Method for establishing and training corrected current setting value and electronic terminal

Abstract

A method for establishing corrected current setting values includes: driving plural light emitting units of a backlight module and measuring a set of measured current values of each light emitting unit corresponding to plural dimming levels; correcting each light emitting unit according to the set of measured current values to generate a set of corrected current preliminary setting values of each light emitting unit; establishing an input data matrix according to the dimming levels, the set of measured current values and the set of corrected current preliminary setting values of a portion of the light emitting units; and inputting the input data matrix into a well-trained model to generate an output data matrix. The output data matrix includes a set of corrected current optimal setting values of each light emitting unit corresponding to the dimming levels.

Description

Technical Field

[0001] This disclosure relates to a current correction method for establishing a correction current setting value for the light-emitting unit of a backlight module. Prior Technology

[0002] Monitors are among the most common electronic devices in modern life, used in various scenarios and situations. Some monitors include a backlight module to provide the light source. This backlight module has multiple light-emitting units, and with local dimming technology, the brightness of these light-emitting units can be independently controlled, thereby improving the monitor's contrast. The brightness of the light-emitting units is controlled and adjusted through current amplitude and pulse width modulation (PWM). However, each light-emitting unit differs in production, manufacturing, and use. If each light-emitting unit is driven with the same current value, the brightness of each unit will be different. Furthermore, the position and density of each light-emitting unit within the backlight module are not uniform. Therefore, the current value required to drive each light-emitting unit will not be the same if a uniform brightness is desired within the backlight module.

[0003] To ensure uniform brightness across all light-emitting units within a backlight module, current measurement and calibration are typically performed for each unit at various brightness levels to obtain a calibration current setting. For example, consider a backlight module with 2400 light-emitting units in one area, representing 256 brightness levels. A single current measurement of each unit takes approximately 0.4 seconds. Therefore, measuring the current at each brightness level for all units in that area would take approximately 245760 seconds. Adding the time required for calibration, the overall operation time is extremely long, making it difficult to reliably perform current measurement and calibration for all light-emitting units in every area of ​​the backlight module on a production line.

[0004] Therefore, the problem that must be overcome is how to quickly provide a more accurate calibration current setting value for each light-emitting unit to drive each light-emitting unit to emit uniform brightness, thereby significantly reducing the overall detection and calibration time, and how to use the light-emitting units on the backlight module or on the irregularly shaped light panel to achieve uniform brightness of the light-emitting units in the backlight module or the irregularly shaped light panel by using different calibration current settings for each light-emitting unit. Summary of the Invention

[0005] The embodiments disclosed herein propose a method for establishing calibration current settings, applicable to backlight modules. This backlight module includes multiple light-emitting units that are driven by current to emit light. The method includes: driving these light-emitting units; measuring the current of each light-emitting unit at various dimming levels to obtain a set of measured current values ​​corresponding to each light-emitting unit at the various dimming levels; calibrating each light-emitting unit based on this set of measured current values ​​to generate a preliminary calibration current setting set for each light-emitting unit; establishing an input data matrix based on a subset of the light-emitting units, obtaining the various dimming levels, the set of measured current values, and the preliminary calibration current setting set; and inputting this input data matrix into a trained model to generate an output data matrix. This output data matrix contains an optimal calibration current setting set for each light-emitting unit at the various dimming levels.

[0006] The method for establishing the calibration current setting value disclosed herein improves upon the conventional method which requires measuring the current value of each luminance level of every light-emitting unit located at different positions. Instead, this method only requires measuring the current value of each luminance level of a subset (rather than all) of the light-emitting units as input for training the current calibration model. Then, by using this method, the calibration current setting value of all light-emitting units at various luminance levels can be quickly inferred. This significantly reduces the time required to measure the current value of each light-emitting unit at different luminance levels and to calibrate the luminance of each light-emitting unit.

[0007] Meanwhile, the electronic terminal provided in this disclosure can quickly infer the correction current setting value of other different brightness levels through current measurement values, so that each light-emitting unit can provide a uniform light source on irregularly shaped light panels or backlight modules.

