Image enhancement processing and display method, device and MicroLED display

Abstract

The embodiment of the present invention discloses an image enhancement processing and display method, device and MicroLED display, which are applied to the field of MicroLED display technology. Among them, the image enhancement method includes: obtaining an image to be displayed; determining a sampling point pixel, the sampling point pixel is the current pixel of the image to be displayed when it is displayed; generating a second-order sampling matrix according to the sampling point pixel; determining whether there is a boundary between two elements in the second-order sampling matrix; when it is determined that there is a boundary, enhancing the pixels at the boundary. By using a second-order sampling matrix to perform image enhancement processing on the image to be displayed on the MicroLED display, not only can the effect of the MicroLED display image be improved, but also the resource overhead in the image processing is small, which is very suitable for the MicroLED display to perform image display processing.

Description

Technical Field

[0001] The present invention relates to the field of MicroLED display technology, and in particular to an image enhancement processing and display method and device for a MicroLED display, and a MicroLED display. Background Art

[0002] MicroLED (micro light-emitting diode) displays have the advantages of self-luminescence, wide color gamut, high brightness and stable operation, and are an important technology for future display applications.

[0003] For the display technology of MicroLED displays in existing solutions, the initial input image must be mapped once to achieve the high image resolution effect of the panel. This mapping method is also called sub-pixel rendering. Through mapping, the information of the initial image can be displayed relatively completely on the panel, but the result of the mapping is a lossy compression of the initial image. Therefore, during the mapping process, there will be a certain degree of loss in the image data information. This loss is inevitable in the image mapping process. More importantly, mapping will cause the pixel value of each pixel to be lost proportionally. Due to the high brightness of MicroLED display devices, compared with TFT-LCD display devices, the image display effect deteriorates more seriously due to the proportional loss of pixel values.

[0004] Generally speaking, the amount of information brought to the observer by each part of the image is unequal. The amount of information of the background image is often less than that of the foreground image. The boundaries in the image where the pixel values ​​jump dramatically are usually the key parts for the observer to obtain information, especially at the boundaries of artificially created images such as text and lines. When the image boundaries become blurred after mapping, the information loss provided by the panel display becomes unacceptable, which is intuitively reflected in the serious distortion of the panel display. Summary of the invention

[0005] In view of this, the embodiments of the present specification provide an image enhancement processing and display method, device and MicroLED display for a MicroLED display to avoid boundary blur caused during the mapping process.

[0006] The embodiments of this specification adopt the following technical solutions:

[0007] The embodiment of this specification provides an image enhancement processing method, including:

[0008] Get the image to be displayed;

[0009] Determine a sampling point pixel, wherein the sampling point pixel is a current pixel of the image to be displayed when it is displayed;

[0010] Generate a second-order sampling matrix based on the sampling point pixels;

[0011] Determine whether there is a boundary between two elements in the second-order sampling matrix;

[0012] When it is determined that there is a boundary, the pixels at the boundary are enhanced.

[0013] The embodiment of this specification provides an image enhancement processing device, including:

[0014] An image input module, which obtains the image to be displayed;

[0015] A sampling module determines a sampling point pixel, wherein the sampling point pixel is a current pixel of the image to be displayed when it is displayed;

[0016] A generation module generates a second-order sampling matrix according to the sampling point pixels;

[0017] A determination module determines whether there is a boundary between two elements in the second-order sampling matrix;

[0018] The enhancement module performs enhancement processing on pixels at the boundary when it is determined that there is a boundary.

[0019] The embodiment of this specification provides an image enhancement display method, which is applied to a MicroLED display. The image enhancement display method includes:

[0020] Preprocessing the image to be displayed to obtain a new image to be displayed for display on the MicroLED display, wherein the preprocessing includes processing the image to be displayed based on the image enhancement processing method according to any one of claims 1 to 10;

[0021] Displaying the new image to be displayed on the MicroLED display.

[0022] The embodiment of this specification provides an image enhancement display device, which is applied to a MicroLED display. The image enhancement display device includes:

[0023] A preprocessing module, which preprocesses the image to be displayed to obtain a new image to be displayed for display on the MicroLED display, wherein the preprocessing includes processing the image to be displayed based on the image enhancement processing method according to any one of claims 1 to 10;

[0024] The display driving module displays the new image to be displayed on the MicroLED display.

[0025] The embodiment of this specification provides a MicroLED display, including:

[0026] MicroLED arrays;

[0027] A processor, the processor being configured to:

[0028] Execute the image enhancement processing method as described in any one of claims 1 to 10 to convert each pixel of the image to be displayed into a target pixel;

[0029] The pixel value of the target pixel is output to the input end of the MicroLED array, so as to display the content of the image to be displayed through the MicroLED array.

[0030] Compared with the prior art, the beneficial effects that can be achieved by at least one of the above technical solutions adopted in the embodiments of this specification include at least:

[0031] By determining whether there is a boundary in the sampling matrix through the associated data of the pixels at the sampling points, the image can be enhanced when the pixels at the boundary are displayed on the MicroLED display panel, thereby retaining key image information and reducing image distortion. This can effectively avoid image boundary blurring due to image remapping and improve the display effect. At the same time, the boundary judgment is achieved through the second-order sampling matrix, which uses less associated data, consumes less corresponding hardware resources, and has less resource overhead for computing and processing, thereby improving processing efficiency, reducing the hardware performance requirements for the display, and adapting to MicroLED display under different hardware conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0033] Figure 1 A schematic diagram of a structure of an image enhancement processing solution for a MicroLED display provided in an embodiment of this specification

[0034] Figure 2 A flowchart of an image enhancement processing method applied to a MicroLED display provided in an embodiment of this specification;

[0035] Figure 3 A schematic diagram of the structure of a second-order sampling matrix in an image enhancement processing method for a MicroLED display provided in an embodiment of this specification;

[0036] Figure 4 A schematic diagram of a Gamma curve in an image enhancement processing method applied to a MicroLED display provided in an embodiment of this specification;

[0037] Figure 5Schematic diagram of different segmentation schemes in an image enhancement processing method for a MicroLED display provided in an embodiment of this specification

[0038] Figure 6 A schematic diagram of processing a front-to-back correlated second-order sampling matrix in an image enhancement processing method for a MicroLED display provided in an embodiment of this specification;

[0039] Figure 7 A flowchart of an image enhancement processing method applied to a MicroLED display provided in an embodiment of this specification;

[0040] Figure 8 A schematic diagram of the structure of an image enhancement processing device applied to a MicroLED display provided in an embodiment of this specification;

[0041] Fig. 9 A flowchart of an image enhancement display method applied to a MicroLED display provided in an embodiment of this specification;

[0042] Fig.10 A schematic diagram of the structure of an image enhancement display device applied to a MicroLED display provided in an embodiment of this specification;

[0043] Fig.11 A schematic diagram of the structure of a MicroLED display provided in an embodiment of this specification. DETAILED DESCRIPTION

[0044] The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0045] The following describes the implementation methods of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The present application can also be implemented or applied through other different specific implementation methods, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, in the absence of conflict, the following embodiments and the features in the embodiments can be combined with each other. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work belong to the scope of protection of the present application.

