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

Abstract

The invention aims to provide an RGBG type display panel sub-pixel rendering method and system based on edge judgment. The method comprises the following steps: acquiring an RGB image to be rendered; rGB pixel information of the RGB image is converted into RG pixel information or BG pixel information; the pixel with the missing sub-pixel information and the adjacent pixel share the missing pixel information. According to the invention, when the RGB data of the source image is converted into the RGBG panel display data, the condition of edge blurring or heavy pseudo color caused by the weighted average calculation of adjacent sub-pixels in the prior art can be overcome.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and particularly to a sub-pixel rendering method and system for an RGBG-type display panel based on edge judgment. Background Art

[0002] In a traditional and mature LCD display screen, generally each basic display unit is composed of a red (R) sub-pixel, a green (G) sub-pixel, and a blue (B) sub-pixel. However, with the continuous improvement of requirements such as display effects, resolution, and yield, organic light-emitting diode (OLED) panels have become the mainstream choice for mid- to high-end mobile phones and computers and other electronic products. In order to achieve a higher resolution, an OLED display screen usually increases the pixel density by reducing the number of sub-pixels contained in a single pixel. Common Pentile and diamond arrangements are typical RGBG-type arrangements. The RGBG-type display panel has a higher yield and pixel density than the RGB-type panel under the same process. However, due to the physical structure that reduces the number of sub-pixels contained in a single pixel, this type of display screen will inevitably have a false color phenomenon at the image edge, and it is necessary to weaken the false color through a rendering algorithm or adjustment process.

[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, which includes the processing of the three-dimensional model or scene, such as modeling, texturing, mapping, lighting calculation, projection transformation, and viewpoint transformation, etc., and finally generating a two-dimensional image.

[0004] Currently, most sub-pixel rendering of RGBG-type display panels uses weighted average calculation by borrowing adjacent pixels. Weighted average calculation by borrowing adjacent pixels can weaken false colors to a certain extent, but there are significant limitations. On the one hand, when borrowing sub-pixel weighted average calculation in the edge area with a large difference in brightness, the direct problem is that the image edge becomes blurred. If the R or B component of the source image is directly discarded in the edge area for display, it will bring a direct false color phenomenon to the naked eye. On the other hand, different manufacturers have different processes, and the opening areas of the sub-pixel area masks and the sub-pixel pitches are different, which will bring differences in display effects. Summary of the Invention

[0005] The purpose of the present invention is to provide a sub-pixel rendering method and system for an RGBG-type display panel based on edge judgment. This method can overcome the situation of edge blurring or heavy false colors caused by borrowing adjacent sub-pixels for weighted average calculation in the prior art when converting the RGB data of the source image into the display data of the RGBG panel.

[0006] A sub-pixel rendering method for an RGBG display panel based on edge judgment, comprising: Obtain an 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 adjacent pixels.

[0007] Preferably, converting the RGB pixel information of the RGB image into RG pixel information includes: Calculate the brightness values of the source image at the point to be rendered and on both sides of the point to be rendered, and calculate the difference between the maximum and minimum brightness values of the three points; According to the comparison result between the preset gradient edge threshold and the brightness value difference, select the filtering coefficient corresponding to the gradient to perform filtering calculation on the R component of the source image: ; ; ; ; Wherein: th2_condition: ; th1_condition: ; th0_condition: ; other_condition: ; It is necessary to satisfy , and the sum of each group of coefficients is 1.

[0008] Preferably, converting the RGB pixel information of the RGB image into RG pixel information includes: Calculate the brightness values of the source image at the point to be rendered and on both sides of the point to be rendered, and calculate the difference between the maximum and minimum brightness values of the three points; According to the comparison result between the preset gradient edge threshold and the brightness value difference, select the filtering coefficient corresponding to the gradient to perform filtering calculation on the G component of the source image: ; ; ; ; Wherein: th2_condition: ; th1_condition:

[0009] th0_condition: ; other_condition: ; It is necessary to satisfy , and the sum of each group of coefficients is 1.