[0008] To make the above-mentioned features and advantages disclosed herein more apparent and understandable, specific embodiments are described below in conjunction with the accompanying drawings for detailed explanation. Simple Explanation of the Diagram

[0009] [Figure 1] is a schematic diagram illustrating a current correction system according to an embodiment. [Figure 2] is a schematic diagram illustrating multiple areas on a display panel and their corresponding light-emitting units according to an embodiment. [Figure 3] is a flowchart of the method for establishing the correction current setting value according to the first embodiment of the present disclosure. [Figure 4] is a schematic diagram of the steps of the method for establishing the calibration current setting value according to the first embodiment of the present disclosure. [Figure 5] is an illustrative schematic diagram of a specific light-emitting unit according to the second embodiment of the present disclosure. [Figure 6] is a flowchart of the training phase of the method for establishing the calibration current setting value according to the first embodiment of the present disclosure, and may also be a flowchart of the method for training the calibration current setting value according to the second embodiment. [Figure 7] is a schematic diagram of the steps of the training method for correcting the current setting value according to the second embodiment of the present disclosure. Implementation

[0010] Figure 1 is a schematic diagram illustrating a current correction system according to an embodiment. Referring to Figure 1, the current correction system 100 includes an electronic terminal 110 and a display device 120. The electronic terminal 110 can be a personal computer, a server, or various electronic devices with computing capabilities. The display device 120 includes a circuit 130, a backlight module 140, and a display panel 150. The circuit 130 includes a timing controller 131 and a microcontroller unit (MCU) 132. The microcontroller 132 can also be replaced with a programmable gate array (FPGA), so it should not be limited to the microcontroller 132 disclosed in this embodiment. The backlight module 140 includes multiple light-emitting units, such as light-emitting diodes, which are driven by the current of the backlight module 140 to provide a backlight source. The display panel 150 is, for example, a liquid crystal display panel. For example, the display device 120 is a liquid crystal display device using full array local dimming (FALD) technology.

[0011] Figure 2 is a schematic diagram illustrating multiple regions and corresponding light-emitting units on a display panel 150 according to an embodiment. Referring to Figures 1 and 2, in the embodiment of Figure 2, the display panel 150 includes 15 regions 151, each region 151 corresponding to multiple light-emitting units 141 included in the backlight module 140. Here, the brightness of each light-emitting unit 141 can be controlled by providing different currents to increase screen contrast. For example, when the image to be displayed in a certain region 151 is darker, the brightness of the corresponding light-emitting unit 141 can be reduced; conversely, when the image to be displayed in a certain region 151 is brighter, the brightness of the corresponding light-emitting unit 141 can be increased. Figure 2 is merely an example; this disclosure does not limit the number of regions 151 included in the display panel 150, nor does it limit how many light-emitting units 141 each region 151 corresponds to. Furthermore, each region 151 can also correspond to a different number of light-emitting units 141, which is also an extension of this disclosure.

[0012] It is worth noting that, due to differences in production, manufacturing, and use, the luminance of different light-emitting units 141 will vary when driven by the same current value. Furthermore, the luminance of the light-emitting unit 141 can be divided into 256 levels, and the current value for each level of luminance of the same light-emitting unit 141 will be different. Different light-emitting units 141 will also have different current values ​​for the same level of luminance. Additionally, as shown in Figure 2, different regions 151 correspond to different numbers of light-emitting units 141. Since the number of light-emitting units 141 in each region 151 is different, i.e., the density of light-emitting units 141 within each region 151 is different, it can be understood that if each region 151 is to present the same level of luminance, the current value of each light-emitting unit 141 in different regions 151 will be slightly different for the same level of luminance due to the different densities of light-emitting units 141 within each region 151.

[0013] Referring to Figure 1, when displaying an image, the timing controller 131 calculates the dimming level of each area 151 on the display panel 150. This dimming level indicates the required backlight brightness. Based on the calculated dimming level, the timing controller 131 calculates a current preset value, which is used to drive the light-emitting unit 141 to generate a specific current. In some embodiments, the current preset value is positively correlated with the dimming level. For example, the dimming level is represented by a value range of 0 to 255, while the current preset value is represented by a value range of 0 to 1023. The mapping relationship between the dimming level and the current preset value can be linear or non-linear. The order of the dimming level and the current preset value is merely an example, and this disclosure is not limited to it. The order of the dimming level's value range can also be the same as the order of the current preset value's value range.

[0014] Due to variations in the manufacturing process, the current preset setting may not be sufficient to drive the light-emitting unit 141 to provide the required brightness; therefore, the current preset setting needs to be calibrated. The microcontroller 132 contains multiple output data matrices, each corresponding to a light-emitting unit 141. The output data matrices record multiple calibrated current optimal settings corresponding to multiple dimming levels. The timing controller 131 can access the output data matrices in the microcontroller 132 based on the calculated dimming level to obtain the corresponding calibrated current optimal setting. Then, it drives the corresponding light-emitting unit 141 based on this calibrated current optimal setting, thus enabling the light-emitting unit 141 to provide the expected brightness. A method for establishing the calibrated current setting is proposed, executed by the electronic terminal 110, and utilizes a generative adversarial network (GAN) to generate the aforementioned output data matrices.

