Display compensation method, brightness control model training method and related device

Abstract

The invention provides a display compensation method, a brightness control model training method and a related device, and the method comprises the steps: inputting the current average image level and the current brightness of a display panel into a brightness control model, and obtaining the correction gray scale of each pixel on the display panel; wherein the brightness control model is obtained by training according to multiple groups of training data, each group of training data comprises brightness training data of the display panel, average image level training data corresponding to the brightness training data and target gray scales of pixels corresponding to the brightness training data, and the pixels can realize local excitation brightness under the target gray scales; determining a driving voltage required for exciting each pixel of the display panel to the corrected gray scale; and outputting a corresponding driving voltage to each pixel. The local excitation brightness deviation caused by the voltage drop can be conveniently compensated by correcting the gray scale based on the brightness control model, hardware of the display device does not need to be changed, and the cost is low.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of display technology, and in particular to a display compensation method, a brightness control model training method and related devices. Background Art

[0002] OLED (Organic Light Emitting Diode) screens are currently widely used in mobile phones, wearables, tablets and other scenarios.

[0003] Local excitation brightness is one of the important indicators to measure the performance of OLED screens and is crucial to providing a high-quality visual experience. Local excitation brightness is a key factor in achieving high dynamic range (HDR) content display. HDR content requires the screen to provide high brightness in a small area to show rich details and contrast. Local excitation brightness affects the local contrast and details of the image displayed on the screen. When watching videos or pictures, high local excitation brightness can provide clearer and more vivid visual effects. In outdoor or direct sunlight environments, high local excitation brightness can improve the visibility of the screen, allowing users to clearly see the screen content even in strong light.

[0004] Local excitation brightness is the peak brightness value obtained only in a local area of ​​the screen under the premise of global excitation brightness. However, due to the voltage drop (i.e. voltage drop) caused by the internal resistance of the wiring in the PANEL (display panel) in the local display, the performance of peak brightness is often affected by the deviation of brightness, chromaticity and gamma curves, which in turn produces local excitation brightness deviation. Summary of the invention

[0005] The embodiments of the present invention provide a display compensation method, a brightness control model training method and related devices to solve the problem of local excitation brightness deviation when the display device is displaying.

[0006] In order to solve the above-mentioned technical problems, the present invention is achieved as follows:

[0007] In a first aspect, an embodiment of the present invention provides a display compensation method, comprising:

[0008] Inputting the current average image level and the current brightness of the display panel into the brightness control model to obtain the corrected grayscale of each pixel on the display panel; wherein the brightness control model is trained according to multiple sets of training data, each set of the training data includes: brightness training data of the display panel, average image level training data corresponding to the brightness training data, and target grayscale of each pixel corresponding to the brightness training data, at which the pixel can achieve local excitation brightness;

[0009] Determining a driving voltage required to excite each pixel of the display panel to the modified grayscale;

[0010] The corresponding driving voltage is output to each pixel.

[0011] In a second aspect, an embodiment of the present invention provides a method for training a brightness control model, comprising:

[0012] Acquire a training data set, the training data set comprising a plurality of sets of training data, each set of the training data comprising: brightness training data of the display panel, average image level training data corresponding to the brightness, and target grayscale of each pixel corresponding to the brightness training data; the pixel can achieve local excitation brightness under the target grayscale;

[0013] The training data set is used to train the brightness control model to be trained to obtain the brightness control model.

[0014] Optionally, the original brightness control model is trained using the training data set to obtain a brightness control model, including:

[0015] Prediction step: inputting the brightness training data and the average image level training data corresponding to the brightness training data into the brightness control model to be trained, and obtaining the predicted grayscale of each pixel output by the current brightness control model to be trained;

[0016] Optimization step: optimizing the parameters of the brightness control model to be trained according to the target grayscale and the predicted grayscale of each pixel;

[0017] The prediction step and the optimization step are iteratively performed until a preset condition is met.

[0018] Optionally, the preset condition is that the difference between the predicted grayscale and the target grayscale is less than a preset threshold;

[0019] Alternatively, the preset condition is that the number of iterations exceeds a preset number.

[0020] Optionally, each pixel includes a plurality of single-color pixels, and the parameters of the brightness control model to be trained corresponding to each single-color pixel are different;

[0021] The optimization step comprises:

[0022] According to the target grayscale and the predicted grayscale of each of the single-color pixels, the parameters of the brightness control model to be trained corresponding to each of the single-color pixels are optimized respectively.

