Display panel compensation method, device, equipment and storage medium

Abstract

The present application discloses a display panel compensation method, apparatus, device, and storage medium. The display panel compensation method includes: obtaining display parameters of the display panel at the current moment, the display parameters including the grayscale of the sub-pixels of the display panel at the current moment and target parameters, the target parameters including at least one of the brightness level, refresh rate, and temperature of the display panel at the current moment; inputting the display parameters into a pre-trained neural network model to obtain the current decay efficiency of the sub-pixels at the current moment; determining the compensation value of the sub-pixels based on the current decay efficiency and the cumulative lighting time of the sub-pixels; and compensating the brightness of the sub-pixels based on the compensation value. According to the embodiments of the present application, it is beneficial to improve the compensation effect.

Description

Technical Field

[0001] The present application relates to the field of display technology, and in particular to a display panel compensation method, device, equipment and storage medium. Background Art

[0002] Organic Light-Emitting Diode (OLED) display panels are a type of self-luminous flat display panel that is currently widely used. OLED display panels use organic light-emitting materials as their red, green, and blue sub-pixels. When a sub-pixel is lit for a long time at high brightness, the organic light-emitting material will age, causing its luminous efficiency to decrease. As a result, the sub-pixel will be unable to display the specified brightness under the same driving current. Due to the different degrees of aging of each pixel, the OLED panel will eventually experience brightness and color non-uniformity.

[0003] DeBurn in (DBI) eliminates uneven brightness caused by aging. Related technologies use OLED aging models to compensate for OLED device brightness. The inventors discovered that the degree of sub-pixel aging is dependent on multiple factors, and using a single aging model is unable to account for complex brightness decay variations, resulting in suboptimal compensation results. Summary of the Invention

[0004] The embodiments of the present application provide a display panel compensation method, device, equipment, and storage medium, which are conducive to improving the compensation effect.

[0005] In a first aspect, an embodiment of the present application provides a display panel compensation method, which includes: obtaining display parameters of the display panel at a current moment, the display parameters including the grayscale of the sub-pixels of the display panel at the current moment and target parameters, the target parameters including at least one of the brightness level, refresh rate, and temperature of the display panel at the current moment; inputting the display parameters into a pre-trained neural network model to obtain the current decay efficiency of the sub-pixel at the current moment; determining the compensation value of the sub-pixel based on the current decay efficiency and the cumulative lighting time of the sub-pixel; and compensating the brightness of the sub-pixel based on the compensation value.

[0006] In a possible implementation of the first aspect, before inputting the display parameters into a pre-trained neural network model to obtain the decay efficiency, the method further includes:

[0007] Obtaining a training sample set, where the training sample set includes multiple training samples, each training sample including a grayscale, a target parameter, and an actual decay efficiency of a sub-pixel in the training sample;

[0008] The preset neural network model is trained using the training samples until the training stop condition is met to obtain the trained neural network model.

[0009] In a possible implementation of the first aspect, the training sample set includes N*M1*M2*M3*M4 training samples, where M1 represents the number of grayscales, M2 represents the number of brightness levels, M3 represents the number of refresh rates, M4 represents the number of temperatures, and N represents the number of sub-pixels. Each training sample includes a brightness attenuation value, where the brightness attenuation value is a brightness attenuation value after the sub-pixel in the training sample is lit for a first duration.

[0010] The actual decay efficiency of the sub-pixel in each training sample is the ratio of the brightness decay value in the training sample to the maximum brightness decay value among the N*M1*M2*M3*M4 brightness decay values.

[0011] In a possible implementation of the first aspect, before obtaining the training sample set, the method further includes:

[0012] For each training sample, perform the following steps:

[0013] Lighting up sub-pixels in the training sample under reference display parameters and obtaining a first brightness, wherein the reference display parameters include a reference grayscale and a reference target parameter;

[0014] Light up the sub-pixels in the training sample according to the grayscale of the training sample and the target parameters;

[0015] After the sub-pixels in the training sample are illuminated for a first duration, the sub-pixels in the training sample are illuminated under the reference display parameters, and a second brightness is obtained;

[0016] Determining a brightness attenuation value of the training sample according to a difference between the second brightness and the first brightness;

[0017] Preferably, the reference target parameter includes at least one of a reference brightness level, a reference refresh rate, and a reference temperature.

[0018] In a possible implementation of the first aspect, using the training samples to train a preset neural network model until a training stop condition is met to obtain a trained neural network model specifically includes:

[0019] For each training sample, perform the following steps:

[0020] The grayscale and target parameters of the training samples are input into the preset neural network model to obtain the predicted decay efficiency;

[0021] Determine the loss function value of the preset neural network model according to the predicted decay efficiency and the actual decay efficiency;

[0022] When the loss function value does not meet the training stop condition, the weight coefficient of the hidden layer in the preset neural network model is adjusted, and the neural network model with the adjusted coefficient is trained using the training sample set until the training stop condition is met to obtain the trained neural network model.

[0023] In a possible implementation of the first aspect, the number of hidden layers is greater than or equal to 2.

[0024] In a possible implementation of the first aspect, determining the compensation value of the sub-pixel according to the current decay efficiency and the accumulated lighting time of the sub-pixel includes:

[0025] Determining a cumulative decay efficiency based on decay efficiencies corresponding to a plurality of second durations within the cumulative lighting duration of the sub-pixel;

[0026] Determine the current recession total efficiency based on the current recession efficiency and the accumulated recession efficiency;

[0027] The compensation value of the sub-pixel is determined according to the current total decay efficiency.