[0015] Figure 3 is a flowchart of a method 300 for establishing a calibration current setting value according to the first embodiment of this disclosure. In step 301, the plurality of light-emitting units 141 included in the backlight module 140 are driven, and then the current of each of these light-emitting units 141 at various dimming levels (0 to 255) is measured to obtain a set of measured current values ​​corresponding to each light-emitting unit 141 at various dimming levels. In other words, the set of measured current values ​​consists of 256 measured current values ​​corresponding to the light-emitting units 141 at dimming levels from 0 to 255. The specific method of step 301 is that the electronic terminal 110 sends out a command to cause the timing controller 131 to drive each light-emitting unit 141 to emit light at multiple dimming levels according to multiple current preset values ​​corresponding to multiple dimming levels (0~255 levels), and to measure the current of each light-emitting unit 141 at multiple dimming levels (0~255 levels) through an ammeter or a power meter to obtain the set of measured current values ​​of each light-emitting unit 141 at multiple dimming levels.

[0016] In step 302, each light-emitting unit 141 is calibrated based on this set of measured current values ​​to generate a preliminary set of calibration current values ​​for each light-emitting unit 141. In other words, the preliminary set of calibration current values ​​consists of 256 preliminary calibration current values ​​corresponding to dimming levels 0 to 255 for each light-emitting unit 141. The specific method of step 302 is as follows: if the measured current value corresponding to a certain dimming level is less than the target current value corresponding to that dimming level, the preliminary calibration current value corresponding to that dimming level is obtained by slightly increasing the current preset value; if the measured current value corresponding to that dimming level is greater than the target current value corresponding to that dimming level, the preliminary calibration current value corresponding to that dimming level is obtained by slightly decreasing the current preset value. The target current value can be an objective target value obtained by the electronic terminal 110 through procedural calculation, a subjective setting value required by the user, or a specification setting value required by the manufacturer of the electronic terminal 110. For example, the preset current setting value corresponding to dimming level 255 can be "995". Under this preset current setting value, the target current value is 64 milliamperes. That is to say, 64 milliamperes can make the light-emitting unit 141 provide the brightness corresponding to the preset current setting value of "995". Step 302 is to determine whether the actual measured current value is greater than or less than 64 milliamperes. If the measured current value corresponding to dimming level 255 is 65.3 milliamperes (greater than the target current value of 64 milliamperes), then the preset current setting value of "995" is slightly reduced to obtain the adjusted setting value of program corresponding to dimming level 255, for example, "994".

[0017] It is worth noting that the aforementioned current values ​​refer to the concepts corresponding to the 1024th order (0~1023) current values ​​exemplified in the example. The current level of the current value can be in the ampere (A) level, milliampere (mA) level, microampere (μA) level, nanoampere (nA) level, but is not limited to these. In addition, the difference between each order is not the same. For example, the difference between the current values ​​of the 801st order and the 802nd order can be 1 microampere, and the difference between the current values ​​of the 802nd order and the 803rd order can be 2 microamperes, but is not limited to these. Furthermore, a specific order can have a specific current value. For example, the current value of the 994th order is a certain value such as 64 milliamperes, but is not limited to these.

[0018] In step 303, an input data matrix is ​​established by obtaining multiple dimming levels, measured current value sets, and preliminary calibration current setting sets based on a portion of the light-emitting units 141. In other words, the input data matrix includes multiple dimming levels (0 to 255 levels) and 256 measured current values ​​and 256 preliminary calibration current setting values ​​corresponding to dimming levels 0 to 255 for a portion of the light-emitting units 141. It is worth noting that the dimming levels are not limited to 0 to 255 levels; the number of dimming levels can be increased or decreased according to actual needs. In addition, the relationship between the dimming level and the corresponding current value level is not limited to one-to-one; in certain cases, it can be one-to-many. For example, each dimming level can correspond to three current value levels (such as generally strong, normal, and generally weak). In throttling mode, it corresponds to the generally weak current value level, and in special development effect mode, it corresponds to the generally strong mode. The above description of the three current value levels is just an example and is not a limitation.

[0019] It is worth mentioning that the input data matrix also includes the index, position (LED position x & y), model (LED model), driver IC model, driver IC serial number, target current value, and current preset setting value for each of the light-emitting units 141. The target current value corresponds to the dimming level, and the current preset setting value corresponds to the target current value. In other words, the input data matrix includes multiple dimming levels (0~255 levels) and 256 measured current values, preliminary calibration current settings, index, position, model, driver IC model, driver IC serial number, target current value, and current preset setting value for each of the light-emitting units 141 at dimming levels from 0 to 255.

