An RGBG type display panel sub-pixel rendering method and system based on edge judgment

Abstract

The application aims to provide an RGBG display panel sub-pixel rendering method and system based on edge judgment, which comprises the following steps: obtaining an RGB image to be rendered; converting RGB pixel information of the RGB image into RG pixel information or BG pixel information; and sharing missing sub-pixel information by adjacent pixels for the pixel with missing sub-pixel information. The application can overcome the edge blur or heavy pseudo-color caused by borrowing adjacent sub-pixel weighted average calculation when converting source image RGB data into RGBG panel display data.

Description

Technical Field

[0001] This invention relates to the field of image processing technology, and specifically to a method and system for rendering sub-pixels of an RGBG type display panel based on edge detection. Background Technology

[0002] Traditional mature LCD displays typically consist of red (R) subpixels, green (G) subpixels, and blue (B) subpixels per basic display unit. However, with the continuous improvement of requirements for display effect, resolution, and yield, Organic Light Emitting Diode (OLED) panels have become the mainstream choice for mid-to-high-end mobile phones, computers, and other electronic products. To achieve higher resolution, OLED displays usually increase pixel density by reducing the number of subpixels in a single pixel. Common Pentile and diamond arrays are typical RGBG arrays. RGBG display panels have higher yield and pixel density than RGB type panels under the same process. However, due to the reduction in the physical structure of the number of subpixels in a single pixel, this type of display inevitably has false color phenomena at the image edges, which need to be reduced through rendering algorithms or process adjustments.

[0003] Image rendering is the process of converting three-dimensional light energy transfer processing into a two-dimensional image. In computer graphics, image rendering involves converting a three-dimensional model or scene into a two-dimensional image. This includes processing the three-dimensional model or scene, such as modeling, texturing, mapping, lighting calculation, projection transformation, and viewpoint transformation, ultimately generating a two-dimensional image.

[0004] Currently, most RGBG display panels use weighted averaging calculations by borrowing neighboring pixels for subpixel rendering. While this method can reduce false color to some extent, it has significant limitations. Firstly, when using weighted averaging in edge areas with large brightness differences, it directly leads to blurred image edges. If the R or B components of the source image are discarded directly in these edge areas, it will result in a visible false color phenomenon. Secondly, different manufacturers use different processes, and the mask aperture area and subpixel spacing vary for different subpixel areas, leading to differences in display effects. Summary of the Invention

[0005] The purpose of this invention is to provide a sub-pixel rendering method and system for RGBG type display panels based on edge judgment. This method can overcome the edge blurring or heavy pseudo-color caused by the use of weighted average calculation of neighboring sub-pixels in the prior art when converting source image RGB data into RGBG panel display data.

[0006] A sub-pixel rendering method for an RGBG type display panel based on edge detection, comprising: Obtain the RGB image to be rendered; Convert the RGB pixel information of the RGB image into RG pixel information or BG pixel information; Pixels that lack sub-pixel information share the missing pixel information with their neighboring pixels.

[0007] Preferably, converting the RGB pixel information of the RGB image to RG pixel information includes: Calculate the brightness values ​​of the point to be rendered and the source images on both sides of the point to be rendered, and calculate the difference between the maximum and minimum brightness values ​​of the three points; Based on the comparison results of the preset gradient edge threshold and the difference in brightness value, the corresponding gradient filtering coefficients are selected to perform filtering calculations on the R component of the source image: ; ; ; ;

[0008] in: ; ;

[0009]

[0010] Must meet The sum of the coefficients in each group is 1.

[0011] Preferably, converting the RGB pixel information of the RGB image to RG pixel information includes: Calculate the brightness values ​​of the point to be rendered and the source images on both sides of the point to be rendered, and calculate the difference between the maximum and minimum brightness values ​​of the three points; Based on the comparison results of the preset gradient edge threshold and the difference in brightness value, the corresponding gradient filtering coefficients are selected to perform filtering calculations on the G component of the source image: ; ; ; ; ; in: ; ; ; ; Must meet The sum of the coefficients in each group is 1.