[0028] In a possible implementation of the first aspect, after determining the current total decay efficiency based on the current decay efficiency and the accumulated decay efficiency, the method further includes:

[0029] The current total decay efficiency is used as the updated cumulative decay efficiency.

[0030] In a possible implementation of the first aspect, determining the compensation value of the sub-pixel according to the current total decay efficiency includes:

[0031] The compensation value corresponding to the current decay total efficiency is determined according to the aging compensation table corresponding to the grayscale of the sub-pixel at the current moment. The aging compensation table includes a correspondence between a plurality of decay total efficiencies and a plurality of compensation values.

[0032] In a possible implementation of the first aspect, before determining the compensation value corresponding to the current total decay efficiency according to the aging compensation table corresponding to the grayscale of the sub-pixel at the current moment, the method further includes:

[0033] For any decaying total efficiency, set the initial compensation value corresponding to the decaying total efficiency;

[0034] If the brightness of the sub-pixel does not meet the target brightness at the initial compensation value, adjust the initial compensation value until the brightness of the sub-pixel meets the target brightness at the adjusted initial compensation value, and use the adjusted initial compensation value as the compensation value corresponding to the total decay efficiency.

[0035] In a possible implementation of the first aspect, the compensation value includes a compensated grayscale value.

[0036] In a possible implementation of the first aspect, the compensated grayscale value increases as the total decay efficiency increases.

[0037] Based on the same inventive concept, in a second aspect, an embodiment of the present application provides a display panel compensation device, the device comprising:

[0038] a data acquisition module, configured to acquire display parameters of the display panel at a current moment, the display parameters including grayscales of sub-pixels of the display panel at the current moment and target parameters, the target parameters including at least one of the brightness level, refresh rate, and temperature of the display panel at the current moment;

[0039] an efficiency determination module, configured to input the display parameters into a pre-trained neural network model to obtain a current decay efficiency of the sub-pixel at the current moment;

[0040] a compensation determination module, configured to determine a compensation value of the sub-pixel according to the current decay efficiency and the accumulated lighting time of the sub-pixel;

[0041] A compensation module is configured to compensate the brightness of the sub-pixel based on the compensation value.

[0042] Based on the same inventive concept, in a third aspect, an embodiment of the present application provides a processor and a memory storing computer program instructions, and when the processor executes the computer program instructions, it implements the display panel compensation method as described in any one of the embodiments in the first aspect.

[0043] Based on the same inventive concept, in a fourth aspect, an embodiment of the present application provides a computer program stored on the computer-readable storage medium, and when the computer program is executed by the processor, it implements the display panel compensation method as described in any one of the embodiments in the first aspect.

[0044] According to the display panel compensation method, device, equipment and storage medium provided in the embodiments of the present application, the neural network model used is no longer a single aging model, and can take into account the influence of multiple factors such as the grayscale displayed by the sub-pixel and the target parameters other than the grayscale on the degree of sub-pixel aging, thereby improving the accuracy of the determined current decay efficiency, and further improving the compensation accuracy, which is conducive to improving the compensation effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Other features, objects and advantages of the present application will become more apparent by reading the following detailed description of non-limiting embodiments with reference to the accompanying drawings, in which the same or similar reference numerals represent the same or similar features and the accompanying drawings are not drawn to scale.

[0046] Figure 1A schematic diagram showing a flow chart of a display panel compensation method provided by an embodiment of the present application;

[0047] Figure 2 Another schematic diagram showing a flow chart of a display panel compensation method provided in an embodiment of the present application;

[0048] Figure 3 Another schematic diagram illustrating a flow chart of a display panel compensation method provided in an embodiment of the present application;

[0049] Figure 4 Another schematic diagram illustrating a flow chart of a display panel compensation method provided in an embodiment of the present application;

[0050] Figure 5 Another schematic diagram illustrating a flow chart of a display panel compensation method provided in an embodiment of the present application;

[0051] Figure 6 Another schematic diagram illustrating a flow chart of a display panel compensation method provided in an embodiment of the present application;

[0052] Figure 7 A schematic structural diagram of a display panel compensation device provided in an embodiment of the present application is shown;

[0053] Figure 8 A schematic structural diagram of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0054] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only configured to explain the present application and are not configured to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is merely to provide a better understanding of the present application by illustrating the examples of the present application.

[0055] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, the elements defined by the phrase "comprising..." do not exclude the presence of other identical elements in the process, method, article, or device comprising the elements.

[0056] It will be apparent to those skilled in the art that various modifications and variations can be made in this application without departing from the spirit or scope of this application. Therefore, this application is intended to cover modifications and variations of this application that fall within the scope of the corresponding claims (technical solutions claimed for protection) and their equivalents. It should be noted that the embodiments provided in the examples of this application can be combined with each other without contradiction.

[0057] Embodiments of the present application provide a display panel compensation method, apparatus, device, and storage medium. The following describes various embodiments of the display panel compensation method, apparatus, device, and storage medium in conjunction with the accompanying drawings.

[0058] Figure 1 A flow chart of a display panel compensation method provided by an embodiment of the present application is shown. Figure 1 As shown, the display panel compensation method provided in the embodiment of the present application includes S110 to S140.

