF (u (h, v), L _BG , X, Y, φ)”
Calculate

In step S007, the contrast “C _X ” of the defect is calculated from the luminance value of the defect area and the luminance value of the periphery of the defect from the equation (2).

Next, in step S008, the obtained frequency component “Power (u (h, v))” and the two-dimensional MTF characteristic “S _MTF (u (h, v), L _BG , X, Y, φ)”.
From this, the recognition limit contrast “C _JND ” for the defect shape is calculated by the following equation.

Finally, in step S009, the defect intensity (Sem) is calculated from the defect contrast “C _X ” and the recognition limit contrast “C _JND ” for the defect shape by the following equation.
Semu = C _X / C _JND Formula (4)

  In the “determination process”, in step S010, the defect strength (Semu) calculated in the “feature amount calculation procedure” is compared with a preset threshold value to determine whether or not the defect is present. If it is determined that there is, information on the detected defects is collected into output data in step S011.

The “feature amount calculation process” and the “determination process” are executed for all candidate areas extracted in the “defect candidate extraction process”.
JP 2005-326323 A Journal of Sciology Vol.60, No.2, p129-136, 2006 Quantification method of intensity of luminance unevenness based on visual system spatial frequency characteristics, Takuto Kishi SEMI D31-1102 Definition of measurement unit (Semu) of luminance unevenness in FPD image quality inspection Journal of the Japan Society of Photography, Vol.65, No.2, p121-127, 2002 Directional dependence of visual system spatial frequency response (1), Tetsuya Ishihara Journal of the Japan Photography Society Vol.65, No.2, p128-133, 2002 Directional dependence of the spatial frequency response of the visual system (2), Tetsuya Ishihara

  In order to apply the image quality inspection using the above MTF characteristics to the calculation of the defect intensity of color unevenness, it is conceivable to use the opposite color component.

In general, the CIELAB color system (standardized by the International Commission on Illumination) is used to represent the color of an object, but the standard conditions (light source (D65), illuminance (1000lx), It is necessary to satisfy the background luminance (L ^* = 50 gray), sample size (4 degrees or more), and the sample is a uniform color). If the standard conditions are not met, the coefficient must be set according to the observation conditions. Arise. However, since there are a wide variety of color irregularities, and the light source and background conditions are also different, it is difficult to meet the reference conditions, and it is difficult to simply apply CIELAB.

  Therefore, the tristimulus value image to be inspected is acquired, the tristimulus value is converted into the opposite color component, the ratio of the recognition limit contrast by the MTF of each opposite color component and the contrast in the opposite color component of the color unevenness is obtained, By weighting and integrating the ratio, the defect intensity of color unevenness can be calculated.

  Here, the opposite color component is Herring's opposite color theory (the retina has three types of receivers that respond to white-black, red-green, and blue-yellow. It is based on the ratio of the response amount of the receiver, and it explains the empirical fact that there is no yellowish red but yellowish red, and well represents human color vision characteristics . w / k is a luminance component, r / g is a chromaticity component for red-green, and b / y is a chromaticity component for blue-yellow. For reference, FIG. 12 shows the spectral characteristics of the color components opposite to the tristimulus values.

  Conversion from the tristimulus values (X, Y, Z) to each value (wk, rg, by) of the opposite color component (w / k, r / g, b / y) is performed by the matrix operation shown in the following equation. Is called.

Also, an example of behavior on the CIELAB color system when only one opposite color component is changed and the other two components are fixed under the conditions shown in FIG. In the a ^* b ^* plane shown in FIG. 13, the chromaticity component r / g changes almost horizontally to the a ^* axis, and the chromaticity component b / y changes slightly with a slight inclination with respect to the b ^* axis. It can be seen that they change in the red-green and blue-yellow directions, respectively. It can be seen that the luminance component w / k component hardly changes and the chromaticity component is removed. In the L ^* c ^* plane shown in FIG. 13, only the luminance component w / k changes greatly, and the chromaticity components r / g and b / y hardly change. As described above, the inverse color component conversion is appropriately performed by the above equation (5). It should be noted that, under other conditions, the axis is changed with each translated position as an axis.