[0023] In a third aspect, an embodiment of the present invention provides a display compensation device, including:

[0024] An execution module, used for inputting the current average image level and the current brightness of the display panel into a brightness control model to obtain a corrected grayscale of each pixel on the display panel; wherein the brightness control model is trained according to a plurality of sets of training data, each set of the training data comprising: brightness training data of the display panel, average image level training data corresponding to the brightness training data, and a target grayscale of each pixel corresponding to the brightness training data, at which the pixel can achieve a local excitation brightness;

[0025] A determination module, used for determining a driving voltage required to excite each pixel of the display panel to the modified grayscale;

[0026] The output module is used to output the corresponding driving voltage to each pixel.

[0027] In a fourth aspect, an embodiment of the present invention provides a training device for a brightness control model, comprising:

[0028] An acquisition module is used to acquire a training data set, wherein the training data set includes multiple groups of training data, each group of the training data includes: brightness training data of the display panel, average image level training data corresponding to the brightness, and target grayscale of each pixel corresponding to the brightness training data; the pixel can achieve local excitation brightness under the target grayscale;

[0029] The training module is used to train the brightness control model to be trained using the training data set to obtain the brightness control model.

[0030] In a fifth aspect, an embodiment of the present invention provides an electronic device, comprising a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps in the display compensation method as described in the first aspect, or implements the steps in the training method of the brightness control model as described in any one of the second aspects.

[0031] In a sixth aspect, an embodiment of the present invention provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps in the display compensation method described in the first aspect are implemented, or the steps in the training method of the brightness control model described in any one of the second aspects are implemented.

[0032] In the seventh aspect, an embodiment of the present invention provides a computer program product, comprising computer instructions, which, when executed by a processor, implement the steps in the display compensation method as described in the first aspect, or implement the steps in the training method of the brightness control model as described in any one of the second aspects.

[0033] In an embodiment of the present invention, by inputting the current average image level and the current brightness of the display panel into a pre-trained brightness control model, a corrected grayscale of each pixel on the display panel is obtained, a driving voltage required to excite each pixel of the display panel to the corrected grayscale is determined, and a corresponding driving voltage is output to each pixel. The brightness control model is obtained by training based on multiple groups of training data, each group of the training data includes: brightness training data of the display panel, average image level training data corresponding to the brightness training data, and target grayscale of each pixel corresponding to the brightness training data. At the target grayscale, the pixel can achieve local excitation brightness. Based on the brightness control model, it is convenient to compensate for the local excitation brightness deviation caused by the voltage drop by correcting the grayscale, without changing the hardware of the display device, and the cost is low. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0035] Figure 1 It is a schematic diagram of the pressure drop principle;

[0036] Figure 2 A schematic diagram showing a flow chart of a compensation method according to an embodiment of the present invention;

[0037] Figure 3 Schematic diagram of a flow chart of a method for training a brightness control model according to an embodiment of the present invention;

[0038] Figure 4 This is a diagram of the training process;

[0039] Figure 5 A principle block diagram showing a compensation device according to an embodiment of the present invention;

[0040] Figure 6 It is a principle block diagram of a training device for a brightness control model according to an embodiment of the present invention;

[0041] Figure 7 The figure is a principle block diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] The terms "first", "second", etc. in this application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way are interchangeable where appropriate, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of one type, and the number of objects is not limited, for example, the first object can be one or more. In addition, "or" in this application represents at least one of the connected objects. For example, "A or B" covers three schemes, namely, Scheme 1: including A but not including B; Scheme 2: including B but not including A; Scheme 3: including both A and B. The character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0044] In the technical solution of the present disclosure, words such as “connect”, “couple” or “connected” are not limited to physical or mechanical connections, but may include electrical connections.