[0020] Figure 4 is a schematic diagram of step 304 of the method for establishing the correction current setting value according to the first embodiment of this disclosure. Referring to Figures 3 and 4 together, in step 304, this input data matrix... The input generative adversarial network is used to train model M to produce an output data matrix. This output data matrix contains a set of optimal correction current settings for all light-emitting units 141 at various dimming levels. In other words, the output data matrix contains various dimming levels (0-255) and 256 optimal correction current settings for all light-emitting units 141 at dimming levels from 0 to 255. The training of model M in step 304 is completed, which can be used to predict the optimal correction current settings.

[0021] Specifically, the flowcharts in Figures 3 and 4 use the measurement results of a subset of the light-emitting units 141 to generate the optimal setting value of the correction current for all light-emitting units 141. This means that by training a generative adversarial network (GAN) to complete the model M, the model predicts the data of all light-emitting units 141 (i.e., the output data matrix, which contains a set of optimal setting values ​​of the correction current for each light-emitting unit 141 at various dimming levels) using data from a subset of the light-emitting units 141 (e.g., 20% of the total number of light-emitting units 141, but this disclosure is not limited to this). In the flowchart of Figure 3, the optimal setting value of the correction current for all light-emitting units 141 is obtained by training a generative adversarial network to complete the model M. Therefore, the method described in the above embodiment can quickly set the current value of the light-emitting units 141, saving a significant amount of brightness calibration time.

[0022] Besides the above-described embodiment (first embodiment), a second embodiment of the method for establishing the calibration current setting value can also follow a similar process. Referring to Figure 5, the main difference between the second and first embodiments is that the second embodiment only drives specific light-emitting units 143 (i.e., a portion of the light-emitting units 144), instead of driving all light-emitting units 144. Details are as follows: multiple specific light-emitting units 143 among the multiple light-emitting units 144 included in the backlight module 142 are driven, and then the current of each of these specific light-emitting units 143 is measured at various dimming levels (0-255) to obtain a set of measured current values ​​corresponding to each specific light-emitting unit 143 at various dimming levels. In other words, the set of measured current values ​​consists of 256 measured current values ​​corresponding to the specific light-emitting units 143 at dimming levels from 0 to 255.

[0023] It should also be noted that the specific light-emitting units 143 circled in Figure 5 are examples of light-emitting units 144 located at the corners of the backlight module 142, but this is not a limitation; light-emitting units 144 located in the middle area or at the edge of the backlight module 142 can also be selected. It is worth noting that because specific light-emitting units 143 differ in production, manufacturing, and use, the luminance of different specific light-emitting units 143 will be different when driven by the same current value. In addition, the luminance of specific light-emitting units 143 can be divided into 256 levels, and the current value of each level of luminance of the same specific light-emitting unit 143 will be different, and the current value of different specific light-emitting units 143 at the same level of luminance will also be different.

[0024] After obtaining the set of measured current values ​​corresponding to each specific light-emitting unit 143 at various dimming levels, each specific light-emitting unit 143 is calibrated according to this set of measured current values ​​to generate a preliminary set of calibration current values ​​for each specific light-emitting unit 143.

[0025] Next, based on these specific light-emitting units 143, various dimming levels, measured current value sets, and preliminary calibration current setting sets are obtained to establish an input data matrix. In other words, the input data matrix contains various dimming levels (0 to 255 levels) and 256 measured current values ​​and 256 preliminary calibration current setting values ​​corresponding to the specific light-emitting units 143 at dimming levels from 0 to 255. It is worth noting that the number of dimming levels is not limited to 0 to 255 levels; the number of dimming levels can be increased or decreased according to actual needs. Furthermore, the number of dimming levels and the corresponding current value levels are not limited to a one-to-one relationship; in specific cases, a one-to-many relationship can be established. For example, each dimming level can correspond to three current value levels (such as generally strong, normal, and generally weak). In throttling mode, it corresponds to the generally weak current value level, and in special development effect mode, it corresponds to the generally strong mode. The above description of the three current value levels is merely an example and is not a limitation.

[0026] Finally (after creating the input data matrix), please refer to Figure 5, and then... Input a trained model M to produce an output data matrix Furthermore, the output data matrix contains a set of optimal correction current settings for each of the light-emitting units 144 corresponding to the various dimming levels.

[0027] The most distinctive feature of the second embodiment disclosed herein is that the specific light-emitting unit 143 of the backlight module 142 includes light-emitting units 144 located at the edges, corners, arcs, etc. of the backlight module 142 and light-emitting units 144 located at the center of the backlight module 142. To ensure uniform luminance in the backlight module 142, the luminance required from the light-emitting unit 144 at the center of the backlight module 142 can be slightly dimmer (i.e., the required current can be slightly smaller) than that required from the specific light-emitting units 143 located at the edges, corners, and arcs of the backlight module 142. This is because the light-emitting unit 144 at the center of the backlight module 142 is surrounded by other light-emitting units 144 that can maintain its luminance, while the specific light-emitting units 143 located at the edges, corners, and arcs of the backlight module 142 do not have enough other light-emitting units 144 around them to maintain their luminance. Therefore, the luminance required from the specific light-emitting units 143 located at the edges, corners, and arcs of the backlight module 142 must be brighter (i.e., the required current must be larger) than that of the light-emitting unit 144 at the center of the backlight module 142. Only in this way can the overall luminance of the backlight module 142 be uniform.