The calculation of contrast with the opposite color component is similar to the above equation (2),
C _wk = Δwk / BG _wk × 100 (6)
C _rg = Δrg / BG _rg × 100 (7)
_{C by = Δby / BG by ×} 100 ··· formula (8)
It is represented by

The tendency of the MTF of the opposite color component is shown in FIG. The luminance component w / k becomes a characteristic curve having a peak at a certain frequency, and has the same tendency as the MTF for the luminance shown in FIG. 11B, and the visual perception of the human luminance component is sensitive to a certain fineness. It is shown that. On the other hand, the chromaticity components r / g and b / y have characteristic curves in which the sensitivity decreases as the frequency increases, and tend to be different from the tendency with respect to the luminance component.
The defect intensity in the opposite color component is the defect contrast in the opposite color component (C _wk , C _rg , C _by ) and the recognition limit contrast (C _JNDwk , C _JNDrg , C _JNDby ) obtained by the corresponding MTF of the opposite color component, respectively. ) By the following formula.
Semu _wk = C _wk / C _JNDwk (9)
Semu _rg = C _rg / C _JNDrg Formula (10)
_{Semu by = C by / C JNDby} ··· formula (11)

However, when performing color unevenness inspection by inputting a color image, the following problems are raised.
(1) Among the opposite color components, the r / g component and the b / y component may take negative values as shown in FIG. For this reason, when the background values of the r / g component and the b / y component are close to 0, the calculated contrast varies significantly due to the sign change. For example, when a JND contrast is experimentally obtained for a circular defect having a sinusoidal luminance distribution, and the contrast is calculated using the formulas (6) to (9), the b / y component tends to vary in positive and negative directions. I can't.
(2) Color unevenness is a complex mixture of opposite color components, and the influence of background values of other components as well as one component appears. It is difficult to objectively express the strength of the defect in which the opposite color components are mixed in a complicated manner by the method of calculating the defect strength in consideration of only the current background value of one component.
(3) The Semu value is quantified by the ratio of each opposite color component to the JND contrast. Even if the ratio of one component is the same as the ratio of another component, the ratio and conspicuousness of the opposite color component are different. The relationship with is different. Even when the opposite color components show the same SEMU value, they do not stand out the same. In other words, when only one component is changed for each opposite color component, when the Semu value in the same conspicuous state is calculated, the same Semu value is not obtained.
(4) Color unevenness can be said to be a complex mixture of opposite color components, and the strength of a defect that can be noticeable for the first time as a result of complex mixing of opposite color components only with the defect strength for one component as in the present situation. Is difficult to express objectively.

  An object of the present invention is to provide an image quality inspection apparatus and an image quality inspection method capable of calculating a defect intensity of a two-dimensional image with high accuracy and stability.

The image quality inspection apparatus according to the present invention is an image quality inspection apparatus that inspects the image quality of a two-dimensional image, and obtains the opposite color component acquisition unit that acquires the opposite color component of the two-dimensional image to be inspected and the opposite color component acquisition unit. A defect intensity calculating means for calculating a defect intensity of the two-dimensional image as a ratio with a recognition limit contrast using the opposite color component, and the defect intensity calculating means specifies a specific opposite color component. When calculating the defect intensity in the region, the specific opposite color component in the specific region and another opposite color component different from the specific opposite color component in the specific region are used. And
According to this image quality inspection apparatus, since the defect intensity for a specific opposite color component is calculated using a plurality of opposite color components, the defect intensity can be calculated with high accuracy and stability.

  You may provide the determination means which determines the image quality of the said two-dimensional image based on the said defect strength calculated by the said defect strength calculation means.

  The defect intensity calculating means calculates a first defect intensity using the opposite color component acquired by the opposite color component acquisition means of the first two-dimensional image to be inspected and a second object to be inspected. The second defect strength is calculated using the opposite color component acquired by the opposite color component acquisition unit of the two-dimensional image, and the determination unit calculates the first defect strength calculated by the defect strength calculation unit. The image difference between the first two-dimensional image and the second two-dimensional image may be determined by comparing the second defect strength.