[0046] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present application, it should be understood by those skilled in the art that an aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number and aspect described herein can be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this device and / or practice this method.

[0047] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application. The drawings only show components related to the present application rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0048] In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the aspects described may be practiced without these specific details. The terms "first", "second", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features described by "first", "second", etc. may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise stated, "multiple" means two or more.

[0049] In existing MicroLED display solutions, the resolution of the initial input image is first remapped to adapt to the display resolution of the MicroLED display panel. That is, when the MicroLED display panel displays the image, an image processing algorithm is used to map the original input image into a new image. However, the image processing algorithm used (such as three-order sliding window processing) is not only complex in algorithm and resource overhead, but also requires hardware with high performance, which restricts the promotion and application of MicroLED displays in different application requirements.

[0050] In addition, lossy compression is performed during mapping, resulting in image distortion, such as proportional distortion of pixels. Such distortion significantly deteriorates the image display effect in the MicroLED display panel. For example, compared with TFT-LCD display devices, the display screen of the MicroLED display panel is seriously distorted, which gives users a poor intuitive experience and affects the application and promotion of MicroLED displays.

[0051] In view of this, after in-depth research and improvement on MicroLED displays and their display solutions, a processing solution for image enhancement in MicroLED displays with low resource overhead and flexible application to different hardware performance is proposed: Figure 1 As shown, for the original input image to be displayed (which can be recorded as the image to be displayed), a second-order sampling matrix is ​​used to perform sliding window sampling processing on each pixel of the image, that is, in the sliding window processing, a corresponding second-order sampling matrix is ​​formed for the current sampling point pixel, and image data processing is performed on each pair of elements in the second-order sampling matrix. According to the processing result, it is determined whether there is a boundary between each pair of elements in the second-order sampling matrix, that is, it is determined whether the current sampling point pixel may be located at the boundary. When it is determined that the current sampling point pixel belongs to the image pixel at the boundary, the boundary pixel is subjected to image enhancement processing and output, and the boundary pixel can be remapped to an enhanced pixel value that is more in line with the display of the MicroLED display panel.

[0052] By adopting a second-order sampling matrix to process image sampling and performing pairwise calculation processing on the elements of the second-order sampling matrix to determine the pixels at the boundary that need to be enhanced, not only can the key information of the image be retained through the enhancement processing and image distortion be reduced, but also the blurring of the image boundary caused by image remapping can be effectively avoided and the display effect can be improved; moreover, the image remapping process has low resource overhead, a small number of hardware required, convenient calculation, high processing efficiency, and low hardware performance requirements, which makes it convenient for images to be displayed in MicroLED display panels under different hardware conditions, and is conducive to the promotion and application of MicroLED display panels in environments with different hardware performance.

[0053] Hereinafter, the image enhancement processing method and device of this specification will be described in detail with reference to the accompanying drawings.

[0054] Figure 2 An image enhancement processing method applied to a MicroLED display is provided in an embodiment of this specification.

[0055] like Figure 2 As shown, the image enhancement processing method provided in the embodiments of this specification may include:

[0056] Step S201, obtaining an image to be displayed.

[0057] In implementation, the image to be displayed may be an original input image to be displayed on the MicroLED display panel, that is, an original input image that needs to be pixel-remapped.

[0058] It should be noted that the image to be displayed may be a grayscale image, an RGB image, etc.

[0059] Step S203 : determining a sampling point pixel, wherein the sampling point pixel may be a current pixel of the image to be displayed when being displayed.

[0060] In the remapping process of each pixel of the original image, the pixel that needs to be mapped can be used as the sampling point pixel. For example, in a progressive scan display, the first pixel in the upper left corner of the image can usually be used as the first sampling point pixel; for example, in a sliding window process, the first pixel of the sliding window can be used as the sampling point pixel.

[0061] Step S205: Generate a second-order sampling matrix according to the sampling point pixels.

[0062] like Figure 3 As shown, a second-order sampling matrix can be created. The second-order sampling matrix may include 4 matrix elements, that is, the elements of the second-order sampling matrix can be marked as matrix element (1, 1), matrix element (1, 2), matrix element (2, 1) and matrix element (2, 2) from left to right and from top to bottom, respectively, and in the created second-order sampling matrix, the values ​​corresponding to each element of the second-order sampling matrix are generated according to the sampling point pixels.

[0063] For example, the pixel value of the sampling point pixel is input into the first matrix element, and the pixel values ​​of the corresponding pixels to the right, below and lower right of the sampling point pixel value are respectively input into the matrix element (1, 2), the matrix element (2, 1) and the matrix element (2, 2), to complete the data input of the second-order sampling matrix, thereby constructing the second-order sampling matrix corresponding to the sampling point pixel, which is convenient for subsequent image data processing according to the second-order sampling matrix.

[0064] In implementation, the pixel value may be the actual pixel value corresponding to the pixel in the original image, or may be the pixel value corresponding to the actual pixel value after transformation (such as smoothing, averaging, weighting, etc.), which may be determined according to actual application needs.

[0065] Step S207, determining whether there is a boundary between any two elements in the second-order sampling matrix, and when it is determined that there is a boundary, enhancing the pixels at the boundary.

[0066] In implementation, the mapping output may be enhanced for pixels at the boundary, while the mapping output may not be enhanced for pixels at non-boundaries.

[0067] In practice, enhancing pixels at the boundary may refer to selectively highlighting interesting image features in an image and attenuating uninteresting image features according to the needs of practical applications, so as to improve the display effect of the image in the MicroLED display panel, that is, the image enhancement process can be used to retain the key content of the original image when it is displayed in the MicroLED display panel during remapping. Therefore, the image enhancement process here may be a mature image enhancement process solution in the prior art, and is not limited here.

