Micro-LED micro-display chip driven digital presentation method of ancient painting

Abstract

The application discloses a Micro-LED micro display chip driven ancient painting digital presentation method, relates to the technical field of digital image processing and display, and extracts a brightness uniformity coefficient, a size coefficient and a contrast ratio unqualified coefficient of image adaptation processing qualified in the target image by taking the image after image adaptation processing of the ancient painting digital image as the target image; obtains a processing qualified coefficient according to the brightness uniformity coefficient, the size coefficient and the contrast ratio unqualified coefficient, and judges whether the target image is qualified in the image adaptation processing process according to the processing qualified coefficient and a preset processing qualified coefficient threshold value; when the target image is qualified in the image adaptation processing process, the target image is driven to present the ancient painting in the AR device through the Micro-LED micro display chip; in this way, the rationality of image adaptation processing can be judged, whether the image can present the ancient painting digital image through the AR device can be judged, and the optimal presentation effect can be ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital image processing and display, and particularly relates to a Micro-LED micro-display chip driven ancient painting digital presentation method. BACKGROUND

[0002] In the application of ancient painting digital display and augmented reality (AR) technology, the closest prior art currently mainly relies on traditional LCD or LED display technology. These technologies, although perform well in general applications, have some obvious limitations in specific art display, especially in the digital presentation of ancient paintings.

[0003] Firstly, traditional display technology usually cannot achieve the high standards required by artworks in terms of color reproduction. For example, LCD technology has inherent color deviation and limited color gamut coverage when displaying colors in the real world, which may lead to the loss of original details and distortion of colors for ancient paintings that require accurate color matching. In addition, the performance of these technologies in terms of brightness and contrast is usually affected by environmental light, especially in outdoor or strong light environments, the display effect is greatly reduced. Secondly, the application of traditional display solutions in AR glasses is limited by volume and weight. Conventional LCD and LED display components are large in size and heavy in weight, which not only limits the flexibility of AR glasses design, but also affects the comfort of users wearing them for a long time. In addition, the pixel density of these technologies often cannot meet the needs of high-resolution image display, which is particularly important in AR glasses that need to display high-definition images to improve user experience. Low pixel density limits the display of image details, affecting the appreciation of ancient painting art works by users;

[0004] To solve this problem, Micro-LED technology is now introduced to overcome these limitations, achieve a more realistic and detailed digital presentation of ancient paintings, and optimize the wearing experience of AR glasses, thereby better meeting the needs of professional display and personal learning, because Micro-LED technology, with its self-emitting characteristics and higher color accuracy, can provide a wider color gamut and more accurate color reproduction, solving the color deviation problem of traditional LCD and LED technology in the digital presentation of ancient paintings; the design of independent pixels allows brightness and contrast to be more flexibly adjusted, even in strong light environments, to maintain clear display effects. In addition, the ultra-high pixel density of Micro-LED makes the image details more rich, accurately presenting the subtle textures and tone changes in ancient paintings, greatly improving the visual effect of AR glasses; at the same time, Micro-LED displays are usually thinner and lighter, helping to reduce the weight of AR glasses and improve wearing comfort. Therefore, this technology not only has significant advantages in improving the quality of digital presentation of artistic works, but also optimizes the design and user experience of AR devices, especially when used for a long time.

[0005] In order to make the digital image of ancient paintings presented on the Micro-LED display screen of the AR device optimal, image adaptation processing such as image size adjustment, brightness and contrast adjustment, etc. is usually required to make it display on the Micro-LED display screen with the best effect; but existing technologies often directly default image adaptation processing to be reasonable and directly present the digital image of ancient paintings through the AR device, resulting in a non-optimal presentation effect. SUMMARY

[0006] The purpose of the present application is to solve the above-mentioned problems and provide a Micro-LED micro-display chip driven ancient painting digital presentation method.