[0010] Preferably, converting the RGB pixel information of the RGB image into BG pixel information includes: Calculating the brightness values of the point to be rendered and the source images on both sides of the point to be rendered, and calculating the difference between the maximum and minimum brightness values of the three points; According to the comparison result between the preset gradient edge threshold and the brightness value difference, select the filtering coefficient corresponding to the gradient to perform filtering calculation on the B component of the source image: ; ; ; ; Wherein: th2_condition: ; th1_condition: ; th0_condition: ; other_condition: ; It is necessary to satisfy , and the sum of each group of coefficients is 1.

[0011] Preferably, converting the RGB pixel information of the RGB image into BG pixel information includes: Calculating the brightness values of the point to be rendered and the source images on both sides of the point to be rendered, and calculating the difference between the maximum and minimum brightness values of the three points; According to the comparison result between the preset gradient edge threshold and the brightness value difference, select the filtering coefficient corresponding to the gradient to perform filtering calculation on the G component of the source image: ; ; ; ; Wherein: th2_condition: ; th1_condition: ; th0_condition: ; other_condition: ; It is required to satisfy , and the sum of each group of coefficients is 1.

[0012] Preferably, the pixels lacking sub-pixel information share the missing pixel information with adjacent pixels, including: Pixels containing only RG sub-pixel information are adjacent to pixels containing only BG sub-pixel information; The pixels containing only RG sub-pixel information copy the B sub-pixel information of the pixels containing only BG sub-pixel information; The pixels containing only BG sub-pixel information copy the R sub-pixel information of the pixels containing only RG sub-pixel information.

[0013] Preferably, after obtaining the RGB image to be rendered, it further includes segmenting the RGB image to be rendered, specifically: Removing the noise of the RGB image; Calculating the grayscale of the denoised RGB image; Calculating the edge gradient direction; Traversing the image to find edge pixel points.

[0014] Preferably, the calculation of the edge gradient direction includes: Using the dilation and erosion algorithm to refine the edge information.

[0015] An RGBG-type display panel sub-pixel rendering system based on edge judgment, including: An image acquisition module for acquiring an RGB image to be rendered; A pixel conversion module for converting the RGB pixel information of the RGB image into RG pixel information or BG pixel information; A pixel processing module is used for pixels lacking sub-pixel information to share the missing pixel information with adjacent pixels.

[0016] An electronic device includes: a chip, a processor, and a memory. The memory is used to store computer program code, and the computer program code includes 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.

[0017] The beneficial effects of the present invention are as follows: The present invention not only realizes the conversion of the RGB pixel information of the source image into the RGBG pixel information required by the RGBG display panel, but also can effectively perform corresponding filtering calculations on the G, R, or B sub-pixel components at the edge position through gradient-based edge judgment, thereby suppressing false colors while avoiding edge blurring. In addition, this algorithm does not require a frame buffer or a line buffer in the implementation of the hardware circuit, which can greatly simplify the hardware circuit design and reduce costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present invention and, together with the specification, are used to explain the principles of the present invention.

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0020] Figure 1 It is a flowchart of a sub-pixel rendering method for an RGBG display panel based on edge judgment according to the present invention; Figure 2 It is a schematic diagram of the conversion of the RGB of the source image of the RGBG display panel according to the present invention to the RGBG panel arrangement; Figure 3 It is a schematic diagram of the RGB to RGBG conversion process at different pixel positions according to the present invention; Figure 4 It is a schematic diagram of the replication and expansion of pixel information at the beginning and end of a row participating in the calculation according to the present invention; Figure 5 It is a schematic diagram of the hardware structure of an electronic device according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0022] It should be noted that all the directional indications (such as up, down, left, right, front, back...) in the embodiments of the present invention are only used to explain the relative positional relationship and movement conditions between components in a specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indications will also change accordingly.

[0023] In addition, the descriptions involving "first", "second", etc. in the present invention are only for descriptive purposes and should not be construed as indicating or implying their relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions between various embodiments can be combined with each other, but it must be based on the ability of those of ordinary skill in the art to implement. When the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the protection scope required by the present invention.

