Data processing method, apparatus and computer-readable storage medium for display panel

Abstract

This application provides a data processing method, apparatus, and computer-readable storage medium for a display panel. The method includes: acquiring brightness compensation data for at least one grayscale bound point; compressing the brightness compensation data for the at least one grayscale bound point based on a target vector quantization algorithm; and storing the compressed brightness compensation data in a storage unit. This application can improve the compression accuracy of brightness compensation data, better preserve the original data characteristics, reduce the distortion of the compressed brightness compensation data, and thus improve the Demura compensation effect.

Description

Technical Field

[0001] This application belongs to the field of display technology, and in particular relates to a data processing method, apparatus and computer-readable storage medium for a display panel. Background Technology

[0002] During the manufacturing process, display panels may exhibit uneven brightness and color due to factors such as manufacturing processes, materials, and equipment. This phenomenon is known as demura. Currently, the mainstream demura method primarily involves external optical compensation. This method uses a camera to capture the brightness data of the display panel, calculates the brightness compensation data using the demura algorithm, and then compresses the data before writing it into a storage unit, thereby achieving brightness compensation for the display panel.

[0003] However, the inventors of this application have found that the current brightness compensation data is significantly distorted after data compression, resulting in poor Demura compensation effect. Summary of the Invention

[0004] This application provides a data processing method, apparatus, and computer-readable storage medium for a display panel, which can improve the compression accuracy of brightness compensation data and reduce the distortion of compressed brightness compensation data.

[0005] In a first aspect, embodiments of this application provide a data processing method for a display panel, the data processing method for a display panel including: acquiring brightness compensation data of at least one grayscale bound point; compressing the brightness compensation data of at least one grayscale bound point based on a target vector quantization algorithm; and storing the compressed brightness compensation data in a storage unit.

[0006] According to the first aspect of this application, based on the target vector quantization algorithm, the brightness compensation data of at least one grayscale binding point is compressed, specifically including: dividing the display panel into multiple partitions, each partition including multiple sub-pixels; for any partition, dividing the brightness compensation data of multiple sub-pixels in the partition at the target grayscale binding point into N intervals, and determining the center value of each interval based on the brightness compensation data of the sub-pixels contained in each interval at the target grayscale binding point, where the target grayscale binding point is any grayscale binding point and N is a positive integer; for any sub-pixel in the partition, generating the index value corresponding to the sub-pixel based on the interval to which the brightness compensation data of the sub-pixel belongs; storing the compressed brightness compensation data in a storage unit, specifically including: for any partition, storing the center value of each interval in the partition and the index value corresponding to each sub-pixel in the partition in the storage unit.

[0007] Thus, in this embodiment, the target vector quantization algorithm is used to compress the brightness compensation data. On the one hand, the brightness compensation data of all sub-pixels in each interval when the target grayscale is bound is replaced by the same center value, which can significantly reduce the amount of data and save storage space. On the other hand, the brightness compensation data can be classified and compressed according to the data size, which can improve the compression accuracy of the brightness compensation data, better preserve the original data characteristics, reduce the distortion of the compressed brightness compensation data, and thus improve the Demura compensation effect.

[0008] According to any of the foregoing embodiments of the first aspect of this application, the center value of each interval is determined based on the brightness compensation data of the sub-pixels contained in each interval when the target grayscale is bound. Specifically, this includes: for any interval, calculating the average value, median, or mode of the brightness compensation data of the sub-pixels contained in the interval when the target grayscale is bound, and taking the average value, median, or mode as the center value of the interval.

[0009] According to any of the foregoing embodiments of the first aspect of this application, before storing the center value of each interval in the partition and the index value corresponding to each sub-pixel in the partition into the storage unit, the data processing method further includes: restricting the number of digits of the center value from the first digit to the second digit, wherein the second digit is less than the first digit; and / or, restricting the number of digits of the index value to the third digit, wherein the third digit is less than the first digit; storing the center value of each interval in the partition and the index value corresponding to each sub-pixel in the partition into the storage unit specifically includes: storing the center value of each interval in the partition after the digit restriction and the index value of each sub-pixel in the partition after the digit restriction to the storage unit; or, storing the center value of each interval in the partition after the digit restriction and the index value corresponding to each sub-pixel in the partition into the storage unit; or, storing the center value of each interval in the partition and the index value of each sub-pixel in the partition after the digit restriction to the storage unit.

[0010] Thus, by limiting the number of bits for the center value and / or subscript value, the data range of the center value and / or subscript value can be further reduced, thereby reducing the data volume of the center value and further reducing the storage cost of the storage unit.

[0011] According to any of the foregoing embodiments of the first aspect of this application, the third digit is less than the second digit.

[0012] This can significantly reduce the data range of the index value, further reducing the amount of data for the index value, saving storage space, and lowering storage costs.

[0013] According to any of the foregoing embodiments of the first aspect of this application, before storing the center value of each interval in the partition and the index value corresponding to each sub-pixel in the partition into the storage unit, the data processing method further includes: compressing the index value corresponding to each sub-pixel based on the Huffman coding algorithm; storing the center value of each interval in the partition and the index value corresponding to each sub-pixel in the partition into the storage unit, specifically including: storing the center value of each interval in the partition and the compressed index value of each sub-pixel in the partition into the storage unit.

[0014] Thus, by further compressing the index values ​​corresponding to each sub-pixel based on the Huffman coding algorithm, lossless compression can be achieved while further reducing the amount of data in the index values, saving storage space and reducing storage costs.

[0015] According to any of the foregoing embodiments of the first aspect of this application, based on the Huffman coding algorithm, the index value corresponding to each sub-pixel is compressed, specifically including: converting the index value with an occurrence frequency of a first frequency into a code of a first length; converting the index value with an occurrence frequency of a second frequency into a code of a second length; wherein the first frequency is greater than the second frequency, and the first length is less than the second length.

[0016] This reduces the average length of the encoded string, thus achieving lossless data compression.

[0017] According to any of the foregoing embodiments of the first aspect of this application, before compressing the brightness compensation data of at least one grayscale bound point based on the target vector quantization algorithm, the data processing method further includes: performing mean compression on the brightness compensation data of at least one grayscale bound point based on the block compression algorithm; and compressing the brightness compensation data of at least one grayscale bound point based on the target vector quantization algorithm, specifically including: compressing the brightness compensation data of at least one grayscale bound point after mean compression based on the target vector quantization algorithm.

[0018] Therefore, by first performing mean compression on the brightness compensation data of at least one grayscale binding point based on the block compression algorithm, the amount of brightness compensation data can be further reduced, saving storage space and reducing storage costs.