[0012] Preferably, converting the RGB pixel information of the RGB image to BG pixel information includes: Calculate the brightness values ​​of the point to be rendered and the source images on both sides of the point to be rendered, and calculate the difference between the maximum and minimum brightness values ​​of the three points; Based on the comparison results of the preset gradient edge threshold and the brightness value difference, the filter coefficients corresponding to the gradient are selected to perform filtering calculations on the B component of the source image: ; ; ; ; ; in: ; ; ; ; Must meet The sum of the coefficients in each group is 1.

[0013] Preferably, converting the RGB pixel information of the RGB image to BG pixel information includes: Calculate the brightness values ​​of the point to be rendered and the source images on both sides of the point to be rendered, and calculate the difference between the maximum and minimum brightness values ​​of the three points; Based on the comparison results of the preset gradient edge threshold and the difference in brightness value, the corresponding gradient filtering coefficients are selected to perform filtering calculations on the G component of the source image: ; ; ; ; ; in: ; ; ; ; Must meet The sum of the coefficients in each group is 1.

[0014] Preferably, the pixel with missing sub-pixel information shares the missing pixel information with its adjacent pixels, including: A pixel containing only RG sub-pixel information is adjacent to a pixel containing only BG sub-pixel information; The pixel containing only RG sub-pixel information copies the B sub-pixel information of the pixel containing only BG sub-pixel information; The pixel containing only BG sub-pixel information copies the R sub-pixel information of the pixel containing only RG sub-pixel information.

[0015] Preferably, after obtaining the RGB image to be rendered, the method further includes segmenting the RGB image to be rendered, specifically: Remove noise from the RGB image; Calculate the grayscale of the denoised RGB image; Calculate the edge gradient direction; Traverse the image to find edge pixels.

[0016] Preferably, the calculation of the edge gradient direction includes: An expansion erosion algorithm is used to refine the edge information.

[0017] An RGBG type display panel sub-pixel rendering system based on edge detection, comprising: The image acquisition module is used to acquire the RGB image to be rendered; A pixel conversion module is used to convert the RGB pixel information of the RGB image into RG pixel information or BG pixel information; The pixel processing module is used to ensure that pixels with missing sub-pixel information share the missing pixel information with their neighboring pixels.

[0018] An electronic device includes a chip, a processor, and a memory, the memory storing computer program code including computer instructions. When the chip executes the computer instructions, the electronic device executes an RGBG type display panel sub-pixel rendering method based on edge judgment.

[0019] The beneficial effects of this invention are as follows: This invention not only converts the RGB pixel information of the source image into the RGBG pixel information required by the RGBG type display panel, but also effectively performs corresponding filtering calculations on the G, R or B sub-pixel components at the edge position through gradient edge judgment, thereby suppressing false color and avoiding edge blurring; in addition, this algorithm does not require frame buffer or line buffer in hardware circuit implementation, which can greatly simplify hardware circuit design and reduce costs. Attached Figure Description

[0020] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a sub-pixel rendering method for an RGBG type display panel based on edge detection according to the present invention; Figure 2 This is a schematic diagram illustrating the RGB conversion of the source image of the RGBG type display panel of the present invention to an RGBG type panel arrangement; Figure 3 This is a schematic diagram of the RGB to RGBG conversion process at different pixel positions according to the present invention; Figure 4 This is a schematic diagram illustrating the calculation of pixel information copying and extension at the beginning and end of the line in this invention. Figure 5 This is a schematic diagram of the hardware structure of an electronic device according to the present invention. Detailed Implementation

[0023] 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 a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0024] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0025] Furthermore, the use of terms such as "first" and "second" in this invention is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" and "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of the various embodiments can be combined with each other, but only on the basis of being achievable by those skilled in the art. When the combination of technical solutions is contradictory or impossible to implement, such a combination of technical solutions should be considered non-existent and not within the scope of protection claimed by this invention.