The defect intensity calculating means, when calculating the defect intensity of the specific opponent-color components, it may be used a background value of the other opposite color components.

The defect intensity calculating means, wherein when calculating the particular defect contrast required for calculating the defect intensity of opponent color components, the difference between the defective area and the background in the particular opposite color component the other opposite color The defect contrast may be calculated by dividing by the background value of the luminance component as the component.

  The defect strength calculation means may calculate a total defect strength by weighting the defect strength calculated for each opposite color component.

  Defect candidate extraction means for extracting defect candidates using the opposite color component acquired by the opposite color component acquisition means, and the defect candidate extraction means calculates the logical sum of the extraction results based on all the opposite color components, You may extract as a defect candidate used as the calculation object of the said defect strength by a defect strength calculation means.

The image quality inspection method of the present invention is obtained in the image quality inspection method for inspecting the image quality of a two-dimensional image by obtaining an opposite color component of the two-dimensional image to be inspected and obtaining the opposite color component. Calculating a defect intensity of the two-dimensional image as a ratio with a recognition limit contrast using an opposite color component, and in the step of calculating the defect intensity, the step of calculating the defect intensity includes a specific step. In calculating the defect intensity for a specific opposite color component in a region, the specific opposite color component in the specific region, and another opposite color component different from the specific opposite color component in the specific region, , Is used .
According to this image quality inspection method, since the defect strength for a specific opposite color component is calculated using a plurality of opposite color components, the defect strength can be calculated with high accuracy and stability.

In the step of calculating the defect intensity, when calculating the defect intensity of the specific opponent-color components, it may be used a background value of the other opposite color components.

In the step of calculating the defect intensity, the time of calculation of the particular defect contrast required for calculating the defect intensity of opponent color components, the difference between the defective area and the background in the particular opposite color component of said other The defect contrast may be calculated by dividing by the background value of the luminance component as the opposite color component .

  In the step of calculating the defect intensity, the total defect intensity may be calculated by weighting the defect intensity calculated for each opposite color component.

  A step of extracting defect candidates using the opposite color component acquired by the step of acquiring the opposite color component, and the step of extracting the defect candidate includes a logical sum of extraction results based on all the opposite color components, You may extract as a defect candidate used as the calculation object of the said defect strength by the said defect strength calculation means.

  According to the image quality inspection device of the present invention, since the defect strength for a specific opposite color component is calculated using a plurality of opposite color components, the defect strength can be calculated with high accuracy and stability.

  According to the image quality inspection method of the present invention, since the defect strength for a specific opposite color component is calculated using a plurality of opposite color components, the defect strength can be calculated with high accuracy and stability.

  Hereinafter, an embodiment of an image quality inspection apparatus according to the present invention will be described with reference to FIGS.

  FIG. 1 is a block diagram illustrating a configuration of an image quality inspection apparatus according to an embodiment.

  A high-pixel monochrome CCD camera 1 combined with a photographic lens is disposed at a position directly opposite the inspection target device 3, thereby imaging the inspection target device. The CCD camera 1 is installed in a state in which the tilt is adjusted accurately and vertically with respect to the panel to be inspected.

  Further, a turret 11 having RGB color filters 11R, 11G, and 11B that respectively transmit light of RGB wavelengths is inserted between the photographing lens of the monochrome CCD camera 1 and the inspection target device 3, and the turret 11 is a stepping motor or servo. It has a structure that can be rotated and positioned at high speed by being supported by a rotational drive unit such as a motor. The turret rotates in a programmable manner in conjunction with the shooting sequence, allowing arbitrary filter selection at high speed.

  Instead of a configuration in which a turret and a monochrome camera are combined, a configuration with a 3CCD camera having a built-in color filter without using a turret is also possible.

  Also, instead of the RGB color filter, consider the spectral characteristics of a monochrome camera and use an X, Y, Z filter designed to directly capture XYZ tristimulus images, or use a two-dimensional colorimeter. For example, it is possible to directly input an image of XYZ tristimulus values.

  The RGB captured image data output from the CCD camera 1 is input to the image quality inspection apparatus 2.