[0045] In addition, the technical features involved in the different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0046] Figure 1 The voltage drop caused by the internal resistance of the PANEL wiring in the local display is shown. Specifically, Figure 1 In the display module, the equivalent resistance of all sub-pixel units in parallel in the display area is defined as Rx, and the ELVDD voltage lead resistance excluding the sub-pixel unit in the display module is defined as R0. In the display module, R0 is a fixed value. Rx and R0 are connected in series and pass the same current (I). Therefore, the resistor R0 occupies a part of the voltage, that is, Vdrop=I×R0. However, in the display module, I will change according to the displayed image, so Vdrop will also change accordingly. Due to the IR drop effect, as a result, the voltage allocated to the sub-pixel units connected in parallel in the display area will also change at the same time, that is, Real ELVDD=ELVDD-I×R0. The phenomenon that the Real ELVDD of the sub-pixel unit is different due to different display loads is called internal resistance voltage drop (IR Drop, that is, the voltage drop referred to in this application).

[0047] The embodiment of the present invention provides a display compensation method, see Figure 2 As shown, Figure 2 The present invention is a flowchart of a compensation method according to an embodiment of the present invention, including:

[0048] Step 11: inputting the current average image level and the current brightness of the display panel into the brightness control model to obtain the corrected grayscale of each pixel on the display panel; wherein the brightness control model is trained according to multiple sets of training data, each set of training data includes: brightness training data of the display panel, average image level training data corresponding to the brightness training data, and target grayscale of each pixel corresponding to the brightness training data, and the pixel can achieve local excitation brightness under the target grayscale;

[0049] Step 12: Determine the driving voltage required to excite each pixel of the display panel to a corrected grayscale;

[0050] Step 13: Output the corresponding driving voltage to each pixel.

[0051] In an embodiment of the present invention, the target grayscale is the grayscale that each pixel should reach under the same group of brightness training data and average image level training data after the voltage drop is offset as set by the user, where the same group refers to the same group of training data. In other words, the target grayscale is the grayscale that corrects the local excitation brightness deviation caused by the voltage drop. In an embodiment of the present invention, the pixel can achieve local excitation brightness under the target grayscale, which means that each pixel can achieve the local excitation brightness that eliminates the local excitation brightness deviation caused by the voltage drop under the target grayscale. Therefore, it can be understood that the brightness control model can output the grayscale that each pixel should have when the local excitation brightness deviation is corrected, that is, the corrected grayscale, based on the input current average image level and the current brightness.

[0052] In the embodiment of the present invention, the specific method for determining the driving voltage in step 12 includes: determining the driving voltage required to excite each pixel of the display panel to the corrected gray scale according to preset gray scale-driving voltage mapping relationship data.

[0053] It should be noted that in some optional embodiments, the embodiments of the present invention can be applied to the DDIC (Display Data Interface Controller, which means in Chinese: Display Data Interface Controller) in the display. The DDIC is electrically connected to the display panel in the display, and is used to output a driving voltage to drive each pixel to emit light to the display panel. The DDIC pre-stores grayscale-driving voltage mapping relationship data; and / or, the DDIC is electrically connected to a register in the display, and the register pre-stores grayscale-driving voltage mapping relationship data, and the DDIC can call the grayscale-driving voltage mapping relationship data pre-stored in the register. Therefore, after the DDIC executes the step of inputting the current average image level and the current brightness of the display panel into the brightness control model to obtain the corrected grayscale of each pixel on the display panel, the DDIC can determine the driving voltage required to excite each pixel of the display panel to the corrected grayscale based on the grayscale-driving voltage mapping relationship data, and output the corresponding driving voltage to each pixel, so that each pixel emits light according to the corrected grayscale, thereby achieving local excitation brightness.

[0054] It should be noted that in the embodiment of the present invention, each pixel of the display panel is excited to the driving voltage required for correcting the grayscale, that is, the voltage that can offset the voltage drop after being input to each pixel, and further can correct the local excitation brightness deviation caused by the voltage drop after being input to each pixel.

[0055] In an embodiment of the present invention, by inputting the current average image level and the current brightness of the display panel into a pre-trained brightness control model, a corrected grayscale of each pixel on the display panel is obtained, a driving voltage required to excite each pixel of the display panel to the corrected grayscale is determined, and a corresponding driving voltage is output to each pixel. The brightness control model is obtained by training based on multiple groups of training data, each group of the training data includes: brightness training data of the display panel, average image level training data corresponding to the brightness training data, and target grayscale of each pixel corresponding to the brightness training data. At the target grayscale, the pixel can achieve local excitation brightness. Based on the brightness control model, it is convenient to compensate for the local excitation brightness deviation caused by the voltage drop by correcting the grayscale, without changing the hardware of the display device, and the cost is low.