[0019] According to any of the foregoing embodiments of the first aspect of this application, the partition includes multiple color sub-pixels; the brightness compensation data of multiple sub-pixels in the partition at the target grayscale binding point is divided into N intervals, and the center value of each interval is determined according to the brightness compensation data of the sub-pixels contained in each interval at the target grayscale binding point, specifically including: for any i-th color sub-pixel in the partition, the brightness compensation data of multiple i-th color sub-pixels in the partition at the target grayscale binding point is divided into N intervals, and the center value of the i-th color sub-pixel in each interval is determined according to the brightness compensation data of the i-th color sub-pixel contained in each interval at the target grayscale binding point, where i is a positive integer, 1≤i≤N; for any sub-pixel in the partition, the subscript value corresponding to the sub-pixel is generated according to the interval to which the brightness compensation data of the sub-pixel belongs, specifically including: for any i-th color sub-pixel in the partition, the subscript value corresponding to the i-th color sub-pixel is generated according to the interval to which the brightness compensation data of the i-th color sub-pixel belongs.

[0020] According to any of the foregoing embodiments of the first aspect of this application, before storing the center value of each interval in the partition and the index value corresponding to each sub-pixel in the partition into the storage unit, the data processing method further includes: compressing the center value of each interval in the partition based on the Huffman coding algorithm; storing the center value of each interval in the partition and the index value corresponding to each sub-pixel in the partition into the storage unit, specifically including: storing the compressed center value of each interval in the partition and the index value corresponding to each sub-pixel in the partition into the storage unit.

[0021] Thus, by further compressing the center value corresponding to each sub-pixel based on the Huffman coding algorithm, lossless compression can be achieved while further reducing the amount of data in the center value, saving storage space and reducing storage costs.

[0022] According to any of the foregoing embodiments of the first aspect of this application, after storing the center value of each interval in the partition and the compressed index value of each sub-pixel in the partition into the storage unit, the data processing method further includes: decompressing the compressed index value of each sub-pixel in the partition based on the decoding algorithm corresponding to the Huffman coding algorithm; for any sub-pixel in the partition, determining the center value corresponding to the sub-pixel at the target grayscale binding point based on the correspondence between the index value and the center value at the target grayscale binding point and the decompressed index value of the sub-pixel; and using the center value corresponding to the sub-pixel as the grayscale compensation value of the sub-pixel at the target grayscale binding point.

[0023] Secondly, embodiments of this application provide a data processing device for a display panel, comprising: an acquisition module for acquiring brightness compensation data of at least one grayscale bound point; a first compression module for compressing the brightness compensation data of at least one grayscale bound point based on a target vector quantization algorithm; and a storage module for storing the compressed brightness compensation data in a storage unit.

[0024] Thirdly, embodiments of this application provide an electronic device, which includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of the data processing method for the display panel provided in the first aspect.

[0025] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the data processing method for the display panel provided in the first aspect.

[0026] The data processing method, apparatus, and computer-readable storage medium for a display panel according to embodiments of this application acquire brightness compensation data for at least one grayscale bound point; compress the brightness compensation data for at least one grayscale bound point based on a target vector quantization algorithm; and store the compressed brightness compensation data in a storage unit. Embodiments of this application apply a target vector quantization algorithm to compress the brightness compensation data, which can classify and compress the brightness compensation data according to its size, improve the compression accuracy of the brightness compensation data, better preserve the original data characteristics, reduce the distortion of the compressed brightness compensation data, and thus improve the Demura compensation effect. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 A schematic flowchart of a data processing method for a display panel provided in an embodiment of this application;

[0029] Figure 2 A flowchart illustrating step S102 of the data processing method for a display panel provided in an embodiment of this application;

[0030] Figure 3 Another flowchart illustrating the data processing method for the display panel provided in this application embodiment;

[0031] Figure 4This is another schematic flowchart illustrating a data processing method for a display panel provided in an embodiment of this application.

[0032] Figure 5 A schematic flowchart of step S401 in the data processing method for the display panel provided in this application embodiment;

[0033] Figure 6 This is another schematic flowchart illustrating a data processing method for a display panel provided in an embodiment of this application.

[0034] Figure 7 This is another schematic flowchart illustrating a data processing method for a display panel provided in an embodiment of this application.

[0035] Figure 8 This is another schematic flowchart illustrating a data processing method for a display panel provided in an embodiment of this application.

[0036] Figure 9 A schematic diagram showing the arrangement of subpixels in a display panel;

[0037] Figure 10 A schematic diagram of a data processing device for a display panel provided in an embodiment of this application;

[0038] Figure 11 A schematic diagram of the hardware structure of the electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0039] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0040] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0041] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0042] Various modifications and variations can be made to this application without departing from its spirit or scope, which will be apparent to those skilled in the art. Therefore, this application is intended to cover modifications and variations falling within the scope of the corresponding claims (the claimed technical solutions) and their equivalents. It should be noted that the embodiments provided in this application can be combined with each other without contradiction.

[0043] Before describing the technical solutions provided in the embodiments of this application, in order to facilitate understanding of the embodiments of this application, this application first specifically explains the problems existing in the related technologies:

[0044] During the manufacturing process, display panels may exhibit uneven brightness and color due to factors such as manufacturing processes, materials, and equipment. This phenomenon is known as demura. Currently, the mainstream demura method primarily involves external optical compensation. This method uses a camera to capture the brightness data of the display panel, calculates the brightness compensation data using the demura algorithm, and writes the brightness compensation data into a storage unit, thereby achieving brightness compensation for the display panel.

[0045] Current storage units (such as Demura SRAM) have a storage capacity of around 16 MB, but the amount of data written to these units is large due to the requirements for the number of grayscale levels captured and the depth of the compensation data. Therefore, it is necessary to compress the brightness compensation data to meet hardware resource requirements.

[0046] Through long-term research, the inventors of this application have discovered that the number of gray levels captured and the accuracy of the compensation data (i.e., brightness compensation data) jointly affect the memory size occupied and the compensation effect. When the memory size is fixed, more gray levels captured result in more accurate calculations, but the corresponding compensation data depth (i.e., data range) is lower, which can easily lead to a grainy appearance, undercompensation, or overcompensation after compensation. Conversely, reducing the number of gray levels captured and increasing the compensation data depth results in good gray level compensation during capture, but the interpolation calculation gray level compensation effect is poor. Therefore, data compression plays a crucial role in this process, requiring the preservation of sufficient captured gray levels while retaining as much compensation data depth as possible to achieve the best compensation effect.

[0047] However, the current method for compressing brightness compensation data is usually average compression, which results in significant distortion of the data after compression, leading to poor Demura compensation performance.