  The image quality inspection apparatus 2 corrects a correction unit 21 that performs later-described correction on the inspection region of the two-dimensional image to be inspected, and acquires the opposite color component of the two-dimensional image that has been corrected by the correction unit 21. Using the color component acquisition unit 22 and the opposite color component acquired by the opposite color component acquisition unit 22, the defect candidate extraction unit 23 that extracts a defect candidate, and the defect intensity for the region extracted by the defect candidate extraction unit 23 Defect strength calculation means 24 to be calculated, and determination means 25 for determining the presence / absence of a defect based on the defect strength calculated by the defect strength calculation means 24.

  The function of the correction means 21 is the process of step S201 described later, the function of the opposite color component acquisition means 22 is the process of steps S202 to S203 described later, and the function of the defect candidate extraction means 23 is the process of steps S204 to S205 described later. The function of the defect strength calculation means 24 corresponds to the processing in steps S207 to S212 described later, and the function of the determination means 25 corresponds to “determination processing” described later.

  FIG. 2 is a flowchart showing an inspection procedure by the image quality inspection apparatus 2. As shown in FIG. 2, the inspection procedure includes an “image correction process” for correcting input RGB image data, and an “image for converting RGB images into images of opposite colors of w / k, r / g, b / y”. "Conversion processing", "Defect candidate extraction processing" for extracting defect candidates from each image, "Feature amount calculation processing" for calculating defect intensity Semu values of the extracted defect candidates, and calculated feature amounts (Sem value) Is compared with a preset threshold value to determine whether or not it is a defect.

  First, in the “image correction process”, in step S201, as pre-processing, each RGB image input from the CCD camera 1 is subjected to shading for optical characteristic correction, defect size support, high-frequency noise removal, and the like. Correction, reduction processing, smoothing processing, and the like are performed.

  Next, in the “image conversion process”, in step S202, the RGB image is converted into three tristimulus value images (X, Y, Z) and output. In step S203, the input three tristimulus value images are converted into three opposite color component images (w / k, r / g, b / y) using the above equation (5) and output. To do.

  In the “defect candidate extraction process”, in step S204, each input opposite color component image (w / k, r / g, b / y) is subjected to a defect using an enhancement filter such as a Laplacian type, a Sobel type, or a projection. Emphasizes the difference between the candidate opposite color component values. In step S205, the image in which the difference between the opposite color component values is emphasized is binarized with a threshold, and a portion exceeding (or not satisfying) the threshold is extracted as a defect candidate and output.

  Next, in step S206, the logical OR (OR) of the defect candidate areas extracted for each opposite color component image by the processing of step S204 to step S205 is taken, and all defect candidate areas are extracted. For example, as shown in FIG. 3, when candidate regions are extracted only with b / y components, not only b / y components but also w / k components and r / g components are subjected to “feature calculation”. "Process" is executed.

In the “feature value calculation process”, defect candidates are normalized in step S207 as in the case of monochrome image input, and in step S208, frequency components Power _i (u _h , u _v ) that form defect shapes.
To extract. Here, i is each opposite color component w / k., R / g, b / y.

On the other hand, in step S209, the spatial frequencies u _h and u _v , the background values BG _wk , BG _rg , BG _by , the defect sizes X and Y, the angle direction φ, etc. for all the opposite color components are used as parameters. The dimension-extended MTF characteristic S _MTF (u _h , u _v , BG _wk , BG _rg , BG _by , X, Y, φ) is calculated. Here, the background values of all the opposite color components are used in consideration of the influence of other background values.

In step S210, the defect contrast C _i for each opposite color component is calculated from the opposite color component value of the defect area and the background value of the luminance component w / k by the following equation.
C _wk = Δwk / BG _wk × 100 (13)
C _rg = Δrg / BG _wk × 100 (14)
C _by = Δby / BG _wk × 100 (15)

  In this way, the contrast calculation is performed by dividing the luminance component w / k background value in any opposite color component.

