KR20240122745A - Method for detecting defects in near-field displays - Google Patents

Abstract

A method for detecting defects in a near-eye display comprises the following steps: dividing a pixel matrix into a plurality of sub-pixel units, each of the plurality of sub-pixel units including M x M pixels; sequentially controlling pixels located at same positions within the M x M pixels of the sub-pixel units to emit light simultaneously to generate M x M sub-test images from an image generator; sequentially capturing the sub-test images generated by the image generator using an imaging device; stacking the sub-test images to generate a test image corresponding to a virtual image, each pixel of the test image having a pixel brightness; comparing the pixel brightness of at least one pixel of the test image with an area pixel brightness of an area pixel of the test image; setting at least one pixel as a defective pixel if the pixel brightness of the at least one pixel is less than a brightness threshold value of the area pixel brightness; and classifying the virtual image as a defective image if the defective pixel is included in a visible range of the test image corresponding to the virtual image.

Description

Method for detecting defects in near-field displays

The present invention relates to the field of near-eye display technology, and more specifically, to a method for detecting defects in a near-eye display.

Near-eye displays generally refer to augmented reality (AR), virtual reality (VR), and head-up/head-mounted displays. A key metric for evaluating the image quality displayed on these displays is visual artifacts, also known as image defects. In the field of near-eye displays, image problems containing defects are particularly serious. Typically, defects in virtual images rendered on near-eye displays are caused by pixel defects in the image generator (e.g., micro LED/OLED). Visually, these defects may appear as dots, lines, or areas with uneven brightness.

In the prior art, an image is captured using a conventional imaging system (e.g., a camera) and then analyzed. However, it is difficult to detect defects using conventional measurements due to crosstalk between virtual image pixels of a near-eye display or low image resolution. In practice, defects still exist, which degrades the image quality. In other words, it is still a difficult task to detect defects in a near-eye display using current imaging systems.

Therefore, there is a need to develop more advanced test patterns to effectively detect defects in virtual images of near-field displays, and to meet the requirements for detecting, identifying and classifying defects in virtual images, thereby enabling subsequent processing and resolution of defects.

In this regard, the present invention provides a method for detecting defects in a near-eye display, which aims to detect, identify and classify defects in a virtual image.

According to a specific embodiment of the present invention, a method for detecting defects in a near-eye display comprises the steps of: dividing a pixel matrix into a plurality of sub-pixel units, each of the plurality of sub-pixel units including M x M pixels, where M is a natural number greater than 2; sequentially controlling pixels located at same positions within the M x M pixels of the sub-pixel units to emit light simultaneously to generate M x M sub-test images from an image generator; sequentially capturing the sub-test images generated by the image generator using an imaging device; stacking the sub-test images to generate a test image corresponding to a virtual image, wherein each pixel of the test image has a pixel brightness; comparing the pixel brightness of at least one pixel of the test image with an area pixel brightness of an area pixel of the test image; setting at least one pixel as a defective pixel if the pixel brightness of the at least one pixel is less than a brightness threshold of the area pixel brightness; A step of classifying a virtual image as a defective image if a defect pixel is included in the visible range of a test image corresponding to the virtual image.

Here, the brightness threshold is between 50% and 70% of the area pixel brightness.

After the step of comparing pixel brightness of at least one pixel of the test image with area pixel brightness of area pixels of the test image, the method further includes the step of designating the given unit area as a defect area if a quantity of pixels within the given unit area of the test image, each pixel having a pixel brightness less than a brightness threshold of the area pixel brightness, exceeds a predetermined quantity threshold.

The visible range includes a first visible range, a second visible range, a third visible range, and a fourth visible range, which are arranged in order from small to large. In addition, in a step of classifying the virtual image as a defective image when a defective pixel is included in the visible range of a test image corresponding to the virtual image, the method includes: a step of classifying the virtual image as a fifth level defective image when the defective pixel is included in the first visible range of the test image or a defective area is included in the second visible range; a step of classifying the virtual image as a fourth level defective image when the defective pixel is included in the second visible range of the test image or a defective area is included in the third visible range; a step of classifying the virtual image as a third level defective image when the defective pixel is included in the third visible range of the test image or a defective area is included in the fourth visible range; a step of classifying the virtual image as a second level defective image when the defective pixel is included in the fourth visible range of the test image; and further comprising a step of classifying the virtual image as a first-level defective image if the fourth visible range of the test image does not include a defective pixel.

In one embodiment, the shape of the visible range is circular.

