Defect Detection Method for Near-Eye Display

Abstract

The method for detecting defects in a near-eye display includes the steps of dividing a pixel matrix into a plurality of sub-pixel units each including M×M pixels, controlling in sequence so as to simultaneously emit light the pixels at the same positions among the M×M pixels of those sub-pixel units, thereby generating M×M sub-test images in an image generator, capturing in sequence the sub-test images in the image generator using an imaging device, generating a test image corresponding to a virtual image by superimposing the sub-test images, with each pixel in the test image having a pixel luminance, comparing the pixel luminance of at least one pixel in the test image with the region pixel luminance of region pixels in the test image, setting at least one pixel as a defective pixel when the pixel luminance of the at least one pixel is smaller than a luminance threshold of the region pixel luminance, and classifying the virtual image as a defective image when the defective pixel is included in a visible range of the test image corresponding to the virtual image.

Description

Technical Field

[0001] The present invention relates to the technical field of near-eye displays, and more specifically, to a method for detecting defects in near-eye displays.

Background Art

[0002] A near-eye display generally refers to an augmented reality (AR), virtual reality (VR), or head-up / head-mounted display. Also, an important index for evaluating the quality of an image displayed on a display is visual artifact, which is also referred to as an image defect. In the field of near-eye displays, the situation where an image contains defects is particularly serious. Generally, defects in virtual images rendered on a near-eye display are caused by defective pixels (e.g., Micro LED / OLED) of an image generator. Such defects may visually cause luminance unevenness of dots, lines, or regions.

[0003] In the prior art, shooting is performed using a conventional imaging system (e.g., a camera), and subsequent analysis is then carried out. However, it is very difficult to detect defects by conventional measurements regarding crosstalk between pixels or image resolution degradation in the virtual image of a near-eye display. Therefore, in the actual situation, defects remain, leading to image quality degradation. That is, there are still problems in detecting defects in near-eye displays by a conventional imaging system.

[0004] Therefore, in order to effectively detect defects in the virtual image of a near-eye display, it is necessary to develop a higher-class test mode so that defects existing in the virtual image can be detected, identified, and classified, and subsequent processing and defect elimination operations can be carried out.

Summary of the Invention

Problems to be Solved by the Invention

[0005] In view of the above, an object of the present invention is to provide a method for detecting defects in a near-eye display that can eliminate the drawbacks of the prior art and effectively improve the image quality of a virtual image by realizing the object of detecting, identifying, and classifying defects in the virtual image. **Means for Solving the Problem**

[0006] To achieve the above object, the present invention discloses a method for detecting defects in a near-eye display. The method is characterized by including the following steps.

[0007] Divide the pixel matrix into a plurality of sub-pixel units. Each of the sub-pixel units includes M×M pixels. M is a natural number greater than 2.

[0008] Among the M×M pixels of those sub-pixel units, by sequentially controlling the pixels at the same position to emit light simultaneously, M×M sub-test images are generated in the image generator.

[0009] Use an imaging device to sequentially capture the sub-test images generated by the image generator.

[0010] Generate a test image corresponding to the virtual image by superimposing the sub-test images. Each pixel in the test image has a pixel luminance.

[0011] Compare the pixel luminance of at least one pixel in the test image with the regional pixel luminance of the regional pixels in the test image.

[0012] When the pixel luminance of the at least one pixel is smaller than the luminance threshold of the regional pixel luminance, set the at least one pixel as a defective pixel.

[0013] When the defective pixel is included in the visible range of the test image corresponding to the virtual image, classify the virtual image as a defective image.

[0014] The luminance threshold value is between 50% and 70% of the luminance of the area pixels.

[0015] After the step of comparing the pixel luminance of at least one pixel in the test image with the area pixel luminance of the area pixels in the test image, the following steps are further included.

[0016] That is, among the plurality of pixels in the unit area in the test image, if the number of pixels whose pixel luminance is smaller than the luminance threshold value of the area pixels is larger than a predetermined quantity threshold value, the unit area is set as a defective area.

[0017] The visible range includes a first visible range, a second visible range, a third visible range, and a fourth visible range, and they are arranged in ascending order. And when the defective pixel is included in the visible range of the test image corresponding to the virtual image, the step of classifying the virtual image as a defective image further includes the following steps.

[0018] If 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, the virtual image is classified as a defective image of level 5.

[0019] If 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, the virtual image is classified as a defective image of level 4.

[0020] If 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, the virtual image is classified as a defective image of level 3.