[0068] Through the above steps S201 to S207, that is, the remapping processing scheme for the original image, not only can the key information required for displaying the original image in the MicroLED display panel be retained, the image distortion of the original image in the remapping can be reduced, and the display effect can be improved; moreover, the image remapping process has low computing resource overhead, high processing efficiency, and low hardware performance requirements for image processing, which is conducive to remapping the original image under lower hardware conditions. It can provide a highly flexible and versatile auxiliary solution for MicroLED display panel display processing for application platforms with various hardware conditions, which is conducive to the application and promotion of MicroLED display panels in various scenarios.

[0069] In some embodiments, for example, when the image to be displayed is an RGB image, three second-order sampling matrices may be constructed, each of which is used to load corresponding R (red) sub-pixels, G (green) sub-pixels, and B (blue) sub-pixels.

[0070] In implementation, when the image to be displayed includes an RGB image, generating a second-order sampling matrix based on the sampling point pixels may include: generating three second-order sampling matrices based on the sampling point pixels to generate second-order sampling matrices corresponding to the R sub-pixels, G sub-pixels and B sub-pixels of the sampling point pixels.

[0071] By using the corresponding second-order sampling matrix to process the sub-pixel image data of each sub-pixel in the RGB image, the calculation of the second-order sampling matrix in image processing can be simplified, resource overhead can be reduced, the hardware performance requirements can be lowered, and the adaptability of the image enhancement processing solution in different hardware performance application platforms can be improved, which is more conducive to the promotion of the image enhancement processing solution in different applications.

[0072] In some implementations, whether there is a boundary between any two elements in the second-order sampling matrix may be determined based on a preset threshold.

[0073] In one implementation, a first threshold may be obtained, and then the difference between two elements may be compared with the first threshold, such as by using a comparator to quickly determine whether there is a boundary between two elements.

[0074] In implementation, determining whether there is a boundary between two elements in the second-order sampling matrix may include:

[0075] Determine a first relationship between a difference in pixel values ​​between two adjacent pixels in the second-order sampling matrix and the first threshold, and when the first relationship satisfies a first preset condition, determine that the two adjacent pixels are similar pixels, wherein the first preset condition is used to characterize a condition that the pixel values ​​of the two pixels are similar.

[0076] In implementation, determining the difference in pixel values ​​between two adjacent pixels in the second-order sampling matrix can be accomplished by a subtractor, that is, the input of the subtractor is the pixel values ​​corresponding to the two elements, and the output of the subtractor is their difference. The difference can then be compared with a first threshold, such as by using a comparator to compare the difference with the first threshold to obtain a first relationship, and finally, based on the comparison result of the first relationship, it is determined whether the two elements are similar pixels or non-similar pixels.

[0077] It should be noted that the first preset condition can be set according to specific applications. For example, the first preset condition is set to a range, which can be used to express the numerical range corresponding to the difference values ​​between similar pixels, and is therefore not specifically limited here.

[0078] In one implementation, a second threshold may be obtained, and then the difference between the two elements may be compared with the second threshold, such as by using a comparator to quickly determine whether there is a boundary between the two elements.

[0079] In implementation, determining whether there is a boundary between two elements in the second-order sampling matrix may include:

[0080] Determine a second relationship between the difference in pixel values ​​between two adjacent pixels in the second-order sampling matrix and the second threshold, and when the second relationship satisfies a second preset condition, determine that the two adjacent pixels are boundary pixels, and the second preset condition is used to characterize the condition that the pixel values ​​of the two pixels jump.

[0081] It should be noted that the first threshold for determining the adjacent pixels and the second threshold for determining the pixels at the boundary are different thresholds, and the two different thresholds can be used to quickly determine whether there is a boundary between any two elements in the second-order sampling matrix. In addition, the determination process using the second threshold is similar to the determination process using the first threshold, and will not be expanded here.

[0082] In one implementation, the first threshold may be combined with the second threshold to determine whether there is a boundary between any two elements in the second-order sampling matrix.

[0083] For example, the first matrix element (1, 1) and the second matrix element (1, 2) in the second-order sampling matrix are determined to be close pixels when the first threshold is determined, and the first matrix element (1, 1) and the third matrix element (2, 1) are determined to be pixels at the boundary when the second threshold is determined. Therefore, it can be determined that there is a boundary between the first matrix element (1, 1) and the third matrix element (2, 1), and there is no boundary between the first matrix element (1, 1) and the second matrix element (1, 2).

[0084] In some embodiments, considering that in the visual perception of human eyes, the change between the pixel value "255" and the pixel value "254" is different from the change between the pixel value "32" and the pixel value "31", the preset threshold value can be an adaptive threshold value set for the input value according to the gamma curve of the MicroLED display, that is, the threshold value can be determined according to the gamma curve, wherein the gamma curve can be as follows: Figure 4 shown.

[0085] In one embodiment, obtaining the first threshold may include: determining the first threshold according to a gamma curve of the MicroLED display;

[0086] In one implementation, obtaining the second threshold may include: determining the second threshold according to a gamma curve of the MicroLED display.

[0087] like Figure 4 As shown, the value of the threshold value changes according to the input pixel value, and the way it changes is determined according to the Gamma curve of the MicroLED display panel. The horizontal axis in the figure is the pixel value of the input pixel (normalized value), and the vertical axis is the pixel value re-determined according to the Gamma curve (normalized value). Therefore, the aforementioned first threshold value and / or second threshold value can be determined according to the Gamma curve of the MicroLED display shown in the figure, that is, the threshold value can be adaptively adjusted according to the input pixel.

[0088] In implementation, the first-order derivative of the Gamma curve can be calculated, and the function value corresponding to the first-order derivative of the Gamma curve corresponding to the average value of the pixel value of the input image can be calculated, and the function value multiplied by the threshold value L is used as the first proportional coefficient, and the function value multiplied by the threshold value K is used as the second proportional coefficient, wherein the values ​​of the thresholds L and K can be preset, such as values ​​determined by experiments, values ​​determined according to empirical values, and values ​​set to initial values ​​(such as non-zero values), etc., and are not limited here.

[0089] In implementation, the first threshold value may be obtained by multiplying the average value of the pixel values ​​of the input image by the first proportionality coefficient, and the second threshold value may be obtained by multiplying the average value of the pixel values ​​of the input image by the second proportionality coefficient.

[0090] By determining the threshold value according to the Gamma curve function, the obtained threshold value is made more consistent with the physical characteristics of the MicroLED display, thereby improving the display effect.

[0091] It should be noted that the figure uses the Gamma curve corresponding to the RGB three colors as an example for schematic illustration. If it is other color modes, the corresponding Gamma curve can be used to determine the threshold.