[0048] In view of the inventors’ above-mentioned research findings, the present application provides a data processing method, apparatus and computer-readable storage medium for a display panel, which can solve the technical problem in the related art that the brightness compensation data is greatly distorted after data compression, resulting in poor Demura compensation effect.

[0049] The technical concept of this application embodiment is as follows: for brightness compensation data with at least one grayscale binding point, a target vector quantization algorithm is applied to compress the brightness compensation data. The target vector quantization algorithm can classify and compress the brightness compensation data according to the data size, improve the compression accuracy of the brightness compensation data, better preserve the original data characteristics, reduce the distortion of the compressed brightness compensation data, and thus improve the Demura compensation effect.

[0050] The data processing method for the display panel provided in the embodiments of this application will be described below.

[0051] Figure 1 This is a schematic flowchart illustrating a data processing method for a display panel provided in an embodiment of this application. Figure 1 As shown, the data processing method of the display panel may include the following steps S101 to S103.

[0052] S101. Obtain the brightness compensation data of at least one grayscale binding point.

[0053] At least one grayscale binding point can be set. The embodiments of this application do not limit the size and number of grayscale binding points. For example, in some examples, the set grayscale binding points may include 16 grayscale, 64 grayscale, and 192 grayscale. As another example, in some examples, the set grayscale binding points may include 16 grayscale, 64 grayscale, 128 grayscale, and 192 grayscale, etc.

[0054] Brightness compensation data can be compensation data obtained by performing external optical compensation on the display panel, i.e., Demura compensation data, used to improve the mura phenomenon of the display panel. For example, brightness compensation data includes, but is not limited to, grayscale compensation values.

[0055] In some examples, optionally, point-to-point compensation, i.e., pixel-level compensation, can be implemented during external optical compensation, where each sub-pixel can correspond to a grayscale compensation value. The brightness compensation data may include the grayscale compensation values ​​corresponding to multiple sub-pixels in the display panel.

[0056] S102. Based on the target vector quantization algorithm, compress the brightness compensation data of at least one grayscale binding point.

[0057] Target vector quantization algorithms include, but are not limited to, the LBG algorithm. The LBG algorithm is a vector quantization (VQ) design algorithm based on training sequences. It first sets the number of center points, then iterates continuously, using a set threshold as the termination condition, and finally obtains the values ​​of the center points. Its advantage lies in its ability to classify data based on size; for example, smaller values ​​can be classified into class A, medium values ​​into class B, and larger values ​​into class C. These three classes are merely illustrative. A larger number of center points results in more classifications and higher compression accuracy. Compared to conventional scalar quantization, vector quantization produces lower distortion.

[0058] In the embodiments of this application, for example, the brightness compensation data of at least one grayscale binding point can be compressed based on a target vector quantization algorithm (such as the LBG algorithm), thereby improving the compression accuracy of the brightness compensation data, better preserving the original data characteristics, and reducing the distortion of the compressed brightness compensation data.

[0059] S103. Store the compressed brightness compensation data into the storage unit.

[0060] After obtaining the compressed brightness compensation data, the compressed brightness compensation data can be stored in a storage unit. This storage unit includes, but is not limited to, the storage unit within the driver chip (such as Demura SRAM).

[0061] The data processing method for the display panel in this embodiment of the application obtains brightness compensation data of at least one grayscale bound point; compresses the brightness compensation data of the at least one grayscale bound point based on a target vector quantization algorithm; and stores the compressed brightness compensation data in a storage unit. This embodiment of the application uses a target vector quantization algorithm to compress the brightness compensation data, which can classify and compress the brightness compensation data according to its size, improve the compression accuracy of the brightness compensation data, better preserve the original data characteristics, reduce the distortion of the compressed brightness compensation data, and thus improve the Demura compensation effect.

[0062] To facilitate understanding, the compression process of the target vector quantization algorithm will be described in detail below.

[0063] Figure 2 This is a schematic flowchart of step S102 in the data processing method for a display panel provided in an embodiment of this application. Figure 2 As shown, according to some embodiments of this application, optionally, S102, based on the target vector quantization algorithm, the brightness compensation data of at least one grayscale binding point is compressed, which may specifically include the following steps S201 to S203.

[0064] S201. Divide the display panel into multiple partitions, each partition including multiple sub-pixels.

[0065] The number of partitions in the display panel and the number of sub-pixels in each partition can be flexibly adjusted according to actual conditions, and this application embodiment does not limit this. For example, in some examples, a partition may include 20*20 pixels.

[0066] S202. For any partition, divide the brightness compensation data of multiple sub-pixels in the partition into N intervals when the target grayscale is bound, and determine the center value of each interval based on the brightness compensation data of the sub-pixels contained in each interval when the target grayscale is bound.

[0067] Wherein, the target grayscale binding point can be any grayscale binding point, and N is a positive integer. For example, the target grayscale binding point can be 16 grayscale, 64 grayscale, 192 grayscale, or other grayscale. Taking a partition containing 20*20 pixels, or 400 pixels, as an example, according to the size of the brightness compensation data, the brightness compensation data of the 400 pixels in this partition at the target grayscale binding point can be divided into N intervals (or regions). In this application embodiment, the size of N is not limited; for example, in some examples, N can be equal to 16.

[0068] The brightness compensation data of all sub-pixels in each interval at the target grayscale point can be replaced by the same center value, that is, one interval corresponds to one center value. Specifically, for any interval, the average, median or mode of the brightness compensation data of the sub-pixels contained in the interval at the target grayscale point can be calculated, and the average, median or mode of the brightness compensation data of the sub-pixels contained in the interval at the target grayscale point is used as the center value of the interval.

[0069] S203. For any sub-pixel in the partition, generate the index value corresponding to the sub-pixel based on the interval to which the brightness compensation data of the sub-pixel belongs.

[0070] In step S202, the interval to which the brightness compensation data of each sub-pixel belongs is known. Different intervals can correspond to different index values; for example, interval A corresponds to index a1, interval B to index b1, and interval C to index c1. The purpose of the index value is to accurately determine the interval to which the sub-pixel belongs and its corresponding center value based on the index value. For example, when the index value of a sub-pixel is b1, it can be determined that the sub-pixel belongs to interval B, and the center value of the sub-pixel is the center value of interval B, which is convenient for subsequent decompression.

[0071] Accordingly, S103, the compressed brightness compensation data is stored in the storage unit, which may specifically include the following steps:

[0072] For any partition, store the center value of each interval in the partition and the index value of each sub-pixel in the partition into the storage unit.

