Display driving device, display driving method, chip and electronic equipment

Abstract

The invention discloses a display driving device, a display driving method, a chip and electronic equipment, and belongs to the technical field of display. The display driving device comprises a processing unit and a storage unit. The processing unit is configured to pre-process a target image displayed by the display panel to obtain a pre-processed image, and the pre-processing is used for reducing high-frequency information in the target image; based on the image data of the preprocessed image, acquiring accumulated data; carrying out lossless compression on the accumulated data to obtain compressed accumulated data; a storage unit configured to store the compressed accumulated data; and the processing unit is further configured to read the compressed accumulated data from the storage unit, decompress the compressed accumulated data to obtain the accumulated data, and perform aging compensation on the display panel based on the accumulated data. Therefore, storage resources and cost can be saved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of display technology, and in particular to a display driving device, a display driving method, a chip and an electronic device. Background Art

[0002] With the development of display technology, display panels are increasingly widely used. After long-term use, the brightness, color and clarity of the display panel will gradually decrease, resulting in poor display effect. This phenomenon is called aging. The display driving device can perform aging compensation on the display panel to eliminate the aging mark of the display panel.

[0003] In the related art, the display driving device includes a processing unit and a storage unit. The processing unit directly obtains the accumulated data based on the image data of the image displayed by the display panel, and stores the accumulated data in the storage unit. When aging compensation is required, the accumulated data is read from the storage unit, and then the aging compensation is performed on the display panel based on the accumulated data. The accumulated data is a kind of data used to measure the aging degree of the display panel.

[0004] The amount of accumulated data on which aging compensation is based is usually large. Directly storing the accumulated data in a storage unit will occupy a large storage space of the storage unit, consume more storage resources, and be costly. Summary of the invention

[0005] The embodiments of the present application provide a display driving device, a display driving method, a chip and an electronic device, which can be used to save storage resources and costs. The technical solution is as follows:

[0006] In one aspect, an embodiment of the present application provides a display driving device, the display driving device comprising a processing unit and a storage unit;

[0007] The processing unit is configured to preprocess the target image displayed on the display panel to obtain a preprocessed image, wherein the preprocessing is used to reduce high-frequency information in the target image; obtain accumulated data based on image data of the preprocessed image; and losslessly compress the accumulated data to obtain compressed accumulated data;

[0008] The storage unit is configured to store the compressed accumulated data;

[0009] The processing unit is further configured to read the compressed accumulated data from the storage unit, decompress the compressed accumulated data to obtain the accumulated data, and perform aging compensation on the display panel based on the accumulated data.

[0010] In a possible implementation, the processing unit is configured to:

[0011] Decomposing the accumulated data into at least two accumulated sub-data;

[0012] performing lossless compression on the at least two accumulated sub-data respectively to obtain compressed data corresponding to the at least two accumulated sub-data respectively;

[0013] The compressed accumulated data is acquired based on the compressed data respectively corresponding to the at least two accumulated sub-data.

[0014] In a possible implementation, any one of the at least two accumulated sub-data includes at least one data block, and any one of the at least one data block includes at least one accumulated sub-value; and the processing unit is configured to:

[0015] For any data block among the at least one data block, when the any data block satisfies the data flatness condition, the any data block is losslessly compressed using a first encoding method to obtain compressed data corresponding to the any data block; when the any data block does not satisfy the data flatness condition, the any data block is losslessly compressed using a second encoding method to obtain compressed data corresponding to the any data block;

[0016] Based on the compressed data corresponding to the at least one data block, the compressed data corresponding to any one of the accumulated sub-data is obtained.

[0017] In a possible implementation, the processing unit is configured to:

[0018] In the case where any of the data blocks meets the data flatness condition, arranging the accumulated sub-values ​​in the any of the data blocks to obtain arranged accumulated sub-values;

[0019] identifying consecutive repetitive portions in the arranged accumulated sub-values;

[0020] Each continuous repetitive part is encoded into a cumulative sub-value and a repetition number corresponding to the continuous repetitive part, so as to obtain compressed data corresponding to any data block.

[0021] In a possible implementation, the processing unit is configured to:

[0022] In a case where any of the data blocks does not satisfy the data flatness condition, for any accumulated sub-value in the any of the data blocks, determining a predicted value corresponding to the any accumulated sub-value;

[0023] Performing entropy coding on a difference between any one of the accumulated sub-values ​​and the predicted value to obtain a compressed value corresponding to any one of the accumulated sub-values;

[0024] Based on the compression values ​​corresponding to the accumulated sub-values ​​in the any data block, the compressed data corresponding to the any data block is obtained.

[0025] In a possible implementation, the processing unit is configured to:

[0026] For any pixel point among the pixels in the target image, if there is a covering pixel point at the position of any pixel point, the pixel value of any pixel point is updated to the pixel value of the covering pixel point; if there is no covering pixel point at the position of any pixel point, the pixel value of any pixel point is updated to the reference value; after the pixel values ​​of all the pixels are updated, the preprocessed image is obtained; wherein, the covering pixel point is a pixel point located at the position of any pixel point after the pixels are moved according to a moving method; the moving method is a moving method of the trajectory point corresponding to the target image in the reference moving trajectory relative to the starting point of the reference moving trajectory; or,

[0027] For any pixel point among the pixels in the target image, based on the pixel values ​​of the pixels in the neighborhood of the pixel point, obtain the filtered pixel value of the pixel point; based on the filtered pixel values ​​of the pixels, obtain the preprocessed image; or,

[0028] Convert the target image from the spatial domain to the frequency domain to obtain a converted image; perform low-pass filtering on the converted image to obtain a filtered image; convert the filtered image from the frequency domain back to the spatial domain to obtain the preprocessed image; or,

[0029] The target image is downsampled to obtain a downsampled image; and the downsampled image is upsampled to obtain the preprocessed image.

[0030] In a possible implementation, the processing unit is configured to:

[0031] Preprocessing the reference image using at least two candidate preprocessing algorithms to obtain preprocessed images corresponding to the at least two candidate preprocessing algorithms, respectively, wherein the at least two candidate preprocessing algorithms are used to reduce high-frequency information in the reference image;

[0032] For any candidate preprocessing algorithm among the at least two candidate preprocessing algorithms, based on the image data of the preprocessed image corresponding to the any candidate preprocessing algorithm, the accumulated data corresponding to the any candidate preprocessing algorithm is obtained; the accumulated data corresponding to the any candidate preprocessing algorithm is losslessly compressed to obtain the compressed accumulated data corresponding to the any candidate preprocessing algorithm; based on the ratio of the data volume of the accumulated data corresponding to the any candidate preprocessing algorithm to the data volume of the compressed accumulated data corresponding to the any candidate preprocessing algorithm, the compression ratio corresponding to the any candidate preprocessing algorithm is determined;

[0033] Determining a target preprocessing algorithm from among the at least two candidate preprocessing algorithms based on compression ratios respectively corresponding to the at least two candidate preprocessing algorithms;

[0034] The target image is preprocessed using the target preprocessing algorithm to obtain the preprocessed image.

[0035] On the other hand, an embodiment of the present application further provides a display driving method, the method comprising:

[0036] Preprocessing the target image displayed on the display panel to obtain a preprocessed image, wherein the preprocessing is used to reduce high-frequency information in the target image;

[0037] acquiring accumulated data based on the image data of the preprocessed image;

[0038] Losslessly compressing the accumulated data to obtain compressed accumulated data;

[0039] storing the compressed accumulated data;

[0040] The compressed accumulated data is read, the compressed accumulated data is decompressed to obtain the accumulated data, and aging compensation is performed on the display panel based on the accumulated data.

[0041] In a possible implementation, the losslessly compressing the accumulated data to obtain compressed accumulated data includes:

[0042] Decomposing the accumulated data into at least two accumulated sub-data;

[0043] performing lossless compression on the at least two accumulated sub-data respectively to obtain compressed data corresponding to the at least two accumulated sub-data respectively;

[0044] The compressed accumulated data is acquired based on the compressed data respectively corresponding to the at least two accumulated sub-data.

[0045] In a possible implementation, any one of the at least two accumulated sub-data includes at least one data block, and any one of the at least one data block includes at least one accumulated sub-value; and the losslessly compressing the at least two accumulated sub-data to obtain compressed data corresponding to the at least two accumulated sub-data includes:

[0046] For any data block among the at least one data block, when the any data block satisfies the data flatness condition, the any data block is losslessly compressed using a first encoding method to obtain compressed data corresponding to the any data block; when the any data block does not satisfy the data flatness condition, the any data block is losslessly compressed using a second encoding method to obtain compressed data corresponding to the any data block;

[0047] Based on the compressed data corresponding to the at least one data block, the compressed data corresponding to any one of the accumulated sub-data is obtained.

[0048] In a possible implementation, when any data block satisfies a data flatness condition, losslessly compressing any data block by using a first encoding method to obtain compressed data corresponding to any data block includes:

[0049] In the case where any of the data blocks meets the data flatness condition, arranging the accumulated sub-values ​​in the any of the data blocks to obtain arranged accumulated sub-values;

[0050] identifying consecutive repetitive portions in the arranged accumulated sub-values;

[0051] Each continuous repetitive part is encoded into a cumulative sub-value and a repetition number corresponding to the continuous repetitive part, so as to obtain compressed data corresponding to any data block.

[0052] In a possible implementation, when any of the data blocks does not satisfy the data flatness condition, losslessly compressing the any of the data blocks using a second encoding method to obtain compressed data corresponding to the any of the data blocks includes:

[0053] In a case where any of the data blocks does not satisfy the data flatness condition, for any accumulated sub-value in the any of the data blocks, determining a predicted value corresponding to the any accumulated sub-value;

[0054] Performing entropy coding on a difference between any one of the accumulated sub-values ​​and the predicted value to obtain a compressed value corresponding to any one of the accumulated sub-values;

[0055] Based on the compression values ​​corresponding to the accumulated sub-values ​​in the any data block, the compressed data corresponding to the any data block is obtained.

[0056] In a possible implementation, preprocessing the target image displayed by the display panel to obtain a preprocessed image includes:

[0057] For any pixel point among the pixels in the target image, if there is a covering pixel point at the position of any pixel point, the pixel value of any pixel point is updated to the pixel value of the covering pixel point; if there is no covering pixel point at the position of any pixel point, the pixel value of any pixel point is updated to the reference value; after the pixel values ​​of all the pixels are updated, the preprocessed image is obtained; wherein, the covering pixel point is a pixel point located at the position of any pixel point after the pixels are moved according to a moving method; the moving method is a moving method of the trajectory point corresponding to the target image in the reference moving trajectory relative to the starting point of the reference moving trajectory; or,

[0058] For any pixel point among the pixels in the target image, based on the pixel values ​​of the pixels in the neighborhood of the pixel point, obtain the filtered pixel value of the pixel point; based on the filtered pixel values ​​of the pixels, obtain the preprocessed image; or,

[0059] Convert the target image from the spatial domain to the frequency domain to obtain a converted image; perform low-pass filtering on the converted image to obtain a filtered image; convert the filtered image from the frequency domain back to the spatial domain to obtain the preprocessed image; or,

[0060] The target image is downsampled to obtain a downsampled image; and the downsampled image is upsampled to obtain the preprocessed image.