[0024] Currently, most sub-pixel rendering of RGBG-type display panels uses weighted average calculation by borrowing adjacent pixels. Weighted average calculation by borrowing adjacent pixels can weaken false colors to a certain extent, but there are significant limitations. On the one hand, when borrowing sub-pixel weighted average calculation in the edge area with a large difference in brightness, the direct problem is that the image edge becomes blurred. If the R or B component of the source image is directly discarded in the edge area for display, it will directly cause false color phenomena to the naked eye. On the other hand, different manufacturers have different processes, and the opening areas of the mask templates in different sub-pixel regions and the sub-pixel pitches are different, which will bring differences in display effects.

[0025] The present invention not only realizes the conversion of the RGB pixel information of the source image into the RGBG pixel information required by the RGBG-type display panel, but also can effectively perform corresponding filtering calculations on the G, R, or B sub-pixel components at the edge position through gradient-based edge judgment, thereby avoiding edge blurring while suppressing false colors. In addition, this algorithm does not require a frame buffer or a line buffer in the implementation of the hardware circuit, which can greatly simplify the hardware circuit design and reduce costs.

[0026] Embodiment 1 A method for sub-pixel rendering of an RGBG-type display panel based on edge judgment, referring to Figure 1 , includes: S100, obtaining an RGB image to be rendered; An RGB image is an image format that uses three color channels: red (Red), green (Green), and blue (Blue). The color of each pixel is composed of the intensities of these three colors, usually ranging from 0 to 255, so that about 16 million different colors can be combined. RGB images are widely used in various digital display devices, such as televisions, computer monitors, and mobile phone screens, and different colors are displayed by adjusting the light intensities of red, green, and blue.

[0027] The RGBG format is a special pixel arrangement method for image sensors, mainly used for high-resolution image capture. The RGBG format is a type of Bayer filter, characterized in that each pixel consists of one red filter, two green filters, and one blue filter. This arrangement can effectively improve the resolution and detail performance of the image, especially excelling in high-resolution image capture. By using the RGBG filter array, the sensor can capture more color information more accurately, thereby improving the quality and clarity of the image. The RGB format is an image storage format that obtains various colors through the changes in the three color channels of red (R), green (G), and blue (B) and their superposition with each other.

[0028] S200, convert the RGB pixel information of the RGB image into RG pixel information or BG pixel information; An image pixel refers to the basic unit that constitutes an image in a digital image. Each pixel is a tiny dot that stores the color and brightness information of the image. The number and arrangement of pixels determine the resolution and quality of the image. Pixels are the basic units that make up a digital image, and the image resolution is usually expressed in pixels per inch (PPI). The number and arrangement of pixels directly affect the clarity and detail performance of the image. Each pixel contains three color components: red (R), green (G), and blue (B), and rich colors can be produced through different intensity combinations. The more pixels there are, the higher the resolution of the image and the richer the detail performance. Pixel information is the specific situation of each pixel containing the three color components of red (R), green (G), and blue (B).

[0029] S300, the pixels lacking sub-pixel information share the missing pixel information with adjacent pixels.

[0030] Each pixel information of the source image contains three sub-pixel components of RGB. After being processed by this algorithm, the pixels of the source image containing RGB information can be converted into pixels containing only RG or BG information, and adjacent pixels share the R or B sub-pixel information. The human eye is more sensitive to green light than red and blue. Therefore, the source image can be restored to a great extent visually. The present invention provides a sub-pixel rendering algorithm for an RGBG-type display panel based on edge judgment and multi-channel filtering, which overcomes the problems of edge blurring or heavy false colors caused by borrowing adjacent sub-pixels for weighted average calculation in the prior art when converting the RGB data of the source image into the display data of the RGBG panel.

[0031] Preferably, referring to Figure 2 and Figure 3 , converting the RGB pixel information of the RGB image into RG pixel information includes: Calculate the brightness values of the source image at the point to be rendered and on both sides of the point to be rendered, and calculate the difference between the maximum and minimum brightness values of the three points; According to the comparison result between the preset gradient edge threshold and the brightness value difference, select the filtering coefficient corresponding to the gradient to perform filtering calculation on the R component of the source image: ; ; ; ; Among them: th2_condition: ; th1_condition: ; th0_condition: ; other_condition: ; It is necessary to satisfy , and the sum of each group of coefficients is 1.