In one embodiment, the shape of the visible range is elliptical.

A method for detecting defects in a near-field display further comprises, prior to the step of dividing the pixel matrix into a plurality of sub-pixel units, the step of calibrating and aligning the pixel matrix and the virtual image.

In summary, the method for detecting defects in a near-eye display of the present invention has the following beneficial effects: it can accurately find defective pixels in a virtual image, identify and classify them, and facilitate subsequent defect processing, thereby achieving the ultimate goal of improving the image quality of the virtual image.

FIG. 1 is a flow chart illustrating steps of a method for detecting defects in a near-eye display according to a specific embodiment of the present invention.
FIG. 2a is a schematic diagram illustrating a pixel matrix of a near-eye display of the present invention.
FIG. 2b is a schematic diagram illustrating the principle of a defect detection method for a near-field display of the present invention.
FIG. 3 is a schematic diagram illustrating defective pixels in a defect detection method for a near-field display of the present invention.
FIG. 4 is a flowchart illustrating steps of a defect classification method of a near-eye display according to a specific embodiment of the present invention.
FIG. 5 is a schematic diagram illustrating a test image and a viewing range according to a specific embodiment of the present invention.
FIG. 6 is a schematic diagram illustrating a test image and a viewing range according to a specific embodiment of the present invention.

The detailed description of the embodiments of the disclosed devices and methods described below is presented herein by way of example and not limitation with reference to the drawings. Although specific embodiments have been illustrated and described in detail, it should be understood that various changes and modifications may be made without departing from the scope of the appended claims. The scope of the present invention is in no way limited by the number, material, shape, relative arrangement, etc. of the components, but is disclosed only as an example of embodiments of the present invention.

The terminology used in the various embodiments disclosed in the present invention is only used to describe particular embodiments and is not intended to limit the disclosed embodiments. The singular as used herein includes the plural unless the context clearly indicates otherwise. Unless defined otherwise, all terms (including technical and scientific terms) used in this description have the same meaning as commonly understood by one of ordinary skill in the art of the present invention. Terms defined in common dictionaries will be interpreted in a meaning consistent with the context of the art and will not be interpreted in an idealized or overly formal sense unless explicitly defined in the disclosed embodiments.

Referring to FIGS. 1, 2A and 2B together, FIG. 1 is a flow chart illustrating a method for detecting defects in a near-eye display according to a specific embodiment of the present invention. FIG. 2A illustrates a schematic diagram of a pixel matrix (1) of the near-eye display of the present invention. FIG. 2B illustrates a schematic diagram of the principle of a defect detection method for the near-eye display of the present invention. As illustrated in FIG. 1, the method for detecting defects in the near-eye display of the present invention includes the following steps: Step S1: dividing a pixel matrix into a plurality of sub-pixel units, each of the plurality of sub-pixel units including M x M pixels, where M is a natural number greater than 2; Step S2: sequentially controlling pixels located at the same positions within the M x M pixels of the sub-pixel units to emit light simultaneously to generate M x M sub-test images from an image generator; Step S3: sequentially capturing the M x M sub-test images displayed by the image generator using an imaging device; Step S4: stacking and integrating the sub-test images to generate a complete test image corresponding to the virtual image, wherein each pixel of the test image has a pixel brightness; Step S5: comparing the pixel brightness of the pixels of the test image with the area pixel brightness of the area pixels of the test image; Step S6: setting the pixel as a defective pixel if the pixel brightness of the pixel is less than a brightness threshold of the area pixel brightness; and Step S7: classifying the virtual image as a defective image if the defective pixel is included in the visible range of the test image corresponding to the virtual image.

As illustrated in FIGS. 1 and 2A, the near-eye display may include a pixel matrix (1) for displaying images or virtual images, wherein the pixel matrix (1) includes a plurality of pixels (12). In this particular embodiment, the pixel matrix (1) is illustrated as having a size of 9 x 9. In actual applications, the size of the pixel matrix and the number of pixels may be determined according to the resolution of the near-eye display, and the shape of the pixel matrix is not limited to the square illustrated in FIG. 2A, and may be rectangular or other shapes.

In this particular embodiment, the image generator can generate virtual images for display in the pixel matrix (1). The size of the virtual image can correspond to the size of the pixel matrix (1). In actual applications, the virtual images can be, but are not limited to, color images; they can also be all white, all gray, or other monochrome images. Additionally, each of the positions of the pixels (12) corresponding to the virtual image includes image data (e.g., brightness, saturation, and other optical characteristics). The image generator can drive the pixels (12) of the pixel matrix (1) based on the image data to render the virtual image from the near-eye display.