[0092] After correction by the Gamma curve, although the input pixel value of the image to be displayed is not proportional to the output pixel value signal, there may be a distortion. The Gamma curve is the measurement parameter of this distortion when it is displayed in the actual display panel, and the threshold can change with the change of pixel value. Therefore, a better image output effect can be determined after using the Gamma curve.

[0093] In some implementations, by performing segmentation calculations in different directions on the second-order sampling matrix, the difference between two elements in different segmentation directions can be obtained, and the difference can be used to determine whether a boundary exists.

[0094] In implementation, when determining whether there is a boundary between two elements in the second-order sampling matrix, it may include:

[0095] Perform at least one of the following segmentation processing on the second-order sampling matrix: horizontal segmentation, vertical segmentation, oblique segmentation, and single-point segmentation;

[0096] Calculate the difference between the opposite parts after segmentation, where the opposite parts can be elements on both sides of the segmentation direction;

[0097] It is determined whether there is a boundary between two elements of the opposing parts according to the difference.

[0098] like Figure 5 As shown in Figure a, we can first assume that there is a boundary between the upper and lower rows of elements in the second-order sampling matrix, and then divide the second-order sampling matrix horizontally. After the assumed division, the difference between the elements of the upper and lower rows in the horizontal and vertical directions is calculated, and then determine whether there is a boundary in the horizontal division based on the difference.

[0099] For example, when the first matrix element (1, 1) and the second matrix element (1, 2) in the second-order sampling matrix are determined to be close pixels, and the third matrix element (2, 1) and the fourth matrix element (2, 2) are also determined to be close pixels, and the first matrix element (1, 1) and the third matrix element (2, 1) are determined to be pixels at the boundary, and the second matrix element (1, 2) and the fourth matrix element (2, 2) are determined to be pixels at the boundary, it can be determined that the second-order sampling matrix has a segmentation boundary in the horizontal direction.

[0100] like Figure 5 As shown in Figure b, we can first assume that there is a boundary between the elements in the left and right columns of the second-order sampling matrix, and then perform vertical (vertical can also be called longitudinal, no distinction is made below) segmentation on the second-order sampling matrix, and after the assumed segmentation, calculate the difference between the elements of the left and right columns in the horizontal and vertical directions, and then determine whether there is a boundary in the vertical segmentation based on the difference.

[0101] For example, when the first matrix element (1, 1) and the third matrix element (2, 1) in the second-order sampling matrix are determined to be close pixels, and the second matrix element (1, 2) and the fourth matrix element (2, 2) are also determined to be close pixels, and the first matrix element (1, 1) and the second matrix element (1, 2) are determined to be pixels at the boundary, and the third matrix element (2, 1) and the fourth matrix element (2, 2) are determined to be pixels at the boundary, it can be determined that the second-order sampling matrix has a segmentation boundary in the vertical direction.

[0102] like Figure 5 As shown in Figure c, we can first assume that there is a boundary between every two elements in the right oblique direction (i.e., the oblique direction from the upper right to the lower left) in the second-order sampling matrix, and then perform horizontal segmentation on the second-order sampling matrix. After the assumed segmentation, calculate the difference between the upper and lower elements in the oblique direction, the horizontal direction, and the vertical direction, and then determine whether there is a boundary in the horizontal segmentation based on the difference.

[0103] For example, when the second matrix element (1, 2) and the third matrix element (2, 1) in the second-order sampling matrix are determined to be adjacent pixels, and the second matrix element (1, 2) (or the third matrix element (2, 1)) and the first matrix element (1, 1) and the fourth matrix element (2, 2) are determined to be pixels at the boundary, it can be determined that the second-order sampling matrix has a segmentation boundary in this oblique segmentation.

[0104] like Figure 5As shown in Figure d, we can first assume that there is a boundary between every two elements in the left oblique direction (i.e., the oblique direction from the upper left to the lower right) in the second-order sampling matrix, and then segment the second-order sampling matrix in the left oblique direction. After the assumed segmentation, the difference between the upper and lower elements in the oblique direction, the horizontal direction, and the vertical direction is calculated, and then determine whether there is a boundary in the horizontal segmentation based on the difference.

[0105] For example, when the first matrix element (1, 1) and the fourth matrix element (2, 2) in the second-order sampling matrix are determined to be adjacent pixels, and the first matrix element (1, 1) (or the fourth matrix element (2, 2)) and the second matrix element (1, 2) and the third matrix element (2, 1) are determined to be boundary pixels, it can be determined that the second-order sampling matrix has a segmentation boundary in this oblique segmentation.

[0106] like Figure 5 As shown in Figures e to h, we can first assume that there are boundaries around each element in the second-order sampling matrix, and then perform single-point segmentation on the second-order sampling matrix. After the assumed segmentation, the difference between the element in the single-point segmentation and its surrounding elements is calculated, and then determine whether there is a boundary in the single-point segmentation direction based on the difference.

[0107] For example, in Figure 5 In Figure e, when the first matrix element (1, 1) and the second matrix element (1, 2), the third matrix element (2, 1), and the fourth matrix element (2, 2) in the second-order sampling matrix are all determined as boundary pixels, it can be determined that the second-order sampling matrix has a segmentation boundary on this single-point segmentation.

[0108] For example, in Figure 5 In the f figure, when the second matrix element (1, 2) in the second-order sampling matrix and the first matrix element (1, 1), the third matrix element (2, 1), and the fourth matrix element (2, 2) are all determined as boundary pixels, it can be determined that the second-order sampling matrix has a segmentation boundary on this single-point segmentation.

[0109] For example, in Figure 5 In the g figure, when the third matrix element (2, 1) in the second-order sampling matrix and the first matrix element (1, 1), the second matrix element (1, 2), and the fourth matrix element (2, 2) are all determined as boundary pixels, it can be determined that the second-order sampling matrix has a segmentation boundary on this single-point segmentation.

[0110] For example, in Figure 5In the h figure, when the fourth matrix element (2, 2) in the second-order sampling matrix and the first matrix element (1, 1), the second matrix element (1, 2), and the third matrix element (2, 1) are all determined as boundary pixels, it can be determined that the second-order sampling matrix has a segmentation boundary on this single-point segmentation.

[0111] In some embodiments, whether there is a boundary between two elements may be determined by comparing the absolute value of the difference between the two elements with a preset threshold.

[0112] During implementation, after calculating the difference between two elements, the absolute value of the difference can be taken, so that the threshold can be a non-negative number. Therefore, in the comparison operation for determining whether a boundary exists, the judgment process of the comparison calculation is simple and the resource overhead is small, which is conducive to applying the image enhancement processing solution to MicroLED display application environments with different hardware performance.