[0007] The Micro-LED micro-display chip driven ancient painting digital presentation method according to the present application comprises:

[0008] The image after image adaptation processing of the digital image of ancient paintings is taken as a target image, and the brightness uniformity coefficient, size coefficient and contrast unqualified coefficient related to image adaptation processing of the target image are extracted;

[0009] The processing qualified coefficient is obtained according to the brightness uniformity coefficient, size coefficient and contrast unqualified coefficient, and whether the target image is qualified in the image adaptation processing process is judged according to the processing qualified coefficient and the preset processing qualified coefficient threshold;

[0010] When the target image is qualified in the image adaptation processing process, the target image is digitally presented on the ancient painting through the Micro-LED micro-display chip driven on the AR device.

[0011] Optionally, the brightness uniformity coefficient comprises:

[0012] The target image is converted into a gray-scale image, and the mean value μY and the standard deviation σY of the gray-scale values in the gray-scale image are calculated. The formula for calculation is:

[0013]

[0014] In the formula, Q is the number of rows of the image, E is the number of columns of the image, and Y(n,m) is the pixel gray-scale value of the gray-scale image at position (n,m);

[0015] The brightness uniformity coefficient is calculated. The formula for calculation is:

[0016]

[0017] In the formula, UK is the brightness uniformity index, and ∈ is a preset constant, which is used to avoid division by zero when calculating the standard deviation.

[0018] Optionally, the size coefficient comprises:

[0019] The width and length of the target image are obtained, which are marked as HJ and HG respectively. The width HJ of the target image is divided by the length HG of the target image to obtain the post-processing proportion coefficient;

[0020] The image width and length of the digital image of the ancient painting before image adaptation processing are marked as FG and RE respectively. The width FG is divided by the length RE to obtain the pre-processing proportion coefficient;

[0021] The absolute difference value of the post-processing proportion coefficient and the pre-processing proportion coefficient is calculated, and the absolute difference value is taken as the size coefficient of the target image.

[0022] Optionally, the contrast unqualified coefficient comprises:

[0023] The target image is subjected to gray-scale processing, and the color image is converted into a gray-scale image. The number of pixels of each gray-scale level in the image is counted to obtain a gray-scale histogram;

[0024] The gray-scale histogram represents the pixel distribution of each gray-scale level in the image;

[0025] The gray-scale histogram is normalized so that the sum of the histogram is 1, i.e. the number of pixels of each gray-scale level is divided by the total number of pixels of the image;

[0026] The normalized histogram reflects the relative distribution of each gray-scale level in the image.

[0027] The contrast of the image is calculated using the normalized gray-scale histogram,

[0028] The calculation formula of the contrast is:

[0029]

[0030] In the formula, Qa is the contrast, p a is the normalized pixel number of the a-th gray level, is the mean value of the normalized histogram, is the center value of the normalized histogram, and L is the number of gray levels;

[0031] The obtained contrast value is normalized to obtain a contrast unqualified coefficient of the image. The calculation formula of the contrast unqualified coefficient is:

[0032]

[0033] In the formula, GL is the contrast unqualified coefficient, and JK is the maximum possible contrast.

[0034] Optionally, obtaining the processing qualified coefficient according to the brightness uniformity coefficient, the size coefficient and the contrast unqualified coefficient comprises:

[0035]

[0036] In the formula, ERT is the processing qualified coefficient, UK, HY and GL are respectively the brightness uniformity coefficient, the size coefficient and the contrast unqualified coefficient, a1, a2 and a3 are respectively preset proportion coefficients of the brightness uniformity coefficient, the size coefficient and the contrast unqualified coefficient, and a1, a2 and a3 are all greater than 0.

[0037] Optionally, judging whether the target image is qualified in the image adaptation processing according to the processing qualified coefficient and a preset processing qualified coefficient threshold comprises:

[0038] Comparing the processing qualified coefficient with the preset processing qualified coefficient threshold, if the processing qualified coefficient is not less than the preset processing qualified coefficient threshold, it is indicated that the target image is qualified in the image adaptation processing, and the target image is digitally presented on the AR device by driving the ancient painting through the Micro-LED micro display chip.