[0056] The embodiment of the present invention provides a method for training a brightness control model. Figure 3 As shown, Figure 3 The flowchart of the training method of the brightness control model according to the embodiment of the present invention includes:

[0057] Step 21: obtaining a training data set, the training data set including multiple sets of training data, each set of training data including: brightness training data of the display panel, average image level training data corresponding to the brightness, and target grayscale of each pixel corresponding to the brightness training data; the pixel can achieve local excitation brightness under the target grayscale;

[0058] Step 22: Use the training data set to train the brightness control model to be trained to obtain the brightness control model.

[0059] In the embodiment of the present invention, the target grayscale is the grayscale that each pixel should reach under the same group of brightness training data and average image level training data after the voltage drop is offset as set by the user, where the same group refers to the same group of training data. In other words, the target grayscale is the grayscale that corrects the local excitation brightness deviation caused by the voltage drop. In the embodiment of the present invention, the pixels can achieve local excitation brightness under the target grayscale, which means that each pixel can achieve the local excitation brightness that eliminates the local excitation brightness deviation caused by the voltage drop under the target grayscale. Therefore, it can be understood that the brightness control model trained by the embodiment of the present invention can correct the grayscale of the local excitation brightness deviation caused by the voltage drop according to the input current average image level and the current brightness output, that is, the corrected grayscale.

[0060] In some embodiments of the present invention, the brightness control model to be trained is optionally trained based on a genetic algorithm (GA) to improve the training efficiency. Genetic algorithm is a heuristic search algorithm that imitates the principles of natural selection and genetics to solve optimization problems. Genetic algorithm was proposed by John H. Holland in the 1970s and elaborated in detail in his book "Adaptability of Natural and Artificial Systems". The basic idea of ​​genetic algorithm is to iteratively optimize candidate solutions by simulating the inheritance and mutation mechanisms in the biological evolution process.

[0061] In an embodiment of the present invention, a training data set is obtained, the training data set includes multiple groups of training data, each group of training data includes: brightness training data of the display panel, average image level training data corresponding to the brightness, and target grayscale of each pixel corresponding to the brightness training data; the pixel can achieve local excitation brightness under the target grayscale; the training data set is used to train the brightness control model to be trained to obtain the brightness control model. The embodiment of the present invention can train a brightness control model of the grayscale that corrects the local excitation brightness deviation caused by the input average image level and the brightness output voltage drop.

[0062] In some embodiments of the present invention, optionally, the original brightness control model is trained using a training data set to obtain a brightness control model, including:

[0063] Prediction step a: inputting brightness training data and average image level training data corresponding to the brightness training data into the brightness control model to be trained, and obtaining the predicted grayscale of each pixel output by the current brightness control model to be trained;

[0064] Optimization step b: Optimizing the parameters of the brightness control model to be trained according to the target grayscale and predicted grayscale of each pixel;

[0065] The prediction step a and the optimization step b are iteratively executed until the preset conditions are met.

[0066] The embodiment of the present invention can realize continuous training optimization of the brightness control model to be trained until the brightness control model is obtained through training, thereby reducing human intervention in training and facilitating improving training efficiency.

[0067] In some embodiments of the present invention, optionally, the preset condition is that the difference between the predicted grayscale and the target grayscale is less than a preset threshold;

[0068] Alternatively, the preset condition is that the number of iterations exceeds a preset number.

[0069] In an embodiment of the present invention, when the preset condition is that the difference between the predicted grayscale and the target grayscale is less than a preset threshold, the preset condition is met, indicating that the difference between the predicted grayscale and the target grayscale output by the current brightness control model to be trained meets the user's usage requirements, and the training can be terminated, and the current brightness control model to be trained is used as the brightness control model to be trained.

[0070] In some embodiments of the present invention, optionally, the preset threshold is 0.1. Under the preset threshold, the brightness control model trained by the embodiment of the present invention can accurately output the corrected grayscale according to the input current average image level and current brightness.

[0071] In some embodiments of the present invention, optionally, each pixel includes a plurality of single-color pixels, and the parameters of the brightness control model to be trained corresponding to each single-color pixel are different;

[0072] Optimizing step b, including:

[0073] According to the target grayscale and the predicted grayscale of each single-color pixel, the parameters of the brightness control model to be trained corresponding to each single-color pixel are optimized respectively.