[0061] In a possible implementation, preprocessing the target image displayed by the display panel to obtain a preprocessed image includes:

[0062] Preprocessing the reference image using at least two candidate preprocessing algorithms to obtain preprocessed images corresponding to the at least two candidate preprocessing algorithms, respectively, wherein the at least two candidate preprocessing algorithms are used to reduce high-frequency information in the reference image;

[0063] For any candidate preprocessing algorithm among the at least two candidate preprocessing algorithms, based on the image data of the preprocessed image corresponding to the any candidate preprocessing algorithm, the accumulated data corresponding to the any candidate preprocessing algorithm is obtained; the accumulated data corresponding to the any candidate preprocessing algorithm is losslessly compressed to obtain the compressed accumulated data corresponding to the any candidate preprocessing algorithm; based on the ratio of the data volume of the accumulated data corresponding to the any candidate preprocessing algorithm to the data volume of the compressed accumulated data corresponding to the any candidate preprocessing algorithm, the compression ratio corresponding to the any candidate preprocessing algorithm is determined;

[0064] Determining a target preprocessing algorithm from among the at least two candidate preprocessing algorithms based on compression ratios respectively corresponding to the at least two candidate preprocessing algorithms;

[0065] The target image is preprocessed using the target preprocessing algorithm to obtain the preprocessed image.

[0066] On the other hand, an embodiment of the present application further provides a chip, wherein the chip includes any of the display driving devices described above.

[0067] On the other hand, an embodiment of the present application further provides an electronic device, the electronic device comprising:

[0068] Display panel;

[0069] And any one of the above-mentioned display driving devices, wherein the display driving device is used to drive the display panel.

[0070] The technical solution provided by the embodiments of the present application brings at least the following beneficial effects:

[0071] According to the technical solution provided by the embodiment of the present application, the storage unit stores compressed accumulated data obtained by losslessly compressing the accumulated data. The amount of compressed accumulated data is small and the accumulated data can be restored losslessly, which is beneficial to reducing the storage space occupied in the storage unit while ensuring the aging compensation effect, thereby saving storage resources and saving costs.

[0072] In addition, the cumulative data is obtained based on the image data of the preprocessed image. The preprocessed image is obtained by reducing the high-frequency information in the image originally displayed by the display panel. The reduction of high-frequency information has less interference on the visual effect of the image, but the cumulative data obtained based on the preprocessed image with less high-frequency information has higher redundancy and is easier to compress, which is conducive to achieving a higher compression rate for lossless compression while ensuring the visual effect, further saving storage resources and costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of 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 creative work.

[0074] Figure 1 is a structural schematic diagram of a display driving device provided in an embodiment of the present application;

[0075] Figure 2 is a schematic diagram of a process of decomposing accumulated data into at least two accumulated sub-data provided by an embodiment of the present application;

[0076] Figure 3 is a schematic diagram of an arrangement result of any cumulative sub-value, a first cumulative sub-value, a second cumulative sub-value, a third cumulative sub-value, a fourth cumulative sub-value, and a fifth cumulative sub-value provided in an embodiment of the present application;

[0077] Figure 4 It is a schematic diagram of a process of losslessly compressing accumulated data to obtain compressed accumulated data provided by an embodiment of the present application;

[0078] Figure 5 is a schematic diagram of an aging compensation process provided by an embodiment of the present application;

[0079] Figure 6 is a flow chart of a display driving method provided by an embodiment of the present application;

[0080] Figure 7 is a schematic diagram of the structure of a chip provided in an embodiment of the present application;

[0081] Figure 8 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0082] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0083] With the development of display technology, the application of display panels is becoming more and more extensive. After long-term use, the brightness, color and clarity of the display panel will gradually decrease, resulting in a poor display effect. This phenomenon is called aging (also known as screen burn-in). In other words, if the display panel displays videos or static images for a long time, it will cause serious aging of the display panel, and ghosting will appear on the display panel after the display panel is restarted. In some embodiments, the display panel may refer to an organic light emitting diode (OLED) display panel. OLED display panels are widely used in various consumer electronic products such as mobile phones, televisions, laptops, desktop computers, etc. due to their advantages such as high image quality, power saving, thin body and wide range of applications, and have become the mainstream in display products.

[0084] The display driving device can perform aging compensation on the display panel to eliminate the aging imprint of the display panel. Exemplarily, the display driving device can use DBI (De burn in) technology to perform aging compensation on the display panel. DBI technology is a technology for eliminating aging imprints. The basic principle of DBI technology is: according to the image displayed by the display panel, the accumulated data based on the aging compensation is obtained, the aging degree of the entire display panel is estimated according to the accumulated data, and the display panel is compensated for aging using compensation parameters matching the aging degree, so as to improve the brightness uniformity of the display panel, reduce image retention, and increase the life of the display panel.

[0085] In the related art, the display driving device includes a processing unit and a storage unit. The processing unit directly obtains the accumulated data based on the image data of the image displayed by the display panel, and stores the accumulated data in the storage unit. When aging compensation is required, the accumulated data is read from the storage unit, and then the aging compensation is performed on the display panel based on the accumulated data.

[0086] The image data of an image includes the pixel value of each pixel in the image. For a color image, the pixel value of each pixel includes sub-pixel values ​​of three components: R (red), G (green), and B (blue). The R component, G component, and B component have different characteristics, and each component has independent color accumulation, so the amount of accumulated data based on aging compensation is very large. Directly storing the accumulated data in a storage unit will occupy a large storage space of the storage unit, consume more storage resources, and be costly.

[0087] The embodiment of the present application provides a display driving device, which can be used to save storage resources and costs. Figure 1 As shown, the display driving device provided in the embodiment of the present application includes a processing unit 1 and a storage unit 2. The storage unit 2 can store data processed by the processing unit 1.

[0088] The processing unit 1 is configured to preprocess the target image displayed on the display panel to obtain a preprocessed image, wherein the preprocessing is used to reduce high-frequency information in the target image; obtain accumulated data based on image data of the preprocessed image; and losslessly compress the accumulated data to obtain compressed accumulated data;

[0089] A storage unit 2, configured to store compressed accumulated data;

[0090] The processing unit 1 is further configured to read the compressed accumulated data from the storage unit 2, decompress the compressed accumulated data to obtain accumulated data, and perform aging compensation on the display panel based on the accumulated data.

[0091] In the display driving device provided in the embodiment of the present application, the storage unit 2 stores compressed accumulated data obtained by losslessly compressing the accumulated data. The amount of compressed accumulated data is small, and the accumulated data can be restored losslessly, which is beneficial to reducing the storage space occupied in the storage unit 2 while ensuring the aging compensation effect, thereby saving storage resources and saving costs.

[0092] In addition, the cumulative data is obtained based on the image data of the preprocessed image. The preprocessed image is obtained by reducing the high-frequency information in the image originally displayed by the display panel. The reduction of high-frequency information has less interference on the visual effect of the image, but the cumulative data obtained based on the preprocessed image with less high-frequency information has higher redundancy and is easier to compress, which is conducive to achieving a higher compression rate for lossless compression while ensuring the visual effect, further saving storage resources and costs.

[0093] The target image is an image based on which the cumulative data is obtained from the image displayed on the display panel. The number of target images may be one or more, and the embodiments of the present application do not limit this. For example, in the case where the number of target images is multiple, the target image may refer to a continuous multi-frame image displayed in the display panel. For example, the target image may refer to a multi-frame image displayed by the display panel for 1000 consecutive hours. When the number of target images is multiple, preprocessing is performed on each target image to obtain a preprocessed image corresponding to each target image, and then cumulative data is obtained based on the image data of the preprocessed images corresponding to the multiple target images. For each target image, the target image may refer to a static image or a frame of image in a video.

[0094] The principle of preprocessing each target image is the same. In the embodiment of the present application, the number of target images is one as an example to introduce the process of preprocessing the target image by the processing unit 1.

[0095] The target image includes a plurality of pixels, each of which has its own pixel value, and the pixel value of each pixel determines the display effect of the target image in the display panel. In some embodiments, the display panel displays the target image means that the display panel displays the target image in full screen, based on which, the pixels in the target image correspond to the pixels on the display panel one by one, and it can also be understood that the pixels in the target image cover the pixels on the display panel one by one, that is, the arrangement of the pixels in the target image is the same as the arrangement of the pixels on the display panel.

[0096] The embodiment of the present application does not limit the representation form of the pixel value of the pixel point in the target image. For example, if the target image is a grayscale image, the pixel value of the pixel point in the target image can be represented by one grayscale data, and if the target image is a color image, the pixel value of the pixel point in the target image can be represented by three grayscale data, and the three grayscale data respectively represent the grayscale data corresponding to the R component, the grayscale data corresponding to the G component, and the grayscale data corresponding to the B component.

[0097] Grayscale data is used to represent the different brightness levels from the darkest to the brightest. The more intermediate levels there are, the more delicate the picture effect can be. Grayscale data can be expressed in bits. The more bits there are, the more complex the image display effect can be. For example, grayscale data can be expressed using 8-bit binary numbers. This grayscale data can present 256 (2 8 =256) levels.

[0098] The purpose of the processing unit 1 preprocessing the target image is to reduce the high-frequency information in the target image. The high-frequency information in the target image refers to the area in the target image where the pixel value changes dramatically, corresponding to the edge, noise and detail of the target image. The human eye is less sensitive to high-frequency information, and the visual attention is mainly focused on low-frequency information. Therefore, the impact of high-frequency information on the visual effect is relatively small. Therefore, reducing the interference of high-frequency information on the visual effect of the target image is small. That is, the preprocessed image can be visually lossless compared to the target image.

[0099] In the embodiment of the present application, the processing unit 1 performs preprocessing on the target image to obtain the preprocessed image in an implementation manner that is not limited, as long as it can ensure that the high-frequency information in the target image can be reduced. The processing unit 1 performs preprocessing on the target image to obtain the preprocessed image in an implementation manner that includes but is not limited to the following four methods:

[0100] Implementation method 1: For any pixel in the target image, if there is a covering pixel at the position of any pixel, update the pixel value of any pixel to the pixel value of the covering pixel; if there is no covering pixel at the position of any pixel, update the pixel value of any pixel to the reference value; after the pixel values ​​of all pixels are updated, a preprocessed image is obtained; wherein, the covering pixel is the pixel located at the position of any pixel after moving each pixel according to the moving method; the moving method is the movement method of the corresponding trajectory point of the target image in the reference moving trajectory relative to the starting point of the reference moving trajectory. This method of preprocessing the target image can be considered as preprocessing the target image using MIA (Moving Image Algorithm). The working principle of MIA is to periodically move the image along a certain trajectory without being noticed by the user. MIA can be used to compensate for the residual image (aging effect) of the OLED display panel.