[0021] If the defective pixel is included in the fourth visible range of the test image, the virtual image is classified as a defective image of level 2.

[0022] When the defective pixel is not included within the fourth visible range of the test image, classify the virtual image as a first-level defective image.

[0023] The shape of the visible range is circular.

[0024] The shape of the visible range is elliptical.

[0025] Before the step of dividing the pixel matrix into a plurality of sub-pixel units, further include the following steps.

[0026] That is, correct and align the pixel matrix and the virtual image.

Advantages of the Invention

[0027] Generally speaking, the method for detecting defects in a near-eye display according to the present invention has the following beneficial effects. That is, since it is possible to accurately discover defective pixels in a virtual image and identify and classify them, it is advantageous for subsequent defect processing, and ultimately the object of improving the image quality of the virtual image can be achieved.

Brief Description of the Drawings

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DETAILED DESCRIPTION OF THE INVENTION

[0029] In order to more easily and clearly understand the advantages, spirit and features of the present invention, the following will use examples and refer to the accompanying drawings for detailed description and discussion. It should be noted that these examples are only representative examples of the present invention, and the specific methods, devices, conditions, materials, etc. exemplified do not limit the present invention or the corresponding embodiments.

[0030] The terms used in the various embodiments disclosed in the present invention are for the purpose of describing specific embodiments and are not intended to limit the various embodiments disclosed in the present invention. Also, unless otherwise clearly indicated in the context, the singular forms used in the specification include the plural forms. Also, unless otherwise limited, all terms (including technical terms and scientific terms) used in this specification have the same meaning as generally understood by those skilled in the art of the various embodiments disclosed in the present invention. The above terms (for example, terms defined in commonly used dictionaries) are construed to have the same meaning as their contextual meaning in the same technical field, unless the terms are clearly limited in the various embodiments disclosed in the present invention, and are not construed to have an idealized meaning or an overly formal meaning.

[0031] Refer to FIGS. 1, 2A and 2B together. FIG. 1 shows a flowchart of the steps in the method for detecting defects in a near-eye display based on a specific embodiment of the present invention. FIG. 2A shows a schematic diagram of the pixel matrix 1 of the near-eye display in the present invention. FIG. 2B shows a schematic principle diagram of the method for detecting defects in the near-eye display in the present invention. As shown in FIG. 1, the method for detecting defects in the near-eye display in the present invention includes the following steps.

[0032] Step S1: Divide the pixel matrix into a plurality of sub-pixel units. Each sub-pixel unit contains M×M pixels. Also, M is a natural number greater than 2.

[0033] Step S2: Among the M×M pixels of those sub-pixel units, control them in order to simultaneously emit light for the pixels at the same position, thereby generating M×M sub-test images in the image generator.

[0034] Step S3: Use the imaging device to sequentially capture the M×M sub-test images displayed on the image generator.

[0035] Step S4: By superimposing and integrating those sub-test images in the program, form a complete test image corresponding to the virtual image. Note that each pixel in the test image has a pixel luminance.

[0036] Step S5: Compare the pixel luminance of the pixels in the test image with the region pixel luminance of the region pixels in the test image.

[0037] Step S6: If the pixel luminance of a pixel is smaller than the luminance threshold of the region pixel luminance, set the pixel as a defective pixel.

[0038] Step S7: If the visible range of the test image corresponding to the virtual image contains defective pixels, classify the virtual image as a defective image.

[0039] As shown in FIG. 1 and FIG. 2A, the near-eye display may include a pixel matrix 1 for displaying an image or a virtual image. Also, the pixel matrix 1 includes a plurality of pixels 12. In this specific embodiment, the case where the pixel matrix 1 has a size of 9×9 is illustrated. In actual applications, the size and the number of pixels of the pixel matrix may be determined based on the resolution of the near-eye display. Also, the shape of the pixel matrix is not limited to the square in FIG. 2A, and may be a rectangle or other shapes.

[0040] In this specific embodiment, the image generator can generate a virtual image to be displayed on 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 image may be a color image, but it is not limited to this, and it may also be a white single-color, gray single-color, or other pure-color image. Furthermore, the positions of the pixels 12 corresponding to the virtual image each contain image data (for example, optical characteristics such as luminance and chromaticity). The image generator can render the virtual image from the near-eye display by driving the pixels 12 of the pixel matrix 1 based on the image data.

[0041] Also, in this specific embodiment, the method for detecting defects in the near-eye display may further include steps of correcting and aligning the pixel matrix 1 and the virtual image. In practical use, the image correction device or adjustment mechanism of the near-eye display aligns the pixel matrix 1 and the virtual image, so that the positions of the respective pixels 12 in the pixel matrix 1 can correspond to the positions of the virtual image respectively.