[0026] Currently, most RGBG display panels use weighted averaging calculations by borrowing neighboring pixels for subpixel rendering. While this method can reduce false color to some extent, it has significant limitations. Firstly, when using weighted averaging in edge areas with large brightness differences, it directly leads to blurred image edges. If the R or B components of the source image are discarded directly in these edge areas, it will result in a visible false color phenomenon. Secondly, different manufacturers use different processes, and the mask aperture area and subpixel spacing vary for different subpixel areas, leading to differences in display effects.

[0027] This invention not only converts the RGB pixel information of the source image into the RGBG pixel information required by the RGBG type display panel, but also effectively performs corresponding filtering calculations on the G, R, or B sub-pixel components at the edge position through gradient edge judgment, thereby suppressing false color and avoiding edge blurring. In addition, this algorithm does not require frame buffer or line buffer in hardware circuit implementation, which can greatly simplify hardware circuit design and reduce costs.

[0028] Example 1 A sub-pixel rendering method for RGBG type display panels based on edge detection, referenced Figure 1 ,include: S100, acquire the RGB image to be rendered; RGB images are an image format that uses three color channels: red, green, and blue. The color of each pixel is composed of the intensities of these three colors, typically ranging from 0 to 255, which can create approximately 16 million different colors. RGB images are widely used in various digital display devices, such as televisions, computer monitors, and mobile phone screens, displaying different colors by adjusting the intensity of red, green, and blue light.

[0029] RGBG format is a special pixel arrangement for image sensors, primarily used for high-resolution image capture. RGBG is a type of Bayer filter, characterized by each pixel consisting of one red filter, two green filters, and one blue filter. This arrangement effectively improves image resolution and detail, performing exceptionally well in high-resolution image capture. By using RGBG filter arrays, sensors can capture more color information more accurately, thus improving image quality and sharpness. RGB format is an image storage format that uses variations in the red (R), green (G), and blue (B) color channels and their superposition to obtain a wide variety of colors.

[0030] S200 converts the RGB pixel information of an RGB image into RG pixel information or BG pixel information; An image pixel is the basic unit that makes up a digital image. Each pixel is a tiny dot that stores color and brightness information. The number and arrangement of pixels determine the image's resolution and quality. Pixels are the basic units that make up digital images, and image resolution is usually expressed in pixels per inch (PPI). The number and arrangement of pixels directly affect the image's sharpness and detail. Each pixel contains three color components: red (R), green (G), and blue (B). Different combinations of intensities can produce a rich variety of colors. The more pixels there are, the higher the image resolution and the richer the detail. Pixel information refers to the specific details of the red (R), green (G), and blue (B) color components contained in each pixel.

[0031] S300, pixels lacking sub-pixel information share the missing pixel information with their neighboring pixels.

[0032] Each pixel in the source image contains three sub-pixel components: RGB. This algorithm converts pixels containing RGB information into pixels containing only RG or BG information. Adjacent pixels share R or B sub-pixel information. Since the human eye is more sensitive to green light than red and blue, the algorithm can visually reproduce the source image to a large extent. This invention provides a sub-pixel rendering algorithm for RGBG display panels based on edge detection and multi-channel filtering. When converting RGB data from the source image to RGBG panel display data, it overcomes the edge blurring or severe false color issues caused by using weighted averages of neighboring sub-pixels in existing technologies.

[0033] Preferably, refer to Figure 2 and Figure 3 Converting RGB pixel information of an RGB image to RG pixel information includes: Calculate the brightness values ​​of the point to be rendered and the source images on both sides of the point to be rendered, and calculate the difference between the maximum and minimum brightness values ​​of the three points; Based on the comparison results of the preset gradient edge threshold and the difference in brightness value, the corresponding gradient filtering coefficients are selected to perform filtering calculations on the R component of the source image: ; ; ; ;

[0034] in: ; ;

[0035]

[0036] Must meet The sum of the coefficients in each group is 1.