[0101] In the above implementation method 1, the processing unit 1 needs to update the pixel value of each pixel in the target image during the process of preprocessing the target image. In the process of updating the pixel value of any pixel in the target image, it is first determined whether there is an overlay pixel at the position of any pixel. The overlay pixel refers to the pixel located at the position of any pixel after the pixels in the target image are moved according to the moving method, and the moving method refers to the movement method of the corresponding trajectory point of the target image in the reference moving trajectory relative to the starting point of the reference moving trajectory.

[0102] The reference movement trajectory may be any form of movement trajectory, for example, a U-shaped movement trajectory, a mouth-shaped movement trajectory, etc. The reference movement trajectory includes a plurality of trajectory points, including a starting point and other trajectory points arranged in sequence from the starting point along the movement direction. The reference movement trajectory may be set based on experience or flexibly adjusted based on the application scenario, which is not limited in the embodiments of the present application.

[0103] The trajectory point corresponding to the target image in the reference movement trajectory may refer to the Kth trajectory point starting from the starting point in the reference movement trajectory. K can be determined based on the ranking M of the target image in all images that need to be preprocessed using implementation method 1 and the total number N of trajectory points in the reference movement trajectory. M is an integer not less than 1, N is an integer greater than 1, and K is an integer not less than 1 and not greater than N. Exemplarily, the remainder obtained after M is divided by N is calculated. If the remainder is 0, N is used as K (that is, the last trajectory point in the reference movement trajectory is used as the trajectory point corresponding to the target image in the reference movement trajectory). If the remainder is not 0, the remainder is used as K.

[0104] Each track point in the reference movement track has a movement mode relative to the starting point, and the movement mode can be represented by a number of upward, downward, leftward, or rightward movements.

[0105] Each pixel point in the target image is moved according to the movement mode of the trajectory point corresponding to the target image in the reference movement trajectory relative to the starting point of the reference movement trajectory. If there is a pixel point located at the position of any pixel point after the movement, it is considered that there is a covered pixel point at the position of any pixel point; if there is no pixel point located at the position of any pixel point after the movement, it is considered that there is no covered pixel point at the position of any pixel point.

[0106] In the case where there is an overlapping pixel at the position of any pixel, the method for updating the pixel value of any pixel is: updating the pixel value of any pixel to the pixel value of the overlapping pixel; in the case where there is no overlapping pixel at the position of any pixel, the method for updating the pixel value of any pixel is: updating the pixel value of any pixel to a reference value. The reference value can be set based on experience or flexibly adjusted according to the application scenario, which is not limited in the embodiments of the present application. For example, the reference value can refer to the maximum value, minimum value, median value, etc. within the value range of the pixel value of the pixel.

[0107] By referring to the method of updating the pixel value of any pixel point, the pixel value of each pixel point in the target image can be updated, and after the pixel value of each pixel point is updated, the preprocessed image is obtained. In other words, the pixel value of the pixel point in the preprocessed image refers to the updated pixel value of the corresponding pixel point in the target image.

[0108] Implementation method 2: For any pixel in the target image, based on the pixel values ​​of the pixels in the neighborhood of any pixel, obtain the filtered pixel value of any pixel; based on the filtered pixel values ​​of each pixel, obtain the preprocessed image. This method of preprocessing the target image can be considered as preprocessing the target image using a spatial domain filtering algorithm. Spatial domain filtering refers to directly filtering the pixel values ​​of the image.

[0109] The neighborhood of any pixel point refers to an area of ​​a certain size centered on any pixel point. For example, the neighborhood of any pixel point may refer to a 5*5 area centered on any pixel point, or a 3*3 area centered on any pixel point, etc., where 5 and 3 refer to the number of pixels.

[0110] Exemplarily, based on the pixel values ​​of the pixels in the neighborhood of any pixel point, an implementation method for obtaining the filtered pixel value of any pixel point includes: determining the weights corresponding to the pixels in the neighborhood of any pixel point, and based on the weights corresponding to the pixels in the neighborhood of any pixel point, performing weighted summation of the pixel values ​​of the pixels in the neighborhood of any pixel point, and using the pixel value obtained after the weighted summation as the filtered pixel value of the any pixel point.

[0111] The embodiments of the present application do not limit the implementation method of determining the weights corresponding to the pixels in the neighborhood of any pixel point. For example, the weights corresponding to the pixels in the neighborhood of any pixel point can be determined based on the distance between the pixels in the neighborhood of any pixel point and the any pixel point. The greater the distance between the pixels in the neighborhood of any pixel point and the any pixel point, the smaller the weight of the pixels in the neighborhood of any pixel point. For example, the method of determining the weights corresponding to the pixels in the neighborhood of any pixel point based on the distance between the pixels in the neighborhood of any pixel point and the any pixel point can be implemented using a Gaussian distribution function. In this case, the spatial domain filtering algorithm used for preprocessing the target image can be considered to be a Gaussian filtering algorithm.

[0112] Exemplarily, based on the pixel values ​​of the pixels in the neighborhood of any pixel point, the implementation method of obtaining the filtered pixel value of any pixel point includes: taking the average value of the pixel values ​​of the pixels in the neighborhood of any pixel point as the filtered pixel value of the any pixel point. In this case, the spatial domain filtering algorithm used for preprocessing the target image can be considered as a mean filtering algorithm.

[0113] Exemplarily, based on the pixel values ​​of the pixels in the neighborhood of any pixel, the implementation method of obtaining the filtered pixel value of any pixel includes: sorting the pixel values ​​of the pixels in the neighborhood of any pixel according to size, and taking the sorted middle value as the filtered pixel value of the any pixel. In this case, the spatial domain filtering algorithm used for preprocessing the target image can be considered as a median filtering algorithm.

[0114] The above-described implementation method of obtaining the filtered pixel value of any pixel point based on the pixel values ​​of the pixels within the neighborhood of any pixel point is only an illustrative example, and the embodiments of the present application are not limited thereto.

[0115] Implementation method 3: Convert the target image from the spatial domain to the frequency domain to obtain a converted image; perform low-pass filtering on the converted image to obtain a filtered image; convert the filtered image from the frequency domain back to the spatial domain to obtain a preprocessed image. This method of preprocessing the target image can be considered as preprocessing the target image using a frequency domain filtering algorithm. Frequency domain filtering refers to filtering after converting the image from the spatial domain to the frequency domain. Studies have found that the frequency filtering algorithm does not cause visually visible losses.

[0116] The process of converting the target image from the spatial domain to the frequency domain can be achieved through Fourier transform. Performing Fourier transform on the target image can convert the target image from the spatial domain to the frequency domain, thereby obtaining a converted image.

[0117] The conversion image is low-pass filtered to remove high-frequency information in the conversion image. Exemplarily, the process of low-pass filtering the conversion image can be implemented using any low-pass filter, and the types of low-pass filters may include but are not limited to: mean filter, Gaussian filter, median filter, bilateral filter, etc.

[0118] The process of converting the filtered image from the frequency domain back to the spatial domain can also be achieved through Fourier transform. Performing Fourier transform on the filtered image can convert the filtered image from the frequency domain back to the spatial domain, and the converted image is used as a preprocessed image.

[0119] Implementation method 4: down-sample the target image to obtain a down-sampled image; up-sample the down-sampled image to obtain a pre-processed image. This method of pre-processing the target image can be considered as pre-processing the target image using a sampling algorithm.

[0120] Downsampling the target image can reduce the size of the target image, that is, reduce the number of pixels in the target image. Exemplarily, the specific operation of downsampling the target image can be to divide the target image into multiple small blocks (such as 2*2, 3*3, 4*4, etc.), and calculate certain statistics (such as average value, maximum value, etc.) of the pixel values ​​of the pixels in each small block to represent the pixel value of the entire small block, thereby reducing the target image and reducing the high-frequency information in the target image.

[0121] Exemplarily, any downsampling method may be used to downsample the target image, and the image obtained after downsampling is called a downsampled image. Exemplarily, the downsampling method may include but is not limited to average pooling, maximum pooling, random pooling, etc.

[0122] Upsampling the downsampled image is used to restore the size of the downsampled image to the size of the target image. Exemplarily, any upsampling method can be used to upsample the downsampled image, and the image obtained after upsampling is called a preprocessed image. Exemplarily, the upsampling method can include but is not limited to nearest neighbor interpolation, bilinear interpolation, bicubic interpolation, etc.

[0123] After preprocessing the target image to obtain the preprocessed image, the processing unit 1 obtains the accumulated data based on the image data of the preprocessed image.

[0124] The image data of the preprocessed image is data used to represent the preprocessed image. Exemplarily, the image data of the preprocessed image includes pixel values ​​of each pixel point in the preprocessed image.

[0125] The accumulated data refers to the data required for calculating the aging compensation parameters of the display panel. The embodiment of the present application does not limit the implementation method of obtaining the accumulated data based on the image data of the pre-processed image.

[0126] Exemplarily, in the case where there are multiple target images, the number of pre-processed images is also multiple, and the image data of the multiple pre-processed images can be accumulated in units of pixels to obtain the accumulated image data of each pixel, and the accumulated data can be obtained based on the accumulated image data of each pixel.

[0127] Exemplarily, based on the accumulated value of the image data of each pixel point, the accumulated data can be obtained by adding data for characterizing the influencing factors of the aging degree to the accumulated image data of each pixel point to obtain the accumulated data. The influencing factors of the aging degree can include but are not limited to: the operating temperature when the display panel displays the target image, the operating voltage when the display panel displays the target image, the material of the display panel, etc.

[0128] Exemplarily, when the number of target images is one, the number of pre-processed images is also one. In this case, data for characterizing factors affecting the degree of aging can be added to the image data of the one pre-processed image to obtain cumulative data.

[0129] Exemplarily, the accumulated data may include accumulated values ​​corresponding to each pixel on the display panel. If the target image is a color image, the accumulated value corresponding to each pixel may include the accumulated value corresponding to the pixel under the R component, the accumulated value corresponding to the G component, and the accumulated value corresponding to the B component. Taking the bit depth of the accumulated data as 24 bits and the number of pixels on the display panel as 1920*1080 as an example, the size of the accumulated data is approximately 1920*1080*3*24bit=18Mbytes.

[0130] Taking the example that the target image refers to a plurality of frames of images displayed by the display panel for 1000 consecutive hours, the accumulated data may also be referred to as aging data obtained after the display panel is aged for 1000 hours.

[0131] Since the amount of accumulated data is large, directly storing the accumulated data will occupy a large storage space of the storage unit 2, thereby wasting storage resources and increasing costs. Therefore, in the embodiment of the present application, after acquiring the accumulated data, the processing unit 1 losslessly compresses the accumulated data to obtain compressed accumulated data, and then stores the compressed accumulated data to save storage space of the storage unit 2, save storage resources and costs.