[0039] Optionally, judging whether the target image is qualified in the image adaptation processing according to the processing qualified coefficient and a preset processing qualified coefficient threshold further comprises:

[0040] If the processing pass coefficient is less than the preset processing pass coefficient threshold, it means that the target image is unqualified in the image adaptation process. Then, the image adaptation process is repeated on the digitized image of the ancient painting until the processing pass coefficient of the image after image adaptation is not less than the preset processing pass coefficient threshold. The image after image adaptation is then digitally presented on the AR device through the Micro-LED micro-display chip.

[0041] The beneficial effects of this invention are:

[0042] This invention proposes a method for digitally presenting ancient paintings driven by a Micro-LED microdisplay chip. The method uses the image after image adaptation processing of the digitized ancient painting as the target image, and extracts the brightness uniformity coefficient, size coefficient, and contrast failure coefficient from the target image to determine if the image adaptation processing is successful. A processing success coefficient is obtained based on these coefficients, and the success coefficient is compared with a preset threshold to determine whether the target image passes the image adaptation processing. When the target image passes the image adaptation processing, it is digitally presented on an AR device using the Micro-LED microdisplay chip. This method allows for the assessment of the rationality of the image adaptation processing, determining whether the image can be presented as a digitized ancient painting on an AR device, ensuring optimal presentation results. Attached Figure Description

[0043] The present invention will now be further described with reference to the accompanying drawings.

[0044] Figure 1 A flowchart of a method for digitally presenting ancient paintings driven by a Micro-LED microdisplay chip. Detailed Implementation

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] This invention provides a method for digitally presenting ancient paintings driven by a Micro-LED microdisplay chip. See also... Figure 1 , Figure 1A flowchart illustrating a method for digitally presenting ancient paintings driven by a Micro-LED microdisplay chip, provided in an embodiment of the present invention. The method includes the following steps:

[0048] The image after image adaptation processing of the digitized image of the ancient painting is used as the target image, and the brightness uniformity coefficient, size coefficient and contrast failure coefficient of the target image with acceptable image adaptation processing are extracted.

[0049] The processing pass coefficient is obtained based on the brightness uniformity coefficient, size coefficient, and contrast failure coefficient. The processing pass coefficient and the preset processing pass coefficient threshold are used to determine whether the target image is qualified in the image adaptation process.

[0050] When the target image passes the image adaptation process, the target image is digitally presented on the AR device using a Micro-LED microdisplay chip to drive the ancient painting.

[0051] Based on the Micro-LED microdisplay chip-driven method for digitally presenting ancient paintings provided in this embodiment of the invention, the rationality of image adaptation processing can be judged through the above method, and it can be determined whether the image can be presented as a digital image of ancient paintings through AR devices, ensuring the best presentation effect.

[0052] It should be noted that there are several significant shortcomings and limitations in using existing technologies to digitally present ancient paintings and display them on AR glasses. First, traditional digital display technologies, such as LCD or traditional LED, often cannot accurately reproduce the colors and details of ancient paintings. The limitations of these technologies in color depth and dynamic range lead to the loss of delicate colors and blurring of details during the digitization process, failing to truly reproduce the artistic value and visual effects of the original work. Second, when these traditional technologies are applied to AR glasses, they often face issues of size and weight, affecting the portability of the device and user comfort. More importantly, these display technologies have relatively high power consumption, which significantly reduces the usage time of AR glasses that need to be worn for extended periods. Finally, the limitations of existing technologies in pixel density also pose a challenge to high-quality image display. When used in AR glasses, this low pixel density prevents the detailed display of image features, thus affecting the overall quality of the digital presentation of ancient paintings and the user's viewing experience. To address this issue, Micro-LED technology has been introduced to overcome these limitations, enabling a more realistic and detailed digital presentation of ancient paintings and optimizing the wearing experience of AR glasses. This is because the superior performance of Micro-LED display chips in color reproduction, contrast, and brightness can effectively solve the color and detail loss problems in the digital display of ancient paintings using traditional technologies. In addition, the low power consumption and small size of Micro-LED make it very suitable for AR glasses, not only improving display quality but also enhancing device portability and user comfort. These improvements enable Micro-LED-based digital presentation technology of ancient paintings to provide a more vivid, realistic, and high-quality visual experience, whether in museum exhibitions or personal learning.