[0059] S110, obtaining display parameters of the display panel at the current moment, the display parameters including the grayscale of the sub-pixels of the display panel at the current moment and target parameters, the target parameters including at least one of the brightness level, refresh rate, and temperature of the display panel at the current moment.

[0060] S120: Input the display parameters into a pre-trained neural network model to obtain the current decay efficiency of the sub-pixel at the current moment.

[0061] S130 , determining a compensation value of the sub-pixel according to the current decay efficiency and the accumulated lighting time of the sub-pixel.

[0062] S140 , compensating the brightness of the sub-pixel based on the compensation value.

[0063] The specific implementation of each of the above steps will be described in detail below.

[0064] According to the display panel compensation method provided in the embodiment of the present application, the neural network model used is no longer a single aging model, and can take into account the influence of multiple factors such as the grayscale displayed by the sub-pixel and the target parameters other than the grayscale on the degree of sub-pixel aging, thereby improving the accuracy of the determined current decay efficiency, and further improving the compensation accuracy, which is conducive to improving the compensation effect.

[0065] The specific implementation methods of the above steps are introduced below.

[0066] First, let me introduce the S110.

[0067] When displaying images, the display panel can switch images frame by frame.

[0068] Optionally, before the display panel displays the i-th frame, a compensation value corresponding to the sub-pixel of the display panel corresponding to the i-th frame can be determined, so that the i-th frame can be displayed based on the determined compensation value. The current moment can be a moment before the i-th frame is displayed.

[0069] In subsequent steps, the compensation value may be determined based on the cumulative lighting time of the sub-pixels. Optionally, the current moment may be a moment before the i-th frame is displayed and after the i-1-th frame is displayed.

[0070] The grayscale of the sub-pixel of the display panel at the current moment may be the grayscale to be displayed by the sub-pixel. Still taking the i-th frame as an example, the grayscale of the sub-pixel at the current moment is the grayscale to be displayed by the sub-pixel corresponding to the i-th frame.

[0071] To more intuitively understand brightness levels, let's take a display panel with a brightness bar as an example. The brightness bar can be used to adjust the display brightness of the display panel, and different positions of the brightness bar can correspond to different brightness levels. Brightness levels can also be understood as display brightness values (DBV).

[0072] The refresh rate is the frequency at which the display panel refreshes the image. The display panel can support multiple refresh rates, such as 1HZ, 10HZ, 30HZ, 60HZ, 90HZ, 120HZ, 144HZ, etc.

[0073] The temperature may be the ambient temperature of the display panel or the temperature of the display panel itself. The required temperature may be acquired by a temperature acquisition device.

[0074] Optionally, the target parameters may include brightness level, refresh rate, and temperature.

[0075] Before introducing S120, let’s first introduce the training process of the neural network model.

[0076] In some optional embodiments, such as Figure 2As shown, before S120, the display panel compensation method provided by the embodiment of the present application may further include S210 and S220.

[0077] S210 , obtaining a training sample set, where the training sample set includes a plurality of training samples, and each training sample includes a grayscale, a target parameter, and an actual decay efficiency of a sub-pixel in the training sample.

[0078] S220, using the training samples to train a preset neural network model until a training stop condition is met, thereby obtaining a trained neural network model.

[0079] In an embodiment of the present application, before using the neural network model, the neural network model is first trained using multiple training samples, so that the parameters of the trained neural network model are more in line with actual needs, and the current decay efficiency obtained using the trained neural network model is more accurate.

[0080] As an example, a certain number of display panel samples can be selected for aging experiments. The display panel samples can be from the same batch as the display panels used in the display panel compensation method according to the embodiment of the present application. Display panels from the same batch undergo essentially the same manufacturing process, and thus exhibit essentially the same aging behavior. Of course, multiple display panel samples can also come from different batches, allowing the trained neural network model to account for the effects of different manufacturing conditions on aging.

[0081] For example, a training sample set may include N*M1*M2*M3*M4 training samples, where M1 represents the number of grayscales, M2 represents the number of brightness levels, M3 represents the number of refresh rates, M4 represents the number of temperatures, and N represents the number of sub-pixels, which can also be understood as the number of display panel samples. Each training sample includes a brightness attenuation value, which is the brightness attenuation value of the sub-pixel in the training sample after it is lit for the first time. N, M1, M2, M3, and M4 are all positive integers. It is understandable that the larger the values of N, M1, M2, M3, and M4, the better the training effect.

[0082] For example, M1 grayscales (Gray) to be displayed, M2 brightness levels (DBV), M3 frequencies (F), M4 temperatures (T), and three colors of sub-pixels, namely red sub-pixels (R), green sub-pixels (G), and blue sub-pixels (B), can be selected to be tested. In each case, N samples can be selected to conduct an aging experiment with the lighting up to H hours. At the beginning and end of the experiment, the brightness values of the samples are measured respectively. The difference between the brightness value at the beginning of the experiment and the brightness value at the end of the experiment is the brightness attenuation value.

[0083] Exemplarily, L_normalization = ΔL / max(ΔL), where L_normalization represents the actual degradation efficiency of the sub-pixel in the training sample, ΔL represents the brightness attenuation value in the training sample, and max(ΔL) represents the maximum brightness attenuation value among the N*M1*M2*M3*M4 brightness attenuation values. In other words, the actual degradation efficiency of the sub-pixel in each training sample is the ratio of the brightness attenuation value in the training sample to the maximum brightness attenuation value among the N*M1*M2*M3*M4 brightness attenuation values.