[0132] In a possible implementation, the processing unit 1 is configured to: decompose the accumulated data into at least two accumulated sub-data; perform lossless compression on the at least two accumulated sub-data respectively to obtain compressed data corresponding to the at least two accumulated sub-data; and obtain compressed accumulated data based on the compressed data corresponding to the at least two accumulated sub-data. By decomposing the accumulated data into at least two accumulated sub-data, the at least two accumulated sub-data can be losslessly compressed respectively to obtain the final compressed accumulated data. The process of losslessly compressing the at least two accumulated sub-data can be performed in parallel, which is conducive to improving the efficiency of obtaining the compressed accumulated data.

[0133] The implementation method of decomposing the accumulated data into at least two accumulated sub-data can be set according to experience, or it can be flexibly adjusted according to actual conditions, and the embodiments of the present application do not limit this. Exemplarily, taking the bit depth of the accumulated data as Sbit as an example, the accumulated data can be decomposed into at least two accumulated sub-data according to the bit. Exemplarily, the accumulated data can be decomposed into 3 accumulated sub-data with a bit depth of S / 3bit according to the high bit, the middle bit and the low bit. Exemplarily, the accumulated data can also be decomposed into 2 accumulated sub-data with a bit depth of S / 2bit according to the high bit and the low bit. S is an integer greater than 1, for example, the value of S can be 24, or it can be 30, etc., which is related to the acquisition method of the accumulated data. In some embodiments, after analyzing the accumulated data, it is found that the high bit of the accumulated data has stronger repeatability and fewer numerical types. After the accumulated data is divided according to the bit, each accumulated sub-data can be more flexibly losslessly compressed to improve the compression effect.

[0134] Exemplarily, the accumulated data includes accumulated values ​​corresponding to each pixel on the display panel, and the accumulated value corresponding to each pixel includes the accumulated value corresponding to the pixel under the R component, the accumulated value corresponding to the G component, and the accumulated value corresponding to the B component. After the accumulated sub-data are decomposed into at least two accumulated sub-data, each accumulated sub-data includes the accumulated sub-values ​​corresponding to each pixel on the display panel, and the accumulated sub-values ​​corresponding to each pixel include the accumulated sub-value corresponding to the pixel under the R component, the accumulated sub-value corresponding to the G component, and the accumulated sub-value corresponding to the B component.

[0135] For example, taking the case where the accumulated value corresponding to each pixel includes the accumulated value corresponding to the pixel under the R component, the accumulated value corresponding to the G component, and the accumulated value corresponding to the B component, and the bit depth of the accumulated value corresponding to each component is 24 bits, the process of decomposing the accumulated data into at least two accumulated sub-data can be as follows: Figure 2 shown.

[0136] The 24-bit cumulative value corresponding to each pixel under the R component ( Figure 2 R (24 bits)) is decomposed into the high 8-bit cumulative sub-value corresponding to the pixel point under the R component ( Figure 2 R(H8bits) in the figure), the 8-bit cumulative subvalue ( Figure 2 R(M8bits)) and the lower 8 bits of the cumulative sub-value ( Figure 2 In the figure, it is represented as R(L8bits). The 24-bit cumulative value corresponding to each pixel under the G component ( Figure 2 denoted as G(24 bits)) is decomposed into the high 8-bit cumulative sub-value corresponding to the pixel point under the G component ( Figure 2 G(H8bits) in the figure), the 8-bit cumulative subvalue ( Figure 2 G(M8bits)) and the lower 8 bits of the cumulative sub-value ( Figure 2 In the figure, it is represented as G(L8bits). The 24-bit cumulative value corresponding to each pixel under the B component ( Figure 2 denoted as B (24 bits)) is decomposed into the high 8-bit cumulative sub-value corresponding to the pixel point under the B component ( Figure 2 In the expression B(H8bits), the 8-bit cumulative subvalue ( Figure 2 In the representation, it is B(M8bits)) and the lower 8 bits of the cumulative sub-value ( Figure 2 It is represented as B(L8bits)).

[0137] The principle of lossless compression performed by the processing unit 1 on different accumulated sub-data is the same. Next, the process of lossless compression performed by the processing unit 1 on any one of at least two accumulated sub-data is taken as an example for description.

[0138] In an exemplary embodiment, the processing unit 1 may use any lossless coding algorithm to perform lossless compression on any accumulated sub-data to obtain compressed data corresponding to the any accumulated sub-data. The lossless coding algorithm may refer to entropy coding, which includes but is not limited to Run-Length Encoding (RLE), Huffman Coding, arithmetic coding, etc.

[0139] In an exemplary embodiment, any accumulated sub-data includes at least one data block, and any data block in the at least one data block includes at least one accumulated sub-value. Each accumulated sub-value included in any data block corresponds to a pixel in the display panel. The embodiment of the present application does not limit the division method of the data blocks in any accumulated sub-data. Exemplarily, any accumulated sub-data can be regarded as a data block as a whole. Exemplarily, any accumulated sub-data includes accumulated sub-values ​​corresponding to each pixel on the display panel. In this case, each pixel can be divided into multiple pixel areas, and the data containing the accumulated sub-values ​​corresponding to the pixels in a pixel area in any accumulated sub-data can be regarded as a data block.

[0140] In an exemplary embodiment, when any accumulated sub-data includes at least one data block, and any data block in at least one data block includes at least one accumulated sub-value, the processing unit 1 may perform lossless compression on any accumulated sub-data in the following manner: for any data block in at least one data block, when any data block satisfies a data flatness condition, the first encoding method is used to perform lossless compression on any data block to obtain compressed data corresponding to any data block; when any data block does not satisfy the data flatness condition, the second encoding method is used to perform lossless compression on any data block to obtain compressed data corresponding to any data block; based on the compressed data corresponding to at least one data block, compressed data corresponding to any accumulated sub-data is obtained. The first encoding method is different from the second encoding method, based on which, according to the data situation of the data block in any accumulated sub-data, a suitable encoding method can be used to perform lossless compression on the data block, thereby facilitating improving the effect of lossless compression.

[0141] If any data block satisfies the data flatness condition, it means that the flatness of the data in the data block is relatively high, and if any data block does not satisfy the data flatness condition, it means that the flatness of the data in the data block is relatively low. Exemplarily, the method for determining whether any data block satisfies the data flatness condition may be: calculating the flatness measurement index corresponding to any data block, if the flatness measurement index is greater than the flatness threshold, then it is determined that any data block satisfies the data flatness condition; if the flatness measurement index is not greater than the flatness threshold, then it is determined that any data block does not satisfy the data flatness condition. The flatness measurement index is used to measure the flatness of the data in any data block, and the flatness measurement index is positively correlated with the flatness of the data in any data block. The flatness threshold can be set based on experience, or it can be flexibly adjusted according to the application scenario, and the embodiments of the present application do not limit this.

[0142] The embodiment of the present application does not limit the method for calculating the flatness measurement index corresponding to any data block. Exemplarily, the method for calculating the flatness measurement index corresponding to any data block includes: calculating the sub-flatness corresponding to each cumulative sub-value in any data block, and determining the flatness measurement index corresponding to any data block based on the sub-flatness corresponding to each cumulative value.

[0143] In an exemplary embodiment, the sub-flatness corresponding to any cumulative sub-value can be determined based on the gradient value corresponding to the any cumulative sub-value. The gradient value corresponding to any cumulative sub-value is used to measure the degree of change of the surrounding cumulative sub-values ​​of the any cumulative sub-value. The greater the degree of change of the surrounding cumulative sub-values ​​of the any cumulative sub-value, the greater the gradient value corresponding to the any cumulative sub-value. Exemplarily, the surrounding cumulative sub-values ​​of any cumulative sub-value refer to the cumulative sub-values ​​of the corresponding pixel points adjacent to the pixel point corresponding to the any cumulative sub-value. Exemplarily, the gradient value corresponding to any cumulative sub-value may refer to the average value of the difference between the surrounding cumulative sub-values ​​and the any cumulative sub-value, or may refer to the sum of the difference between the surrounding cumulative sub-values ​​and the any cumulative sub-value, etc.

[0144] The sub-flatness corresponding to any cumulative sub-value is negatively correlated with the gradient value corresponding to the cumulative sub-value, that is, the greater the degree of change of the surrounding cumulative sub-values ​​of any cumulative sub-value, the smaller the sub-flatness corresponding to the cumulative sub-value. For example, the reciprocal of the gradient value corresponding to any cumulative sub-value can be used as the sub-flatness corresponding to the cumulative sub-value.

[0145] The embodiment of the present application does not limit the method of determining the flatness measurement index corresponding to any data block based on the sub-flatness corresponding to each cumulative value. Exemplarily, the method of determining the flatness measurement index corresponding to any data block based on the sub-flatness corresponding to each cumulative value may be: taking the sum of the sub-flatness corresponding to each cumulative value as the flatness measurement index corresponding to any data block. Exemplarily, the method of determining the flatness measurement index corresponding to any data block based on the sub-flatness corresponding to each cumulative value may also be: taking the average value of the sub-flatness corresponding to each cumulative value as the flatness measurement index corresponding to any data block. Exemplarily, the method of determining the flatness measurement index corresponding to any data block based on the sub-flatness corresponding to each cumulative value may also be: taking the maximum value or minimum value among the sub-flatness corresponding to each cumulative value as the flatness measurement index corresponding to any data block.

[0146] When any data block meets the data flatness condition, the first encoding method is used to perform lossless compression on the data block; when any data block does not meet the data flatness condition, the second encoding method is used to perform lossless compression on the data block. The first encoding method is a method that is more suitable for lossless compression of data with higher flatness (that is, it can achieve a higher compression rate for data with higher flatness), and the second encoding method is a method that is more suitable for lossless compression of data with lower flatness (that is, it can achieve a higher compression rate for data with lower flatness).

[0147] In a possible implementation, when any data block satisfies the data flatness condition, the processing unit 1 uses the first encoding method to perform lossless compression on any data block, and the implementation method of obtaining the compressed data corresponding to any data block includes: when any data block satisfies the data flatness condition, arranging the accumulated sub-values ​​in any data block to obtain the arranged accumulated sub-values; identifying the continuous repeated parts in the arranged accumulated sub-values; encoding each continuous repeated part into the accumulated sub-value and the number of repetitions corresponding to the continuous repeated part, and obtaining the compressed data corresponding to any data block. In this case, the first encoding method can be considered as a run length encoding encoding method.

[0148] Exemplarily, the manner of arranging the accumulated sub-values ​​in any data block may be to arrange the accumulated sub-values ​​in any data block according to the arrangement order of the pixel points corresponding to the accumulated sub-values ​​in any data block on the display panel.