[0073] Thus, in this embodiment, the target vector quantization algorithm is used to compress the brightness compensation data. On the one hand, the brightness compensation data of all sub-pixels in each interval when the target grayscale is bound is replaced by the same center value, which can significantly reduce the amount of data and save storage space. On the other hand, the brightness compensation data can be classified and compressed according to the data size, which can improve the compression accuracy of the brightness compensation data, better preserve the original data characteristics, reduce the distortion of the compressed brightness compensation data, and thus improve the Demura compensation effect.

[0074] In some specific examples, a partition may include sub-pixels of multiple colors, such as red sub-pixels, green sub-pixels, and blue sub-pixels. Accordingly, S202 may specifically include the following steps:

[0075] For any i-th color sub-pixel in the partition, divide the brightness compensation data of multiple i-th color sub-pixels in the partition into N intervals when binding the target grayscale point, and determine the center value of the i-th color sub-pixel in each interval based on the brightness compensation data of the i-th color sub-pixels contained in each interval when binding the target grayscale point, where i is a positive integer, 1≤i≤N.

[0076] In S202, for any given partition, for example, the brightness compensation data of multiple red sub-pixels in the partition at the target grayscale point can be divided into N intervals, and the center value corresponding to the red sub-pixels in each interval can be determined based on the brightness compensation data of the red sub-pixels in each interval at the target grayscale point. Similarly, the brightness compensation data of multiple green sub-pixels in the partition at the target grayscale point can be divided into N intervals, and the center value corresponding to the green sub-pixels in each interval can be determined based on the brightness compensation data of the green sub-pixels in each interval at the target grayscale point. Likewise, the brightness compensation data of multiple blue sub-pixels in the partition at the target grayscale point can be divided into N intervals, and the center value corresponding to the blue sub-pixels in each interval can be determined based on the brightness compensation data of the blue sub-pixels in each interval at the target grayscale point.

[0077] Accordingly, S203, for any sub-pixel in the partition, generate the index value corresponding to the sub-pixel based on the interval to which the brightness compensation data of the sub-pixel belongs, which may specifically include the following steps:

[0078] For any i-th color sub-pixel in the partition, generate the index value corresponding to the i-th color sub-pixel based on the interval to which the brightness compensation data of the i-th color sub-pixel belongs.

[0079] Given that the brightness compensation data intervals for each red, green, and blue sub-pixels are known, we can generate the following for each red, green, and blue sub-pixel: For each red sub-pixel, its corresponding index can be generated based on its brightness compensation data interval. Similarly, for each green sub-pixel, its index can be generated based on its brightness compensation data interval. And for each blue sub-pixel, its index can be generated based on its brightness compensation data interval.

[0080] Figure 3 This is another schematic flowchart illustrating the data processing method for the display panel provided in an embodiment of this application. For example... Figure 3As shown, according to some embodiments of this application, optionally, before storing the center value of each interval in the partition and the index value corresponding to each sub-pixel in the partition into the storage unit in S103, the data processing method of the display panel may further include the following steps S301 and / or S302.

[0081] S301. The number of digits in the center value is restricted from the first digit to the second digit, and the second digit is less than the first digit.

[0082] For example, in some cases, the first digit can be 8 digits, and the second digit can be less than 8 digits, such as 4 or 5 digits. When the center value (or brightness compensation data) is 8 digits, the data range of the center value (or brightness compensation data) is large. However, when the number of digits of the center value is limited to 4 or 5 digits, the data range of the center value becomes smaller, and thus the data volume of the center value also becomes smaller, thereby further reducing the data volume of the center value and further reducing the storage cost of the storage unit.

[0083] For example, in some specific embodiments, the second digit can be a 4-digit number, with the highest digit being the sign bit, and the sign being ±, such as ±0000 to ±1111, meaning the grayscale compensation range is ±15, and exceeding this range will result in direct overflow. Alternatively, in some specific embodiments, the second digit can be a 5-digit number, with the highest digit being the sign bit, and the sign being ±, such as ±00000 to ±11111, meaning the grayscale compensation range is ±31, and exceeding this range will result in direct overflow.

[0084] S302. Limit the number of digits in the subscript value to the third digit, where the third digit is less than the first digit.

[0085] Similarly, the number of digits in the index value can be limited to less than the first digit, such as 4 or 5 digits. When the number of digits in the index value is limited to 4 or 5 digits, the data range of the index value becomes smaller, and thus the data volume of the index value also becomes smaller, thereby further reducing the data volume of the index value and further reducing the storage cost of the storage unit. It can be understood that the original number of digits in the index value is the first digit or greater than the first digit.

[0086] For example, in some specific embodiments, the third digit can be a 4-digit number, with the highest digit being the sign bit and the sign being ±, such as ±0000 to ±1111, or ±15. If the value exceeds this range, it will overflow. Alternatively, in some specific embodiments, the third digit can be a 5-digit number, with the highest digit being the sign bit and the sign being ±, such as ±00000 to ±11111, or ±31. If the value exceeds this range, it will overflow.

[0087] The following explanation uses N=16 and the subscript value as an example of 4 digits.

[0088] Table 1 schematically shows the center values ​​of the 16 intervals and the corresponding index values ​​of each center value.

[0089] Table 1

[0090]

[0091] As shown in Table 1, in some examples, the brightness compensation data of multiple sub-pixels in each partition when binding to the target grayscale can be divided into 16 intervals, with A to Q representing the center values ​​of the 16 intervals respectively. Each center value can correspond to an index value (or marker value). For example, center value A corresponds to index value 0, center value B corresponds to index value 1, ..., center value Q corresponds to index value 15. Each sub-pixel in the partition can be assigned an index value; for example, 20*20 pixels in the partition can be assigned 400 index values, and the set of index values ​​can be denoted as index. The set of center values ​​can be denoted as value.

[0092] Accordingly, S103, for any partition, the center value of each interval in the partition and the index value corresponding to each sub-pixel in the partition are stored in the storage unit, which may specifically include the following steps:

[0093] Store the center value of each interval in the partition after limiting the number of bits and the index value of each sub-pixel in the partition after limiting the number of bits into the storage unit;

[0094] Alternatively, the center value of each interval in the partition after limiting the number of bits and the index value corresponding to each sub-pixel in the partition can be stored in the storage unit;

[0095] Alternatively, the center value of each interval in the partition and the index value of each sub-pixel in the partition after limiting the number of bits can be stored in the storage unit.

[0096] In other words, at least one of the center value and the subscript value can be limited in the number of digits, but this application does not limit this.

[0097] Thus, by limiting the number of bits for the center value and / or subscript value, the data range of the center value and / or subscript value can be further reduced, thereby reducing the data volume of the center value and further reducing the storage cost of the storage unit.