[0084] In an embodiment of the present application, for N*M1*M2*M3*M4 training samples, including N*M1*M2*M3*M4 brightness attenuation values, the N*M1*M2*M3*M4 brightness attenuation values are normalized, and the normalized brightness attenuation values are used as the actual decay efficiency of the sub-pixels in the training samples. In this way, each brightness attenuation value is compared with the maximum brightness attenuation value, that is, the aging degree of different training samples is compared. In this way, the impact of different factors such as grayscale, brightness level, refresh rate, and temperature on aging can be more accurately estimated.

[0085] In some optional embodiments, such as Figure 3 As shown, before S210 , for each training sample, the display panel compensation method provided by the embodiment of the present application may further include S310 to S340 .

[0086] S310 , lighting up a sub-pixel under a reference display parameter and acquiring a first brightness, where the reference display parameter includes a reference grayscale and a reference target parameter.

[0087] S320 , lighting up the sub-pixels in the training sample according to the grayscale, brightness level, refresh rate, and temperature included in the training sample.

[0088] S330 , after the sub-pixels in the training sample are illuminated for the first time period, the sub-pixels in the training sample are illuminated under the reference display parameters, and a second brightness is obtained.

[0089] S340: Determine a brightness attenuation value of the training sample according to the difference between the second brightness and the first brightness.

[0090] For each training sample, S310 to S340 may be executed respectively.

[0091] In the embodiment of the present application, the first brightness can be understood as the brightness value at the beginning of the experiment, and the second brightness can be understood as the brightness value at the end of the experiment. At the beginning and end, the display parameters are both benchmark display parameters, that is, the display conditions are consistent, and the brightness attenuation value determined under consistent conditions is also more accurate.

[0092] The reference target parameter may include at least one of a reference brightness level, a reference refresh rate, and a reference temperature.

[0093] The reference target parameter and the target parameter acquired in S110 have the same parameter type.

[0094] For example, the specific values of the grayscale, brightness level, refresh rate, and temperature in the training sample may be different from the specific values of the reference grayscale, reference brightness level, reference refresh rate, and reference temperature in the reference display parameters.

[0095] As an example, a brightness acquisition device can be used to capture brightness. However, due to the inherent characteristics of the brightness acquisition device, the accuracy of the brightness data collected at high brightness is greater than that collected at low brightness. To ensure the accuracy of the acquired brightness, the grayscale in the baseline display parameters can be greater than a first threshold, such as 60 grayscales; and the brightness level in the baseline display parameters can be greater than a second threshold, such as 100 nits. The above first and second thresholds are merely examples and can be set according to actual needs. This application does not limit them.

[0096] As another example, the reference display parameters may include 255 grayscales, a maximum brightness level, a refresh rate of 120 Hz, and a temperature of 20°. Of course, the reference display parameters may also be set to other values.

[0097] For example, the first duration may be H hours, and the specific value of H may be set according to actual needs.

[0098] In some optional embodiments, such as Figure 4 As shown, S220 may specifically include S221 to S223.

[0099] S221, inputting the grayscale and target parameters of the training sample into a preset neural network model to obtain the predicted decay efficiency.

[0100] S222: Determine the loss function value of the preset neural network model based on the predicted decay efficiency and the actual decay efficiency.

[0101] S223, when the loss function value does not meet the training stop condition, adjust the weight coefficient of the hidden layer in the preset neural network model, and use the training sample set to train the neural network model with the adjusted coefficient until the training stop condition is met, thereby obtaining the trained neural network model.

[0102] For each training sample, S221 to S223 may be executed respectively.

[0103] In an embodiment of the present application, the grayscale and target parameters are used as the input of the neural network model, the decay efficiency is used as the output of the neural network model, and the loss function value is used as the judgment condition for training to adjust the weight coefficient of the hidden layer in the neural network model. In this way, the trained neural network model is more suitable for application scenarios of predicting decay efficiency, and the predicted decay efficiency will also be more accurate.

[0104] In the embodiment of the present application, the target parameters may include brightness level, refresh rate and temperature.

[0105] It is understandable that the actual decay efficiency is determined according to the actually collected brightness value.

[0106] For example, the loss function value may include a mean square error, which may be a difference between a predicted decay efficiency and an actual decay efficiency.

[0107] If the loss function value is less than a preset loss threshold, the training stopping condition is considered satisfied. If the loss function value is greater than or equal to the preset loss threshold, the training stopping condition is considered not satisfied. To improve the accuracy of the decay efficiency predicted by the neural network model, the preset loss threshold can be sufficiently small, even close to 0.

[0108] The neural network model may include K hidden layers, where K is an integer greater than or equal to 2. The neural network model is trained to obtain weight coefficients W1, W2, ..., WK for each hidden layer.

[0109] The specific values of the weight coefficients W1, W2, ..., WK corresponding to sub-pixels of different colors may be different, and the weight coefficients W1, W2, ..., WK corresponding to sub-pixels of different colors may be stored separately.

[0110] The above is the training process of the neural network model. Next, S120 will be introduced.

[0111] In S120, the grayscale of the sub-pixel at the current moment, the brightness level, refresh rate, and temperature of the display panel at the current moment can be directly input into the trained neural network model, so that the current decay efficiency of the sub-pixel at the current moment can be directly obtained using the neural network model.