[0113] In some implementations, after the determination is completed, the determination results in each segmentation mode are outputted so that subsequent processing can use the segmentation results for rapid processing.

[0114] In implementation, when the second-order sampling matrix is ​​processed by the segmentation mode, the second-order sampling matrix can be judged after segmentation, and the judgment result can be output, that is, the judgment result corresponding to each segmentation process is output, wherein the judgment result is the judgment result corresponding to the aforementioned segmentation process, and the judgment result is the judgment result of whether there is a boundary between two elements of the opposing parts in the second-order sampling matrix. For example, in horizontal segmentation, it is determined that there is a horizontal segmentation line, and the segmentation result can be a horizontal line judgment result. Correspondingly, vertical segmentation can correspond to a vertical line judgment result, oblique segmentation can correspond to an oblique line judgment result, and single-point segmentation can correspond to a single-point judgment result, etc.

[0115] In some embodiments, after determining whether there are boundaries between any two elements in a second-order sampling matrix, the determination result of the second-order sampling matrix can be stored so that the determination result can be used as a threshold parameter corresponding to the next second-order sampling matrix. This threshold parameter is a priori parameter for determining whether there are boundaries between any two elements in the second-order sampling matrix.

[0116] By storing the judgment result of the previous second-order sampling matrix, the judgment result can be used as a priori parameter for the judgment processing of the next second-order sampling matrix. The correlation between the current second-order sampling matrix and the previous second-order sampling matrix can be determined through a small amount of calculation based on the prior parameter. The possible segmentation direction in the current second-order sampling matrix can be quickly determined based on the correlation, which can provide a more accurate judgment mode while reducing the calculation overhead.

[0117] For example, the aforementioned Figure 5As shown in Figure a, the second-order sampling matrix will perform horizontal line segmentation and then perform horizontal line judgment. If it is determined that there is a boundary, the judgment result is a horizontal line, and the judgment value at the horizontal line (such as the pixels on both sides of the horizontal line) can be stored as a priori parameters of the next sampling matrix; in the second-order sampling matrix in Figure b, if it is determined that there is a vertical boundary after longitudinal segmentation, the judgment result is a vertical line, and the judgment value of the vertical line can be stored as a priori parameters of the next sampling matrix; in Figures c and d, if it is determined that there is a diagonal boundary in the sampling matrix, the pixels at the boundary where the judgment result is the diagonal line can be stored; similarly, in Figures e, f, g and h, the boundary of the sampling matrix at a single point is determined according to the stored single-point judgment, and the corresponding judgment value is output, and the output judgment result is a single-point boundary judgment value.

[0118] By storing the judgment results, the relationship between the second-order sampling matrix and the boundary can be recorded. Then, in the judgment processing of the next second-order sampling matrix, the stored results can be used to quickly determine whether the current second-order sampling matrix has a correlation with other matrices, so as to perform subsequent rapid processing based on the correlation.

[0119] In some implementations, boundary determination processing may be performed based on the correlation between second-order sampling matrices.

[0120] In implementation, determining whether there is a boundary between two elements in the second-order sampling matrix may include:

[0121] Determine whether the current second-order sampling matrix is ​​associated with a previous second-order sampling matrix;

[0122] When there is an association, it is determined whether there is a boundary between any two elements in the second-order sampling matrix according to a preset association strategy.

[0123] It should be noted that the association strategy can be a strategy for processing according to the association between matrices. For example, when the two matrices before and after belong to the association matrix, it can be quickly determined that the current matrix also has a similar determination result based on the determination result of the previous matrix. Therefore, the association strategy can be set according to the actual application scenario and is not limited here.

[0124] like Figure 6 As shown in , when there is an oblique boundary in the image, the two adjacent second-order sampling matrices are usually correlation matrices, so the correlation can be used for rapid determination in the boundary determination. Figure 6 Figure a in the figure is the second-order sampling matrix and the corresponding judgment result output when the first pixel in the first row is the sampling point pixel. Figure 6 Figure b in the figure is the second-order sampling matrix corresponding to the second pixel in the first row as the sampling point pixel and the corresponding judgment result output. By analogy, the output results corresponding to each matrix can be obtained, which will not be explained one by one here.

[0125] In some embodiments, when it is determined that there is a boundary, the pixels at the boundary can be enhanced by selecting points for output, that is, the enhancement of the pixels at the boundary may include: selecting points for the pixels at the boundary and outputting pixel values, so that the pixels at the boundary meet preset display conditions based on the selected point output pixel values.

[0126] In implementation, when it is determined that there is a boundary between two elements, the pixel value of the current sampling point can be selected and output to ensure that the pixel value jump of the boundary image is obvious, thereby reducing the severe compression effect of mapping on the output image.

[0127] In some embodiments, when it is determined that there is no boundary, the image can be output as a normal image, that is, when it is determined that there is no boundary between two elements, the pixel value of the current sampling point is mapped and output according to the normal image mode, which will not cause serious compression loss to the output image.

[0128] During implementation, pixels at the boundary and pixels at non-boundary locations may be remapped according to their respective corresponding mapping strategies and then output.

[0129] Specifically, when it is determined that the two adjacent pixels are close pixels, the image enhancement processing method further includes: outputting the pixel values ​​of the two adjacent pixels that are close pixels according to a first preset mapping strategy;

[0130] Alternatively, when it is determined that the two adjacent pixels are boundary pixels, the image enhancement processing method further includes: outputting the pixel values ​​of the two adjacent pixels that are boundary pixels according to a second preset mapping strategy;

[0131] The first preset mapping strategy and the second preset mapping strategy are different mapping strategies.

[0132] It should be noted that the first preset mapping strategy and the second preset mapping strategy can be pre-set according to actual application needs, wherein the first preset mapping strategy is used to map adjacent pixels into a mapping strategy that is more suitable for display in the MicroLED display panel during remapping, and the second preset mapping strategy is used to map pixels at the boundary into a mapping strategy that is more suitable for boundary display in the MicroLED display panel during remapping. Through different mapping strategies, the display effect of the remapped image in the MicroLED display panel is more in line with the visual effect of the human eye, thereby improving the display performance of the MicroLED display.