Next, in step S211, from the obtained frequency component Power _i (u _h , u _v ) and the two-dimensional MTF characteristic S _MTF (u _h , u _v , BG _i , X, Y, φ) The recognition limit contrast C _JNDi for the defect shape in the opposite color component is calculated.

Finally, in step S212, from the defect contrast C _i in the target opposite color component and the recognition limit contrast C _JNDi for the defect shape, the defect for the target opposite color component is _{calculated according} to the above equations (9) to (11). The intensity (Semu) is calculated.

In the “determination process”, in step S212, the defect intensities Sem _wk , Sem _rg , and Sem _by in each opposite color component (w / k, r / g, b / y) calculated by the “feature amount calculation process” are weighted. the function f (Semu _i), a defect of the same conspicuous degree weighted so that the same defect intensity at each opposite color components, the overall total defect intensity function _{g (Semu wk, Semu rg,} Semu by) by Defect strength (Semu) is calculated.

Here, the weighting function f (Semu _i), for example,
f (Semu _i ) = Semu _i ^α Expression (17)
Using, for example, comprehensive defect intensity function _{g (Semu wk, Semu rg,} Semu by) as,
^{_{Semu = Semu wk α × Semu rg}} β × Semu by γ ··· formula (18)
Etc. are used.

  In step S213, the calculated total defect strength (Semu) is compared with a preset threshold value to determine whether or not the defect is present. If it is determined that the defect is present, step S213 is performed. In S214, the detected defect information is collected into output data.

  As described above, according to the image quality inspection apparatus of the present embodiment, the contrast of each opposite color component is divided by the background value of the luminance component w / k by dividing the difference between the defect area in each opposite color component and the background. By acquiring, it is possible to improve the problem of sign change that occurs when the background value of each opposite color component is near zero.

  Further, by obtaining the MTF characteristics for each opposite color component as a function of the background values for all the opposite color components, the influence of the background values of the other components can be taken into consideration.

  Further, by correcting the defect strength calculated for each opposite color component with a weighting function, the same conspicuous defect in the opposite color component can be expressed as the same defect strength.

  Furthermore, for all defect candidate areas extracted with any of the opposite color components, the defect strengths for all opposite color components are calculated, and then the corrected defect strength calculated for each opposite color component is calculated. The total defect strength is calculated using the total defect strength function. For this reason, it is possible to objectively express the strength of the defect that is conspicuous for the first time as a result of complicated mixing of the opposite color components.

  In this way, high-precision output that quantifies the defect strength of color irregularities of various shapes that occur in actual devices, which could not be objectively quantified until now, and outputs an objective numerical value as the defect strength. Inspection is possible.

  FIG. 4 is a block diagram illustrating a configuration example when an image of XYZ tristimulus values is directly input.

  In the example of FIG. 4, X / Y / Z filters 12X and 12Y designed to directly capture an image of XYZ tristimulus values instead of the RGB color filter in FIG. 1 in consideration of the spectral characteristics of the monochrome camera. , 12Z are used. An image of XYZ tristimulus values may be directly input by using a two-dimensional colorimeter.

  In the case of the configuration shown in FIG. 4, the input image data R, G, B in FIG. 1 is changed to the input image data X, Y, Z. In this case, the tristimulus value image data X, Y, and Z are directly input to the opposite color component acquisition unit 22A of the image quality inspection apparatus 2A. In the “image correction process”, instead of the process of step S201 in FIG. 2, as a pre-process, the input tristimulus value image data X, Y, and Z are corrected for optical characteristics and dealt with defect sizes. In addition, shading correction, reduction processing, smoothing processing, and the like are performed to remove high-frequency noise. In the “image conversion process”, the process corresponding to step S202 is not required, and the input three tristimulus value images are converted into three opposite color component images (w / k, r /) by the process corresponding to step S203. g, b / y) and output. The subsequent processing is the same as in the example of FIGS.

  In this case, it is not necessary to convert RGB images into XYZ images, so that the processing time can be shortened.

  FIG. 5 is a block diagram illustrating a configuration example of an image quality inspection apparatus that determines the degree of defect by combining a plurality of feature amounts.