[0032] For example Figure 3 As shown in the schematic diagram of the RGB to RGBG conversion process at different pixel positions, the sub-pixel arrangement of the RGBG type panel is generally an alternating arrangement of RG and BG rows. According to whether the starting pixel of the image is RGBG or BGRG, the rendering methods of RGBG type sub-pixels at different coordinate positions are different (the coordinate starts from 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 is to calculate the RG information at the corresponding position of the screen according to the RGB information of the source pixel, and the second conversion process is to calculate the BG information at the corresponding position of the screen according to the RGB information of the source pixel.

[0033] Specific calculation of the first conversion process: First, calculate the brightness values of the source image at the point to be rendered and on both sides of it, obtain the difference between the maximum and minimum brightness values of the three points, and then compare according to the difference in brightness values and the preset gradient edge threshold, and select the filtering coefficient corresponding to the gradient to perform filtering calculation on the R and G components of the source image.

[0034] Preferably, converting the RGB pixel information of the RGB image into RG pixel information includes: Calculate the brightness values of the source image at the point to be rendered and on both sides of the point to be rendered, and calculate the difference between the maximum and minimum brightness values of the three points; According to the comparison result between the preset gradient edge threshold and the brightness value difference, select the filtering coefficient corresponding to the gradient to perform filtering calculation on the G component of the source image: ; ; ; ; Wherein: th2_condition: ; th1_condition: ; th0_condition: ; other_condition: ; It is required to satisfy , and the sum of each group of coefficients is 1.

[0035] Preferably, converting the RGB pixel information of the RGB image into BG pixel information includes: Calculating the brightness values of the point to be rendered and the source images on both sides of the point to be rendered, and calculating the difference between the maximum and minimum values of the brightness values of the three points; According to the comparison result between the preset gradient edge threshold and the brightness value difference, selecting the filtering coefficient corresponding to the gradient to perform filtering calculation on the B component of the source image: ; ; ; ; Wherein: th2_condition: ; th1_condition: ; th0_condition: ; other_condition:

[0036] It is required to satisfy , and the sum of each group of coefficients is 1.

[0037] Preferably, converting the RGB pixel information of the RGB image into BG pixel information includes: Calculating the brightness values of the point to be rendered and the source images on both sides of the point to be rendered, and calculating the difference between the maximum and minimum values of the brightness values of the three points; According to the comparison result of the preset gradient-based edge threshold and the luminance value difference, select the filtering coefficient corresponding to the gradient to perform filtering calculation on the G component of the source image: ;

[0038] ;

[0039] ;

[0040] ;

[0041] where: th2_condition: ;

[0042] th1_condition: ;

[0043] th0_condition: ;

[0044] other_condition: ; It is necessary to satisfy , and the sum of each group of coefficients is 1.

[0045] Specific calculation of the second conversion process: First, calculate the luminance values of the point to be rendered and the source images on its two sides, obtain the difference between the maximum and minimum luminance values of these three points, and then compare the luminance value difference with the preset gradient-based edge threshold, and select the filtering coefficient corresponding to the gradient to perform filtering calculation on the B and G components of the source image.

[0046] Preferably, referring to Figure 4 , the pixels lacking sub-pixel information share the missing pixel information with adjacent pixels, including: Pixels containing only RG sub-pixel information are adjacent to pixels containing only BG sub-pixel information; Because if there are three adjacent pixels containing only RG pixel information or only BG pixel information continuously, then the pixel containing only RG pixel information or only BG pixel information in the middle cannot obtain the missing color component information from the adjacent pixels. Therefore, in the embodiments of the present invention, the pixels containing only RG pixel information are adjacent to the pixels containing only BG pixel information, which is convenient for each pixel to supplement the missing color component information through the adjacent pixel.

[0047] 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.