[0112] Specifically, the storage module of the display panel can store the weight coefficients corresponding to the trained neural network model, such as the weight coefficients W1, W2...WK; in addition, the forward propagation module of the trained neural network model can be set in the driving chip corresponding to the display panel. The forward propagation module can output the decay efficiency based on the input grayscale, brightness level, refresh rate, temperature and the weight coefficients corresponding to the trained neural network model. If the input corresponds to the current moment, the output corresponds to the current decay efficiency.

[0113] Next, let’s introduce S130.

[0114] In some optional embodiments, such as Figure 5 As shown, S130 may specifically include S131 to S133.

[0115] S131 , determining a cumulative decay efficiency according to decay efficiencies corresponding to a plurality of second durations within the cumulative lighting duration of the sub-pixel.

[0116] S132: Determine the current total decay efficiency based on the current decay efficiency and the accumulated decay efficiency.

[0117] S133: Determine a compensation value of the sub-pixel according to the current total decay efficiency.

[0118] In the embodiment of the present application, by determining the cumulative decay efficiency within the cumulative lighting time and further determining the current decay total efficiency, the compensation value can be determined based only on the current decay total efficiency, thereby improving efficiency.

[0119] In S131, the cumulative lighting time of the sub-pixels in the display panel may be the total lighting time of the display panel from the first lighting time of the display panel to the current time. The display panel may be provided with a corresponding timing module, which can obtain display parameters of the display panel once every second time interval through the timing module, and input the obtained display parameters into a pre-trained neural network model, thereby obtaining decay efficiencies corresponding to multiple second time intervals.

[0120] Specifically, from the first time the display panel is lit up, the display parameters of the display panel can be obtained for the first time after the first and second time periods, and the obtained display parameters can be input into the pre-trained neural network model, so that the decay efficiency S1 corresponding to the first and second time periods can be obtained. It can be understood that before obtaining the decay efficiency S1, the cumulative decay efficiency is 0, and the decay efficiency S1 can be updated to the cumulative decay efficiency.

[0121] Then, after a second time interval, the display parameters of the display panel can be obtained for the second time, and the obtained display parameters can be input into the pre-trained neural network model, so that the decay efficiency S2 corresponding to the second time interval can be obtained. The sum of the decay efficiency S2 and the decay efficiency S1 is used to update the cumulative decay efficiency, and the cumulative decay efficiency is S1+S2.

[0122] Then, at the third second time interval, the display parameters of the display panel can be obtained for the third time, and the obtained display parameters can be input into the pre-trained neural network model, so that the decay efficiency S3 corresponding to the third second time interval can be obtained. The cumulative decay efficiency is updated by adding the sum of the decay efficiency S3 and the cumulative decay efficiency (S1+S2), and the cumulative decay efficiency is S1+S2+S3.

[0123] And so on. Taking the cumulative lighting time including n second durations as an example, the cumulative decay efficiency can be obtained as S1+S2+S3+…+S n .

[0124] Specifically, in S132 , the sum of the current decay efficiency and the accumulated decay efficiency may be taken as the current total decay efficiency.

[0125] In some optional embodiments, after S132 , the display panel compensation method provided by the embodiment of the present application may further include: using the current total decay efficiency as the updated cumulative decay efficiency.

[0126] In this way, when determining the total decay efficiency corresponding to subsequent moments, the updated cumulative decay efficiency can be directly used, which can improve efficiency.

[0127] In some optional embodiments, such as Figure 6 As shown, S133 may specifically include S1331.

[0128] S1331 , determining a compensation value corresponding to a current total decay efficiency according to an aging compensation table corresponding to a grayscale of a sub-pixel at a current moment, wherein the aging compensation table includes a correspondence between a plurality of total decay efficiencies and a plurality of compensation values.

[0129] As shown in Table 1, an aging compensation table corresponding to a certain grayscale is given.

[0130] Table 1

[0131] Total decay efficiency 0 1000 2000 3000 4000 Compensation value 0 1 1.5 2 3

[0132] The storage module of the display panel may store an aging compensation table, so that during compensation, the stored aging compensation table may be directly called.

[0133] For example, different grayscales may correspond to different aging compensation tables.

[0134] In some optional embodiments, before S1331 , an aging compensation table may be determined first.

[0135] Specifically, before S1331, the display panel compensation method provided in the embodiment of the present application may also include: for any decay total efficiency, setting an initial compensation value corresponding to the decay total efficiency; if the brightness of the sub-pixel does not meet the target brightness under the initial compensation value, adjusting the initial compensation value until the brightness of the sub-pixel meets the target brightness under the adjusted initial compensation value, and using the adjusted initial compensation value as the compensation value corresponding to the decay total efficiency.

[0136] For example, using the highest grayscale as an example, the brightness-time decay curve for that highest grayscale can be determined. Based on the lighting duration, the total decay efficiency corresponding to that lighting duration can be determined. Furthermore, the brightness decay value corresponding to that lighting duration can be determined on the brightness-time decay curve. An initial compensation value can be set based on the brightness decay value. This initial compensation value, when set, is closer to the required compensation value, thereby improving efficiency.

[0137] Of course, the aging compensation table may also be determined in other ways, and this application does not impose any specific limitation on this.

[0138] For example, if the current total efficiency of the decay is 1000.4, and there is no value for this total efficiency in the aging compensation table, the compensation value corresponding to the current total efficiency of 1000.4 can be determined using a linear difference method. For example, the compensation value corresponding to the current total efficiency of 1000.4 can be equal to 1 + (1000.4 - 1000) * (1.5 - 1) / (2000 - 1000) ≈ 1.