  As shown in FIG. 5, in the image quality inspection apparatus 2B, in addition to the defect intensity calculation means 24, a feature quantity calculation means group 26 such as a first feature quantity calculation means, a second feature quantity calculation means,. In the determination unit 25A, the opposite color component defect intensity calculated by the defect intensity calculation unit 24 and other feature amounts (position information, size, color information, defect peripheral area calculated by the feature amount calculation unit group 26 are provided. And the like, etc.) are combined to determine the degree of defect.

  Depending on the object to be inspected, it may not be determined only by the defect strength (that is, the degree of the defect perceived by the eyes), but depending on the type of defect, the position where the defect occurs, etc., it may be desired to detect a defect even if the defect strength is small. By combining with secondary feature quantities (position information, size, color information, feature quantities of defect peripheral areas, etc.) that cannot be expressed by the defect intensity, it is possible to make a determination taking that point into consideration.

  Further, when the entire background is rough in the target image, it may become visually inconspicuous even with the same defect strength. In addition, when there is another defect in the vicinity of the defect, it may be visually noticeable even if the defect strength is small. In order to deal with such a case, the corrected determination is made possible by combining with the feature amount around the defect.

  FIG. 6 is a block diagram illustrating a configuration example in which correction is performed using the camera output value / tristimulus value conversion parameter of the CCD camera 1.

  As shown in FIG. 6, when the opposite color image is generated, the opposite color component acquisition unit 22 reads the camera output value / tristimulus value conversion parameter of the CCD camera 1 from the file 27 and corrects the camera output value.

  As a result, the actual tristimulus value information can be given to the opposite color component acquisition means 22, so that the defect strength can be calculated with high accuracy.

  FIG. 7 is a block diagram showing an example in which a spectral luminance meter is used.

  In the example of FIG. 7, the spectral luminance meter 5 is used in combination with the CCD camera 1, and the tristimulus value of the inspection target device 3 is measured through the RGB color filter. As shown in FIG. 7, the spectral luminance data obtained by the spectral luminance meter 5 is given to the conversion parameter generation unit 51 together with the RGB image data from the CCD camera 1. The conversion parameter generation unit 51 generates a camera output value / tristimulus value conversion parameter of the CCD camera 1 from the spectral luminance data and the RGB image data, and stores it in the file 27. This camera output value / tristimulus value conversion parameter is used by the opposite color component acquisition means 22 as in the case of FIG. Spectral luminance is measured when changing the device to be inspected, when changing lots, when changing device types, or during maintenance.

  As described above, by using the camera output value / tristimulus value conversion parameter obtained by measuring the device 3 to be inspected in real time, it is possible to calculate the defect intensity corresponding to the variation of the light source and the imaging device and the change with time.

  FIG. 8 is a block diagram illustrating a configuration example using the recognition limit sensitivity model parameter.

  In the example of FIG. 8, the recognition limit sensitivity model parameter is prepared as the external file 28, and the recognition limit sensitivity model parameter is given to the image quality inspection apparatus 2 and used for calculation of the defect intensity. The recognition limit sensitivity model parameter is passed to some or all of the processes in the defect candidate extraction unit 23, the defect strength calculation unit 24, and the determination unit 25 corresponding to the opposite components.

  Thus, by inputting the recognition limit sensitivity model parameter from the outside, the maintainability is improved and the defect strength can be calculated with high accuracy. For example, by measuring the characteristics of a device to be inspected in real time, and generating and using a recognition limit sensitivity model parameter from the data, it is possible to calculate the defect intensity corresponding to variations in characteristics between devices and changes over time.

  FIG. 9 is a block diagram showing a configuration example in which the image quality inspection method of the present invention is applied to an image difference evaluation apparatus.

  In the example of FIG. 9, image data B to be compared with image data A (for example, an image obtained by compressing image data A or an image transmitted image data A) is input to image difference evaluation device 6, and correction is performed on image data A. The correction means 61b, the opposite color component acquisition means 62b, the defect intensity calculation means 63b, and the determination means 64b are applied to the image data B by the means 61a, the opposite color component acquisition means 62a, the defect intensity calculation means 63a, and the determination means 64a, respectively. Quantify the defect strength. Furthermore, the defect intensity of both is compared by the judgment comparison part 65, and the change of an image is test | inspected.