[0149] Each continuous repeating part includes only one cumulative sub-value, which is the cumulative sub-value corresponding to the continuous repeating part; the cumulative sub-value may appear once or multiple times in the continuous repeating part, and the number of times the cumulative sub-value appears in the continuous repeating part is the number of repetitions corresponding to the continuous repeating part.

[0150] Exemplarily, the accumulated sub-values ​​and the number of repetitions corresponding to the continuous repeated parts can be represented by binary numerical values, so that the compressed data corresponding to any data block is a binary code stream data.

[0151] For example, taking the accumulated sub-value after arrangement as AAABBCDDDD, there are 4 consecutive repeating parts in the accumulated sub-value after arrangement, namely AAA, BB, C and DDDD. The consecutive repeating part AAA can be encoded as 3A, the consecutive repeating part BB can be encoded as 2B, the consecutive repeating part C can be encoded as 1C, and the consecutive repeating part DDDD can be encoded as 4D. Among them, 1, 2, 3, 4, A, B, C, and D can all be represented by binary values.

[0152] In a possible implementation, when any data block does not meet the data flatness condition, the processing unit 1 uses the second encoding method to perform lossless compression on any data block, and the implementation method of obtaining the compressed data corresponding to any data block includes: when any data block does not meet the data flatness condition, for any accumulated sub-value in any data block, determining the predicted value corresponding to any accumulated sub-value; performing entropy encoding on the difference between any accumulated sub-value and the predicted value to obtain the compressed value corresponding to any accumulated sub-value; based on the compressed values ​​corresponding to each accumulated sub-value in any data block, obtaining the compressed data corresponding to any data block. In this case, the second encoding method can be considered as an encoding method of first predicting and then entropy encoding.

[0153] In an exemplary embodiment, a method for determining a predicted value corresponding to any cumulative sub-value includes: when there is a target cumulative sub-value among the cumulative sub-values ​​in any data block that satisfies an association condition with the any cumulative sub-value, determining the predicted value corresponding to any cumulative sub-value based on the target cumulative sub-value.

[0154] The association condition can be set based on experience or flexibly adjusted according to the application scenario. Under different association conditions, the target cumulative sub-values ​​are different, and the method of determining the predicted value corresponding to any cumulative sub-value based on the target cumulative sub-value is also different. Next, the method of determining the predicted value corresponding to any cumulative sub-value based on the target cumulative sub-value is introduced in combination with the specific situation of the target cumulative sub-value.

[0155] In an exemplary embodiment, the target cumulative sub-value includes a first cumulative sub-value, a second cumulative sub-value, a third cumulative sub-value, a fourth cumulative sub-value, and a fifth cumulative sub-value. The pixel corresponding to the first cumulative sub-value is located on the left side of the pixel corresponding to any cumulative sub-value and is adjacent to the pixel corresponding to any cumulative sub-value, the pixel corresponding to the second cumulative sub-value is located above the pixel corresponding to any cumulative sub-value and is adjacent to the pixel corresponding to any cumulative sub-value, the pixel corresponding to the third cumulative sub-value is located on the left side of the pixel corresponding to the second cumulative sub-value and is adjacent to the pixel corresponding to the second cumulative sub-value, the pixel corresponding to the fourth cumulative sub-value is located on the right side of the pixel corresponding to the second cumulative sub-value and is adjacent to the pixel corresponding to the second cumulative sub-value, and the pixel corresponding to the fifth cumulative sub-value is located on the left side of the pixel corresponding to the third cumulative sub-value and is adjacent to the pixel corresponding to the third cumulative sub-value.

[0156] When the specific situation of the target cumulative sub-value is the above situation, the method of determining the predicted value corresponding to any cumulative sub-value based on the target cumulative sub-value may be: determining the filtered value corresponding to the second cumulative sub-value based on the second cumulative sub-value, the third cumulative sub-value and the fourth cumulative sub-value; determining the filtered value corresponding to the third cumulative sub-value based on the third cumulative sub-value, the second cumulative sub-value and the fifth cumulative sub-value; determining the mixed value corresponding to the second cumulative sub-value based on the filtered value corresponding to the second cumulative sub-value and the second cumulative sub-value; determining the mixed value corresponding to the third cumulative sub-value based on the filtered value corresponding to the third cumulative sub-value and the third cumulative sub-value; determining the predicted value corresponding to any cumulative sub-value based on the first cumulative sub-value, the mixed value corresponding to the second cumulative sub-value and the mixed value corresponding to the third cumulative sub-value. That is, the predicted value corresponding to any cumulative sub-value can be realized through the three steps of filtering, mixing and predicting.

[0157] Exemplarily, the arbitrary cumulative sub-value is recorded as P, and the first cumulative sub-value, the second cumulative sub-value, the third cumulative sub-value, the fourth cumulative sub-value and the fifth cumulative sub-value are recorded as a, b, c, d and e respectively. The arbitrary cumulative sub-value, the first cumulative sub-value, the second cumulative sub-value, the third cumulative sub-value, the fourth cumulative sub-value and the fifth cumulative sub-value are arranged according to the positions of the pixels corresponding to them, and the following can be obtained: Figure 3 The results of the arrangement are shown.

[0158] Exemplarily, the process of determining the filtered value corresponding to the second cumulative sub-value based on the second cumulative sub-value, the third cumulative sub-value, and the fourth cumulative sub-value and determining the filtered value corresponding to the third cumulative sub-value based on the third cumulative sub-value, the second cumulative sub-value, and the fifth cumulative sub-value can be performed by applying a low-pass filter [0.25 0.5 0.25] to Figure 3 For example, based on the second cumulative sub-value, the third cumulative sub-value and the fourth cumulative sub-value, the process of determining the filtered value corresponding to the second cumulative sub-value can be implemented based on the following formula 1, and based on the third cumulative sub-value, the second cumulative sub-value and the fifth cumulative sub-value, the process of determining the filtered value corresponding to the third cumulative sub-value can be implemented based on the following formula 2:

[0159] filtB=(c+2*b+d+2)>>2 (Formula 1)

[0160] filtC=(e+2*c+b+2)>>2 (Formula 2)

[0161] Among them, filtB represents the filter value corresponding to the second accumulated sub-value; b represents the second accumulated sub-value; c represents the third accumulated sub-value; d represents the fourth accumulated sub-value; e represents the fifth accumulated sub-value; filtC represents the filter value corresponding to the third accumulated sub-value; >2 represents a shift to the right by two bits.

[0162] Exemplarily, the process of determining the mixed value corresponding to the second cumulative sub-value based on the filtered value corresponding to the second cumulative sub-value and the second cumulative sub-value, and determining the mixed value corresponding to the third cumulative sub-value based on the filtered value corresponding to the third cumulative sub-value and the third cumulative sub-value is used to mix the filtered value and the original value to generate the mixed value used in the prediction. Exemplarily, the process of determining the mixed value corresponding to the second cumulative sub-value based on the filtered value corresponding to the second cumulative sub-value and the second cumulative sub-value can be implemented based on Formula 3; the process of determining the mixed value corresponding to the third cumulative sub-value based on the filtered value corresponding to the third cumulative sub-value and the third cumulative sub-value can be implemented based on Formula 4:

[0163] diffB=CLAMP(flitB-b,-0.5,0.5), blendB=diffB+b (Formula 3)

[0164] diffC=CLAMP(filtC-c,-0.5,0.5), blendC=diffC+c (Formula 4)

[0165] Among them, filtB represents the filter value corresponding to the second accumulated sub-value; b represents the second accumulated sub-value; diffB represents the correction value of the difference between the filter value corresponding to the second accumulated sub-value and the second accumulated sub-value; blendB represents the mixed value corresponding to the second accumulated sub-value; filtC represents the filter value corresponding to the third accumulated sub-value; c represents the third accumulated sub-value; diffC represents the correction value of the difference between the filter value corresponding to the third accumulated sub-value and the third accumulated sub-value; blendC represents the mixed value corresponding to the third accumulated sub-value.

[0166] The CLAMP function is a function used to limit a value within a specified range. Its basic function is to limit a value between a minimum value (-0.5) and a maximum value (0.5). If the value is between the minimum and maximum values, it returns the value; if the value is greater than the maximum value, it returns the maximum value; if the value is less than the minimum value, it returns the minimum value.

[0167] Exemplarily, based on the first cumulative sub-value, the mixed value corresponding to the second cumulative sub-value, and the mixed value corresponding to the third cumulative sub-value, the process of determining the predicted value corresponding to any cumulative sub-value can be implemented based on Formula 5:

[0168] P′=a+blendB-blendC (Formula 5)

[0169] Wherein, P′ represents the predicted value corresponding to any cumulative sub-value; a represents the first cumulative sub-value; blendB represents the mixed value corresponding to the second cumulative sub-value; blendC represents the mixed value corresponding to the third cumulative sub-value.

[0170] In an exemplary embodiment, the target cumulative sub-value includes a first cumulative sub-value, a second cumulative sub-value, and a third cumulative sub-value, wherein the pixel corresponding to the first cumulative sub-value is located to the left of and adjacent to the pixel corresponding to any cumulative sub-value, the pixel corresponding to the second cumulative sub-value is located above and adjacent to the pixel corresponding to any cumulative sub-value, and the pixel corresponding to the third cumulative sub-value is located to the left of and adjacent to the pixel corresponding to the second cumulative sub-value.

[0171] When the specific situation of the target cumulative sub-value is the above situation, the method for determining the predicted value corresponding to any cumulative sub-value based on the target cumulative sub-value may be: calculating the sum of the first cumulative sub-value and the second cumulative sub-value, and taking the difference between the sum and the third cumulative sub-value as the predicted value corresponding to the any cumulative sub-value. The method for determining the predicted value corresponding to any cumulative sub-value based on the target cumulative sub-value may also be: taking the average of the first cumulative sub-value, the second cumulative sub-value and the third cumulative sub-value as the predicted value corresponding to the any cumulative sub-value.

[0172] In an exemplary embodiment, there may not be a target cumulative sub-value that satisfies the association condition with any cumulative sub-value in any data block. In this case, the method for determining the predicted value corresponding to any cumulative sub-value may be: taking a set value as the predicted value corresponding to any cumulative sub-value. The set value may be set based on experience or flexibly adjusted based on the application scenario. For example, the set value may refer to an intermediate value within the value range of the cumulative sub-value.

[0173] After obtaining the predicted value corresponding to any cumulative sub-value, entropy coding is performed on the difference between any cumulative sub-value and the predicted value to obtain a compressed value corresponding to any cumulative sub-value. The embodiment of the present application does not limit the entropy coding method. For example, the entropy coding method may include but is not limited to run length coding, Huffman coding, arithmetic coding, etc.

[0174] By obtaining the compressed value corresponding to any accumulated sub-value, the compressed values ​​corresponding to each accumulated sub-value in any data block can be obtained, and then the summary data of the compressed values ​​corresponding to each accumulated sub-value in any data block can be used as the compressed data corresponding to any data block.