[0037] like Figure 3 The diagram illustrates the RGB to RGBG conversion process at different pixel positions. The subpixel arrangement of an RGBG panel is generally an alternating arrangement of RG and BG rows. Depending on whether the starting pixel of the image is RGBG or BGRG, the rendering method of RGBG subpixels at different coordinate positions is different (the starting coordinate is 0, j is the vertical position coordinate, and i is the horizontal position coordinate). Specifically, it can be divided into two calculation processes: the first conversion process calculates the RG information of the corresponding position on the screen based on the RGB information of the source pixel, and the second conversion process calculates the BG information of the corresponding position on the screen based on the RGB information of the source pixel.

[0038] The specific calculations for the first conversion process are as follows: First, the brightness values ​​of the point to be rendered and the source images on both sides are calculated. The difference between the maximum and minimum brightness values ​​of these three points is obtained. Then, the difference in brightness values ​​is compared with the preset gradient edge threshold, and the corresponding gradient filtering coefficients are selected to perform filtering calculations on the R and G components of the source image.

[0039] Preferably, converting the RGB pixel information of an RGB image to RG pixel information includes: Calculate the brightness values ​​of the point to be rendered and the source images on both sides of the point to be rendered, and calculate the difference between the maximum and minimum brightness values ​​of the three points; Based on the comparison results of the preset gradient edge threshold and the difference in brightness value, the corresponding gradient filtering coefficients are selected to perform filtering calculations on the G component of the source image: ; ; ; ; ; in: ; ; ; ; Must meet The sum of the coefficients in each group is 1.

[0040] Preferably, converting the RGB pixel information of an RGB image to BG pixel information includes: Calculate the brightness values ​​of the point to be rendered and the source images on both sides of the point to be rendered, and calculate the difference between the maximum and minimum brightness values ​​of the three points; Based on the comparison results of the preset gradient edge threshold and the brightness value difference, the filter coefficients corresponding to the gradient are selected to perform filtering calculations on the B component of the source image: ; ; ; ; ; in: ; ; ; ; Must meet The sum of the coefficients in each group is 1.

[0041] Preferably, converting the RGB pixel information of an RGB image to BG pixel information includes: Calculate the brightness values ​​of the point to be rendered and the source images on both sides of the point to be rendered, and calculate the difference between the maximum and minimum brightness values ​​of the three points; Based on the comparison results of the preset gradient edge threshold and the difference in brightness value, the corresponding gradient filtering coefficients are selected to perform filtering calculations on the G component of the source image: ; ; ; ; ; in: ; ; ; ; Must meet The sum of the coefficients in each group is 1.

[0042] The specific calculations for the second conversion process are as follows: First, the brightness values ​​of the point to be rendered and the source images on both sides are calculated. The difference between the maximum and minimum brightness values ​​of these three points is obtained. Then, the difference in brightness values ​​is compared with the preset gradient edge threshold, and the corresponding gradient filtering coefficients are selected to perform filtering calculations on the B and G components of the source image.

[0043] Preferably, refer to Figure 4 Pixels lacking sub-pixel information share the missing pixel information with their neighboring pixels, including: A pixel containing only RG sub-pixel information is adjacent to a pixel containing only BG sub-pixel information; Because if three consecutive pixels containing only RG pixel information or only BG pixel information are adjacent, the middle pixel containing only RG pixel information or only BG pixel information cannot obtain the missing color component information from the adjacent pixels. Therefore, in this embodiment of the invention, the pixel containing only RG pixel information is adjacent to the pixel containing only BG pixel information, so that each pixel can supplement the missing color component information through the adjacent pixel.