[0028] However, the selection of a specific light-emitting unit 143 can be any of the light-emitting units 144, or the entire boundary, the entire arc, the entire perimeter, any polygon with a range, any shape with a range, any arc with a range, or selected in a specified manner, but is not limited thereto.

[0029] After the selected specific light-emitting unit 143 is established using the correction current setting method provided in this disclosure, the individual current values ​​that need to be set for all light-emitting units 144 (including the specific light-emitting unit 143) distributed at various positions on the backlight module 142 can be deduced. This allows for the rapid provision of the correction current setting value, resulting in uniform brightness in the backlight module 142.

[0030] It is worth noting that the specific light-emitting unit 143 disclosed herein may also refer to the light-emitting unit 144 that has not yet completed training or is currently being trained. The light-emitting unit 144 may also refer to the light-emitting unit 144 that can bring out the data of the light-emitting unit 144 that has been trained and completed training when it encounters similar environmental conditions in the future (such as being arranged in similar polygons, similar shapes, similar arcs or combinations thereof, or in similar arrangement densities), so as to achieve the goal of predicting the data of the entire light-emitting unit 144 by using only the specific light-emitting unit 143.

[0031] Figure 6 is a flowchart of the training phase of the method for establishing the correction current setting value according to the first embodiment of this disclosure. Alternatively, it can be a flowchart of the training phase of the correction current setting value according to the second embodiment. In step 501, multiple training backlight modules are obtained, each training backlight module including multiple training light-emitting units. Step 501 involves using multiple (e.g., 5, but this disclosure is not limited to) different training backlight modules to train the generator of the generative adversarial network to obtain a trained model, thereby improving the prediction accuracy of the generative adversarial network. Furthermore, another advantage of this disclosure is that only a small amount of data is required for training (e.g., only 5 training backlight modules), saving time spent collecting large amounts of data for training.

[0032] In step 502, the multiple training light-emitting units included in each of these training backlight modules are driven, and then the current of each training light-emitting unit is measured at various dimming levels (0-255) to obtain a set of training measurement current values ​​corresponding to each training light-emitting unit at various dimming levels. In other words, the set of training measurement current values ​​consists of 256 training measurement current values ​​corresponding to the training light-emitting units at dimming levels from 0 to 255. The specific method of step 502 is similar to that of step 301, and will not be described again here.

[0033] In step 503, each training light-emitting unit is calibrated according to this set of training measurement current values ​​to generate a preliminary set of training calibration current values ​​for each training light-emitting unit. In other words, the preliminary set of training calibration current values ​​consists of 256 preliminary training calibration current values ​​corresponding to dimming levels from 0 to 255 for each training light-emitting unit. The specific method of step 503 is similar to that of step 302, and will not be described again here.

[0034] In step 504, based on these training light-emitting units, a real data matrix is ​​established by obtaining various dimming levels, sets of training measurement current values, and sets of preliminary training correction current settings. In other words, the real data matrix includes various dimming levels (0 to 255 levels) and 256 training measurement current values ​​and 256 preliminary training correction current settings corresponding to the dimming levels from 0 to 255 for each of the training light-emitting units. It is worth noting that the dimming levels are not limited to 0 to 255 levels; the number of dimming levels can be increased or decreased according to actual needs. In addition, the relationship between the dimming level and the corresponding current value level is not limited to one-to-one; in certain cases, it can be one-to-many. For example, each dimming level can correspond to three current value levels (such as generally strong, normal, and generally weak). In throttling mode, it corresponds to the generally weak current value level, and in special development effect mode, it corresponds to the generally strong mode. The above description of the three current value levels is just an example and is not a limitation.