[0139] Exemplarily, the compensation value may include a compensation grayscale value;

[0140] Exemplarily, the compensated grayscale value increases as the total decay efficiency increases.

[0141] Next, let’s introduce S140.

[0142] For example, the grayscale of the sub-pixel at the current moment is 190, that is, the grayscale that the sub-pixel is about to display is 190, and the compensation value is 1, then the grayscale of the sub-pixel after compensation is 191, and the new grayscale display signal of the sub-pixel is 191, and then the sub-pixel can be driven to display according to the data voltage corresponding to the grayscale value 191.

[0143] Based on the same inventive concept, the embodiment of the present application also provides a display panel compensation device. Figure 7 As shown, the display panel compensation device 700 provided in the embodiment of the present application may include a data acquisition module 701 , an efficiency determination module 702 , a compensation determination module 703 and a compensation module 704 .

[0144] A data acquisition module 701 is configured to acquire display parameters of the display panel at a current moment, the display parameters including the grayscale of the sub-pixels of the display panel at a current moment and target parameters, the target parameters including at least one of the brightness level, refresh rate, and temperature of the display panel at a current moment;

[0145] The efficiency determination module 702 is used to input the display parameters into the pre-trained neural network model to obtain the current decay efficiency of the sub-pixel at the current moment;

[0146] The compensation determination module 703 is used to determine the compensation value of the sub-pixel according to the current decay efficiency and the cumulative lighting time of the sub-pixel;

[0147] Compensation module 704, used to compensate the brightness of the sub-pixel based on the compensation value

[0148] According to the display panel compensation device provided in the embodiment of the present application, the neural network model used is no longer a single aging model. It can take into account the influence of multiple factors such as the grayscale displayed by the sub-pixel and the target parameters other than the grayscale on the degree of sub-pixel aging, thereby improving the accuracy of the determined current decay efficiency, and further improving the compensation accuracy, which is conducive to improving the compensation effect.

[0149] In some optional embodiments, the display compensation device provided in the embodiments of the present application may further include a training module, the training module being configured to:

[0150] Before inputting the display parameters into a pre-trained neural network model to obtain the decay efficiency, the method further includes:

[0151] Obtaining a training sample set, where the training sample set includes a plurality of training samples, each training sample including a grayscale, a target parameter, and an actual decay efficiency of a sub-pixel in the training sample;

[0152] The preset neural network model is trained using the training samples until the training stop condition is met to obtain the trained neural network model.

[0153] In some optional embodiments, the training sample set includes N*M1*M2*M3*M4 training samples, where M1 represents the number of grayscales, M2 represents the number of brightness levels, M3 represents the number of refresh rates, M4 represents the number of temperatures, and N represents the number of sub-pixels. Each training sample includes a brightness attenuation value, which is a brightness attenuation value after the sub-pixel in the training sample is lit for a first time period.

[0154] The actual decay efficiency of the sub-pixel in each training sample is the ratio of the brightness decay value in the training sample to the maximum brightness decay value among the N*M1*M2*M3*M4 brightness decay values.

[0155] In some optional embodiments, the training module may be specifically used to:

[0156] For each training sample, perform the following steps:

[0157] Lighting up sub-pixels in the training sample under reference display parameters and obtaining a first brightness, the reference display parameters including a reference grayscale and a reference target parameter;

[0158] Light up the sub-pixels in the training sample according to the grayscale, brightness level, refresh rate, and temperature included in the training sample;

[0159] After the sub-pixels in the training sample are illuminated for a first duration, the sub-pixels in the training sample are illuminated under the reference display parameters, and a second brightness is obtained;

[0160] A brightness attenuation value of the training sample is determined according to a difference between the second brightness and the first brightness.

[0161] The reference target parameter may include at least one of a reference brightness level, a reference refresh rate, and a reference temperature.

[0162] In some optional embodiments, the training module may be specifically used to:

[0163] For each training sample, perform the following steps:

[0164] The grayscale and target parameters of the training samples are input into the preset neural network model to obtain the predicted decay efficiency;

[0165] Determine the loss function value of the preset neural network model according to the predicted decay efficiency and the actual decay efficiency;

[0166] When the loss function value does not meet the training stop condition, the weight coefficient of the hidden layer in the preset neural network model is adjusted, and the neural network model with the adjusted coefficient is trained using the training sample set until the training stop condition is met to obtain the trained neural network model.

[0167] In some optional embodiments, the number of hidden layers is greater than or equal to 2.

[0168] In some optional embodiments, the efficiency determination module 702 may be specifically configured to:

[0169] Determining a cumulative decay efficiency based on decay efficiencies corresponding to a plurality of second durations within the cumulative lighting duration of the sub-pixel;

[0170] Determine the current recession total efficiency based on the current recession efficiency and the accumulated recession efficiency;

[0171] The compensation value of the sub-pixel is determined according to the current total decay efficiency.

[0172] In some optional embodiments, the efficiency determination module 702 may further be used to:

[0173] The current total decay efficiency is used as the updated cumulative decay efficiency.