[0133] In some embodiments, Figure 7 As shown, after obtaining the input image (i.e. the image to be displayed, which is an RGB image), the following processing can be performed:

[0134] Step S110, performing matrix sampling on the sampling point pixels, that is, generating a second-order sampling matrix corresponding to the sampling point pixels, such as inputting the pixel values ​​of the other three pixels near the sampling point pixels into the second-order sampling matrix according to the sampling point pixels;

[0135] For example, obtain the initial input RGB image and the corresponding sampling point position, and construct three second-order sampling matrices M R 、M G and M B , corresponding to the R, G, B sub-pixels of the image respectively; specifically, the sampling matrix is ​​shown in 3, each second-order sampling matrix is ​​marked as the first matrix element (1, 1), the second matrix element (1, 2), the third matrix element (2, 1) and the fourth matrix element (2, 2) from left to right and from top to bottom, respectively, and the pixel value is input into the matrix to complete the input of the matrix;

[0136] Step S120, completing input value calculation for the second-order sampling matrix, such as calculating pixel differences between two pixels;

[0137] For example, the sampling points in the matrix are subtracted from each other and their absolute values ​​are taken. There are 6 absolute value differences in the second-order sampling matrix: the difference between element (1, 1) and element (1, 2), the difference between element (1, 1) and element (2, 1), the difference between element (1, 1) and element (2, 2), the difference between element (1, 2) and element (2, 1), the difference between element (1, 2) and element (2, 2), and the difference between element (2, 1) and element (2, 2). The calculation of the input value is completed, and 6 groups of absolute value differences are obtained.

[0138] For example, taking the absolute value of the above difference, 6 sets of absolute value differences are obtained to complete the input value of the second-order sampling matrix;

[0139] Step S130, obtaining the threshold required for boundary determination, such as calculating a new threshold corresponding to the current second-order sampling matrix;

[0140] For example, according to the Gamma curve, an adaptive threshold is set for the input value, and the first threshold L for determining the similar pixels is R , L G and L B and the second threshold K used to determine the boundary R , K G and K B , the threshold value may be changed according to the input pixel value, and the change mode may be determined according to the Gamma curve of the MicroLED display panel;

[0141] Step S140, determining whether there is a boundary according to a threshold value, such as determining the input value according to the threshold value to determine whether there is a boundary;

[0142] For example, according to the set initial threshold, the decision matrix M R 、M G and M B Whether the pixel values ​​of adjacent pixels in the image are similar, whether the pixel transition is smooth or the pixel jump is large, among which:

[0143] When the absolute value difference calculated in the sampling matrix is ​​less than the threshold L R , L G and L B When , it is determined that the adjacent pixel values ​​are similar;

[0144] When the calculated absolute value difference in the sampling matrix is ​​greater than the threshold K R , K G and K B When , it is determined that the region has a boundary;

[0145] Otherwise, the transition of the judgment area is smooth and there is no boundary;

[0146] Step S150: After the determination is completed, the determination result may be stored, such as storing the threshold of the determination boundary as a priori parameter, such as storing the pixels at the boundary as the determination value, etc.;

[0147] In implementation, the stored judgment value is used as a priori parameter of the next sampling matrix to provide more accurate mode judgment;

[0148] In implementation, the matrix can be judged based on the relevance. Figure 5 In Figures a to c, the sampling matrix in Figure a will make a horizontal line judgment. After the sampling matrix in Figure b is vertically segmented, if it is judged to be a vertical line, the judgment value of the vertical line is stored as a priori parameter of the next sampling matrix. In Figures c and d, the sampling matrix is ​​determined to be located at the boundary of the diagonal line according to the stored diagonal line judgment value, and the corresponding judgment value is output, and the output judgment result is the diagonal line boundary judgment value; in Figures e to h, the sampling matrix is ​​determined to be located at the boundary of a single point according to the stored single point judgment, and the corresponding judgment value is output, and the output judgment result is the single point boundary judgment value;

[0149] In implementation, the output determination results may include: horizontal line boundary determination results, vertical line boundary determination results, oblique line boundary determination results, and single point boundary determination results;

[0150] Step S160, performing corresponding output processing according to the determination result, for example, when there is a boundary, step S161 can be executed, and two pixels can be remapped and output according to the boundary mode, and when there is no boundary, step S162 is executed, and two pixels are mapped and output according to the common image;

[0151] For example, the output mode is determined according to the judgment result, and the output value is calculated according to the output mode. It should be noted that the relationship between the output mode and the output value can be a preset mapping relationship according to the actual application scenario. For example, the output mode corresponding to the boundary usually has a more obvious jump in the output value, which can highlight the display effect of the boundary; for example, the output mode corresponding to the non-boundary has a smooth transition in the output value, which can make the human eye feel more comfortable, etc. Therefore, no specific limitation is made;

[0152] The output process corresponding to the boundary mode output in step S161 may be: determining that there is a boundary in the current sampling matrix according to the judgment value, selecting points to output the values, and ensuring that the pixel value jump of the boundary image is obvious; the output process corresponding to the ordinary image mapping output in step S162 may be: determining that there is no boundary in the current sampling matrix according to the judgment value, mapping and outputting the current sampling point according to the ordinary image, and ensuring that the transition between adjacent pixels is smooth;

[0153] Step S170, determine whether the above processing has been completed for the entire image, such as determining whether the second-order sampling matrix has completed the sampling of the initial image. If not, return to step S110 to continue processing the next pixel point. If the processing of the entire image is completed, end.

[0154] In some embodiments, the second-order sampling matrix can be subjected to sliding window processing in the image to be displayed according to a preset sliding window processing strategy. For example, the second-order sampling matrix can perform the image enhancement processing method described in any of the aforementioned embodiments on each pixel point from left to right and from top to bottom until all pixels are processed.

[0155] Based on the same inventive concept, the embodiments of this specification also provide an image enhancement processing device corresponding to the aforementioned image enhancement processing method, which can be applied to MicroLED displays.

[0156] like Figure 8 As shown, the image enhancement processing device 800 may include: an image input module 810, which obtains the image to be displayed; a sampling module 830, which determines the sampling point pixels, and the sampling point pixels are the current pixels of the image to be displayed when being displayed; a generation module 850, which generates a second-order sampling matrix according to the sampling point pixels; a determination module 870, which determines whether there is a boundary between each pair of elements in the second-order sampling matrix; and an enhancement module 890, which, when it is determined that there is a boundary, enhances the pixels at the boundary.

[0157] Optionally, the sampling module 830 may include a RAM memory (not shown in the figure), and the RAM memory is used to generate a second-order sampling matrix according to the sampling point pixels, so that the second-order sampling matrix can complete the sampling of the input image using the RAM memory.

[0158] Optionally, the determination module 870 may include: a subtractor unit (not shown in the figure), determining the difference in pixel values ​​between each pair of elements in the second-order sampling matrix; and a comparator unit (not shown in the figure), determining whether there is a boundary between each pair of elements in the second-order sampling matrix based on the difference.