[0098] In some specific embodiments, the third digit can be less than the second digit. For example, the center value can be limited to 5 digits, and the subscript value to 4 digits. This can significantly reduce the data range of the subscript values, further reducing the amount of data in the subscript values, saving storage space, and lowering storage costs.

[0099] Figure 4This is another schematic flowchart illustrating a data processing method for a display panel provided in an embodiment of this application. For example... Figure 4 As shown, according to some embodiments of this application, optionally, before storing the center value of each interval in the partition and the index value corresponding to each sub-pixel in the partition into the storage unit in S103, the data processing method of the display panel may further include the following step S401.

[0100] S401. Based on the Huffman coding algorithm, the index value corresponding to each sub-pixel is compressed.

[0101] Huffman coding is a variable-step coding method based on probability distribution. Higher probabilities correspond to higher weights, resulting in shorter code lengths. Huffman coding first creates a tree based on the frequency of index values. Then, it generates a specific code for each index value using this tree structure. Indices with higher frequencies receive shorter codes, while those with lower frequencies receive longer codes. This reduces the average length of the encoded string, achieving lossless data compression.

[0102] Accordingly, S103, for any partition, the center value of each interval in the partition and the index value corresponding to each sub-pixel in the partition are stored in the storage unit, which may specifically include the following steps:

[0103] The center value of each interval in the partition and the compressed index value of each subpixel in the partition are stored in the storage unit.

[0104] Thus, by further compressing the index values ​​corresponding to each sub-pixel based on the Huffman coding algorithm, lossless compression can be achieved while further reducing the amount of data in the index values, saving storage space and reducing storage costs.

[0105] Huffman coding is a variable-length coding algorithm. The data size of the index value obtained based on the LBG algorithm is fixed, which is convenient for calculating the probability distribution. Huffman coding can effectively reduce the data size (number of bits) of the index value.

[0106] Figure 5 This is a schematic flowchart of step S401 in the data processing method for a display panel provided in an embodiment of this application. Figure 5 As shown, according to some embodiments of this application, optionally, S401, based on the Huffman coding algorithm, the index value corresponding to each sub-pixel is compressed, which may specifically include the following steps S501 and S502.

[0107] S501. Convert the index value of the occurrence frequency of the first frequency into an encoding of the first length.

[0108] S502, Convert the index value of the occurrence frequency of the second frequency into an encoding of the second length.

[0109] Among them, the first frequency is greater than the second frequency, and the first length is less than the second length.

[0110] It should be noted that the first and second frequencies are merely examples, and can be any frequencies. In addition to the first and second frequencies, other frequencies such as the third and fourth frequencies may also be included, but this application does not limit this. That is, the higher the frequency, the shorter the length of the converted code can be. This reduces the average length of the encoded string, thereby achieving lossless data compression.

[0111] Figure 6 This is another schematic flowchart illustrating a data processing method for a display panel provided in an embodiment of this application. For example... Figure 6 As shown, according to some embodiments of this application, optionally, before compressing the brightness compensation data of the at least one grayscale binding point based on the target vector quantization algorithm in S102, the data processing method of the display panel may further include the following step S601.

[0112] S601. Based on the block compression algorithm, perform mean compression on the brightness compensation data of at least one grayscale binding point.

[0113] Block compression, also known as 1x1, 1x2, 2x1, or 2x2 block compression, allows for the selection of block sizes (compression ratios) to perform average compression on luminance compensation data, depending on the compression requirements. For example, for a set of data in rows and four columns... Performing block1*2 compression involves averaging every row and every two columns to obtain the result. It becomes 2 rows and 2 columns.

[0114] Accordingly, S102, based on the target vector quantization algorithm, the brightness compensation data of at least one grayscale binding point is compressed, which may specifically include the following steps:

[0115] Based on the target vector quantization algorithm, the brightness compensation data after the mean compression of at least one gray level bound point is compressed.

[0116] That is, the brightness compensation data can be compressed in blocks first, and then the compressed brightness compensation data can be compressed by vector quantization.

[0117] Therefore, by first performing mean compression on the brightness compensation data of at least one grayscale binding point based on the block compression algorithm, the amount of brightness compensation data can be further reduced, saving storage space and reducing storage costs.

[0118] Figure 7 This is another schematic flowchart illustrating a data processing method for a display panel provided in an embodiment of this application. For example... Figure 7 As shown, according to some embodiments of this application, optionally, before storing the center value of each interval in the partition and the index value corresponding to each sub-pixel in the partition into the storage unit in S103, the data processing method of the display panel may further include the following step S701.

[0119] S701, based on the Huffman coding algorithm, compresses the center value of each interval in the partition.

[0120] Similarly, Huffman coding first creates a tree using the frequency of center values, and then generates a specific code for each center value based on the structure of this tree. Center values ​​with higher frequencies use shorter codes, while those with lower frequencies use longer codes. This reduces the average length of the encoded string, thus achieving lossless data compression.

[0121] Accordingly, S103, the center value of each interval in the partition and the index value corresponding to each sub-pixel in the partition are stored in the storage unit, specifically including:

[0122] The compressed center value of each interval in the partition and the index value of each sub-pixel in the partition are stored in the storage unit.

[0123] Thus, by further compressing the center value corresponding to each sub-pixel based on the Huffman coding algorithm, lossless compression can be achieved while further reducing the amount of data in the center value, saving storage space and reducing storage costs.

[0124] Figure 8 This is another schematic flowchart illustrating a data processing method for a display panel provided in an embodiment of this application. For example... Figure 8 As shown, according to some embodiments of this application, optionally, after storing the center value of each interval in the partition and the compressed sub-index value of each sub-pixel in the partition into the storage unit in S103, the data processing method of the display panel may further include the following steps S801 to S803.

[0125] S801, a decoding algorithm based on the Huffman coding algorithm, decompresses the compressed index values ​​of each sub-pixel in the partition.

[0126] When performing Demura compensation, the compressed index values ​​of each sub-pixel in the partition can be decompressed based on the decoding algorithm corresponding to the Huffman coding algorithm to obtain the decompressed index values ​​of each sub-pixel in the partition.

[0127] S802. For any sub-pixel in the partition, determine the center value corresponding to the sub-pixel when it is bound to the target gray level, based on the correspondence between the index value and the center value when the sub-pixel is bound to the target gray level and the index value after the sub-pixel is decompressed.

[0128] As mentioned earlier, different center values ​​(or intervals) can correspond to different index values. As shown in Table 1, a correspondence between the index value and the center value when binding the target grayscale point can be established in advance. For any sub-pixel in the partition, the center value corresponding to the sub-pixel when binding the target grayscale point can be determined based on the decompressed index value of the sub-pixel and this correspondence.