As described above, by applying the image quality inspection method of the present invention to the image difference evaluation apparatus, the difference / deterioration of the image can be evaluated with a quantitative value based on the visual recognition limit sensitivity, and the evaluation that is easy to understand objectively. The value can be calculated. Moreover, the deterioration with respect to each shape can be evaluated. For example, it can be used for the following objects.
(1) Evaluation of image compression performance This is performed by setting the images before and after compression as image data A and B.
(2) Image transmission performance evaluation This is performed by setting the images before and after transmission to the image data A and B.
(3) An inspection image of a printed matter change is performed by setting an image obtained by imaging a printed matter as image data A, and then setting an image obtained by capturing the printed matter as image B.
(4) Inspection data for changes in painted object, stability of object position accuracy, or addition of illumination condition correction function and object position correction function by image processing, and image data of image of painted object as master image data This is performed by setting an image obtained by capturing an image of what has been painted to A in image B.

の各波長帯域の光をそれぞれ透過させるフィルタとモノクロカメラを組み合わせ、各波長帯域の光から反対色成分を演算して取得してもよい。
具備する構成をとることを特徴とする検査装置
（３）ｘｙ表色系などで示される色度図上の独立した少なくとも３点の色度フィルタとモノクロカメラを組み合わせ、各波長帯域の光から反対色成分を演算して取得してもよい。
（４）撮像素子と撮影レンズの間に複屈折素子を具備し、モアレを除去又は軽減するようにしてもよい。 The image quality inspection apparatus according to the present invention is not limited to the above-described embodiment, and may be, for example, the following forms.
(1) A filter that transmits light in each wavelength band of cyan, magenta, and yellow may be combined with a monochrome camera, and an opposite color component may be calculated and acquired from the light in each wavelength band.
(2) XYZ color matching function

Alternatively, a filter that transmits light in each wavelength band and a monochrome camera may be combined, and the opposite color components may be calculated from the light in each wavelength band.
Inspection apparatus characterized by comprising (3) Combining at least three independent chromaticity filters and a monochrome camera on a chromaticity diagram represented by an xy color system, etc. Color components may be calculated and acquired.
(4) A birefringence element may be provided between the imaging element and the photographic lens so that moire is removed or reduced.

  As described above, according to the image quality inspection apparatus of the present invention, since the defect strength for a specific opposite color component is calculated using a plurality of opposite color components, the defect strength can be calculated with high accuracy and stability.

  The scope of application of the present invention is not limited to the above embodiment. The present invention can be widely applied to an image quality inspection apparatus and an image quality inspection method for inspecting the image quality of a two-dimensional image.

1 is a block diagram illustrating a configuration of an image quality inspection apparatus according to an embodiment. The flowchart which shows the test | inspection procedure by an image quality test | inspection apparatus. The figure which shows the extraction method of a candidate area | region. The block diagram which shows the structural example in the case of inputting the image of an XYZ tristimulus value directly. The block diagram which shows the structural example of the image quality inspection apparatus which determines the grade of a defect combining a some feature-value. The block diagram which shows the structural example which correct | amends by a camera output value / tristimulus value conversion parameter. The block diagram which shows the example which uses a spectral luminance meter. The block diagram which shows the structural example using a recognition limit sensitivity model parameter. The block diagram which shows the structural example which applied the image quality inspection method of this invention to the image difference evaluation apparatus. The flowchart which shows the test | inspection procedure by the image quality inspection apparatus using the MTF characteristic with respect to a brightness | luminance. It is a figure which shows a visual sensitivity characteristic etc., (a) is a figure which shows a light and dark sine wave-like stripe pattern, (b) is a figure which shows the human visual sensitivity characteristic with respect to a spatial frequency, (c) is a range of a certain visual angle. The figure which shows the state which displayed the sine wave of a certain wave number. The figure which shows the spectral characteristic of a color component opposite to a tristimulus value. The figure which shows an example of behavior on a CIELAB color system. The figure which shows the tendency of MTF of an opposite color component.