[0074] In an embodiment of the present invention, the parameters of the brightness control model to be trained corresponding to each single-color pixel are optimized according to the target grayscale and predicted grayscale of each single-color pixel. The embodiment of the present invention finely separates the parameters of the brightness control model to be trained corresponding to each single-color pixel, and optimizes them in a targeted manner, thereby achieving more accurate parameter optimization and ensuring that the trained brightness control model can accurately obtain the corrected grayscale.

[0075] In some embodiments of the present invention, optionally, single-color pixels include three types: R (red), G (green), and B (blue).

[0076] The present invention will be further described below through some specific embodiments.

[0077] Model training data collection:

[0078] See also Figure 4 As shown, 10 pieces of R / G / B data (data data, i.e. target grayscale) are set at 1600nit, 2000nit, 2500nit and 3000nit respectively under APL10%, 20% and 30% white screen, i.e. data values ​​of PANEL R / G / B pixels are written.

[0079] Model training:

[0080] Set DATA R / G / B The relationship is:

[0081] DATA R =a0 R +a1 R *APL+a2 R *Lv+a3 R *APL^2+a4 R *Lv^2+a5 R *APL*lv

[0082] DATA G =a0 G +a1 G *APL+a2 G *Lv+a3 G *APL^2+a4 G *Lv^2+a5 G *APL*lv

[0083] DATA B =a0 B +a1 B *APL+a2 B *Lv+a3 B *APL^2+a4 B *Lv^2+a5B *APL*lv

[0084] The raw data collected is DATA R / G / B (representing R / G / B data, i.e. target grayscale), APL (i.e. average picture level training data) and Lv (i.e. display panel brightness training data), and use genetic algorithm to identify the six parameters a0, a1, a2, a3, a4, and a5, including the following key steps:

[0085] 1. Initialize the population: randomly generate a set of candidate solutions to form an initial population. Each candidate solution is called an individual, and the collection of individuals is called a population;

[0086] The design population size is 100, the dimension is 6 (6 parameters), and the upper and lower limits of the parameters are [-2000, 2000].

[0087] 2. Fitness evaluation: Calculate the fitness value for each individual. The fitness value is usually related to the objective function of the problem and reflects the individual's ability to solve the problem;

[0088] The expression of the fitness function is as follows:

[0089] F[i]=DATA[i]-

[0090] [a0+a1*APL[i]+a2*Lv[i]+a3*APL[i]^2+a4*Lv[i]^2+a5*APL[i]*lv[i]];

[0091] Where F[i] is the fitness at time i; APL[i] is the average image level at time i; Lv[i] is the brightness at time i; DATA[i] is the target grayscale at time i;

[0092] a0+a1*APL[i]+a2*Lv[i]+a3*APL[i]^2+a4*Lv[i]^2+a5*APL[i]*lv[i] is the predicted grayscale at moment i. Since we are doing parameter fitting, the smaller the value of F[i], the better.

[0093] 3. Selection: Select individuals for reproduction based on their fitness values. Individuals with higher fitness values ​​have a greater probability of being selected.

[0094] 4. Crossover: A pair is randomly selected from the selected individuals for crossover to produce new offspring by exchanging some parts of the individuals.

[0095] 5. Mutation: Perform mutation operations on the newly generated offspring, that is, randomly change certain characteristics of the offspring individuals to increase the diversity of the population.

[0096] 6. The formation of a new generation of population: Based on the new individuals produced by crossover and mutation, a new generation of population is formed.

[0097] 7. Termination condition judgment: Check whether the preset conditions are met, such as reaching the maximum number of iterations, the fitness value reaching the preset threshold, etc. If met, the algorithm ends; otherwise, return to step 2 to continue iteration.

[0098] 8. Output the optimal solution: Select the individual with the highest fitness value from the final population as the optimal solution to the problem (a0, a1, a2, a3, a4, a5).