[0129] S803. Use the center value corresponding to the sub-pixel as the grayscale compensation value when the sub-pixel is bound to the target grayscale point.

[0130] For any sub-pixel within a partition, after obtaining the center value of the sub-pixel at the target grayscale binding point, this center value can be used as the grayscale compensation value for the sub-pixel at the target grayscale binding point. The grayscale compensation value can be used to correct the grayscale to be displayed for that sub-pixel, achieving Demura compensation.

[0131] It should be noted that, when the brightness compensation data has undergone block compression, S803 may specifically include the following steps one and two.

[0132] Step 1: Based on the decoding algorithm corresponding to the block compression algorithm, decompress the center value corresponding to the sub-pixel.

[0133] Step 2: Use the center value of the decompressed sub-pixel as the grayscale compensation value when the sub-pixel is bound to the target grayscale point.

[0134] It should be noted that when the compression ratio of block compression is 1*1, it is not necessary to decompress the center value of the sub-pixel based on the decoding algorithm corresponding to the block compression algorithm. Instead, the center value of the sub-pixel can be directly used as the gray level compensation value of the sub-pixel when it is bound to the target gray level.

[0135] According to some embodiments of this application, optionally, before compressing the brightness compensation data of at least one grayscale binding point based on the target vector quantization algorithm in S102, the data processing method of the display panel may further include the following steps:

[0136] Select a filter kernel of a preset size and perform mean filtering on the brightness compensation data.

[0137] In this way, mean filtering can reduce the volatility of brightness compensation data, which helps to reduce the data range of brightness compensation data.

[0138] According to some embodiments of this application, optionally, the number of intervals (or center values) corresponding to each partition can be flexibly adjusted according to actual needs. For example, the number of intervals (or center values) corresponding to each partition can be reduced, thereby reducing the amount of data for the center values.

[0139] To facilitate understanding, the following explanation will be provided with reference to some specific application examples.

[0140] For example, in some specific embodiments, three gray levels (R / G / B, 16 / 64 / 192) are selected as Demura gray level binding points, the resolution of the display panel is 2400*720, the display panel arrangement is Real RGB, and the number of bits for brightness compensation data is 8. Figure 9 This is a schematic diagram illustrating the arrangement of subpixels in a display panel. For example... Figure 9 As shown, in some examples, the display panel can adopt a Real RGB layout. That is, along the row direction X of the display panel, the sub-pixel column containing red sub-pixel 1001, the sub-pixel column containing green sub-pixel 1002, and the sub-pixel column containing blue sub-pixel 1003 can be arranged alternately. First, a light-emitting device can sequentially write grayscale images of R / G / B at different grayscale binding points (16 / 64 / 192) onto the display panel. A camera is used to acquire the brightness data of the display screen at different grayscale binding points of R / G / B, acquiring a total of 9 image brightness data. Based on the brightness data of R / G / B at the above 3 grayscale binding points, the Demura algorithm is used to calculate the brightness compensation data of the 3 grayscale binding points, i.e., the compensation grayscale. Point-to-point compensation is used, and the number of brightness compensation data is: 2400*720*3*3. The original data size of the brightness compensation data is: 2400*720*3*3*8=124416000bit. In this code, 2400*720 represents the resolution, the first 3 represents the three colors RGB, the second 3 represents the three gray levels, and 8 represents that each number is 8 bits.

[0141] Then, based on the block compression algorithm, mean compression is performed on the brightness compensation data of at least one grayscale bound point. Taking Block 1*1 mean compression as an example, the data size of the brightness compensation data after mean compression is: 2400*720*3*3*8=124416000 bits.

[0142] Next, the luminance compensation data of at least one grayscale bound point is compressed based on the LBG algorithm. For example, a 20*20 pixel partition is used as a unit, and then based on the LBG algorithm, each partition is further divided into 16 intervals (or regions), resulting in 16 center values ​​for these intervals. The center value is limited to 5 digits, and the index value is limited to 4 digits.

[0143] The data size of the center value is: 2400*720*3*3*5 / 20 / 20*16=3110400 bits. 5 represents 5 bits, 20*20 represents the number of pixels in the partition, and 16 represents the number of center values ​​corresponding to one partition.

[0144] Next, based on the Huffman coding algorithm, the index values ​​corresponding to each sub-pixel are compressed. Since the index values ​​range from 0 to 15, based on the probability distribution and Huffman coding rules, a compression efficiency of 2 times is expected. Therefore, the data size of the compressed index values ​​is 10,368,000 bits.

[0145] Therefore, the total data size is: 10368000 + 3110400 = 13478400 ≈ 12.854 Mb.

[0146] Based on the data processing method for the display panel provided in the above embodiments, this application also provides specific implementations of a data processing device for the display panel. Please refer to the following embodiments.

[0147] Figure 10 This is a schematic diagram of a data processing device for a display panel provided in an embodiment of this application. Figure 10 As shown, the data processing device 10 for the display panel provided in this application embodiment includes the following modules:

[0148] The acquisition module 101 is used to acquire brightness compensation data for at least one grayscale binding point;

[0149] The first compression module 102 is used to compress the brightness compensation data of at least one grayscale binding point based on the target vector quantization algorithm.

[0150] Storage module 103 is used to store the compressed brightness compensation data into the storage unit.

[0151] The data processing apparatus for the display panel in this embodiment acquires brightness compensation data for at least one grayscale bound point; compresses the brightness compensation data for the at least one grayscale bound point based on a target vector quantization algorithm; and stores the compressed brightness compensation data in a storage unit. This embodiment applies a target vector quantization algorithm to compress the brightness compensation data, which can classify the brightness compensation data according to its size, improve the compression accuracy of the brightness compensation data, better preserve the original data characteristics, reduce the distortion of the compressed brightness compensation data, and thus improve the Demura compensation effect.

[0152] In some embodiments, the first compression module 102 is specifically used to divide the display panel into multiple partitions, each partition including multiple sub-pixels; for any partition, the brightness compensation data of the multiple sub-pixels in the partition at the target grayscale binding point is divided into N intervals, and the center value of each interval is determined according to the brightness compensation data of the sub-pixels contained in each interval at the target grayscale binding point, where the target grayscale binding point is any grayscale binding point and N is a positive integer; for any sub-pixel in the partition, the index value corresponding to the sub-pixel is generated according to the interval to which the brightness compensation data of the sub-pixel belongs. The storage module 103 is specifically used to store the center value of each interval in the partition and the index value corresponding to each sub-pixel in the partition into a storage unit for any partition.