[0044] The pixel containing only RG sub-pixel information copies the B sub-pixel information of the pixel containing only BG sub-pixel information; The pixel containing only BG sub-pixel information copies the R sub-pixel information of the pixel containing only RG sub-pixel information.

[0045] Preferably, after obtaining the RGB image to be rendered, the process further includes segmenting the RGB image to be rendered, specifically: Remove noise from RGB images; The primary purpose of image denoising is to improve image quality, making it more suitable for subsequent computer vision processing and analysis. Image denoising techniques significantly enhance image sharpness and detail by removing noise, thereby improving the overall visual quality. Image denoising refers to the process of reducing noise in digital images. Noise can originate from various factors, such as lighting interference, sensor noise, and digital conversion noise. This noise degrades image quality and affects subsequent computer vision processing and analysis.

[0046] The loss function is designed to optimize convergence speed and denoising effect, and is expressed as follows: ; ; ; Where x is the denoised image and y is the image before denoising. This is a regularization constant that controls gradient stability in the loss function; it is set to 0.001. For the Laplace operator.

[0047] Calculate the grayscale of the denoised RGB image; The main benefits of image grayscale conversion include simplifying the image matrix, improving computational efficiency, reducing data dimensionality, simplifying image processing algorithms, and providing better contrast and brightness information, making image features easier to analyze and recognize. Since images captured by cameras typically have more than 256 colors, the maximum inter-class variance method is used for preprocessing the target image to better extract the target area while ignoring background interference. This method is based on the image's grayscale histogram, using the maximum inter-class variance between the target and background as the threshold selection criterion. Its basic calculation method is as follows: Assuming the image consists of a target and a background, since the target area generally has different grayscale values ​​than the background, the grayscale values ​​of the image are statistically analyzed based on the histogram as levels. A threshold is selected between these levels to divide the image into two classes: target and background. If the inter-class variance between the two classes is maximized, then it is the optimal segmentation threshold.

[0048] Calculate the edge gradient direction; The purpose of image gradient direction is to indicate the direction of the fastest change in grayscale value in an image. In image processing, the gradient direction is the direction of the fastest change in a function, and its maximum value is easily found along the gradient direction. Image gradient refers to the rate of change between the grayscale value of each pixel in an image and the grayscale values ​​of its surrounding pixels. It can be used to detect edges and texture information in an image. In practical calculations, the derivative is usually approximated using differences, especially for digital images, where gradient values ​​can be calculated by convolving small regions of templates.

[0049] Canny edge extraction is performed on the segmented binary image of the target to obtain the target edge with a width of one pixel, thus obtaining the contour of the target region. To ensure accuracy, each pixel in the binary image needs to be regarded as a rectangular region. Thus, each edge must be associated with two rectangular regions. If the two regions have the same color, the attribute of this edge is recorded as 0; otherwise, the attribute of this edge is recorded as 1. The point sequence formed by these pixels constitutes the contour.

[0050] Traverse the image to find edge pixels.

[0051] Edge pixels are important features in image processing and analysis, typically located at the boundaries between different regions in an image. Edge pixels exhibit significant changes in grayscale value or brightness within localized areas; they are pixels whose brightness or grayscale value changes drastically within an image. These pixels are usually located between the object and the background, between objects, or between an object and its shadow. The existence of edge pixels is caused by factors such as surface discontinuities, depth discontinuities, changes in object properties, or changes in scene lighting.

[0052] Preferably, calculating the edge gradient direction includes: An expansion erosion algorithm is used to refine the edge information.

[0053] The implementation steps of the dilation and erosion algorithms are as follows: Selecting a structuring element: Choose an appropriate structuring element (also known as a kernel or convolution template) based on the target image features to be processed.

[0054] Dilation operation: Slide the structuring element on the image, perform convolution calculation at each position, and replace the pixel value of the reference point with the maximum pixel value of the area covered by the structuring element.