[0099] According to the identification results, DATA R / G / B , the relationship between Lv and APL (i.e. brightness control model):

[0100] DATA R =a0 R +a1 R *APL+a2 R *Lv+a3 R *APL^2+a4 R *Lv^2+a5 R *APL*lv;

[0101] DATA G =a0 G +a1 G *APL+a2 G *Lv+a3 G *APL^2+a4 G *Lv^2+a5 G *APL*lv;

[0102] DATA B =a0 B +a1 B *APL+a2 B *Lv+a3 B *APL^2+a4 B *Lv^2+a5 B *APL*lv.

[0103] Afterwards, the obtained brightness control model is placed in DDIC, for DDIC to execute under the control of the whole machine command to input the current average image level and the current brightness of the display panel into the brightness control model, to obtain the corrected grayscale of each pixel on the display panel, and after determining the driving voltage required to excite each pixel of the display panel to the corrected grayscale, output data to the screen display (i.e., output the corresponding driving voltage to each pixel).

[0104] The embodiment of the present invention provides a display compensation device, see Figure 5As shown, Figure 5 The schematic diagram of the compensation device according to the embodiment of the present invention is shown in FIG. 5 , and the compensation device 50 includes:

[0105] The execution module 51 is used to input the current average image level and the current brightness of the display panel into the brightness control model to obtain the corrected grayscale of each pixel on the display panel; wherein the brightness control model is trained according to multiple sets of training data, each set of the training data includes: brightness training data of the display panel, average image level training data corresponding to the brightness training data, and target grayscale of each pixel corresponding to the brightness training data, at which the pixel can achieve local excitation brightness;

[0106] A determination module 52, configured to determine a driving voltage required to excite each pixel of the display panel to the modified grayscale;

[0107] The output module 53 is used to output the corresponding driving voltage to each pixel.

[0108] The display compensation device provided in the embodiment of the present application can achieve Figure 2 The various processes implemented by the method embodiment and achieving the same technical effect are not described here to avoid repetition.

[0109] The embodiment of the present invention provides a training device for a brightness control model. Figure 6 As shown, Figure 6 1 is a principle block diagram of a training device for a brightness control model according to an embodiment of the present invention. The training device 60 for a brightness control model includes:

[0110] An acquisition module 61 is used to acquire a training data set, wherein the training data set includes multiple groups of training data, each group of the training data includes: brightness training data of the display panel, average image level training data corresponding to the brightness, and target grayscale of each pixel corresponding to the brightness training data; the pixel can achieve local excitation brightness under the target grayscale;

[0111] The training module 62 is used to train the brightness control model to be trained using the training data set to obtain the brightness control model.

[0112] In some embodiments of the present invention, optionally,

[0113] The training module 62 is further used for the prediction step: inputting the brightness training data and the average image level training data corresponding to the brightness training data into the brightness control model to be trained, and obtaining the predicted grayscale of each pixel output by the current brightness control model to be trained;

[0114] The training module 62 is further used for an optimization step: optimizing the parameters of the brightness control model to be trained according to the target grayscale and the predicted grayscale of each pixel;

[0115] The prediction step and the optimization step are iteratively performed until a preset condition is met.

[0116] In some embodiments of the present invention, optionally,

[0117] The preset condition is that the difference between the predicted grayscale and the target grayscale is less than a preset threshold;

[0118] Alternatively, the preset condition is that the number of iterations exceeds a preset number.

[0119] In some embodiments of the present invention, optionally, each pixel includes a plurality of single-color pixels, and the parameters of the brightness control model to be trained corresponding to each single-color pixel are different;

[0120] The training module 62 is further configured to optimize the parameters of the brightness control model to be trained corresponding to each of the single-color pixels according to the target grayscale and the predicted grayscale of each of the single-color pixels.

[0121] The display compensation device provided in the embodiment of the present application can achieve Figure 3 to Figure 4 The various processes implemented by the method embodiment shown achieve the same technical effect and will not be described again here to avoid repetition.

[0122] An embodiment of the present invention provides an electronic device 70, see Figure 7 As shown, Figure 7 This is a principle block diagram of an electronic device 70 according to an embodiment of the present invention, including a processor 71, a memory 72, and a program or instruction stored in the memory 72 and executable on the processor 71. When the program or instruction is executed by the processor, the steps of any display compensation method of the present invention are implemented, or the steps of any brightness control model training method described in the present invention are implemented.