[0053] By adopting Micro-LED technology, the following benefits are achieved: High color accuracy and contrast: Micro-LED provides a wider color gamut and higher contrast, allowing each stroke of the ancient painting to be presented in a way that is closer to the original, thus significantly improving the visual effect of the artwork; High pixel density: High pixel density ensures that even on smaller display screens, such as AR glasses, extremely fine image details can be displayed, optimizing the user's viewing experience; Miniaturization and lightweight: The miniaturized design of Micro-LED makes AR glasses lighter, improving the comfort of users wearing them for extended periods; Through these technological improvements, the digital display quality of ancient paintings is significantly optimized, and the user experience of AR glasses is improved; In addition, compared with existing technologies, this invention functionally achieves a transformation from simple image display to a high-fidelity, highly interactive visual experience, allowing the appreciation of ancient paintings to be enjoyed anytime and anywhere through digital and augmented reality technologies, not just in physical galleries.

[0054] For example, restoring the Ming Dynasty painter Zhang Longzhang's painting "Evening Snow on the River" using Micro-LED technology first requires high-precision scanning and digitization of the original painting, accurately capturing every detail and converting it into a digital image. To ensure perfect restoration of the painting's colors, layers, and details, image adjustment technologies such as color calibration, resolution adjustment, and brightness and contrast optimization are employed. Through Micro-LED display technology, these meticulously restored digital images are presented. The Micro-LED display, with its high resolution, high brightness, and accurate color reproduction, effectively presents the delicate brushstrokes and rich colors of "Evening Snow on the River." To further enhance the display effect, the painting is not only transformed from a static display to a dynamic one, but details can also be magnified or reduced on the screen, allowing viewers to appreciate Zhang Longzhang's depiction of the snowy river scene more closely. Furthermore, the integration with AR glasses allows the painting to transcend the limitations of physical display space, eliminating the need for users to view the exhibits in a fixed location or at a fixed time. After wearing AR glasses, users can project paintings onto any location in the real world anytime, anywhere using AR technology. The paintings are seamlessly integrated with the real-world scene, creating an art experience that feels like traveling through time. AR glasses can dynamically adjust the angle and view of the paintings based on the user's perspective, movements, and position, making them more realistic and greatly enhancing interactivity and immersion.

[0055] The benefits of this technological combination lie in its ability to transform traditional static displays of artworks into interactive and dynamic ones, enabling viewers to gain a deeper understanding and appreciation. Simultaneously, Micro-LED and AR technologies overcome the limitations of traditional display methods. Viewers can enjoy artworks anytime, anywhere, without spatial or temporal constraints, experiencing unprecedented convenience and immersion. Furthermore, high-precision digital restoration avoids damage to the original artwork caused by prolonged display, providing an innovative approach to cultural relic preservation. Therefore, this display method combining Micro-LED and AR technologies not only enhances the accessibility and dissemination of artworks but also offers a completely new perspective and form for the modern display of ancient artworks.

[0056] To achieve the best possible display effect for digitized images of ancient paintings on the Micro-LED display screen of an AR device, image adaptation processing is typically required, such as adjusting the image size, brightness, and contrast. This ensures optimal display on the Micro-LED screen. However, existing technologies often assume that the image adaptation processing is satisfactory before directly displaying the digitized images of ancient paintings on the AR device, resulting in suboptimal presentation. Therefore, this paper analyzes the image after the image adaptation processing of the digitized ancient paintings to determine its suitability before proceeding with the digitization of the ancient paintings on the AR device using a Micro-LED microdisplay chip, thus achieving the best possible presentation effect.