[0055] Erosion operation: Similarly, slide the structuring element on the image, perform convolution calculation at each position, and replace the pixel value of the reference point with the minimum pixel value of the area covered by the structuring element.

[0056] Results processing: Depending on the needs, the results of expansion and erosion operations can be combined, such as opening operations (erosion first, then expansion) and closing operations (expansion first, then erosion) to achieve better processing results.

[0057] Define the dilatational erosion operator: ; ; Where f is the source RGB image and S is the structuring element.

[0058] Structural elements are a key element in morphological image processing. Generally, the size, shape, and orientation of a structuring element all affect the edge detection performance. Small structuring elements have weak noise reduction capabilities but can detect good edge details, while large structuring elements have strong noise reduction capabilities but detect coarser edges. If a square structuring element is chosen, the resulting edges are strongly connected boundaries, meaning the boundaries are continuous; if a non-square structuring element is used, the edges are weakly connected. When implementing edge detection for an image, the appropriate structuring element must be selected based on the image's texture features. Generally, unless there are special requirements, a 3×3 square structuring element is often chosen. Since the algorithm simply uses dilatational erosion edge detection and requires continuous edges, a 3×3 square structuring element is selected.

[0059] Dilation and erosion are morphological image processing methods primarily used for morphological operations in image processing. Dilation and erosion are two fundamental morphological operations. Dilation expands the highlighted or white areas of an image, enlarging the overall target image. The dilation operator is "⊕". It involves convolving a template B with image A, scanning each pixel in the image, and performing an AND operation between the template elements and the binary image elements. If both results are 0, the target pixel is set to 0; otherwise, it is set to 1. This process calculates the maximum pixel value within the area covered by B and replaces the reference pixel value with this value. Dilation can be used to enlarge target regions, often filling small holes in an image or connecting adjacent objects.

[0060] Erosion: The erosion operation reduces or thins out bright or white areas in an image, resulting in a smaller overall image size. The erosion operator is "-". It calculates the minimum pixel value in the area covered by template B through convolution with image A, and then replaces the pixel value of the reference point with this minimum value. Erosion can eliminate noise in an image while reducing the boundary values ​​of the target image. Dilation and erosion operations can be used for image segmentation, repeatedly applying them to extract specific shapes or structures from an image.

[0061] Example 2 An RGBG type display panel sub-pixel rendering system based on edge detection, comprising: The image acquisition module is used to acquire the RGB image to be rendered; The pixel conversion module is used to convert the RGB pixel information of an RGB image into RG pixel information or BG pixel information. The pixel processing module is used to ensure that pixels with missing sub-pixel information share the missing pixel information with their neighboring pixels.

[0062] Example 3 An electronic device includes a chip, a processor, and a memory. The memory stores computer program code, which includes computer instructions. When the chip executes the computer instructions, the electronic device executes an edge-based RGBG type display panel sub-pixel rendering method.

[0063] refer to Figure 5 The electronic device 2 includes a processor 21, a memory 22, an input device 23, and an output device 24. The processor 21, memory 22, input device 23, and output device 24 are coupled together via connectors, which may include various interfaces, transmission lines, or buses, etc., and are not limited in this embodiment of the invention. It should be understood that in the various embodiments of the invention, coupling refers to mutual connection through a specific method, including direct connection or indirect connection through other devices, such as through various interfaces, transmission lines, buses, etc.

[0064] The processor 21 can be one or more graphics processing units (GPUs). If the processor 21 is a GPU, the GPU can be a single-core GPU or a multi-core GPU. Optionally, the processor 21 can be a processor group composed of multiple GPUs, with the multiple processors coupled to each other via one or more buses. Optionally, the processor can also be other types of processors, etc., and this embodiment of the invention is not limited thereto.

[0065] The memory 22 can be used to store computer program instructions, as well as various types of computer program code, including program code for executing the present invention. Optionally, the memory includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or compact disc read-only memory (CD-ROM), which is used for related instructions and data.