[0159] Optionally, when the image to be displayed includes an RGB image, generating a second-order sampling matrix according to the sampling point pixels includes: generating three second-order sampling matrices according to the sampling point pixels to generate second-order sampling matrices corresponding to R sub-pixels, G sub-pixels and B sub-pixels of the sampling point pixels.

[0160] Optionally, the image enhancement processing device may further include: an acquisition module (not shown in the figure) for acquiring a threshold value of a determination boundary.

[0161] In implementation, the acquisition module can be configured as follows:

[0162] Obtaining a first threshold value;

[0163] Determining whether there is a boundary between any two elements in the second-order sampling matrix comprises:

[0164] Determine a first relationship between a difference in pixel values ​​between two adjacent pixels in the second-order sampling matrix and the first threshold, and when the first relationship satisfies a first preset condition, determine that the two adjacent pixels are similar pixels, wherein the first preset condition is used to characterize that the pixel values ​​of the two pixels are similar;

[0165] and / or, obtaining a second threshold;

[0166] Determining whether there is a boundary between any two elements in the second-order sampling matrix comprises:

[0167] Determine a second relationship between a difference in pixel values ​​between two adjacent pixels in the second-order sampling matrix and the second threshold, and when the second relationship satisfies a second preset condition, determine that the two adjacent pixels are boundary pixels, wherein the second preset condition is used to characterize a condition in which the pixel values ​​of the two pixels are jumpy;

[0168] The first threshold and the second threshold are different thresholds.

[0169] Optionally, obtaining the first threshold includes: determining the first threshold according to a gamma curve of the MicroLED display;

[0170] And / or, obtaining the second threshold includes: determining the second threshold according to a gamma curve of the MicroLED display.

[0171] Optionally, determining whether there is a boundary between any two elements in the second-order sampling matrix includes:

[0172] Perform at least one of the following segmentation processing on the second-order sampling matrix: horizontal segmentation, vertical segmentation, oblique segmentation, and single-point segmentation;

[0173] Calculate the difference between the opposite parts after segmentation;

[0174] It is determined whether there is a boundary between two elements of the opposing parts in the second-order sampling matrix according to the difference.

[0175] Optionally, after determining whether there is a boundary between two elements of the opposing parts in the second-order sampling matrix according to the difference, the image enhancement processing device also includes: an output module (not shown in the figure), outputting the judgment result corresponding to each segmentation processing, and the judgment result is the judgment result of whether there is a boundary between two elements of the opposing parts in the second-order sampling matrix.

[0176] Optionally, the image enhancement processing device further includes: a mapping module (not shown in the figure) for remapping the input image to generate a new image.

[0177] In implementation, the mapping module is configured as follows:

[0178] When it is determined that the two adjacent pixels are close pixels, the pixel values ​​of the two adjacent pixels that are close pixels are output according to a first preset mapping strategy;

[0179] Alternatively, when it is determined that the two adjacent pixels are boundary pixels, the pixel values ​​of the two adjacent pixels that are boundary pixels are output according to a second preset mapping strategy;

[0180] The first preset mapping strategy and the second preset mapping strategy are different mapping strategies.

[0181] Optionally, the pixels at the boundary are enhanced, including: selecting points at the pixels at the boundary and outputting pixel values, so that the pixels at the boundary meet preset display conditions based on the pixel values ​​output at the selected points.

[0182] Optionally, determining whether there is a boundary between any two elements in the second-order sampling matrix includes:

[0183] Determine whether the current second-order sampling matrix is ​​associated with a previous second-order sampling matrix;

[0184] When there is an association, it is determined whether there is a boundary between any two elements in the second-order sampling matrix according to a preset association strategy.

[0185] Optionally, after determining whether there are boundaries between every two elements in the second-order sampling matrix, the image enhancement processing device also includes: a storage module (not shown in the figure), storing the judgment result corresponding to the current second-order sampling matrix, so as to use the judgment result as the threshold parameter corresponding to the next second-order sampling matrix, and the threshold parameter is a priori parameter used to determine whether there are boundaries between every two elements in the second-order sampling matrix.

[0186] Based on the same inventive concept, the embodiments of this specification also provide an image enhancement display method, which can be based on the image enhancement processing method provided by any of the aforementioned embodiments. After the image enhancement processing, the image can be displayed through a MicroLED display.

[0187] like Fig. 9 As shown, the image enhancement display method may include:

[0188] Step S901: preprocessing the image to be displayed to obtain a new image to be displayed for display on the MicroLED display, wherein the preprocessing includes processing the image to be displayed based on the image enhancement processing method described in any one of the above embodiments;

[0189] Step S903: display the new image to be displayed on the MicroLED display.

[0190] After remapping the input image through preprocessing, a new image suitable for display on the MicroLED display is obtained, and the new image can be displayed on the MicroLED display.

[0191] Based on the same inventive concept, the embodiments of this specification also provide an image enhancement display device corresponding to the aforementioned image enhancement display method, which can be applied to MicroLED displays.

[0192] like Fig.10 As shown, the image enhancement display device 1000 may include:

[0193] A preprocessing module 1010, which preprocesses the image to be displayed to obtain a new image to be displayed for display on the MicroLED display, wherein the preprocessing includes processing the image to be displayed based on the image enhancement processing method described in any of the above embodiments;

[0194] The display driving module 1030 displays the new image to be displayed on the MicroLED display.

[0195] After remapping the input image through the preprocessing module, a new image suitable for display on the MicroLED display is obtained, and then the new image can be displayed on the MicroLED display. In addition to improving the MicroLED display effect during remapping, a good display effect can also be obtained through simple hardware processing.

[0196] Based on the same inventive concept, an embodiment of this specification also provides a MicroLED display, wherein the MicroLED display is used to execute the image enhancement processing method described in any of the aforementioned embodiments to enhance and display the input image and then display it on the MicroLED display panel.

[0197] like Fig.11 As shown, the MicroLED display 1100 may include:

[0198] MicroLED array 1110 may be a display array panel composed of a plurality of MicroLEDs (micro light emitting diodes);

[0199] Processor 1130, the processor 1130 is configured to: execute the image enhancement processing method as described in any of the aforementioned embodiments, convert each pixel of the image to be displayed into a target pixel; and output the pixel value of the target pixel to the input end of the MicroLED array 1110, so as to display the content of the image to be displayed through the MicroLED array 1110.

[0200] It should be noted that the processor can be a device used for processing, such as a CPU (central processing unit), an MCU (microcontroller), an NPU (network processor), a GPU (graphics processing unit), etc., which is not limited here.