[0057] In one embodiment, the image obtained from the digitized image of the ancient painting and processed for image adaptation is used as the target image, and the brightness uniformity coefficient, size coefficient, and contrast failure coefficient of the target image are extracted to indicate whether the image adaptation processing is qualified.

[0058] The brightness uniformity coefficient includes:

[0059] Convert the target image to a grayscale image, and calculate the mean μY and standard deviation σY of the grayscale values ​​in the grayscale image. The calculation formula is as follows:

[0060]

[0061] In the formula, Q is the number of rows in the image, E is the number of columns in the image, and Y(n,m) is the pixel gray value at position (n,m) in the grayscale image.

[0062] The formula for calculating the brightness uniformity coefficient is as follows:

[0063]

[0064] In the formula, UK is the brightness uniformity index, and ∈ is a preset constant used to avoid division by zero when calculating the standard deviation.

[0065] It should be noted that ∈ is a preset constant, typically used to avoid division by zero and ensure the stability of the calculation process. For calculating the image brightness uniformity index, the general range of values ​​for ∈ can be set according to different needs; common ranges include:

[0066] 10 -5 This value is usually small enough to effectively avoid division by zero without significantly affecting the calculation result.

[0067] 10 -6 When higher precision is required, a smaller value can be selected to ensure the accuracy of the calculation even in extreme cases;

[0068] 10-8 This value can be used for certain high-precision applications, especially when numerical stability is required.

[0069] The specific values ​​will be determined by professionals based on the actual situation, and will not be limited or elaborated upon.

[0070] It should be noted that a higher brightness uniformity coefficient indicates a more satisfactory image adaptation process for the digitized ancient painting. This is because a higher uniformity coefficient signifies a more even distribution of brightness within the image, resulting in less overall brightness fluctuation and clearer, more easily identifiable details. In the presentation of digitized ancient paintings, especially when showcasing intricate artistic details, uniform brightness better reveals the work's depth and color richness. Excessive or uneven brightness can cause some details to disappear or certain areas to be too bright or too dark, affecting the viewer's overall appreciation of the artwork. Therefore, a high brightness uniformity coefficient means that excessive brightness distortion or unevenness has been avoided during image processing, ensuring that the digitized image faithfully reflects the original's visual effects and artistic style, thereby enhancing the overall quality and artistic value reproduction of the digitized ancient painting. For example, in the Ming Dynasty painter Zhang Longzhang's painting "Evening Snow on the River," the snow scene and distant mountains exhibit extremely delicate layers, with subtle variations in brightness and contrast. If the brightness of the digitally processed image is uneven, it may cause overexposure of the white parts of the snow or loss of details in the shadows of the mountains, thus affecting the viewer's perception of the painting's artistic conception. By increasing the brightness uniformity coefficient, image processing can effectively preserve these details, allowing the purity of the snow scene and the depth of the distant mountains to be presented, enhancing the visual effect, and ensuring that the digital image can better convey the delicate brushstrokes and exquisite style of the original work. This allows viewers to appreciate the unique charm of Zhang Longzhang's snowy landscape depicted through the contrast of light and shadow when viewing this work.

[0071] In one embodiment, the size factor includes:

[0072] Obtain the width and length of the target image, labeled as HJ and HG respectively. Divide the width HJ of the target image by the length HG of the target image to obtain the scaling factor after processing.

[0073] The width and length of the digitized image of the ancient painting before image adaptation processing are labeled as FG and RE, respectively. The width FG is divided by the length RE to obtain the scaling factor before processing.

[0074] Calculate the absolute difference between the scaling factor after processing and the scaling factor before processing, and use the absolute difference as the size factor of the target image.