[0066] Input device 23 is used to input data and / or signals, and output device 24 is used to output data and / or signals.

[0067] The output device 24 and the input device 23 can be independent devices or an integrated device.

[0068] This invention not only converts the RGB pixel information of the source image into the RGBG pixel information required by the RGBG type display panel, but also effectively performs corresponding filtering calculations on the G, R, or B sub-pixel components at the edge position through gradient edge judgment, thereby suppressing false color and avoiding edge blurring. In addition, this algorithm does not require frame buffer or line buffer in hardware circuit implementation, which can greatly simplify hardware circuit design and reduce costs.

[0069] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A sub-pixel rendering method for an RGBG type display panel based on edge detection, characterized in that, include: Obtain the RGB image to be rendered; Convert the RGB pixel information of the RGB image into RG pixel information or BG pixel information; Pixels lacking sub-pixel information share the missing pixel information with their neighboring pixels; including: A pixel containing only RG sub-pixel information is adjacent to a pixel containing only BG sub-pixel information; the pixel containing only RG sub-pixel information copies the B sub-pixel information of the pixel containing only BG sub-pixel information; the pixel containing only BG sub-pixel information copies the R sub-pixel information of the pixel containing only RG sub-pixel information. After obtaining the RGB image to be rendered, the process further includes segmenting the RGB image to be rendered, specifically: Remove noise from the RGB image; Calculate the grayscale of the denoised RGB image; Calculate the edge gradient direction; refine the edge information using the dilatational erosion algorithm; Traverse the image to find edge pixels; Converting the RGB pixel information of the RGB image to RG pixel information includes: Calculate the brightness values ​​of the point to be rendered and the source images on both sides of the point to be rendered, and calculate the difference between the maximum and minimum brightness values ​​of the three points; Based on the comparison results of the preset gradient edge threshold and the difference in brightness value, the corresponding gradient filtering coefficients are selected to perform filtering calculations on the R component of the source image: ； ； ； ； ； in: ； ； ； ； Must meet The sum of the coefficients in each group is 1; Based on the comparison results of the preset gradient edge threshold and the difference in brightness value, the corresponding gradient filtering coefficients are selected to perform filtering calculations on the G component of the source image: ； ； ； ； ； in: ； ； ； ； Must meet The sum of the coefficients in each group is 1; Converting the RGB pixel information of the RGB image to BG pixel information includes: Calculate the brightness values ​​of the point to be rendered and the source images on both sides of the point to be rendered, and calculate the difference between the maximum and minimum brightness values ​​of the three points; Based on the comparison results of the preset gradient edge threshold and the brightness value difference, the filter coefficients corresponding to the gradient are selected to perform filtering calculations on the B component of the source image: ； ； ； ； ； in: ； ； ； ； Must meet The sum of the coefficients in each group is 1; Based on the comparison results of the preset gradient edge threshold and the difference in brightness value, the corresponding gradient filtering coefficients are selected to perform filtering calculations on the G component of the source image: ； ； ； ； ； in: ； ； ； ； Must meet The sum of the coefficients in each group is 1.

2. A sub-pixel rendering system for an RGBG type display panel based on edge detection, characterized in that, include: The image acquisition module is used to acquire the RGB image to be rendered; A pixel conversion module is used to convert the RGB pixel information of the RGB image into RG pixel information or BG pixel information; The pixel processing module is used to allow pixels with missing sub-pixel information to share the missing pixel information with adjacent pixels. The system is applied to the RGBG type display panel sub-pixel rendering method based on edge judgment as described in claim 1.

3. An electronic device, characterized in that, include: A chip, a processor, and a memory, the memory being used to store computer program code, the computer program code including computer instructions, wherein, when the chip executes the computer instructions, the electronics are applied to a sub-pixel rendering method for an RGBG type display panel based on edge judgment as described in claim 1.

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