[0201] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0202] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

[0203] It should be noted that the above embodiments can be freely combined as needed. The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered as the protection scope of the present invention.

Claims

1. An image enhancement processing method, characterized in that: Applied to MicroLED display, the image enhancement processing method includes: Get the image to be displayed; Determine a sampling point pixel, where the sampling point pixel is a current pixel of the image to be displayed when it is displayed; Generate a second-order sampling matrix according to the sampling point pixels; Determine whether there is a boundary between any two elements in the second-order sampling matrix; When it is determined that there is a boundary, the pixels at the boundary are enhanced.

2. The image enhancement processing method according to claim 1, characterized in that: When the image to be displayed includes an RGB image, generating a second-order sampling matrix according to the sampling point pixels includes: generating three second-order sampling matrices according to the sampling point pixels to generate second-order sampling matrices corresponding to the R sub-pixels, G sub-pixels and B sub-pixels of the sampling point pixels.

3. The image enhancement processing method according to claim 1, characterized in that: The image enhancement processing method further includes: obtaining a first threshold; Determining whether there is a boundary between any two elements in the second-order sampling matrix comprises: Determine a first relationship between a difference in pixel values ​​between two adjacent pixels in the second-order sampling matrix and the first threshold, and when the first relationship satisfies a first preset condition, determine that the two adjacent pixels are similar pixels, wherein the first preset condition is used to characterize that the pixel values ​​of the two pixels are similar; and / or, obtaining a second threshold; Determining whether there is a boundary between any two elements in the second-order sampling matrix comprises: Determine a second relationship between a difference in pixel values ​​between two adjacent pixels in the second-order sampling matrix and the second threshold, and when the second relationship satisfies a second preset condition, determine that the two adjacent pixels are boundary pixels, wherein the second preset condition is used to characterize a condition in which the pixel values ​​of the two pixels are jumpy; The first threshold and the second threshold are different thresholds.

4. The image enhancement processing method according to claim 3, characterized in that: Obtaining the first threshold includes: determining the first threshold according to a gamma curve of the MicroLED display; And / or, obtaining the second threshold includes: determining the second threshold according to a gamma curve of the MicroLED display.

5. The image enhancement processing method according to claim 3, characterized in that: Determining whether there is a boundary between any two elements in the second-order sampling matrix comprises: Perform at least one of the following segmentation processing on the second-order sampling matrix: horizontal segmentation, vertical segmentation, oblique segmentation, and single-point segmentation; Calculate the difference between the opposite parts after segmentation; It is determined whether there is a boundary between two elements of the opposing parts in the second-order sampling matrix according to the difference.

6. The image enhancement processing method according to claim 5, characterized in that: After determining whether there is a boundary between two elements of the opposing parts in the second-order sampling matrix according to the difference, the image enhancement processing method also includes: outputting a judgment result corresponding to each segmentation processing, and the judgment result is a judgment result of whether there is a boundary between two elements of the opposing parts in the second-order sampling matrix.

7. The image enhancement processing method according to claim 3, characterized in that: When it is determined that the two adjacent pixels are close pixels, the image enhancement processing method further includes: outputting the pixel values ​​of the two adjacent pixels that are close pixels according to a first preset mapping strategy; Alternatively, when it is determined that the two adjacent pixels are boundary pixels, the image enhancement processing method further includes: outputting the pixel values ​​of the two adjacent pixels that are boundary pixels according to a second preset mapping strategy; The first preset mapping strategy and the second preset mapping strategy are different mapping strategies.

8. The image enhancement processing method according to claim 1, characterized in that: The pixels at the boundary are enhanced, including: selecting points and outputting pixel values ​​for the pixels at the boundary, so that the pixels at the boundary meet preset display conditions based on the pixel values ​​output from the selected points.

9. The image enhancement processing method according to claim 1, characterized in that: Determining whether there is a boundary between any two elements in the second-order sampling matrix comprises: Determine whether the current second-order sampling matrix is ​​associated with a previous second-order sampling matrix; When there is an association, it is determined whether there is a boundary between any two elements in the second-order sampling matrix according to a preset association strategy.

10. The image enhancement processing method according to claim 1, characterized in that: After determining whether there is a boundary between any two elements in the second-order sampling matrix, the image enhancement processing method further includes: The judgment result corresponding to the current second-order sampling matrix is ​​stored to use the judgment result as a threshold parameter corresponding to the next second-order sampling matrix, wherein the threshold parameter is a priori parameter used to determine whether there is a boundary between two elements in the second-order sampling matrix.

11. An image enhancement processing device, characterized in that: Applied to MicroLED display, the image enhancement processing device comprises: An image input module obtains the image to be displayed; A sampling module determines a sampling point pixel, where the sampling point pixel is a current pixel of the image to be displayed when it is displayed; A generating module, generating a second-order sampling matrix according to the sampling point pixels; A determination module, determining whether there is a boundary between any two elements in the second-order sampling matrix; The enhancement module performs enhancement processing on pixels at the boundary when it is determined that there is a boundary.

12. The image enhancement processing device according to claim 11, characterized in that: The sampling module includes a RAM memory, and the RAM memory is used to generate a second-order sampling matrix according to the sampling point pixels.

13. The image enhancement processing device according to claim 11, characterized in that: The determination module comprises: A subtractor unit, for determining the difference between pixel values ​​of two elements in the second-order sampling matrix; A comparator unit determines whether there is a boundary between any two elements in the second-order sampling matrix according to the difference.

14. An image enhancement display method, characterized in that: Applied to MicroLED display, the image enhancement display method comprises: Preprocessing the image to be displayed to obtain a new image to be displayed for display on the MicroLED display, wherein the preprocessing includes processing the image to be displayed based on the image enhancement processing method according to any one of claims 1 to 10; Displaying the new image to be displayed on the MicroLED display.

15. An image enhancement display device, characterized in that: Applied to a MicroLED display, the image enhancement display device comprises: A preprocessing module, which preprocesses the image to be displayed to obtain a new image to be displayed for display on the MicroLED display, wherein the preprocessing includes processing the image to be displayed based on the image enhancement processing method according to any one of claims 1 to 10; The display driving module displays the new image to be displayed on the MicroLED display.

16. A MicroLED display, characterized in that: include: MicroLED arrays; A processor, the processor being configured to: Execute the image enhancement processing method as described in any one of claims 1 to 10 to convert each pixel of the image to be displayed into a target pixel; The pixel value of the target pixel is output to the input end of the MicroLED array, so as to display the content of the image to be displayed through the MicroLED array.

Images