[0075] It's important to note that a smaller size factor indicates a more successful image adaptation process for digitized ancient paintings. This is because a smaller size factor means less change in the aspect ratio after adaptation, resulting in a more closely approximate image shape compared to the original. Maintaining the original aspect ratio is crucial for digitizing ancient paintings, as any excessive stretching or compression will distort the artwork visually, damaging its composition and details. For example, figures, scenery, or structures in ancient paintings may rely on specific proportions to achieve their artistic effect. If the aspect ratio changes significantly during digitization, the viewer's visual experience will be affected, potentially preventing them from truly perceiving the original style and intent. Therefore, a smaller size factor not only ensures a more visually faithful reproduction of the original artwork but also improves the accuracy of image display, allowing viewers to better appreciate every detail and layer of the artwork. For instance, the Ming Dynasty painter Zhang Longzhang's painting "Evening Snow on the River" uses meticulous composition and perfectly proportioned proportions to depict the vastness of the snow scene and the river and sky. If the aspect ratio changes significantly during digitization, it may distort the perspective in the painting, affecting the viewer's overall perception of the artwork. This is especially true in a painting like "Evening Snow on the River," which depicts a river, snowscape, and distant mountains. The vast sense of space and profound atmosphere originally conveyed through proportion can be lost if the image is distorted due to improper size adaptation. Therefore, ensuring a smaller size factor and a digital image that closely approximates the original allows viewers to more accurately perceive how Zhang Longzhang uses delicate proportion control and spatial arrangement to depict the ethereal quality of the snowscape and the vastness of the river, enhancing their deeper understanding and appreciation of the work's artistic value.

[0076] In one embodiment, the contrast rejection factor includes:

[0077] The target image is converted to grayscale by performing grayscale processing, and the number of pixels at each grayscale level in the image is counted to obtain a grayscale histogram.

[0078] A grayscale histogram represents the pixel distribution at each gray level in an image;

[0079] Normalize the grayscale histogram so that the sum of the histograms is 1, which is the number of pixels at each grayscale level divided by the total number of pixels in the image.

[0080] The normalized histogram reflects the relative distribution of each gray level in the image.

[0081] The contrast of the image is calculated using the normalized gray-level histogram.

[0082] The formula for calculating contrast is:

[0083]

[0084] In the formula, Qa is the contrast ratio, p a It is the number of normalized pixels at the a-th gray level. It is the mean of the normalized histogram. It is the center value of the normalized histogram, and L is the number of gray levels;

[0085] The obtained contrast values ​​are normalized to obtain the contrast failure coefficient of the image. The formula for calculating the contrast failure coefficient is as follows:

[0086]

[0087] Where GL is the contrast ratio failure coefficient and JK is the maximum possible contrast ratio.

[0088] It's important to note that the maximum possible contrast ratio depends on the number of gray levels in the image. This is because image contrast is determined by the differences between different gray levels. With a finite number of gray levels, the maximum possible contrast ratio is finite, as an infinitely high contrast ratio cannot be achieved within a finite range of gray levels. For example, in an 8-bit grayscale image, the gray level range is 0 to 255, meaning the darkest pixel is black (0) and the brightest pixel is white (255). Therefore, the maximum possible contrast ratio within this range is 255. As the number of gray levels increases, the range of grayscale variations that can be represented also increases, thus increasing the maximum possible contrast ratio. For example, for a 16-bit grayscale image, the gray level range is 0 to 65535, resulting in an even higher maximum possible contrast ratio.