[0042] As shown in FIG. 2B, in step S1, the pixel matrix is divided into a plurality of sub-pixel units 11. In this specific embodiment, the pixel matrix is divided into 4 sub-pixel units 11. That is, each sub-pixel unit 11 contains 3×3 pixels 12. In actual applications, the number of sub-pixel units and the number of pixels within the sub-pixel units may be determined based on the size of the pixel matrix.

[0043] In step S2, the image generator controls the pixel 12 in the sub-pixel unit 11 at the same position to emit light simultaneously. As shown in FIG. 2B, the image generator first controls all the pixels 12 at the upper left corner in all the sub-pixel units 11 to emit light simultaneously, and controls the other pixels not to emit light. In step S3, the imaging system can generate the sub-test image 2A by photographing / acquiring the image presented by the pixel matrix 1 at that time. The imaging device may be a light measuring device (LMD). Further, the light measuring device may include a colorimeter / camera and an NED / lens. When the imaging system generates the sub-test image, the pixel luminance values of each lit pixel 12 in the sub-test image are also recorded.

[0044] Furthermore, after the imaging system obtains the sub-test image 2A in which all the pixels 12 at the upper left corner in all the sub-pixel units 11 emit light, the image generator controls the pixel 12 at the central position in the first column in all the sub-pixel units 11 to emit light, and controls the other pixels not to emit light. Then, the imaging system obtains the sub-test image 2B. In this specific embodiment, the sub-pixel unit 11 includes nine pixels. Therefore, the image generator sequentially controls the pixel 12 in the sub-pixel unit 11 at the same position to emit light simultaneously. When the image generator controls all the pixels 12 at the lower right corner in all the sub-pixel units 11 to emit light simultaneously and the imaging system obtains the sub-test image 2I, that is, when all the pixels 12 in the sub-pixel unit 11 light up in sequence, nine sub-test images are generated.

[0045] In this specific embodiment, since each sub-pixel unit 11 is a 3×3 sub-pixel matrix, the interval between each luminous pixel 12 in each sub-test image is three times the interval between adjacent pixels. Therefore, when the pixels 12 of each sub-pixel unit 11 emit light, all the luminous pixels can maintain the maximum distance, and the luminance of the pixels 12 is not affected by interference or crosstalk from the luminance of other pixels. As a result, when the imaging system acquires the sub-test images, it can record the true pixel luminance values of each emitted pixel 12.

[0046] After nine sub-test images are generated by the imaging system, in step S4, the imaging system can generate a test image 3 corresponding to the virtual image by superimposing all the sub-test images. In actual applications, the imaging system can render the test image 3 corresponding to the virtual image by sequentially displaying the sub-test images. Each sub-test image is an image in which non-overlapping pixels 12 at different positions within the sub-pixel unit 11 emit light. Therefore, after the imaging system displays all the sub-test images, all the pixels 12 of the pixel matrix 1 emit light without remainder to form a complete test image 3. Moreover, the luminance of each pixel 12 in the test image 3 all includes the true emission luminance value.

[0047] Refer to FIGS. 1 and 3 together. FIG. 3 shows a schematic diagram of defective pixels in the near-eye display defect detection method according to the present invention. The image shown in FIG. 3 is a test image 3 corresponding to a virtual image generated by the aforementioned imaging system by superimposing all sub-test images. In step S5, after the test image 3 is generated, the pixel luminance of pixel 12 in the test image 3 is compared with the region pixel luminance of the region pixels in the test image. In actual applications, the near-eye display may further include a comparison analysis chip. As shown in FIG. 3, when the comparison analysis chip detects pixel 12', the region pixel 13' may be a 3×3 pixel adjacent or close to pixel 12'. Also, the region pixel luminance may be the average pixel luminance value of 3×3 pixels. Since the luminance of each pixel 12 in the test image 3 is the true emission luminance value, in the near-eye display defect detection method according to the present invention, defective pixels are detected by identifying from the luminance levels between the pixel and the adjacent pixels. It should be noted that the number of pixels within the region pixel may be determined according to requirements or design.