[0174] In some optional embodiments, the compensation determination module 703 may be specifically configured to:

[0175] Determining a compensation value corresponding to a current decay total efficiency according to an aging compensation table corresponding to a grayscale of the sub-pixel at a current moment, wherein the aging compensation table includes a correspondence between a plurality of decay total efficiencies and a plurality of compensation values;

[0176] In some optional embodiments, the compensation determination module 703 may further be configured to:

[0177] For any decaying total efficiency, set the initial compensation value corresponding to the decaying total efficiency;

[0178] If the brightness of the sub-pixel does not meet the target brightness at the initial compensation value, adjust the initial compensation value until the brightness of the sub-pixel meets the target brightness at the adjusted initial compensation value, and use the adjusted initial compensation value as the compensation value corresponding to the total decay efficiency.

[0179] In some optional embodiments, the compensation value includes a compensated grayscale value;

[0180] In some optional embodiments, the compensated grayscale value increases as the total decay efficiency increases.

[0181] The display panel compensation device in the embodiments of the present application can be a device, or a component, integrated circuit, or chip in a terminal. The device can be a mobile electronic device or a non-mobile electronic device. For example, the mobile electronic device can be a mobile phone, a tablet computer, a laptop computer, a PDA, an in-vehicle electronic device, a wearable device, an ultra-mobile personal computer (UMPC), a netbook, or a personal digital assistant (PDA), etc. The non-mobile electronic device can be a server, a network attached storage (NAS), a personal computer (PC), a television (TV), a teller machine, or a self-service machine, etc., and the embodiments of the present application do not specifically limit this.

[0182] The display panel compensation device provided in the embodiment of the present application can achieve Figure 1 To avoid repetition, the various processes in the embodiment of the display panel compensation method are not described again here.

[0183] Figure 8 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application is shown.

[0184] The electronic device may include a processor 801 and a memory 802 storing computer program instructions.

[0185] Specifically, the processor 801 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiment of the present invention.

[0186] The memory 802 may include a large-capacity memory for data or instructions. By way of example and not limitation, the memory 802 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 802 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 802 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 802 is a non-volatile solid-state memory. In a specific embodiment, the memory 802 includes a read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or a flash memory, or a combination of two or more of these. Exemplarily, the memory may include a non-volatile transient memory.

[0187] The processor 801 reads and executes computer program instructions stored in the memory 802 to implement any one of the display panel compensation methods in the above embodiments.

[0188] In one example, the electronic device may further include a communication interface 803 and a bus 810. Figure 8 As shown, the processor 801, the memory 802, and the communication interface 803 are connected via a bus 810 and communicate with each other.

[0189] The communication interface 803 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiment of the present invention.

[0190] Bus 810 comprises hardware, software or both, couples the parts of electronic equipment to each other.For example, and not limitation, bus can comprise accelerated graphics port (AGP) or other graphics bus, enhanced industry standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industry standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations.In suitable cases, bus 810 can comprise one or more buses.Although the embodiment of the present invention describes and shows specific bus, the present invention considers any suitable bus or interconnection.

[0191] The electronic device can execute the display panel compensation method in the embodiment of the present application, thereby realizing the combination Figure 1 and Figure 7 Described display panel compensation method and display panel compensation device.

[0192] The present application also provides a computer-readable storage medium having a computer program stored thereon. When executed by a processor, the computer program implements the display panel compensation method described in the above-described embodiment and achieves the same technical effects. To avoid repetition, the above-described computer-readable storage medium may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc., without limitation herein.

[0193] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or communication link via a data signal carried in a carrier wave. "Computer-readable medium" can include any medium capable of storing or transmitting information. Examples of computer-readable media include electronic circuits, semiconductor memory devices, ROMs, flash memories, erasable ROMs (EROMs), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0194] According to an embodiment of the present application, the computer-readable storage medium may be a non-transitory computer-readable storage medium.

[0195] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps. In other words, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0196] Aspects of the present application have been described above with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine so that these instructions executed via the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. This processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor or a field programmable logic circuit. It is also understood that each box in the block diagram and / or the flowchart and the combination of the boxes in the block diagram and / or the flowchart can also be implemented by the dedicated hardware that performs the specified function or action, or can be implemented by the combination of dedicated hardware and computer instructions.

[0197] While the embodiments described above are not exhaustive, they do not limit the present application to the specific embodiments described. Clearly, numerous modifications and variations are possible based on the above description. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present application, thereby enabling those skilled in the art to better utilize the present application and its modifications. The present application is limited only by the claims and their full scope and equivalents.

Claims

1. A display panel compensation method, characterized in that: The method comprises: Obtaining display parameters of the display panel at a current moment, the display parameters including grayscales of sub-pixels of the display panel at the current moment and target parameters, the target parameters including at least one of a brightness level, a refresh rate, and a temperature of the display panel at the current moment; Inputting the display parameters into a pre-trained neural network model to obtain a current decay efficiency of the sub-pixel at the current moment; determining a compensation value for the sub-pixel according to the current decay efficiency and the accumulated lighting time of the sub-pixel; compensating the brightness of the sub-pixel based on the compensation value; Before inputting the display parameters into a pre-trained neural network model to obtain the decay efficiency, the method further includes: Acquire a training sample set, the training sample set including a plurality of training samples, each of the training samples including a grayscale, a target parameter, and an actual decay efficiency of a sub-pixel in the training sample; Using the training samples to train a preset neural network model until a training stop condition is met, thereby obtaining a trained neural network model; The training sample set includes N*M1*M2*M3*M4 training samples, where M1 represents the number of grayscales, M2 represents the number of brightness levels, M3 represents the number of refresh rates, M4 represents the number of temperatures, and N represents the number of sub-pixels. Each training sample includes a brightness attenuation value, which is the brightness attenuation value of the sub-pixel in the training sample after it is lit for a first time period. The actual decay efficiency of the sub-pixel in each training sample is the ratio of the brightness decay value in the training sample to the maximum brightness decay value among N*M1*M2*M3*M4 brightness decay values.