Additionally, in this particular embodiment, the method for detecting defects in a near-eye display may further include the following step, namely, the step of correcting and aligning the pixel matrix (1) and the virtual image. In practice, the pixel matrix (1) and the virtual image may be aligned using an image corrector or adjustment mechanism of the near-eye display, so that each pixel (12) position of the pixel matrix (1) corresponds to a position of the virtual image, respectively.

As illustrated in FIG. 2b, in step S1, the pixel matrix is divided into a plurality of sub-pixel units (11). In this particular embodiment, the pixel matrix is divided into four sub-pixel units (11), which means that each sub-pixel unit (11) includes 3 x 3 pixels (12). In practical applications, the number of sub-pixel units and the number of pixels within each sub-pixel unit may also be determined according to the size of the pixel matrix.

In step S2, the image generator controls pixels (12) at the same positions of the sub-pixel units (11) to light up simultaneously. As shown in FIG. 2B, the image generator first controls pixels (12) at the upper left corners of all the sub-pixel units (11) to light up simultaneously and other pixels to remain off. In step S3, the imaging system captures/photographs a current image indicated by the pixel matrix (1) to generate a sub-test image (2A). The imaging device may be a light measuring device (LMD) that may include a colorimeter/camera and a NED/lens. When the imaging system generates the sub-test image, the imaging system also records the pixel brightness values of each illuminated pixel (12) in the sub-test image.

In addition, after the imaging system captures the sub-test image (2A) in the state where the upper left pixels (12) of all the sub-pixel units (11) are turned on, the image generator controls the pixels (12) in the middle position of the first row of all the sub-pixel units (11) to turn on simultaneously and the other pixels to remain off, and the imaging system captures another sub-test image (2B). In this particular embodiment, since the sub-pixel unit (11) includes nine pixels, the image generator sequentially controls the pixels (12) in the same position within the sub-pixel units (11) to emit light simultaneously. When the image generator controls the pixels (12) in the lower right corners of all the sub-pixel units (11) to turn on simultaneously and the imaging system captures the sub-test image (2I), nine sub-test images are generated after all the pixels (12) of the sub-pixel units (11) are turned on sequentially.

In this embodiment, since each sub-pixel unit (11) is a 3 x 3 sub-pixel matrix, the spacing between each illuminated pixel (12) in each sub-test image is three times the spacing between adjacent pixels. Therefore, when the pixels (12) of each sub-pixel unit (11) are turned on, all the illuminated pixels can maintain the furthest distance from each other, and the brightness of the pixel (12) does not interfere or cross with the brightness of other pixels. This allows the imaging system to record the actual pixel brightness value of each illuminated pixel (12) when capturing the sub-test images.

In step S4, after the imaging system generates nine sub-test images, the imaging system can stack all the sub-test images to generate a test image (3) corresponding to the virtual image. In practical applications, the imaging system can sequentially display the sub-test images to render the test image (3) corresponding to the virtual image. Since each sub-test image is an image of different non-overlapping illumination pixels (12) of the sub-pixel unit (11), displaying all the sub-test images turns on all the pixels (12) of the pixel matrix (1) to form a complete test image (3), where each pixel (12) of the test image (3) includes an actual value of the illumination brightness.

Referring to FIGS. 1 and 3, FIG. 3 is a schematic diagram illustrating a defective pixel in a defect detection method for a near-eye display of the present invention. The image presented in FIG. 3 is a test image (3) corresponding to a virtual image generated by an imaging system, which is a stack of all sub-test images. In step S5, after generating the test image (3), the pixel brightness of the pixels (12) of the test image (3) and the area pixel brightness of the pixel area of the test image are compared. In practical applications, the near-eye display may additionally include a comparison and analysis chip. As illustrated in FIG. 3, when the comparison and analysis chip inspects the pixel (12'), adjacent or neighboring pixels forming a 3 x 3 area around the pixel (12') called area pixels (13') may be examined, and the area pixel brightness may be an average pixel brightness value of these 3 x 3 pixels. Since each pixel (12) of the test image (3) has a true illumination brightness value, the defect detection method for the near-eye display of the present invention identifies defective pixels by comparing the brightness levels between the pixels and their neighboring pixels. In particular, the number of pixels in the area pixels may be determined according to requirements or designs.