[0153] In some embodiments, the first compression module 102 is specifically used to calculate, for any interval, the average value, median or mode of the brightness compensation data of the sub-pixels contained in the interval when the target grayscale is bound, and to use the average value, median or mode as the center value of the interval.

[0154] In some embodiments, the data processing device 10 for the display panel provided in this application further includes a bit-limiting module, used to limit the number of bits of the center value from a first digit to a second digit, wherein the second digit is less than the first digit; and / or, to limit the number of bits of the subscript value to a third digit, wherein the third digit is less than the first digit. The storage module 103 is specifically used to store the bit-limited center values ​​of each interval in the partition and the bit-limited subscript values ​​of each subpixel in the partition into a storage unit; or, to store the bit-limited center values ​​of each interval in the partition and the corresponding subscript values ​​of each subpixel in the partition into a storage unit; or, to store the bit-limited center values ​​of each interval in the partition and the bit-limited subscript values ​​of each subpixel in the partition into a storage unit.

[0155] In some embodiments, the third digit is less than the second digit.

[0156] In some embodiments, the data processing device 10 for the display panel provided in this application further includes a second compression module, used to compress the index value corresponding to each sub-pixel based on the Huffman coding algorithm. The storage module 103 is specifically used to store the center value of each interval in the partition and the compressed index value of each sub-pixel in the partition into a storage unit.

[0157] In some embodiments, the second compression module is specifically used to convert the index value with a first frequency of occurrence into an encoding of a first length; and to convert the index value with a second frequency of occurrence into an encoding of a second length; wherein the first frequency is greater than the second frequency, and the first length is less than the second length.

[0158] In some embodiments, the data processing device 10 for the display panel provided in this application further includes a third compression module, used to perform mean compression on the brightness compensation data of at least one grayscale bound point based on a block compression algorithm. The first compression module 102 is specifically used to compress the brightness compensation data after mean compression of at least one grayscale bound point based on a target vector quantization algorithm.

[0159] In some embodiments, the partition includes multiple color sub-pixels. The first compression module 102 is specifically used to, for any i-th color sub-pixel in the partition, divide the brightness compensation data of multiple i-th color sub-pixels in the partition at the target grayscale binding point into N intervals, and determine the center value of each interval to which the i-th color sub-pixel belongs based on the brightness compensation data of the i-th color sub-pixels contained in each interval at the target grayscale binding point, where i is a positive integer, 1≤i≤N. The storage module 103 is specifically used to, for any i-th color sub-pixel in the partition, generate the index value corresponding to the i-th color sub-pixel based on the interval to which the brightness compensation data of the i-th color sub-pixel belongs.

[0160] In some embodiments, the second compression module is further configured to compress the center values ​​of each interval in the partition based on the Huffman coding algorithm. The storage module 103 is specifically configured to store the compressed center values ​​of each interval in the partition and the index values ​​corresponding to each sub-pixel in the partition into a storage unit.

[0161] In some embodiments, the data processing device 10 for the display panel provided in this application further includes a decompression module, which is used to decompress the compressed index values ​​of each sub-pixel in the partition based on the decoding algorithm corresponding to the Huffman coding algorithm; for any sub-pixel in the partition, the center value corresponding to the sub-pixel at the target grayscale binding point is determined according to the correspondence between the index value and the center value at the target grayscale binding point and the decompressed index value of the sub-pixel; and the center value corresponding to the sub-pixel is used as the grayscale compensation value of the sub-pixel at the target grayscale binding point.

[0162] Figure 10 Each module / unit in the device shown has the function of implementing each step in the data processing method of the display panel provided in the above method embodiment, and can achieve its corresponding technical effect. For the sake of brevity, it will not be described in detail here.

[0163] Based on the data processing method for the display panel provided in the above embodiments, this application also provides specific implementation methods for electronic devices. Please refer to the following embodiments.

[0164] Figure 11 A schematic diagram of the hardware structure of the electronic device provided in an embodiment of this application is shown.

[0165] The electronic device may include a processor 1101 and a memory 1102 storing computer program instructions.

[0166] Specifically, the processor 1101 may include a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0167] Memory 1102 may include mass storage for data or instructions. For example, and not limitingly, memory 1102 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. In one example, memory 1102 may include removable or non-removable (or fixed) media, or memory 1102 may be non-volatile solid-state memory. Memory 1102 may be internal or external to an electronic device.

[0168] In one example, memory 1102 may be read-only memory (ROM). In one example, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0169] Memory 1102 may include read-only memory (ROM), random access memory (RAM), disk storage media device, optical storage media device, flash memory device, electrical, optical, or other physical / tangible memory storage device. Therefore, generally, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to one aspect of this application.

[0170] The processor 1101 reads and executes the computer program instructions stored in the memory 1102 to implement the methods / steps in the above method embodiments and achieve the corresponding technical effects achieved by the method embodiments in executing their methods / steps. For the sake of brevity, these will not be elaborated here.

[0171] In one example, the electronic device may also include a communication interface 1103 and a bus 1110. For example, Figure 11 As shown, the processor 1101, memory 1102, and communication interface 1103 are connected through bus 1110 and complete communication with each other.

[0172] The communication interface 1103 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0173] Bus 1110 includes hardware, software, or both, that couples components of an electronic device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 1110 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.

[0174] Furthermore, in conjunction with the data processing method for the display panel in the above embodiments, this application embodiment can provide a computer-readable storage medium for implementation. This computer-readable storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the data processing methods for the display panel in the above embodiments. Examples of computer-readable storage media include non-transitory computer-readable storage media, such as electronic circuits, semiconductor memory devices, ROM, random access memory, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, and hard disks.

[0175] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0176] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0177] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0178] The aspects of this application have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by dedicated hardware performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0179] The above description is only a specific embodiment of the present application. Those skilled in the art will clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the scope of protection of the present application.

Claims

1. A data processing method for a display panel, characterized in that, include: Obtain brightness compensation data for at least one grayscale binding point; Based on the target vector quantization algorithm, the brightness compensation data of the at least one grayscale binding point is compressed; The compressed brightness compensation data is stored in the storage unit; The compression of the brightness compensation data of the at least one grayscale binding point based on the target vector quantization algorithm specifically includes: The display panel is divided into multiple partitions, and each partition includes multiple sub-pixels; For any given partition, the brightness compensation data of multiple sub-pixels in the partition at the target grayscale binding point is divided into N intervals, and the center value of each interval is determined based on the brightness compensation data of the sub-pixels contained in each interval at the target grayscale binding point. The target grayscale binding point is any grayscale binding point, and N is a positive integer. For any sub-pixel in the partition, generate the index value corresponding to the sub-pixel based on the interval to which the brightness compensation data of the sub-pixel belongs; The step of storing the compressed brightness compensation data in the storage unit specifically includes: For any given partition, the center value of each interval in the partition and the index value corresponding to each sub-pixel in the partition are stored in the storage unit; The step of determining the center value of each interval based on the brightness compensation data of the sub-pixels contained in each interval when binding points to the target grayscale specifically includes: For any given interval, calculate the average, median, or mode of the brightness compensation data of the sub-pixels contained in the interval when the target grayscale is bound, and use the average, median, or mode as the center value of the interval.