[0048] Preferably, after obtaining the RGB image to be rendered, it further includes segmenting the RGB image to be rendered, specifically: Remove the noise in the RGB image; The main function of image denoising is to improve the image quality and make it more suitable for subsequent computer vision processing and analysis. Image denoising technology can significantly enhance the clarity and detail performance of the image by removing the noise in the image, thereby improving the overall visual effect of the image. Image denoising refers to the process of reducing the noise in digital images. The noise may come from various factors, such as light interference, sensor noise, digital conversion noise, etc. These noises will reduce the quality of the image and affect subsequent computer vision processing and analysis.

[0049] Design a loss function to optimize the convergence speed and denoising effect, expressed as: ; ; ; where x is the denoised image and y is the image before denoising, is the regularization term constant, which controls the gradient stability in the loss function and is set to 0.001, is the Laplacian operator.

[0050] Calculate the grayscale of the denoised RGB image; The main functions of image grayscale conversion include simplifying the image matrix, improving the calculation efficiency, reducing the data dimension, simplifying the image processing algorithm, and providing better contrast and brightness information, making the image features easier to analyze and identify. Since the images captured by cameras are generally above 256 colors, in order to better extract the targets in the image, ignore the background interference, and highlight the content of the target area, the maximum between-class variance method is selected to preprocess the target image. This method is based on the grayscale histogram of the image and takes the maximum between-class variance of the target and the background as the threshold selection criterion. Its basic calculation method is: assume that the image is composed of the target and the background. Since the target area generally has different grayscale values from the background, based on the histogram statistics, the grayscale levels of the image are selected at the levels between the levels to divide the image into two categories: the target and the background. If the between-class variance of the two categories is the largest, then it is the optimal segmentation threshold.

[0051] Calculate the edge gradient direction; The role of the image gradient direction is to indicate the direction in which the gray level changes fastest in the image. In image processing, the direction of the gradient is the direction in which the function changes fastest, and it is easy to find the maximum value along the gradient direction. The image gradient refers to the rate of change between the gray value of each pixel point in the image and the gray values of its surrounding pixel points. It can be used to detect edge and texture information in the image. In actual calculations, differences are usually used to approximate derivatives. Especially for digital images, the gradient value can be calculated by convolving with a small area template.

[0052] Perform canny edge extraction on the segmented target binary image to obtain the target edge with a single pixel width, and then obtain the contour of the target area. To ensure accuracy, each pixel in the binary image needs to be regarded as a rectangular area. In this way, each edge line must be associated with two rectangular areas. If the colors of the two areas are the same, the attribute of this edge is recorded as 0, otherwise the attribute of this edge is recorded as 1. The point sequence composed of these pixels constitutes the contour.

[0053] Traverse the image to find edge pixel points.

[0054] Edge pixel points are important features in image processing and image analysis. They are usually located on the boundaries between different regions in the image. The gray value or brightness of edge pixel points changes significantly in the local area. Edge pixel points refer to the pixel points where the brightness or gray value changes drastically in the image. These pixel points are usually located between the target and the background, between targets, between the target and its shadow, etc. The existence of edge pixel points is caused by factors such as discontinuous surface direction, discontinuous depth, change of object attributes, or change of scene illumination in the image.

[0055] Preferably, calculating the edge gradient direction includes: Use the dilation and erosion algorithm to refine the edge information.

[0056] The implementation steps of the dilation and erosion algorithm are as follows: Select the structuring element: According to the characteristics of the target image to be processed, select a suitable structuring element (also called a kernel or convolution template).

[0057] 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 value of the pixel points in the area covered by the structuring element.

[0058] 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 value of the pixel points in the area covered by the structuring element.

[0059] Result processing: According to needs, the results of dilation and erosion operations can be combined, such as opening (erosion followed by dilation) and closing (dilation followed by erosion), to achieve better processing effects.

[0060] Define dilation and erosion operators: ; ; where f is the source RGB image and S is the structuring element.