In step S6, when the brightness of the pixel is lower than a brightness threshold of the area pixel brightness, the pixel is designated as a defective pixel. In practical applications, the brightness threshold may be set between 50% and 70% of the area pixel brightness. As illustrated in FIG. 3, the pixel (12') may be defined as a defective pixel (also referred to as a darker spot) when its brightness is greater than 70% of the area pixel brightness of the area pixels (13'). In a specific embodiment, the pixel (12') may also be defined as a defective pixel when its brightness is greater than 50% and less than 70% of the area pixel brightness of the area pixels (13'). Additionally, the pixel (12'') may be defined as a defective pixel (also referred to as a dead pixel) when its brightness is less than 50% of the area pixel brightness of the area pixels (13''). Therefore, pixels (12', 12'') are identified as point defect pixels.

The defect detection method for a near-eye display of the present invention can detect a plurality of pixels in addition to detecting whether a single pixel is defective. As illustrated in FIG. 1, in this specific embodiment, after performing step S5, the following step S61 may also be performed: If the number of pixels in a unit area of a test image whose pixel brightness is less than a brightness threshold value of the area pixel brightness exceeds a predetermined quantity threshold value, the unit area is designated as a defective area. In actual applications, the quantity threshold value may be set if the number of defective pixels exceeds a specific value or exceeds 30% of the total number of pixels in the unit area, but is not limited thereto. Assuming that the unit area is a 10 x 10 matrix and 70 pixels in the unit area are identified as defective pixels, the unit area is also defined as a defective area. Additionally, if two or three adjacent or connected pixels are identified as defective pixels (as shown in regions (B, B') of Fig. 3), these defective pixels can be defined as linear defects.

Therefore, the defect detection method for a near-eye display of the present invention can establish a criterion for judging defective pixels through a test image corresponding to a virtual image, effectively detect the severity and distribution range of defective pixels, and thereby form an index for judging the quality of virtual images.

In step S7, if the viewable range of the test image corresponding to the virtual image includes defective pixels, the virtual image is classified as a defective image. The present invention can also identify and define the positions of the defective pixels, thereby determining whether the virtual image requires additional processing and optimization. In practical applications, the viewable range may be an area covered by a Field of View (FOV), and the comparison and analysis chip of the near-eye display can determine and classify the virtual image as a defective image according to whether the viewable range includes defective pixels.

Referring to FIGS. 4 and 5 together, FIG. 4 is a flowchart illustrating a defect classification method for a near-eye display according to a specific embodiment of the present invention. FIG. 5 schematically illustrates test images and visible ranges according to a specific embodiment of the present invention. As illustrated in FIG. 5, this specific embodiment includes a first visible range (A1), a second visible range (A2), a third visible range (A3), and a fourth visible range (A4) arranged in order of the visible ranges from the smallest to the largest. In addition, the first visible range (A1), the second visible range (A2), the third visible range (A3), and the fourth visible range (A4) are circular and located at the center of the test image (3) corresponding to the virtual image. As illustrated in FIG. 1 and FIG. 4, step S7 of FIG. 1 can be described in more detail as follows: Step S71: Determine whether point defect pixels are included in the first visible range (A1) of the test image (3) or whether a defect area is included in the second visible range (A2). If the result is positive, classify the virtual image as a fifth level defective image (step S711); If the result is negative, proceed to the following step S72: Determine whether point defect pixels are included in the second visible range (A2) of the test image (3) or whether a defect area is included in the third visible range (A3). If the result is positive, classify the virtual image as a fourth level defective image (step S721); If the result is negative, the process proceeds to the following step S73: It is determined whether point defect pixels are included in the third visible range (A3) of the test image (3) or whether a defect area is included in the fourth visible range (A4). If the result is positive, the virtual image is classified as a third level defective image (step S731); If the result is negative, the process proceeds to the following step S74: It is determined whether point defect pixels are included in the fourth visible range (A4) of the test image (3). If the result is positive, the virtual image is classified as a second level defective image (step S741); If the result is negative, the virtual image is classified as a first level defective image (step S75).

In practical applications, the first visible range (A1) corresponds to an area covered by a FOV (field of view) of F15, the second visible range (A2) corresponds to an FOV of F30, the third visible range (A3) corresponds to an FOV of F45, and the fourth visible range (A4) corresponds to an FOV of F60. In addition, a higher defect image level means a higher quality of the corresponding virtual image, where the first level defective image is the highest image level, and the fifth level defective image is the lowest image level. In addition, when the image level of the virtual image is lower than the fourth level defective image, the near-eye display can issue a warning signal indicating that processing and optimization are required for the virtual image.