[0089] It's important to note that a smaller contrast ratio disqualification factor indicates a more successful image adaptation process for the digitized ancient painting. This is because a smaller disqualification factor means the image retains higher contrast during adaptation, making the distinction between brightness and shadow more pronounced. Contrast is a key indicator of image quality, especially in the digitization of ancient paintings, highlighting details and depth. For example, the delicate brushstrokes, tonal variations, and lighting effects on figures in many ancient paintings rely on appropriate contrast. If the image contrast is too low, details may become blurry, making some artistic elements difficult to discern and even affecting the overall visual experience. Conversely, an image with a smaller contrast ratio disqualification factor means its brightness range and tonal variations have been effectively preserved, allowing viewers to experience richer details and a greater sense of three-dimensionality in the digital display. This is crucial for the accurate presentation of artworks, ensuring that the effect seen on the screen matches the delicate texture of the original, enhancing the visual impact and aesthetic value. For example, Zhang Longzhang's "Evening Snow on the River" from the Ming Dynasty is renowned for its delicate brushstrokes and layered snowscape. The variations in light and shadow and the details of falling snowflakes showcase an extremely high level of artistry. If the contrast of the image is not properly matched during the digitization process, the sense of depth and layering between the snowscape and the distant river surface may not be clearly presented, potentially losing subtle variations and affecting the original visual effect. Especially for works like "Evening Snow on the River," with its interplay of light and shadow and delicate gray-white layers, insufficient contrast makes it difficult for viewers to experience the beauty of snowflakes gradually appearing and disappearing in the twilight and the profound sense of space in the painting. Therefore, ensuring proper contrast matching not only enhances the visual impact of the artwork but also helps viewers better perceive Zhang Longzhang's skillful depiction of the interplay between snow and river, allowing the ancient painting to radiate a stronger artistic charm in digital display.

[0090] In one embodiment, the processing pass coefficient is obtained based on the brightness uniformity coefficient, size coefficient, and contrast failure coefficient, including:

[0091]

[0092] In the formula, ERT is the processing pass coefficient, UK, HY, and GL are the brightness uniformity coefficient, size coefficient, and contrast failure coefficient, respectively, and a1, a2, and a3 are the preset proportional coefficients of the brightness uniformity coefficient, size coefficient, and contrast failure coefficient, respectively, and a1, a2, and a3 are all greater than 0.

[0093] It should be noted that a1, a2, and a3 are set by professionals according to the actual situation. Generally, the sum of a1, a2, and a3 is 1. For example, a1, a2, and a3 can be 0.3, 0.3, and 0.4 respectively, or other numbers. There are no specific restrictions.

[0094] In one embodiment, determining whether the target image is qualified during the image adaptation process based on the processing qualification coefficient and a preset processing qualification coefficient threshold includes:

[0095] The processing pass coefficient is compared with the preset processing pass coefficient threshold. If the processing pass coefficient is not less than the preset processing pass coefficient threshold, it means that the target image is qualified in the image adaptation process. The target image is then digitally presented on the AR device by driving the ancient painting through the Micro-LED micro-display chip.

[0096] If the processing pass coefficient is less than the preset processing pass coefficient threshold, it means that the target image is unqualified in the image adaptation process. Then, the image adaptation process is repeated on the digitized image of the ancient painting until the processing pass coefficient of the image after image adaptation is not less than the preset processing pass coefficient threshold. The image after image adaptation is then digitally presented on the AR device through the Micro-LED micro-display chip.

[0097] It should be noted that the preset processing qualification threshold is set by professionals based on the actual situation, and no specific limitations or details are provided.

[0098] It should be noted that the calculated processing pass coefficient is compared with a preset processing pass coefficient threshold. If the processing pass coefficient of the target image is not less than the preset threshold, it means that the image adaptation processing has met the pass standard and can be directly displayed on the AR device through the Micro-LED microdisplay chip to show the digital effect of the ancient painting. If the processing pass coefficient is lower than the preset threshold, it means that the target image has failed to meet the quality requirements and the image adaptation processing needs to be repeated. After reprocessing, a new processing pass coefficient needs to be calculated again and compared with the preset threshold until the image processing pass coefficient reaches or exceeds the preset standard. When the image adaptation processing meets the standard, the image is driven and displayed on the AR device through Micro-LED technology, ensuring that the digital effect of the ancient painting is realistically restored and achieving the best viewing experience. This evaluation method based on the processing pass coefficient effectively avoids human intervention and subjective judgment, ensuring quality control and consistency in the digital display process of ancient paintings.