[0061] The structuring element is a key point in morphological image processing. Generally speaking, the size, shape, and directionality of the structuring element will affect the edge detection effect of the image. Small-sized structuring elements have weak noise removal ability but can detect good edge details. Large-sized structuring elements have strong noise removal ability but the detected edges are thicker. If a square structuring element is selected, the resulting edge is a strongly connected boundary, that is, the boundary is continuous. If a non-square structuring element is used, the edge is weakly connected. When specifically implementing edge detection of an image, an appropriate structuring element should be selected according to the texture characteristics of the image. Generally, without special requirements, a 3×3 square structuring element is often chosen. Since only a simple dilation and erosion-based edge detection method is used in the algorithm and continuous edges need to be obtained, a 3×3 square structuring element is selected.

[0062] The dilation and erosion algorithm is a morphological image processing method mainly used for morphological operations in image processing. Dilation and erosion are two basic morphological operations. Dilation: The dilation operation expands the highlighted areas or white parts in the image, making the target image as a whole enlarged. The operator of dilation is "⊕". By performing convolution calculation between template B and image A, scanning each pixel point in the image, doing an "AND" operation between the template element and the binary image element. If all are 0, the target pixel point is 0, otherwise it is 1, so as to calculate the maximum value of the pixel points in the area covered by B and replace the pixel value of the reference point with this value. The dilation operation can be used to expand the target area and is often used to fill small holes in the image or connect adjacent target objects.

[0063] Erosion: The erosion operation reduces and refines the highlighted areas or white parts in the image, making the target image as a whole shrunk. The operator of erosion is "-". By performing convolution calculation between template B and image A, obtaining the minimum value of the pixel points in the area covered by B and replacing the pixel value of the reference point with this minimum value. The erosion operation can eliminate noise points in the image and at the same time shrink the boundary value of the target image. The dilation and erosion operations can be used for image segmentation, by repeatedly applying the dilation and erosion operations to extract specific shapes or structures in the image.

[0064] Example 2 An RGBG-type display panel sub-pixel rendering system based on edge judgment, comprising: An image acquisition module for acquiring an RGB image to be rendered; A pixel conversion module for converting the RGB pixel information of the RGB image into RG pixel information or BG pixel information; The pixel processing module is used for pixels lacking sub-pixel information to share the missing pixel information with adjacent pixels.

[0065] Example 3 An electronic device, comprising: a chip, a processor, and a memory, the memory is used for storing computer program code, the computer program code includes computer instructions, and when the chip executes the computer instructions, the electronic device executes an RGBG-type display panel sub-pixel rendering method based on edge judgment.

[0066] Reference 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, the memory 22, the input device 23, and the output device 24 are coupled through a connector, and the connector includes various interfaces, transmission lines, or buses, etc., which are not limited in the embodiments of the present invention. It should be understood that in various embodiments of the present invention, coupling means being interconnected in a specific manner, including being directly connected or indirectly connected through other devices, for example, being connected through various interfaces, transmission lines, buses, etc.

[0067] The processor 21 may be one or more graphics processing units (GPUs). When the processor 21 is a single GPU, the GPU may be a single-core GPU or a multi-core GPU. Optionally, the processor 21 may be a processor group composed of multiple GPUs, and the multiple processors are coupled to each other through one or more buses. Optionally, the processor may also be other types of processors, etc., which are not limited in the embodiments of the present invention.

[0068] The memory 22 can be used to store computer program instructions and various computer program codes including the program codes for executing the solution of the present invention. Optionally, the memory includes but is not limited to a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), or a compact disc read-only memory (CD-ROM), and this memory is used for relevant instructions and data.

[0069] The input device 23 is used to input data and / or signals, and the output device 24 is used to output data and / or signals. The output device 24 and the input device 23 can be independent devices or an integral device.

[0070] The present invention not only realizes the conversion of the RGB pixel information of the source image into the RGBG pixel information required by the RGBG type display panel, but also can effectively perform corresponding filtering calculations on the G, R or B sub-pixel components at the edge position through gradient-based edge judgment, so as to avoid edge blurring while suppressing false colors; in addition, this algorithm does not require a frame buffer or a line buffer in the implementation of the hardware circuit, which can greatly simplify the hardware circuit design and reduce costs.

[0071] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A sub-pixel rendering method for an RGBG display panel based on edge judgment, characterized in that: include: Get the RGB image to be rendered; Converting the RGB pixel information of the RGB image into RG pixel information or BG pixel information; A pixel with missing sub-pixel information shares the missing pixel information with adjacent pixels.