[0048] In step S6, if the pixel luminance of the pixel is smaller than the luminance threshold of the region pixel luminance, the pixel is set as a defective pixel. In actual applications, the luminance threshold may be set between 50% and 70% of the region pixel luminance. As shown in FIG. 3, if the luminance of pixel 12' is greater than 70% of the region pixel luminance of region pixel 13', pixel 12' may be set and defined as a defective pixel. Also, the pixel 12' may be referred to as a dark pixel. In a specific embodiment, if the luminance of pixel 12' is greater than 50% and smaller than 70% of the region pixel luminance of region pixel 13', pixel 12' may be set and defined as a defective pixel. Further, if the luminance of pixel 12'' is smaller than 50% of the region pixel luminance of region pixel 13'', pixel 12'' may be set and defined as a defective pixel. Also, the pixel 12'' may be referred to as a dead pixel. Note that pixel 12' and pixel 12'' are point defective pixels.

[0049] In the method for detecting defects in a near-eye display according to the present invention, not only is it possible to detect whether a single pixel is a defective pixel, but it is also possible to detect a plurality of pixels. As shown in FIG. 1, in this specific embodiment, after executing step S5, step S61 may be executed. That is, among a plurality of pixels in a unit area within a test image, if the number of pixels whose pixel luminance is smaller than the luminance threshold of the area pixel luminance is larger than a predetermined quantity threshold, the unit area is set as a defective area. In actual applications, the predetermined quantity threshold may be that the number of defective pixels in the unit area exceeds a certain numerical value, or that the number of defective pixels exceeds 30% of the total number of pixels in the unit area, but it is not limited thereto. For example, if the unit area is a 10×10 matrix and 70 pixels included in the unit area are identified as defective pixels, the unit area is also defined as a defective area. Also, if two adjacent or consecutive pixels or three pixels are all identified as defective pixels (shown in regions B and B' in FIG. 3), these defective pixels may be defined as linear defects.

[0050] Therefore, in the method for detecting defects in a near-eye display according to the present invention, it is possible to set the judgment criteria for defective pixels by using a test image corresponding to a virtual image. As a result, the severity and distribution range of defective pixels are effectively detected, and an index for judging the quality of the virtual image is formed.

[0051] In step S7, if a defective pixel is included in the visible range of the test image corresponding to the virtual image, the virtual image is classified as a defective image. In the present invention, by identifying and defining the position of the defective pixel, it may be possible to determine whether subsequent processing and optimization are required for the virtual image. In actual applications, the visible range may be the area covered by the field of view (FOV). Moreover, the comparison analysis chip of the near-eye display can determine and classify that the virtual image is a defective image based on whether a defective pixel is included in the visible range.

[0052] Refer to FIGS. 4 and 5 together. FIG. 4 shows a flowchart of steps in a method for classifying defects of a near-eye display based on a specific embodiment of the present invention. FIG. 5 shows a schematic diagram of a test image and a visible range based on a specific embodiment of the present invention. As shown in FIG. 5, in this specific embodiment, the visible range includes a first visible range A1, a second visible range A2, a third visible range A3, and a fourth visible range A4 arranged in ascending order. Further, the first visible range A1, the second visible range A2, the third visible range A3, and the fourth visible range A4 are circular and are located at the center position of the test image 3 corresponding to the virtual image. As shown in FIGS. 1 and 4, step S7 in FIG. 1 may further be as follows.

[0053] In step S71, it is determined whether there are point defect pixels in the first visible range A1 of the test image 3, or whether there is a defect area in the second visible range A2. If the determination result is YES, the virtual image is classified as a defect image of the fifth level (step S711). On the other hand, if the determination result is NO, step S72 is executed. That is, it is determined whether there are point defect pixels in the second visible range A2 of the test image 3, or whether there is a defect area in the third visible range A3. If the determination result is YES, the virtual image is classified as a defect image of the fourth level (step S721). On the other hand, if the determination result is NO, step S73 is executed. That is, it is determined whether there are point defect pixels in the third visible range A3 of the test image 3, or whether there is a defect area in the fourth visible range A4. If the determination result is YES, the virtual image is classified as a defect image of the third level (step S731). On the other hand, if the determination result is NO, step S74 is executed. That is, it is determined whether there are point defect pixels in the fourth visible range A4 of the test image 3. If the determination result is YES, the virtual image is classified as a defect image of the second level (step S741), but if the determination result is NO, the virtual image is classified as a defect image of the first level (step S75).

[0054] In actual applications, the first visible range A1 is the area covered by the viewing angle F15, the second visible range A2 is the area covered by the viewing angle F30, the third visible range A3 is the area covered by the viewing angle F45, and the fourth visible range A4 is the area covered by the viewing angle F60. Furthermore, the higher the image level of the defective image, the higher the quality of the corresponding virtual image. Note that the image level of the defective image at the first level is the highest, and the image level of the defective image at the fifth level is the lowest. Also, when the image level of the virtual image is smaller than that of the defective image at the fourth level, the near-eye display may issue a warning signal to alert that the processing and optimization of the virtual image are necessary.