[0035] Figure 7 is a schematic diagram based on steps 505 and 506. In step 505, based on the real data matrix... Obtain the input data matrix for training And the training input data matrix Input generator To generate the output data matrix for training In this embodiment, it is a self-generated data matrix. A portion is sampled as a training input data matrix Alternatively, random sampling data can be used to build the training input data matrix. For example, a real data matrix An 11×2400 data matrix is ​​used (the 2400 corresponds to 2400 training light-emitting units, and the 11 corresponds to the following 11 parameters: light-emitting unit number, light-emitting unit X position, light-emitting unit Y position, light-emitting unit model, driver circuit model, driver circuit serial number, dimming level, target current value, current preset value, measured current value, and preliminary setting value of correction current). 128 samples are randomly selected from these 2400 samples to create an 11×128 data matrix, which serves as the training input data matrix. And this training is performed using the input data matrix Generator of input generative adversarial network To generate the output data matrix for training The training uses an input data matrix. The number of data entries is less than the output data matrix used for training. The number of data entries, the output data matrix used in this training The number of data entries equals the actual data matrix. The number of data entries is [number], and both are data matrices of 11×2400. This training uses the output data matrix. This includes a set of optimal training correction current settings for each training emitting unit at various dimming levels. In other words, it's a training output data matrix. It includes multiple dimming levels (0-255) and 256 optimal settings for the training correction current corresponding to dimming levels 0 to 255 for the training light-emitting unit. Step 505: Generator For the generator of the untrained generative adversarial network Therefore, step 506 is still required to train the generator of the generative adversarial network. After training, the trained model M of the generative adversarial network is obtained.

[0036] In step 506, the real data matrix is... With the training output data matrix Discriminator for input generative adversarial networks And then according to the discriminator The loss function Loss is obtained from the discrimination result, and the generator of the generative adversarial network is trained based on the loss function Loss. With discriminator The loss functions of the optimized generator and discriminator are less than the loss threshold. The specific steps of step 506 are detailed below.

[0037] In step 506, the real data matrix is... With the training output data matrix Input discriminator Discriminator Used to store real data matrix With the training output data matrix The comparison is performed to produce a discrimination result, and then based on the discriminator... The loss function Loss is obtained from the identification result (real or fake), and the generator is trained based on the loss function Loss. With discriminator In other words, generator With discriminator The loss function Loss is used as the optimization basis, and the neural network weights with the minimum loss value in the training loop are saved as the optimal network model weight settings. In addition, the generator's initial weights are generated using a truncated normal distribution method, and the mean is set to 0 and the standard deviation is set to 0.02. The above values ​​are only examples and this disclosure is not limited to them.

[0038] The loss function, Loss, is the binary cross-entropy. The optimized loss functions of the generator and discriminator are less than the critical loss values. It's worth noting that the critical loss values ​​vary depending on the number of training iterations. The optimal (minimum) loss value during training must be less than a set value within the training iteration range. In other words, the critical loss values ​​are selected based on the number of training iterations. For example, with 10,000 training iterations, the critical loss value for the generator can be set to 1.5 and the critical loss value for the discriminator can be set to 0.8. This means that the minimum value of the generator's loss function should be less than 1.5 and the minimum value of the discriminator's loss function should be less than 0.8 for the results to be acceptable. These values ​​are merely illustrative and are not strictly limited in this disclosure.

[0039] The method of training the generator and discriminator based on the loss function is called error backpropagation. The concept of error backpropagation is to feed back the error of the loss function to the generator and discriminator, allowing them to use this information to perform gradient descent to update their weights, thereby further reducing the error. Furthermore, when Nash equilibrium or all training input data matrices are reached... When the discrimination result is equal to 0.5, it means that the data generated by the generator is close to the real data, so the training is terminated.

[0040] In short, there are two ways to obtain the trained model M: first, by judging the discriminator. If the resulting identification result falls within a acceptable range, and if it does not, the process is repeated to generate another training output data matrix. The generator then generates another identification result until that result falls within the acceptable range. The trained model M is obtained by completing the training based on the identification result.

[0041] Secondly, the process is repeated to generate multiple output data matrices for this training. The generator generates multiple identification results. When any of these results fails to fall within a valid range, the generator... The model M with the smallest value among the identification results is selected for training. In other words, the trained model M is the generator that has completed training.

[0042] In addition, this disclosure also proposes an electronic terminal to train a generator for a generative adversarial network, thereby obtaining a trained model of the generative adversarial network, and then using the trained model to generate the above-mentioned output data matrix, which is used to generate multiple correction current settings for multiple light-emitting units of a backlight module.

[0043] When the backlight module 142 (as shown in Figure 5, the backlight module 142 is, for example, an irregularly shaped light panel, but this disclosure is not limited to this) needs to be trained, for the specific light-emitting units 143 arranged at the edges, corners, arcs, etc. of the backlight module 142, since the slope, angle, curvature and other characteristics at the edges, corners, arcs, etc. are more complex, more training times and deeper learning are required. As for the non-specific light-emitting units (the light-emitting units 144 arranged at the center of the backlight module 142), it is not necessary to consider the complex characteristics of the slope, angle, curvature and other characteristics at the edges, corners, arcs, etc., and the light-emitting units 144 that are more linear and have lower simulation difficulty can be used to reduce the number of training times. In summary, during the training phase, the training luminescent units are divided into multiple training-specific luminescent units and multiple training-non-specific luminescent units. The training-specific luminescent units are deployed around the training-non-specific luminescent units. During this training phase, the proportion of the training-specific luminescent units in the training input data matrix is ​​greater than the proportion of the training-non-specific luminescent units, so as to carry out deeper learning and training for edges, corners, arcs, etc., and finally generate a trained model.