2. The RGBG display panel sub-pixel rendering method based on edge judgment according to claim 1, characterized in that: Converting the RGB pixel information of the RGB image into RG pixel information includes: Calculate the brightness value of the point to be rendered and the source image on both sides of the point to be rendered, and calculate the difference between the maximum and minimum brightness values ​​of the three points; According to the comparison result of the preset gradient edge threshold and the brightness value difference, the filter coefficient corresponding to the gradient is selected to filter the R component of the source image: ； ； ； ； in: th2_condition: ; th1_condition: ; th0_condition: ; other_condition: ; Need to meet , the sum of each group of coefficients is 1.

3. The RGBG display panel sub-pixel rendering method based on edge judgment according to claim 1, characterized in that: Converting the RGB pixel information of the RGB image into RG pixel information includes: Calculate the brightness value of the point to be rendered and the source image on both sides of the point to be rendered, and calculate the difference between the maximum and minimum brightness values ​​of the three points; According to the comparison result of the preset gradient edge threshold and the brightness value difference, the filter coefficient corresponding to the gradient is selected to filter the G component of the source image: ； ； ； ； in: th2_condition: ; th1_condition: ; th0_condition: ; other_condition: ; Need to meet , the sum of each group of coefficients is 1.

4. The RGBG display panel sub-pixel rendering method based on edge judgment according to claim 1, characterized in that: Converting the RGB pixel information of the RGB image into BG pixel information includes: Calculate the brightness value of the point to be rendered and the source image on both sides of the point to be rendered, and calculate the difference between the maximum and minimum brightness values ​​of the three points; According to the comparison result of the preset gradient edge threshold and the brightness value difference, the filter coefficient corresponding to the gradient is selected to filter the B component of the source image: ； ； ； ； in: th2_condition: ; th1_condition: ; th0_condition: ; other_condition: ; Need to meet , the sum of each group of coefficients is 1.

5. The method for sub-pixel rendering of an RGBG display panel based on edge judgment according to claim 1, characterized in that: Converting the RGB pixel information of the RGB image into BG pixel information includes: Calculate the brightness value of the point to be rendered and the source image on both sides of the point to be rendered, and calculate the difference between the maximum and minimum brightness values ​​of the three points; According to the comparison result of the preset gradient edge threshold and the brightness value difference, the filter coefficient corresponding to the gradient is selected to filter the G component of the source image: ； ； ； ； in: th2_condition: ; th1_condition: ; th0_condition: ; other_condition: ; Need to meet , the sum of each group of coefficients is 1.

6. The RGBG display panel sub-pixel rendering method based on edge judgment according to claim 1, characterized in that: The pixel missing sub-pixel information and the adjacent pixel sharing the missing pixel information include: 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.

7. The RGBG display panel sub-pixel rendering method based on edge judgment according to claim 1, characterized in that: After obtaining the RGB image to be rendered, the step further includes segmenting the RGB image to be rendered, specifically: removing noise from the RGB image; Calculate the grayscale of the denoised RGB image; Calculate edge gradient direction; Traverse the image to find edge pixels.

8. The RGBG display panel sub-pixel rendering method based on edge judgment according to claim 1, characterized in that: The calculating edge gradient direction comprises: The dilation-erosion algorithm is used to refine the edge information.

9. A sub-pixel rendering system for an RGBG display panel based on edge judgment, characterized in that: include: An image acquisition module, used to acquire an RGB image to be rendered; A pixel conversion module, used for converting the RGB pixel information of the RGB image into RG pixel information or BG pixel information; The pixel processing module is used for pixels with missing sub-pixel information to share the missing pixel information with adjacent pixels.

10. An electronic device, characterized in that: include: A chip, a processor and a memory, wherein the memory is used to store computer program codes, wherein the computer program codes include computer instructions. When the chip executes the computer instructions, the electronic device executes a sub-pixel rendering method for an RGBG display panel based on edge judgment as described in any one of claims 1 to 8.

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