[0123] An embodiment of the present invention provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the various processes of the embodiment of the display compensation method as described in any one of the above items, or the various processes of the embodiment of the training method of the brightness control model as described in any one of the above items, can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0124] The readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc. In some examples, the readable storage medium may be a non-transitory readable storage medium.

[0125] An embodiment of the present invention also provides a computer program product, including computer instructions. When the computer instructions are executed by a processor, the various processes of the embodiment of the display compensation method described in any one of the above items, or the various processes of the embodiment of the training method of the brightness control model described in any one of the above items, can achieve the same technical effect. To avoid repetition, they are not described here.

[0126] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A display compensation method, characterized in that: include: Inputting the current average image level and the current brightness of the display panel into the brightness control model to obtain the corrected grayscale of each pixel on the display panel; wherein the brightness control model is trained according to multiple sets of training data, each set of the training data includes: brightness training data of the display panel, average image level training data corresponding to the brightness training data, and target grayscale of each pixel corresponding to the brightness training data, at which the pixel can achieve local excitation brightness; Determining a driving voltage required to excite each pixel of the display panel to the modified grayscale; The corresponding driving voltage is output to each pixel.

2. A method for training a brightness control model, characterized in that: include: Acquire a training data set, the training data set comprising a plurality of sets of training data, each set of the training data comprising: brightness training data of the display panel, average image level training data corresponding to the brightness, and target grayscale of each pixel corresponding to the brightness training data; the pixel can achieve local excitation brightness under the target grayscale; The training data set is used to train the brightness control model to be trained to obtain the brightness control model.

3. The training method of the brightness control model according to claim 2, characterized in that: The original brightness control model is trained using the training data set to obtain a brightness control model, including: Prediction step: inputting the brightness training data and the average image level training data corresponding to the brightness training data into the brightness control model to be trained, and obtaining the predicted grayscale of each pixel output by the current brightness control model to be trained; Optimization step: optimizing the parameters of the brightness control model to be trained according to the target grayscale and the predicted grayscale of each pixel; The prediction step and the optimization step are iteratively performed until a preset condition is met.

4. The training method of the brightness control model according to claim 3, characterized in that: The preset condition is that the difference between the predicted grayscale and the target grayscale is less than a preset threshold; Alternatively, the preset condition is that the number of iterations exceeds a preset number.

5. The training method of the brightness control model according to claim 3, characterized in that: Each of the pixels includes a plurality of single-color pixels, and the parameters of the brightness control model to be trained corresponding to each of the single-color pixels are different; The optimization step comprises: According to the target grayscale and the predicted grayscale of each of the single-color pixels, the parameters of the brightness control model to be trained corresponding to each of the single-color pixels are optimized respectively.

6. A display compensation device, characterized in that: include: An execution module, used for inputting the current average image level and the current brightness of the display panel into a brightness control model to obtain a corrected grayscale of each pixel on the display panel; wherein the brightness control model is trained according to a plurality of sets of training data, each set of the training data comprising: brightness training data of the display panel, average image level training data corresponding to the brightness training data, and a target grayscale of each pixel corresponding to the brightness training data, at which the pixel can achieve a local excitation brightness; A determination module, used for determining a driving voltage required to excite each pixel of the display panel to the modified grayscale; The output module is used to output the corresponding driving voltage to each pixel.

7. A training device for a brightness control model, characterized in that: include: An acquisition module is used to acquire a training data set, wherein the training data set includes multiple groups of training data, each group of the training data includes: brightness training data of the display panel, average image level training data corresponding to the brightness, and target grayscale of each pixel corresponding to the brightness training data; the pixel can achieve local excitation brightness under the target grayscale; The training module is used to train the brightness control model to be trained using the training data set to obtain the brightness control model.

8. An electronic device, characterized in that: It includes a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps in the display compensation method as described in claim 1, or implements the steps in the training method of the brightness control model as described in any one of claims 2 to 5.

9. A readable storage medium, characterized in that: The readable storage medium stores a program or instruction, and when the program or instruction is executed by the processor, it implements the steps in the display compensation method as claimed in claim 1, or implements the steps in the training method of the brightness control model as described in any one of claims 2 to 5.

10. A computer program product, characterized in that The method comprises computer instructions, which, when executed by a processor, implement the steps in the display compensation method as claimed in claim 1, or implement the steps in the training method of the brightness control model as claimed in any one of claims 2 to 5.

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