2. The method according to claim 1, characterized in that Before obtaining the training sample set, the method further includes: For each training sample, perform the following steps: Lighting up the sub-pixels in the training sample under a reference display parameter and obtaining a first brightness, wherein the reference display parameter includes a reference grayscale and a reference target parameter; Lighting up sub-pixels in the training sample according to the grayscale and target parameters of the training sample; After the sub-pixels in the training sample are illuminated for the first duration, the sub-pixels in the training sample are illuminated under the reference display parameters, and a second brightness is obtained; A brightness attenuation value of the training sample is determined according to a difference between the second brightness and the first brightness.

3. The method according to claim 2, characterized in that The reference target parameter includes at least one of a reference brightness level, a reference refresh rate, and a reference temperature.

4. The method according to claim 1, wherein The method of training a preset neural network model using the training samples until a training stop condition is met to obtain a trained neural network model specifically includes: For each training sample, perform the following steps: Inputting the grayscale and target parameters of the training sample into the preset neural network model to obtain the predicted decay efficiency; Determining a loss function value of the preset neural network model according to the predicted decay efficiency and the actual decay efficiency; When the loss function value does not meet the training stop condition, the weight coefficient of the hidden layer in the preset neural network model is adjusted, and the neural network model with the adjusted coefficient is trained using the training sample set until the training stop condition is met, thereby obtaining the trained neural network model.

5. The method according to claim 4, characterized in that The number of the hidden layers is greater than or equal to 2.

6. The method according to claim 1, wherein The determining, according to the current decay efficiency and the accumulated lighting time of the sub-pixel, a compensation value of the sub-pixel includes: Determining a cumulative decay efficiency according to decay efficiencies corresponding to a plurality of second durations within the cumulative lighting duration of the sub-pixel; Determining a current total decay efficiency according to the current decay efficiency and the accumulated decay efficiency; A compensation value of the sub-pixel is determined according to the current total decay efficiency.

7. The method according to claim 6, characterized in that After determining the current decay total efficiency according to the current decay efficiency and the accumulated decay efficiency, the method further includes: The current total decay efficiency is used as the updated cumulative decay efficiency.

8. The method according to claim 6, characterized in that The determining the compensation value of the sub-pixel according to the current total decay efficiency includes: The compensation value corresponding to the current decay total efficiency is determined according to an aging compensation table corresponding to the grayscale of the sub-pixel at the current moment, wherein the aging compensation table includes a correspondence between a plurality of decay total efficiencies and a plurality of compensation values.

9. The method according to claim 8, characterized in that Before determining the compensation value corresponding to the current total decay efficiency according to the aging compensation table corresponding to the grayscale of the sub-pixel at the current moment, the method further includes: For any decaying total efficiency, setting an initial compensation value corresponding to the decaying total efficiency; If the brightness of the sub-pixel does not meet the target brightness at the initial compensation value, adjust the initial compensation value until the brightness of the sub-pixel meets the target brightness at the adjusted initial compensation value, and use the adjusted initial compensation value as the compensation value corresponding to the total decay efficiency.

10. The method according to claim 1, characterized in that The compensation value includes a compensated grayscale value.

11. The method according to claim 10, characterized in that The compensation grayscale value increases with the increase of the total decay efficiency.

12. A display panel compensation device, characterized in that: include: a data acquisition module, configured to acquire display parameters of the display panel at a current moment, the display parameters including grayscales of sub-pixels of the display panel at the current moment and target parameters, the target parameters including at least one of the brightness level, refresh rate, and temperature of the display panel at the current moment; an efficiency determination module, configured to input the display parameters into a pre-trained neural network model to obtain a current decay efficiency of the sub-pixel at the current moment; a compensation determination module, configured to determine a compensation value of the sub-pixel according to the current decay efficiency and the accumulated lighting time of the sub-pixel; a compensation module, configured to compensate the brightness of the sub-pixel based on the compensation value; A training module is used to obtain a training sample set, wherein the training sample set includes multiple training samples, each of which includes a grayscale, a target parameter, and an actual decay efficiency of a sub-pixel in the training sample; a preset neural network model is trained using the training samples until a training stop condition is met to obtain a trained neural network model; the training sample set includes N*M1*M2*M3*M4 training samples, wherein M1 represents the number of grayscales, M2 represents the number of brightness levels, M3 represents the number of refresh rates, M4 represents the number of temperatures, and N represents the number of sub-pixels; each of the training samples includes a brightness attenuation value, which is the brightness attenuation value of the sub-pixel in the training sample after being lit for a first time period; the actual decay efficiency of the sub-pixel in each training sample is the ratio of the brightness attenuation value in the training sample to the maximum brightness attenuation value among the N*M1*M2*M3*M4 brightness attenuation values.

13. An electronic device, characterized in that: include: A processor and a memory storing computer program instructions, wherein the processor implements the display panel compensation method according to any one of claims 1 to 12 when executing the computer program instructions.

14. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the display panel compensation method according to any one of claims 1 to 12 is implemented.

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