2. The data processing method according to claim 1, characterized in that, Before storing the center value of each interval in the partition and the index value corresponding to each sub-pixel in the partition into the storage unit, the data processing method further includes: The number of digits in the center value is limited from the first digit to the second digit, where the second digit is less than the first digit. And / or, limit the number of digits of the subscript value to a third digit, the third digit being less than the first digit; The step of storing the center value of each interval in the partition and the index value corresponding to each sub-pixel in the partition into the storage unit specifically includes: The center value of each interval in the partition after limiting the number of bits and the index value of each sub-pixel in the partition after limiting the number of bits are stored in the storage unit; Alternatively, the center value of each interval in the partition after limiting the number of bits and the index value corresponding to each sub-pixel in the partition can be stored in the storage unit; Alternatively, the center value of each interval in the partition and the index value of each sub-pixel in the partition after limiting the number of bits can be stored in the storage unit.

3. The data processing method according to claim 2, characterized in that, The third digit is less than the second digit.

4. The data processing method according to claim 1, characterized in that, Before storing the center value of each interval in the partition and the index value corresponding to each sub-pixel in the partition into the storage unit, the data processing method further includes: Based on the Huffman coding algorithm, the index value corresponding to each sub-pixel is compressed; The step of storing the center value of each interval in the partition and the index value corresponding to each sub-pixel in the partition into the storage unit specifically includes: The center value of each interval in the partition and the compressed index value of each sub-pixel in the partition are stored in the storage unit.

5. The data processing method according to claim 4, characterized in that, The compression of the index value corresponding to each sub-pixel based on the Huffman coding algorithm specifically includes: Convert the index value that appears at the first frequency into an encoding of the first length; Convert the index value that appears at the second frequency into an encoding of the second length; Wherein, the first frequency is greater than the second frequency, and the first length is less than the second length.

6. The data processing method according to claim 1, characterized in that, Before compressing the brightness compensation data of the at least one grayscale binding point based on the target vector quantization algorithm, the data processing method further includes: Based on the block compression algorithm, mean compression is performed on the brightness compensation data of the at least one grayscale binding point. The compression of the brightness compensation data of the at least one grayscale binding point based on the target vector quantization algorithm specifically includes: Based on the target vector quantization algorithm, the brightness compensation data after the mean compression of the at least one grayscale binding point is compressed.

7. The data processing method according to claim 1, characterized in that, The partition includes multiple color sub-pixels; The step of dividing the brightness compensation data of multiple sub-pixels in the partition into N intervals when binding the target grayscale point, and determining the center value of each interval based on the brightness compensation data of the sub-pixels contained in each interval when binding the target grayscale point, specifically includes: For any i-th color sub-pixel in the partition, divide the brightness compensation data of multiple i-th color sub-pixels in the partition into N intervals when the target gray level is bound. Based on the brightness compensation data of the i-th color sub-pixels contained in each interval when the target gray level is bound, determine the center value of the i-th color sub-pixel in each interval, where i is a positive integer and 1≤i≤N. For any sub-pixel in the partition, based on the interval to which the brightness compensation data of the sub-pixel belongs, the index value corresponding to the sub-pixel is generated, specifically including: For any i-th color sub-pixel in the partition, generate the index value corresponding to the i-th color sub-pixel according to the interval to which the brightness compensation data of the i-th color sub-pixel belongs.

8. The data processing method according to claim 1, characterized in that, Before storing the center value of each interval in the partition and the index value corresponding to each sub-pixel in the partition into the storage unit, the data processing method further includes: Based on the Huffman coding algorithm, the center value of each interval in the partition is compressed; The step of storing the center value of each interval in the partition and the index value corresponding to each sub-pixel in the partition into the storage unit specifically includes: The compressed center value of each interval in the partition and the index value corresponding to each sub-pixel in the partition are stored in the storage unit.

9. The data processing method according to claim 4, characterized in that, After storing the center values ​​of each interval in the partition and the compressed index values ​​of each sub-pixel in the partition into the storage unit, the data processing method further includes: Based on the decoding algorithm corresponding to the Huffman coding algorithm, the compressed index value of each sub-pixel in the partition is decompressed; For any sub-pixel in the partition, the center value corresponding to the sub-pixel at the target gray level binding point is determined according to the correspondence between the index value and the center value at the target gray level binding point and the index value after the sub-pixel is decompressed. The center value corresponding to the sub-pixel is used as the grayscale compensation value of the sub-pixel when it is at the target grayscale binding point.

10. A data processing device for a display panel, characterized in that, include: The acquisition module is used to acquire brightness compensation data for at least one grayscale binding point; The first compression module is used to compress the brightness compensation data of the at least one grayscale binding point based on the target vector quantization algorithm. The storage module is used to store the compressed brightness compensation data into the storage unit; The compression of the brightness compensation data of the at least one grayscale binding point based on the target vector quantization algorithm specifically includes: The display panel is divided into multiple partitions, and each partition includes multiple sub-pixels; For any given partition, the brightness compensation data of multiple sub-pixels in the partition at the target grayscale binding point is divided into N intervals, and the center value of each interval is determined based on the brightness compensation data of the sub-pixels contained in each interval at the target grayscale binding point. The target grayscale binding point is any grayscale binding point, and N is a positive integer. For any sub-pixel in the partition, generate the index value corresponding to the sub-pixel based on the interval to which the brightness compensation data of the sub-pixel belongs; The step of storing the compressed brightness compensation data in the storage unit specifically includes: For any given partition, the center value of each interval in the partition and the index value corresponding to each sub-pixel in the partition are stored in the storage unit; The step of determining the center value of each interval based on the brightness compensation data of the sub-pixels contained in each interval when binding points to the target grayscale specifically includes: For any given interval, calculate the average, median, or mode of the brightness compensation data of the sub-pixels contained in the interval when the target grayscale is bound, and use the average, median, or mode as the center value of the interval.

11. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the steps of the data processing method for the display panel as described in any one of claims 1 to 9.

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