[0055] Also, in this specific embodiment, when line defect pixels are included 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 defective image at the fifth level. Also, when line defect pixels are included between the second visible range A2 and the third visible range A3 of the test image 3, the virtual image may be classified as a defective image at the fourth level. Also, when line defect pixels are included between the third visible range A3 and the fourth visible range A4 of the test image 3, the virtual image may be classified as a defective image at the third level.

[0056] Note that the shape of the visible range may be not only the aforementioned circular shape but also other shapes. Also, refer to FIG. 6. FIG. 6 shows a schematic diagram of the test image 3' and the visible range A' based on a specific embodiment of the present invention. As shown in FIG. 6, the shape of the visible range A' may be an ellipse to adapt to the human visual field when using a near-eye display.

[0057] In summary, the method for detecting defects of the near-eye display in the present invention has the following beneficial effects. That is, by setting the judgment criteria for defective pixels, it is possible to accurately discover the defective pixels in the virtual image and identify and classify them. Thereby, the severity and distribution range of the defective pixels are effectively detected, which is advantageous for subsequent defect processing, and thus the purpose of finally improving the image quality of the virtual image can be achieved.

[0058] The detailed description of the above preferred specific embodiments is intended to more clearly describe the features and spirit of the present invention, and is not intended to limit the scope of the present invention by the preferred specific embodiments disclosed above. Rather, it is intended to cover various modifications and equivalent adjustments within the scope of the claims of the present invention. Although the present invention has been disclosed as above by way of embodiments, it is not the gist of limiting the present invention. Those skilled in the art can make various modifications and supplements without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention shall be in accordance with the provisions of the appended claims.

Claims

1. A method for detecting defects in a near-eye display, comprising: dividing a pixel matrix into a plurality of sub-pixel units, each of said sub-pixel units including M×M pixels, where M is a natural number greater than 2; generating M×M sub-test images in an image generator by sequentially controlling the pixels at the same positions among the M×M pixels of those sub-pixel units to emit light simultaneously; sequentially capturing the sub-test images generated by the image generator using an imaging device; generating a test image corresponding to a virtual image by superimposing the sub-test images, wherein each pixel in the test image has a pixel luminance; comparing the pixel luminance of at least one pixel in the test image with the region pixel luminance of region pixels in the test image; when the pixel luminance of the at least one pixel is less than a luminance threshold of the region pixel luminance, setting the at least one pixel as a defective pixel; classifying the virtual image as a defective image when the defective pixel is included in a visible range of the test image corresponding to the virtual image.

2. The method for detecting defects in a near-eye display according to claim 1, wherein the luminance threshold is between 50% and 70% of the region pixel luminance.

3. After the step of comparing the pixel luminance of at least one pixel in the test image with the region pixel luminance of the region pixels in the test image, further comprising: when the number of pixels having a pixel luminance less than the luminance threshold of the region pixel luminance among a plurality of pixels in a unit region of the test image is greater than a predetermined quantity threshold, setting the unit region as a defective region.

4. 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 ascending order of size. When the defective pixel is included in the visible range of the test image corresponding to the virtual image, the step of classifying the virtual image as a defective image further comprises: If the defective pixel is included within the first visible range of the test image, or if the defective region is included within the second visible range, classifying the virtual image as a defective image of level 5; If the defective pixel is included within the second visible range of the test image, or if the defective region is included within the third visible range, classifying the virtual image as a defective image of level 4; If the defective pixel is included within the third visible range of the test image, or if the defective region is included within the fourth visible range, classifying the virtual image as a defective image of level 3; If the defective pixel is included within the fourth visible range of the test image, classifying the virtual image as a defective image of level 2; If the defective pixel is not included within the fourth visible range of the test image, classifying the virtual image as a defective image of level 1, the method for detecting a defect in a near-eye display according to claim 3, characterized by including the above steps.

5. The method for detecting a defect in a near-eye display according to claim 1, characterized in that the shape of the visible range is circular.

6. The method for detecting a defect in a near-eye display according to claim 1, characterized in that the shape of the visible range is elliptical.

7. Before the step of dividing the pixel matrix into a plurality of sub-pixel units, further including: The method for detecting a defect in a near-eye display according to claim 1, characterized by including the step of correcting and aligning the pixel matrix and the virtual image.

Images