[0175] It should be noted that the above description is only exemplified by taking the example that the processing unit 1 decomposes the accumulated data into at least two accumulated sub-data and performs lossless compression respectively, and the embodiments of the present application are not limited to this. In some embodiments, the processing unit 1 can also directly perform lossless compression on the accumulated data as a whole. The principle of performing lossless compression on the accumulated data as a whole is the same as the principle of performing lossless compression on any accumulated sub-data described above, and will not be repeated here.

[0176] After losslessly compressing any data block using the first encoding method or the second encoding method, compressed data corresponding to any data block is obtained. According to the method of obtaining compressed data corresponding to any data block, compressed data corresponding to at least one data block can be obtained, and then the summary data of the compressed data corresponding to at least one data block is used as the compressed data corresponding to any accumulated sub-data. At this point, the lossless compression process of one accumulated sub-data is completed.

[0177] The above steps are repeated, and compressed data corresponding to at least two accumulated sub-data are obtained according to the method of obtaining compressed data corresponding to any accumulated sub-data, and then the summary data of the compressed data corresponding to the at least two accumulated sub-data are used as the compressed accumulated data. In some embodiments, the compressed accumulated data can be represented by a binary value.

[0178] Exemplarily, the process of losslessly compressing the accumulated data to obtain the compressed accumulated data can be as follows: Figure 4 As shown. The accumulated data is decomposed into at least two accumulated sub-data; for any accumulated sub-data, when any data block in any accumulated sub-data meets the data flatness condition, lossless compression is performed using run length coding; when any data block in any accumulated sub-data does not meet the data flatness condition, lossless compression is performed using prediction followed by entropy coding. Based on this method, lossless compression of each accumulated sub-data is completed to obtain compressed accumulated data.

[0179] After obtaining the compressed accumulated data, the storage unit 2 stores the compressed accumulated data. The embodiment of the present application does not limit the type of the storage unit 2, as long as it has a storage function. Exemplarily, the type of the storage unit 2 can be SRAM (Static Random Access Memory). The characteristic of SRAM is that as long as the power is kept on, the data stored therein can be kept constant without periodic refresh.

[0180] After the compressed accumulated data is stored in the storage unit 2, when it is necessary to perform aging compensation on the display panel based on the accumulated data, the processing unit 1 reads the compressed accumulated data from the storage unit 2, decompresses the compressed accumulated data to obtain the accumulated data, and performs aging compensation on the display panel based on the accumulated data.

[0181] Exemplarily, the process of decompressing the compressed accumulated data is the reverse process of losslessly compressing the accumulated data. Since the compressed accumulated data is obtained by losslessly compressing the accumulated data, lossless accumulated data can be obtained after decompression.

[0182] Exemplarily, the process of performing aging compensation on the display panel based on the accumulated data includes: determining the aging compensation parameters corresponding to the accumulated data according to the correspondence between the accumulated data and the aging compensation parameters, and performing aging compensation on the display panel based on the aging compensation parameters. Exemplarily, the correspondence between the accumulated data and the aging compensation parameters can be represented by a curve or a table. The correspondence between the accumulated data and the aging compensation parameters can be set based on experience or flexibly adjusted based on actual conditions, and the embodiments of the present application do not limit this.

[0183] Exemplarily, the process of performing aging compensation on a display panel based on aging compensation parameters includes: calculating pixel values ​​required to be compensated for each pixel point on the display panel based on the aging compensation parameters; before the display panel displays an image, compensating original pixel values ​​of corresponding pixel points in the image to be displayed according to the pixel values ​​required to be compensated for each pixel point; and displaying the image with compensated pixel values ​​on the display panel.

[0184] For example, the aging compensation process can be as follows: Figure 5 As shown. A preprocessing algorithm (MIA, spatial domain filtering algorithm, frequency domain filtering algorithm or sampling algorithm) is used to reduce the high-frequency information in the target image to obtain a preprocessed image; based on the image data of the processed image, cumulative data is obtained; the cumulative data is losslessly compressed to obtain compressed cumulative data; the compressed cumulative data is stored in SRAM. When aging compensation is required, the compressed cumulative data is read from the SRAM, the compressed cumulative data is decompressed to obtain the cumulative data, the aging compensation parameters are determined according to the cumulative data, and the display panel is aging compensated using the aging compensation parameters. Among them, the bit depths of the target image, the preprocessed image and the aging compensation parameters are all 8 bits, and the bit depth of the cumulative data is 24 bits.

[0185] In an exemplary embodiment, RMSE (Root Mean Square Error) and CR (Compression Ratio) are selected as key indicators to compare the effects of different preprocessing algorithms for reducing high-frequency information on the compression ratio (also known as compression efficiency). By adjusting the parameters of the four preprocessing algorithms (MIA, spatial filtering algorithm, frequency filtering algorithm, and sampling algorithm), the RMSE between the preprocessed image and the target image is constrained to be between 6 and 6.2 (RMSE between 6 and 6.2 has a smaller visual loss), and then the CR of the accumulated data obtained under different preprocessing algorithms for the same static image is compared. In the experiment, five different natural images were selected, such as landscapes, portraits, animations, games, and user interfaces. The comparison results of CR are shown in Table 1, where CR refers to the ratio of the amount of data (e.g., the number of bits) of the accumulated data obtained based on the preprocessed image to the amount of data (e.g., the number of bits) of the compressed accumulated data obtained after lossless compression of the accumulated data.

[0186] Table 1

[0187]

[0188]

[0189] From the comparison in Table 1, it can be seen that the four preprocessing methods for reducing high-frequency information proposed in the embodiments of the present application can all achieve a large compression ratio, which is beneficial to reducing the amount of compressed accumulated data and saving storage resources and costs. In addition, according to Table 1, under the same visual loss, using the frequency domain filtering algorithm and MIA as preprocessing algorithms can improve the compression ratio of the accumulated data. Both methods can achieve a compression ratio of 3:1. In other words, under the same compression ratio, the visual loss of these two algorithms is smaller.

[0190] The embodiment of the present application proposes an efficient lossless compression algorithm suitable for high-bit-depth accumulated data (also referred to as DBI accumulated data). Four preprocessing algorithms are used to reduce the high-frequency information in the target image (also referred to as video source data) while ensuring the display effect, so that the accumulated data is easier to compress, a higher compression ratio can be achieved, storage resources can be reduced, and costs can be reduced. Among them, the preprocessing algorithms of MIA and frequency domain filtering algorithms have better performance, and the compression ratio can reach 3:1, that is, the preprocessing algorithms of MIA and frequency domain filtering algorithms can reduce the space for storing accumulated data to 1 / 3 of the original.

[0191] In an exemplary embodiment, the processing unit 1 preprocesses the target image to obtain a preprocessed image, which may include the following steps 1 to 4:

[0192] Step 1: Preprocess the reference image using at least two candidate preprocessing algorithms to obtain preprocessed images corresponding to the at least two candidate preprocessing algorithms, respectively. The at least two candidate preprocessing algorithms are used to reduce high-frequency information in the reference image.

[0193] At least two candidate preprocessing algorithms are algorithms that can reduce high-frequency information in the image. The embodiment of the present application does not limit the number and specific type of the at least two candidate preprocessing algorithms. In some embodiments, the at least two candidate preprocessing algorithms may include at least two of the MIA, spatial domain filtering algorithm, frequency domain filtering algorithm, or sampling algorithm mentioned above.

[0194] The reference image is an image required to measure the compression ratios corresponding to at least two candidate preprocessing algorithms. The reference image can be selected based on experience or flexibly adjusted according to the application scenario, which is not limited in the embodiments of the present application. In some embodiments, the reference image can be an image displayed by the display panel or an image to be displayed by the display panel. Based on this, it can be ensured that the number of pixels in the reference image is the same as the number of pixels in the target image, thereby ensuring the reliability of the compression ratios corresponding to at least two candidate preprocessing algorithms determined based on the reference image.

[0195] Exemplarily, in the case where there are multiple target images, the reference image may refer to a portion of the multiple target images.

[0196] Each candidate preprocessing algorithm has its own preprocessing process. By preprocessing the reference image according to the preprocessing process of each candidate preprocessing algorithm, the preprocessed images corresponding to at least two candidate preprocessing algorithms can be obtained. Exemplarily, taking at least two candidate preprocessing algorithms including at least two of the MIA, spatial domain filtering algorithm, frequency domain filtering algorithm or sampling algorithm mentioned above as an example, the reference image is preprocessed using at least two candidate preprocessing algorithms to obtain the implementation method of the preprocessed images corresponding to at least two candidate preprocessing algorithms can refer to the relevant content of the preprocessing of the target image introduced in the above text, which will not be repeated here.

[0197] In an exemplary embodiment, for each candidate preprocessing algorithm, different image differences may be caused when different parameters are used, and the image difference refers to the difference between the image obtained after being processed by the candidate preprocessing algorithm and the image not processed by the candidate preprocessing algorithm. The embodiment of the present application does not limit the representation method of the image difference. For example, the image difference can be represented by RMSE.

[0198] In the process of preprocessing the reference image using at least two candidate preprocessing algorithms respectively, the parameters of each candidate preprocessing algorithm can be adjusted so that the image difference constraints corresponding to each candidate preprocessing algorithm are within a certain range, so as to improve the comparability of the compression ratios corresponding to each candidate preprocessing algorithm. For example, when the image difference is represented by RMSE, the RMSE constraints corresponding to each candidate preprocessing algorithm can be made between 6 and 6.2.

[0199] Step 2: For any candidate preprocessing algorithm among at least two candidate preprocessing algorithms, based on the image data of the preprocessed image corresponding to any candidate preprocessing algorithm, obtain the cumulative data corresponding to any candidate preprocessing algorithm; perform lossless compression on the cumulative data corresponding to any candidate preprocessing algorithm to obtain the compressed cumulative data corresponding to any candidate preprocessing algorithm; determine the compression ratio corresponding to any candidate preprocessing algorithm based on the ratio of the data volume of the cumulative data corresponding to any candidate preprocessing algorithm to the data volume of the compressed cumulative data corresponding to any candidate preprocessing algorithm.

[0200] The implementation principle of obtaining the accumulated data and the compressed accumulated data corresponding to any candidate preprocessing algorithm based on the preprocessed image corresponding to any candidate preprocessing algorithm is the same as the implementation principle of obtaining the accumulated data and the compressed accumulated data based on the preprocessed image introduced above, and will not be repeated here.

[0201] Exemplarily, the accumulated data and the compressed accumulated data are both represented by binary values, the data volume of the accumulated data may refer to the total number of bits in the binary value representing the accumulated data, and the data volume of the compressed accumulated data may refer to the total number of bits in the binary value representing the compressed accumulated data.