Explanation of symbols

2 Image quality inspection apparatus 22 Opposite color component acquisition means 23 Defect candidate extraction means 24 Defect strength calculation means 25 Judgment means 62a Opposite color component acquisition means 63a Defect candidate extraction means 64a Defect strength calculation means 62b Opposite color component acquisition means 63b Defect candidate extraction means 64b Defect strength calculation means 65 Determination means

Claims (12)

In an image quality inspection device that inspects the image quality of a two-dimensional image,
An opposite color component acquisition means for acquiring an opposite color component of a two-dimensional image to be inspected;
Using the opposite color component acquired by the opposite color component acquisition means, the defect intensity calculation means for calculating the defect intensity of the two-dimensional image as a ratio with the recognition limit contrast;
With
The defect intensity calculating means calculates the defect intensity in the specific area for the specific opposite color component, the specific opposite color component in the specific area, and the specific opposite color component in the specific area. An image quality inspection apparatus using another opposite color component different from the above . The image quality inspection apparatus according to claim 1, further comprising a determination unit that determines an image quality of the two-dimensional image based on the defect intensity calculated by the defect intensity calculation unit. The defect intensity calculating means calculates a first defect intensity using the opposite color component acquired by the opposite color component acquisition means of the first two-dimensional image to be inspected and a second object to be inspected. A second defect strength is calculated using the opposite color component acquired by the opposite color component acquisition means of the two-dimensional image of
The determination unit compares the first defect intensity and the second defect intensity calculated by the defect intensity calculation unit to compare an image difference between the first two-dimensional image and the second two-dimensional image. The image quality inspection apparatus according to claim 2, wherein: The said defect intensity | strength calculation means uses the background value of said other opposite color component when calculating the said defect intensity | strength of the said specific opposite color component, The any one of Claims 1-3 characterized by the above-mentioned. The image quality inspection apparatus described. The defect intensity calculating means, wherein when calculating the particular defect contrast required for calculating the defect intensity of opponent color components, the difference between the defective area and the background in the particular opposite color component the other opposite color The image quality inspection apparatus according to claim 1, wherein the defect contrast is calculated by dividing by a background value of a luminance component as a component. The image quality inspection according to claim 1, wherein the defect strength calculation unit calculates a total defect strength by weighting the defect strength calculated for each opposite color component. apparatus. A defect candidate extraction means for extracting a defect candidate using the opposite color component acquired by the opposite color component acquisition means;
7. The defect candidate extraction unit extracts a logical sum of extraction results based on all opposite color components as a defect candidate for which the defect intensity is calculated by the defect intensity calculation unit. The image quality inspection apparatus according to any one of the above. In an image quality inspection method for inspecting the image quality of a two-dimensional image,
Obtaining an opposite color component of the two-dimensional image to be inspected;
Calculating the defect intensity of the two-dimensional image as a ratio with the recognition limit contrast using the opposite color component obtained by obtaining the opposite color component;
With
In the step of calculating the defect intensity, when calculating the defect intensity for a specific opposite color component in a specific area, the specific opposite color component in the specific area and the specific opposite in the specific area. An image quality inspection method using another opposite color component different from a color component . 9. The image quality inspection method according to claim 8, wherein the step of calculating the defect intensity uses a background value of the other opposite color component when calculating the defect intensity of the specific opposite color component. . In the step of calculating the defect intensity, the time of calculation of the particular defect contrast required for calculating the defect intensity of opponent color components, the difference between the defective area and the background in the particular opposite color component of said other 10. The image quality inspection method according to claim 8, wherein the defect contrast is calculated by dividing by a background value of a luminance component as an opposite color component . The step of calculating the defect intensity calculates a total defect intensity by weighting the defect intensity calculated for each opposite color component. Image quality inspection method. Extracting a defect candidate using the opposite color component acquired by the step of acquiring the opposite color component;
9. The step of extracting defect candidates, wherein a logical sum of extraction results based on all opposite color components is extracted as a defect candidate as a defect intensity calculation target by the defect intensity calculating means. The image quality inspection method according to claim 1.

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