[0044] When the backlight module 142 (as shown in Figure 5, the backlight module 142 is, for example, an irregularly shaped light panel, but this disclosure is not limited thereto) is used to infer using the trained generative adversarial network model M, that is, to predict the optimal setting value of the correction current, since the proportion of the total area occupied by the edges, corners, arcs, etc. of the backlight module 142 is smaller than the proportion of the total area occupied by the center of the backlight module 142, in the method for establishing the correction current setting value in the second embodiment of this disclosure, the non-specific light-emitting units and the specific light-emitting units can be used as a reference. The input data matrix is ​​established by the various dimming levels of element 143, the measured current value group, and the initial setting value group of the correction current. When the input data matrix is ​​input into the trained model, the proportion of specific light-emitting units 143 arranged at the edges, corners, arcs, etc. in the input data matrix is ​​designed to be smaller than the proportion of non-specific light-emitting units arranged at the center, thereby conforming to the actual shape of the lamp panel. The output data matrix generated by the trained model can obtain the optimal setting value group of the correction current to achieve uniform brightness.

[0045] Therefore, in the deduction process, this disclosure can quickly predict the appropriate correction current setting value for each light-emitting unit at different positions on the lamp board. This allows the lamp board containing the light-emitting unit to save a significant amount of time in measuring the current value of each light-emitting unit at different brightness levels and in correcting the brightness of each light-emitting unit when producing different shapes or irregular shapes.

[0046] In summary, conventional methods require lengthy measurements of the different current values ​​corresponding to each light-emitting unit at individual luminance levels from 0 to 255, followed by current correction before the entire backlight module can achieve uniform luminance. In contrast, the method for establishing the correction current setting value disclosed in this paper, through the establishment of a predictive model and training with a small but sufficient amount of current and luminance data, is not limited by fundamental differences in the production, manufacturing, and use of light-emitting units, nor by differences in the position and density of light-emitting units. Regardless of the polygonal shape, arbitrary shape, random arc, or specific position of the light-emitting unit, the method for establishing the correction current setting value disclosed in this paper can predict the appropriate correction current setting value for each light-emitting unit at different positions, significantly improving the time efficiency of measuring current and correcting luminance for light-emitting units. Furthermore, it has the benefit of achieving uniform luminance on backlight modules with different arrangement shapes or unit densities of light-emitting units.

[0047] In other words, the application scenarios disclosed herein are not limited to situations where the arrangement of the light-emitting units is any polygon, arbitrary shape, random arc, or combination thereof, or when the arrangement density of the light-emitting units on the same lamp board is inconsistent. To achieve consistent luminance on the lamp board, the current value driving each light-emitting unit will differ, and each light-emitting unit will have differences in production, manufacturing, and use. Through the method for establishing the calibration current setting value disclosed herein, the appropriate calibration current setting value corresponding to each light-emitting unit at different positions on the lamp board can be quickly predicted. This significantly reduces the time spent measuring the current value of each light-emitting unit at different luminance levels and calibrating the luminance of each light-emitting unit when producing lamp boards with different shapes or irregular shapes.

[0048] The foregoing has outlined the features of several embodiments, thus enabling those skilled in the art to better understand the nature of this disclosure. Those skilled in the art should recognize that this disclosure can be readily used as a basis to design or modify other processes and structures, thereby achieving the same objectives and / or advantages as the embodiments described herein. Those skilled in the art should also understand that these equivalent constructions do not depart from the spirit and scope of this disclosure, and that various changes, substitutions, and modifications can be made without departing from the spirit and scope of this disclosure.

[0049] 100: Current Correction System 110: Electronic Terminal 120: Display device 130: Circuit 131: Timing Controller 132: Microcontroller 140, 142: Backlight module 141, 144: Light-emitting units 143: Specific light-emitting unit 150: Display panel 151: Region 300: Method for establishing the calibration current setpoint 301~304, 501~506: Steps Discriminator : Generator Output data matrix Training output data matrix Loss: Loss Function Model training complete Real Data Matrix Input data matrix Training input data matrix

[0050] Domestic storage information (please note in order of storage institution, date, and number) none Overseas storage information (please note in the order of storage country, institution, date, and number) none