[0202] In an exemplary embodiment, based on the ratio of the amount of accumulated data corresponding to any candidate preprocessing algorithm to the amount of compressed accumulated data corresponding to any candidate preprocessing algorithm, the method for determining the compression ratio corresponding to any candidate preprocessing algorithm can be: taking the ratio of the amount of accumulated data corresponding to any candidate preprocessing algorithm to the amount of compressed accumulated data corresponding to any candidate preprocessing algorithm as the compression ratio corresponding to any candidate preprocessing algorithm.

[0203] In an exemplary embodiment, based on the ratio of the amount of accumulated data corresponding to any candidate preprocessing algorithm to the amount of compressed accumulated data corresponding to any candidate preprocessing algorithm, the implementation method for determining the compression ratio corresponding to any candidate preprocessing algorithm can also be: rounding the ratio of the amount of accumulated data corresponding to any candidate preprocessing algorithm to the amount of compressed accumulated data corresponding to any candidate preprocessing algorithm (e.g., rounding down or rounding up), and using the rounded value as the compression ratio corresponding to any candidate preprocessing algorithm.

[0204] Step 3: Based on the compression ratios respectively corresponding to the at least two candidate preprocessing algorithms, determine a target preprocessing algorithm from the at least two candidate preprocessing algorithms.

[0205] The embodiment of the present application does not limit the implementation method of determining the target preprocessing algorithm from at least two candidate preprocessing algorithms based on the compression ratios corresponding to the at least two candidate preprocessing algorithms, and can be flexibly set according to the requirements for the compression ratio.

[0206] Exemplarily, for the case where the compression ratio requirement is the higher the better, based on the compression ratios corresponding to at least two candidate preprocessing algorithms, the implementation method of determining the target preprocessing algorithm among at least two candidate preprocessing algorithms can be: taking the preprocessing algorithm with the highest compression ratio among the at least two candidate preprocessing algorithms as the target preprocessing algorithm.

[0207] In an exemplary embodiment, in the process of determining the target preprocessing algorithm, in addition to considering the compression ratios corresponding to at least two candidate preprocessing algorithms, the computational load corresponding to at least two candidate preprocessing algorithms may also be considered. The computational load corresponding to any candidate preprocessing algorithm may refer to the computational load required for preprocessing the reference image based on any preprocessing algorithm.

[0208] In an exemplary embodiment, based on the compression ratios corresponding to the at least two candidate preprocessing algorithms, the implementation method of determining the target preprocessing algorithm from the at least two candidate preprocessing algorithms may be: the preprocessing algorithm with the smallest amount of calculation among the candidate preprocessing algorithms whose compression ratio is not less than the compression ratio threshold is used as the target preprocessing algorithm. The compression ratio threshold may be set based on experience or flexibly adjusted based on the application scenario, and this is not limited in the embodiments of the present application.

[0209] Step 4: Use the target preprocessing algorithm to preprocess the target image to obtain a preprocessed image.

[0210] After determining the target preprocessing algorithm, the target image is preprocessed using the preprocessing flow corresponding to the target preprocessing algorithm to obtain a preprocessed image. In an exemplary embodiment, the parameters used to preprocess the target image using the target preprocessing algorithm are the same as the parameters used to preprocess the reference image using the target preprocessing algorithm in the process of determining the compression ratio corresponding to the target preprocessing algorithm.

[0211] Based on the above steps 1 to 4, the processing unit 1 can select a suitable target preprocessing algorithm from at least two candidate preprocessing algorithms to preprocess the target image according to the compression ratio requirements, so that the compressed accumulated data obtained according to the preprocessed image can achieve the expected compression ratio.

[0212] In an embodiment of the present application, the storage unit stores compressed accumulated data obtained by losslessly compressing the accumulated data. The amount of compressed accumulated data is small, and the accumulated data can be restored losslessly, which is beneficial to reducing the storage space occupied in the storage unit while ensuring the aging compensation effect, thereby saving storage resources and saving costs.

[0213] In addition, the cumulative data is obtained based on the image data of the preprocessed image. The preprocessed image is obtained by reducing the high-frequency information in the image originally displayed by the display panel. The reduction of high-frequency information has less interference on the visual effect of the image, but the cumulative data obtained based on the preprocessed image with less high-frequency information has higher redundancy and is easier to compress, which is conducive to achieving a higher compression rate for lossless compression while ensuring the visual effect, further saving storage resources and costs.

[0214] The present application also provides a display driving method, which can be applied to the display driving device described in the above embodiment. Figure 6 , the display driving method includes the following steps 601 to 605.

[0215] Step 601: preprocessing the target image displayed on the display panel to obtain a preprocessed image, where the preprocessing is used to reduce high-frequency information in the target image.

[0216] In one possible implementation, a target image displayed on a display panel is preprocessed to obtain a preprocessed image, including: for any pixel point in the target image, when there is a covering pixel point at the position of any pixel point, updating the pixel value of any pixel point to the pixel value of the covering pixel point; when there is no covering pixel point at the position of any pixel point, updating the pixel value of any pixel point to a reference value; after the pixel values ​​of all pixels are updated, the preprocessed image is obtained; wherein the covering pixel point is a pixel point located at the position of any pixel point after moving each pixel point according to a moving method; and the moving method is a moving method of a trajectory point corresponding to the target image in a reference moving trajectory relative to a starting point of the reference moving trajectory.

[0217] In one possible implementation, a target image displayed on a display panel is preprocessed to obtain a preprocessed image, including: for any pixel in the target image, based on the pixel values ​​of the pixels in the neighborhood of the pixel, obtaining a filtered pixel value of the pixel; and obtaining the preprocessed image based on the filtered pixel values ​​of the pixels.

[0218] In one possible implementation, the target image displayed on the display panel is preprocessed to obtain a preprocessed image, including: converting the target image from the spatial domain to the frequency domain to obtain a converted image; low-pass filtering the converted image to obtain a filtered image; converting the filtered image from the frequency domain back to the spatial domain to obtain the preprocessed image.

[0219] In a possible implementation, the target image displayed on the display panel is preprocessed to obtain a preprocessed image, including: downsampling the target image to obtain a downsampled image; and upsampling the downsampled image to obtain the preprocessed image.

[0220] In one possible implementation, a target image displayed on a display panel is preprocessed to obtain a preprocessed image, including: using at least two candidate preprocessing algorithms to preprocess a reference image respectively to obtain preprocessed images corresponding to the at least two candidate preprocessing algorithms respectively, wherein the at least two candidate preprocessing algorithms are used to reduce high-frequency information in the reference image; for any candidate preprocessing algorithm among the at least two candidate preprocessing algorithms, based on image data of the preprocessed image corresponding to any candidate preprocessing algorithm, obtaining accumulated data corresponding to any candidate preprocessing algorithm; losslessly compressing the accumulated data corresponding to any candidate preprocessing algorithm to obtain compressed accumulated data corresponding to any candidate preprocessing algorithm; determining a compression ratio corresponding to any candidate preprocessing algorithm based on a ratio of a data volume of the accumulated data corresponding to any candidate preprocessing algorithm to a data volume of the compressed accumulated data corresponding to any candidate preprocessing algorithm; determining a target preprocessing algorithm among the at least two candidate preprocessing algorithms based on the compression ratios corresponding to the at least two candidate preprocessing algorithms respectively; and preprocessing the target image using the target preprocessing algorithm to obtain a preprocessed image.

[0221] Step 602: Based on the image data of the pre-processed image, cumulative data is obtained.

[0222] Step 603: Losslessly compress the accumulated data to obtain compressed accumulated data.

[0223] In one possible implementation, losslessly compressing the accumulated data to obtain compressed accumulated data includes: decomposing the accumulated data into at least two accumulated sub-data; losslessly compressing the at least two accumulated sub-data respectively to obtain compressed data corresponding to the at least two accumulated sub-data; and obtaining compressed accumulated data based on the compressed data corresponding to the at least two accumulated sub-data respectively.

[0224] In a possible implementation, any one of the at least two accumulated sub-data includes at least one data block, and any one of the at least one data block includes at least one accumulated sub-value; the at least two accumulated sub-data are respectively losslessly compressed to obtain compressed data corresponding to the at least two accumulated sub-data, including: for any one of the at least one data block, when any one of the data blocks meets a data flatness condition, the first encoding method is used to losslessly compress the any one of the data blocks to obtain compressed data corresponding to the any one of the data blocks; when any one of the data blocks does not meet the data flatness condition, the second encoding method is used to losslessly compress the any one of the data blocks to obtain compressed data corresponding to the any one of the data blocks; based on the compressed data corresponding to the at least one data block, the compressed data corresponding to any one of the accumulated sub-data is obtained.

[0225] In one possible implementation, when any data block satisfies a data flatness condition, any data block is losslessly compressed using a first encoding method to obtain compressed data corresponding to any data block, including: when any data block satisfies a data flatness condition, the accumulated sub-values ​​in any data block are arranged to obtain the arranged accumulated sub-values; identifying continuous repeated parts in the arranged accumulated sub-values; encoding each continuous repeated part into a cumulative sub-value and a number of repetitions corresponding to the continuous repeated part to obtain compressed data corresponding to any data block.

[0226] In one possible implementation, when any data block does not satisfy the data flatness condition, any data block is losslessly compressed using a second encoding method to obtain compressed data corresponding to any data block, including: when any data block does not satisfy the data flatness condition, for any accumulated sub-value in any data block, determining a predicted value corresponding to any accumulated sub-value; performing entropy encoding on a difference between any accumulated sub-value and the predicted value to obtain a compressed value corresponding to any accumulated sub-value; and obtaining compressed data corresponding to any data block based on the compressed values ​​corresponding to each accumulated sub-value in any data block.

[0227] Step 604: Store the compressed accumulated data.

[0228] Step 605: Read the compressed accumulated data, decompress the compressed accumulated data to obtain accumulated data, and perform aging compensation on the display panel based on the accumulated data.

[0229] The above embodiment of the display driving method and the above embodiment of the display driving device belong to the same concept and have similar beneficial effects as the above embodiment of the display driving device. For technical details not disclosed in the embodiment of the display driving method, please refer to the description of the embodiment of the display driving device of this application for understanding, and no further description is given here.

[0230] See also Figure 7 In one embodiment, a chip is further provided, the chip comprising the display driver device in any possible implementation manner described above. Optionally, the chip may be a display driver chip.

[0231] See also Figure 8 In one embodiment, an electronic device is also provided, the electronic device comprising:

[0232] Display panel;

[0233] And a display driver in any of the above possible implementations, the display driver is used to drive a display panel. Exemplarily, the display driver drives the display panel in a manner that the display driver provides an aging compensation parameter to the display panel to perform aging compensation on an image to be displayed on the display panel.

[0234] Exemplarily, the electronic device may be a smart phone, a tablet computer, a player, a laptop computer, a desktop computer, etc.

[0235] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.) and signals involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0236] The terms "first", "second", etc. in this application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the application described here can be implemented in an order other than those illustrated or described here. The embodiments described in the above exemplary embodiments do not represent all embodiments consistent with the application. On the contrary, they are only examples of devices and methods consistent with some aspects of the application.