Additionally, in this specific embodiment, if line defect pixels are found between the first visible range (A1) and the second visible range (A2) of the test image (3), the virtual image may be classified as a fifth level defective image. If line defect pixels are included between the second visible range (A2) and the third visible range (A3), the virtual image may be classified as a fourth level defective image. If line defect pixels are included between the third visible range (A3) and the fourth visible range (A4), the virtual image may be classified as a third level defective image.

The shape of the visible range may take other forms besides the circular shape mentioned above. Refer to Fig. 6 which shows an outline of a test image (3') and a visible range (A') according to a specific embodiment of the present invention. As shown in Fig. 6, the shape of the visible range (A') may be elliptical to better match the human field of vision when using a near-eye display.

In summary, the defect detection method for a near-eye display of the present invention has the following advantages: it sets a criterion for judging defective pixels, and accurately identifies and classifies defective pixels in virtual images. Through this, the severity and distribution range of defective pixels are effectively detected, and subsequent defect processing is facilitated, ultimately aiming at improving the image quality of virtual images.

Through the examples and explanations mentioned above, it is hoped that the features and spirit of the present invention are well explained. More importantly, the present invention is not limited to the embodiments described herein. Those skilled in the art will readily observe that numerous modifications and changes in the device can be made while maintaining the teachings of the present invention. Accordingly, the above disclosure should be construed as limited only by the scope and scope of the appended claims.

Claims (7)

A method for detecting defects in a near-eye display,
A step of dividing a pixel matrix into a plurality of sub-pixel units, each of the plurality of sub-pixel units including M x M pixels, where M is a natural number greater than 2;
A step of generating M x M sub-test images from an image generator by sequentially controlling pixels located at the same positions within the M x M pixels of the sub-pixel units to emit light simultaneously;
A step of sequentially capturing the sub-test images generated by the image generator using an imaging device;
A step of stacking the above sub-test images to generate a test image corresponding to a virtual image, wherein each pixel of the test image has pixel brightness;
A step of comparing pixel brightness of at least one pixel of the test image with area pixel brightness of an area pixel of the test image;
a step of setting the at least one pixel as a defect pixel if the pixel brightness of the at least one pixel is less than a brightness threshold of the pixel brightness of the area; and
A step of classifying the virtual image as a defective image if the defective pixel is included in the visible range of the test image corresponding to the virtual image;
A method for detecting defects in a near-field display, comprising: In paragraph 1,
A method for detecting defects in a near-eye display, wherein the brightness threshold is between 50% and 70% of the brightness of the pixels in the area. In paragraph 1,
After the step of comparing the pixel brightness of the at least one pixel of the test image with the area pixel brightness of the area pixel of the test image,
A method for detecting defects in a near-eye display, further comprising the step of designating the given unit area as a defect area if a quantity of pixels within the given unit area of the test image, each pixel having a pixel brightness less than the brightness threshold of the pixel brightness of the area, exceeds a predetermined quantity threshold. In the third paragraph,
The above visible range includes a first visible range, a second visible range, a third visible range, and a fourth visible range arranged in order from small to large, and if the defect pixel is included in the visible range of the test image corresponding to the virtual image, the step of classifying the virtual image as the defective image comprises:
A step of classifying the virtual image as a fifth level defect image when the defective pixel is included in the first visible range of the test image or the defective area is included in the second visible range;
A step of classifying the virtual image as a fourth level defect image when the defective pixel is included in the second visible range of the test image or the defective area is included in the third visible range;
A step of classifying the virtual image as a third level defect image when the defective pixel is included in the third visible range of the test image or the defective area is included in the fourth visible range;
a step of classifying the virtual image as a second level defect image if the defect pixel is included in the fourth visible range of the test image; and
A step of classifying the virtual image as a first level defective image if the defective pixel is not included in the fourth visible range of the test image;
A method for detecting defects in a near-field display, the method further comprising: In paragraph 1,
A method for detecting defects in a near-eye display, wherein the shape of the above visible range is circular. In paragraph 1,
A method for detecting defects in a near-eye display, wherein the shape of the above visible range is elliptical. In paragraph 1,
Before the step of dividing the above pixel matrix into the plurality of sub-pixel units,
A method for detecting defects in a near-field display, further comprising the step of correcting and aligning the pixel matrix and the virtual image.

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