[0099] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A method for digitally presenting ancient paintings driven by Micro-LED microdisplay chips, characterized in that, Includes the following steps: The image after image adaptation processing of the digitized image of the ancient painting is used as the target image, and the brightness uniformity coefficient, size coefficient and contrast failure coefficient of the target image with acceptable image adaptation processing are extracted. The processing pass coefficient is obtained based on the brightness uniformity coefficient, size coefficient, and contrast failure coefficient. The processing pass coefficient and the preset processing pass coefficient threshold are used to determine whether the target image is qualified in the image adaptation process. When the target image passes the image adaptation process, the target image will be digitally presented on the AR device using a Micro-LED microdisplay chip to drive the ancient painting. The contrast ratio failure factor includes: The target image is converted to grayscale by performing grayscale processing, and the number of pixels at each grayscale level in the image is counted to obtain a grayscale histogram. A grayscale histogram represents the pixel distribution at each gray level in an image; Normalize the grayscale histogram so that the sum of the histograms is 1, which is the number of pixels at each grayscale level divided by the total number of pixels in the image. The normalized histogram reflects the relative distribution of each gray level in the image; The contrast of the image is calculated using the normalized gray-level histogram. The formula for calculating contrast is: In the formula, It's about contrast. It is the first The number of normalized pixels at each gray level It is the mean of the normalized histogram. It is the center value of the normalized histogram. It represents the number of gray levels; The obtained contrast values ​​are normalized to obtain the contrast failure coefficient of the image. The formula for calculating the contrast failure coefficient is as follows: in, It is the contrast ratio failure factor. It is the maximum possible contrast ratio; The processing pass coefficient is obtained based on the brightness uniformity coefficient, size coefficient, and contrast failure coefficient, including: In the formula, To handle the pass / fail coefficient, , , These are the brightness uniformity coefficient, size coefficient, and contrast ratio non-compliance coefficient, respectively. These are the preset proportional coefficients for brightness uniformity coefficient, size coefficient, and contrast non-compliance coefficient, respectively. All are greater than 0.

2. The method for digitally presenting ancient paintings driven by Micro-LED microdisplay chips according to claim 1, characterized in that, The brightness uniformity coefficient includes: Convert the target image to a grayscale image and calculate the mean of the grayscale values ​​in the grayscale image. and standard deviation The calculation formula is: In the formula, The number of rows in the image. The number of columns in the image. For grayscale images at position The pixel grayscale value; The formula for calculating the brightness uniformity coefficient is as follows: In the formula, The brightness uniformity index, This is a preset constant used to avoid division by zero when calculating the standard deviation.

3. The method for digitally presenting ancient paintings driven by Micro-LED microdisplay chips according to claim 1, characterized in that, The size factor includes: Obtain the width and length of the target image, and label them as follows: and , the width of the target image Divide by the length of the target image , thus obtaining the processed proportionality coefficient; The width and length of the digitized image of the ancient painting before image adaptation processing are marked as follows: and , width Divide by length , thus obtaining the scaling factor before processing; Calculate the absolute difference between the scaling factor after processing and the scaling factor before processing, and use the absolute difference as the size factor of the target image.

4. The method for digitally presenting ancient paintings driven by Micro-LED microdisplay chips according to claim 1, characterized in that, Determining whether a target image is qualified during image adaptation processing based on a processing qualification coefficient and a preset processing qualification coefficient threshold includes: The processing pass coefficient is compared with the preset processing pass coefficient threshold. If the processing pass coefficient is not less than the preset processing pass coefficient threshold, it means that the target image is qualified in the image adaptation process. The target image is then digitally presented on the AR device by driving the ancient painting through the Micro-LED micro-display chip.

5. The method for digitally presenting ancient paintings driven by a Micro-LED microdisplay chip according to claim 1, characterized in that, Judging whether a target image is qualified in the image adaptation process based on the processing qualification coefficient and the preset processing qualification coefficient threshold also includes: If the processing pass coefficient is less than the preset processing pass coefficient threshold, it means that the target image is unqualified in the image adaptation process. Then, the image adaptation process is repeated on the digitized image of the ancient painting until the processing pass coefficient of the image after image adaptation is not less than the preset processing pass coefficient threshold. The image after image adaptation is then digitally presented on the AR device through the Micro-LED micro-display chip.

Images