[0237] It should be understood that the "plurality" mentioned in this article refers to two or more. "And / or" describes the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects are in an "or" relationship.

[0238] The above description is only an exemplary embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A display driving device, characterized in that: The display driving device comprises a processing unit and a storage unit; The processing unit is configured to preprocess the target image displayed on the display panel to obtain a preprocessed image, wherein the preprocessing is used to reduce high-frequency information in the target image; acquiring accumulated data based on the image data of the preprocessed image; Losslessly compressing the accumulated data to obtain compressed accumulated data; The storage unit is configured to store the compressed accumulated data; The processing unit is further configured to read the compressed accumulated data from the storage unit, decompress the compressed accumulated data to obtain the accumulated data, and perform aging compensation on the display panel based on the accumulated data.

2. The device according to claim 1, characterized in that The processing unit is configured to: Decomposing the accumulated data into at least two accumulated sub-data; performing lossless compression on the at least two accumulated sub-data respectively to obtain compressed data corresponding to the at least two accumulated sub-data respectively; The compressed accumulated data is acquired based on the compressed data respectively corresponding to the at least two accumulated sub-data.

3. The device according to claim 2, characterized in that Any of the at least two accumulated sub-data includes at least one data block, and any of the at least one data block includes at least one accumulated sub-value; the processing unit is configured to: For any data block among the at least one data block, when the any data block satisfies a data flatness condition, losslessly compressing the any data block by using a first encoding method to obtain compressed data corresponding to the any data block; When any of the data blocks does not satisfy the data flatness condition, losslessly compressing the any of the data blocks using a second encoding method to obtain compressed data corresponding to the any of the data blocks; Based on the compressed data corresponding to the at least one data block, the compressed data corresponding to any one of the accumulated sub-data is obtained.

4. The device according to claim 3, characterized in that The processing unit is configured to: In the case where any of the data blocks meets the data flatness condition, arranging the accumulated sub-values ​​in the any of the data blocks to obtain arranged accumulated sub-values; identifying consecutive repetitive portions in the arranged accumulated sub-values; Each continuous repetitive part is encoded into a cumulative sub-value and a repetition number corresponding to the continuous repetitive part, so as to obtain compressed data corresponding to any data block.

5. The device according to claim 3, characterized in that The processing unit is configured to: In a case where any of the data blocks does not satisfy the data flatness condition, for any accumulated sub-value in the any of the data blocks, determining a predicted value corresponding to the any accumulated sub-value; Performing entropy coding on a difference between any one of the accumulated sub-values ​​and the predicted value to obtain a compressed value corresponding to any one of the accumulated sub-values; Based on the compression values ​​corresponding to the accumulated sub-values ​​in the any data block, the compressed data corresponding to the any data block is obtained.

6. The device according to any one of claims 1 to 5, characterized in that: The processing unit is configured to: For any pixel point among the pixels in the target image, if there is a covered pixel point at the position of the any pixel point, updating the pixel value of the any pixel point to the pixel value of the covered pixel point; In the case that the covering pixel point does not exist at the position where any pixel point is located, the pixel value of any pixel point is updated to the reference value; after the pixel values ​​of each pixel point are updated, the preprocessed image is obtained; wherein the covering pixel point is a pixel point located at the position where any pixel point is located after the each pixel point is moved according to a moving method; the moving method is a moving method of a trajectory point corresponding to the target image in a reference moving trajectory relative to a starting point of the reference moving trajectory; or, For any pixel point among the pixels in the target image, based on the pixel values ​​of the pixels in the neighborhood of the pixel point, obtain the filtered pixel value of the pixel point; based on the filtered pixel values ​​of the pixels, obtain the preprocessed image; or, Convert the target image from the spatial domain to the frequency domain to obtain a converted image; perform low-pass filtering on the converted image to obtain a filtered image; convert the filtered image from the frequency domain back to the spatial domain to obtain the preprocessed image; or, The target image is downsampled to obtain a downsampled image; and the downsampled image is upsampled to obtain the preprocessed image.

7. The device according to any one of claims 1 to 5, characterized in that: The processing unit is configured to: Preprocessing the reference image using at least two candidate preprocessing algorithms to obtain preprocessed images corresponding to the at least two candidate preprocessing algorithms, respectively, wherein the at least two candidate preprocessing algorithms are used to reduce high-frequency information in the reference image; For any candidate preprocessing algorithm among the at least two candidate preprocessing algorithms, based on image data of a preprocessed image corresponding to the any candidate preprocessing algorithm, acquiring accumulated data corresponding to the any candidate preprocessing algorithm; Losslessly compressing the accumulated data corresponding to any of the candidate preprocessing algorithms to obtain compressed accumulated data corresponding to any of the candidate preprocessing algorithms; Determine a compression ratio corresponding to any candidate preprocessing algorithm based on a ratio of the amount of accumulated data corresponding to any candidate preprocessing algorithm to the amount of compressed accumulated data corresponding to any candidate preprocessing algorithm; Determining a target preprocessing algorithm from among the at least two candidate preprocessing algorithms based on compression ratios respectively corresponding to the at least two candidate preprocessing algorithms; The target image is preprocessed using the target preprocessing algorithm to obtain the preprocessed image.

8. A display driving method, characterized in that: The method comprises: Preprocessing the target image displayed on the display panel to obtain a preprocessed image, wherein the preprocessing is used to reduce high-frequency information in the target image; acquiring accumulated data based on the image data of the preprocessed image; Losslessly compressing the accumulated data to obtain compressed accumulated data; storing the compressed accumulated data; The compressed accumulated data is read, the compressed accumulated data is decompressed to obtain the accumulated data, and aging compensation is performed on the display panel based on the accumulated data.

9. The method according to claim 8, characterized in that The losslessly compressing the accumulated data to obtain compressed accumulated data includes: Decomposing the accumulated data into at least two accumulated sub-data; performing lossless compression on the at least two accumulated sub-data respectively to obtain compressed data corresponding to the at least two accumulated sub-data respectively; The compressed accumulated data is acquired based on the compressed data respectively corresponding to the at least two accumulated sub-data.

10. The method according to claim 9, characterized in that Any one of the at least two accumulated sub-data includes at least one data block, and any one of the at least one data block includes at least one accumulated sub-value; and the losslessly compressing the at least two accumulated sub-data to obtain compressed data corresponding to the at least two accumulated sub-data includes: For any data block among the at least one data block, when the any data block satisfies the data flatness condition, the any data block is losslessly compressed using a first encoding method to obtain compressed data corresponding to the any data block; when the any data block does not satisfy the data flatness condition, the any data block is losslessly compressed using a second encoding method to obtain compressed data corresponding to the any data block; Based on the compressed data corresponding to the at least one data block, the compressed data corresponding to any one of the accumulated sub-data is obtained.

11. The method according to claim 10, characterized in that When any of the data blocks meets the data flatness condition, losslessly compressing the any of the data blocks by using the first encoding method to obtain compressed data corresponding to the any of the data blocks includes: In the case where any of the data blocks meets the data flatness condition, arranging the accumulated sub-values ​​in the any of the data blocks to obtain arranged accumulated sub-values; identifying consecutive repetitive portions in the arranged accumulated sub-values; Each continuous repetitive part is encoded into a cumulative sub-value and a repetition number corresponding to the continuous repetitive part, so as to obtain compressed data corresponding to any data block.

12. The method according to claim 10, characterized in that When any of the data blocks does not satisfy the data flatness condition, losslessly compressing the any of the data blocks by using a second encoding method to obtain compressed data corresponding to the any of the data blocks includes: In a case where any of the data blocks does not satisfy the data flatness condition, for any accumulated sub-value in the any of the data blocks, determining a predicted value corresponding to the any accumulated sub-value; Performing entropy coding on a difference between any one of the accumulated sub-values ​​and the predicted value to obtain a compressed value corresponding to any one of the accumulated sub-values; Based on the compression values ​​corresponding to the accumulated sub-values ​​in the any data block, the compressed data corresponding to the any data block is obtained.

13. The method according to any one of claims 8 to 12, characterized in that: The preprocessing of the target image displayed on the display panel to obtain a preprocessed image includes: For any pixel point among the pixels in the target image, if there is a covering pixel point at the position of any pixel point, the pixel value of any pixel point is updated to the pixel value of the covering pixel point; if there is no covering pixel point at the position of any pixel point, the pixel value of any pixel point is updated to the reference value; after the pixel values ​​of all the pixels are updated, the preprocessed image is obtained; wherein, the covering pixel point is a pixel point located at the position of any pixel point after the pixels are moved according to a moving method; the moving method is a moving method of the trajectory point corresponding to the target image in the reference moving trajectory relative to the starting point of the reference moving trajectory; or, For any pixel point among the pixels in the target image, based on the pixel values ​​of the pixels in the neighborhood of the pixel point, obtain the filtered pixel value of the pixel point; based on the filtered pixel values ​​of the pixels, obtain the preprocessed image; or, Convert the target image from the spatial domain to the frequency domain to obtain a converted image; perform low-pass filtering on the converted image to obtain a filtered image; convert the filtered image from the frequency domain back to the spatial domain to obtain the preprocessed image; or, The target image is downsampled to obtain a downsampled image; and the downsampled image is upsampled to obtain the preprocessed image.

14. The method according to any one of claims 8 to 12, characterized in that: The preprocessing of the target image displayed on the display panel to obtain a preprocessed image includes: Preprocessing the reference image using at least two candidate preprocessing algorithms to obtain preprocessed images corresponding to the at least two candidate preprocessing algorithms, respectively, wherein the at least two candidate preprocessing algorithms are used to reduce high-frequency information in the reference image; For any candidate preprocessing algorithm among the at least two candidate preprocessing algorithms, based on the image data of the preprocessed image corresponding to the any candidate preprocessing algorithm, the accumulated data corresponding to the any candidate preprocessing algorithm is obtained; the accumulated data corresponding to the any candidate preprocessing algorithm is losslessly compressed to obtain the compressed accumulated data corresponding to the any candidate preprocessing algorithm; based on the ratio of the data volume of the accumulated data corresponding to the any candidate preprocessing algorithm to the data volume of the compressed accumulated data corresponding to the any candidate preprocessing algorithm, the compression ratio corresponding to the any candidate preprocessing algorithm is determined; Determining a target preprocessing algorithm from among the at least two candidate preprocessing algorithms based on compression ratios respectively corresponding to the at least two candidate preprocessing algorithms; The target image is preprocessed using the target preprocessing algorithm to obtain the preprocessed image.

15. A chip, characterized in that: The chip includes the display driving device according to any one of claims 1 to 7.

16. An electronic device, characterized in that: The electronic device comprises: Display panel; And the display driving device according to any one of claims 1 to 7, wherein the display driving device is used to drive the display panel.

Images