Claims

1. A method for establishing a calibration current setting value, applicable to a backlight module, the backlight module including a plurality of light-emitting units, the light-emitting units being driven by current to emit light, the method comprising: The system drives the light-emitting units, and measures the current of each light-emitting unit at various dimming levels to obtain a set of measured current values ​​for each light-emitting unit at each dimming level. Based on the set of measured current values, each light-emitting unit is calibrated to generate a preliminary set of calibration current values ​​for each light-emitting unit. Based on a subset of the light-emitting units, an input data matrix is ​​established by obtaining the various dimming levels, the set of measured current values, and the preliminary set of calibration current values. The input data matrix is ​​then input into a trained model to generate an output data matrix, which contains a set of optimal calibration current values ​​for each light-emitting unit at each dimming level. The input data matrix also includes a number, location, model, driver circuit model, driver circuit serial number, target current value, and current preset setting value for each light-emitting unit, where the target current value corresponds to the dimming level, and the current preset setting value corresponds to the target current value.

2. The method of establishing as described in claim 1 further includes a training phase for training a generator to obtain the trained model, the training phase including: Multiple training backlight modules are acquired, each of which includes multiple training light-emitting units; the training light-emitting units are driven, and the current of each training light-emitting unit is measured at various dimming levels to obtain a training measurement current value set corresponding to each of the training light-emitting units at the various dimming levels; each of the training light-emitting units is calibrated based on the training measurement current value set to generate a preliminary training calibration current setting value set for each of the training light-emitting units; a real data matrix is ​​established based on the various dimming levels, the training measurement current value set, and the preliminary training calibration current setting value set. A portion of the real data matrix is ​​sampled as a training input matrix. This training input matrix is ​​then input into the generator to produce a training output matrix. The number of data points in the training input matrix is ​​less than the number of data points in the training output matrix, and the number of data points in the training output matrix is ​​equal to the number of data points in the real data matrix. The real data matrix and the training output matrix are then input into a discriminator. The discriminator compares the training output matrix with the real data matrix to generate a discrimination result. The generator completes training based on the discrimination result to obtain the trained model.

3. The establishment method as described in request item 2, wherein, During the training phase, it is determined whether the discrimination result generated by the discriminator falls within a qualified range. If it does not fall within the qualified range, the process is repeated, sampling a portion of the real data matrix as the input data matrix for training and inputting it into the generator to generate the output data matrix for training, until the discrimination result falls within the qualified range. The generator then completes the training based on the discrimination result to obtain the trained model.

4. The establishment method as described in request item 2, wherein, During the training phase, a portion of the real data matrix is ​​sampled as the input data matrix for training and input into the generator to produce the output data matrix for training. The real data matrix and the output data matrix for training are then input into the discriminator, which compares the output data matrix for training with the real data matrix to produce a discrimination result. The above steps are performed multiple times to produce multiple discrimination results. When each of these discrimination results does not fall within a qualified range, the generator completes the training based on the smallest value among the discrimination results to obtain the trained model.

5. The method of establishing as described in claim 3 or 4 further includes: A loss function is obtained based on the discrimination result of the discriminator. And the generator and the discriminator are trained based on the loss function.

6. The method of establishment as described in claim 5, wherein, During this training phase, the loss function that optimizes the generator and the discriminator is less than a loss threshold.

7. An electronic terminal, wherein the electronic terminal is used to obtain the optimal set of correction current settings for driving the light-emitting units of the backlight module according to the method for establishing correction current settings as described in any one of claims 1 to 6.

8. A method for establishing a calibration current setting value, applicable to a backlight module, the backlight module including a plurality of light-emitting units, the light-emitting units being driven by current to emit light, the method comprising: Multiple specific light-emitting units among the light-emitting units are driven, and the current of each specific light-emitting unit is measured at multiple dimming levels to obtain a set of measured current values ​​corresponding to each of the specific light-emitting units at the multiple dimming levels; each of the specific light-emitting units is calibrated based on the set of measured current values ​​to generate a set of preliminary calibration current settings for each of the specific light-emitting units; an input data matrix is ​​established based on the multiple dimming levels of the specific light-emitting units, the set of measured current values, and the set of preliminary calibration current settings; and the input data matrix is ​​input into a trained model to generate an output data matrix, and the output data matrix contains a set of optimal calibration current settings corresponding to each of the light-emitting units at the multiple dimming levels; wherein the input data matrix also includes a number, a position, a model, a driver circuit model, a driver circuit serial number, a target current value, and a current preset setting value for each of the light-emitting units, wherein the target current value corresponds to the dimming level, and the current preset setting value corresponds to the target current value.

9. The method of establishing a generator as described in claim 8 further includes a training phase for training a generator to obtain the trained model, wherein the training phase includes: Multiple training backlight modules are acquired, each of which includes multiple training light-emitting units; the training light-emitting units are driven, and the current of each training light-emitting unit is measured at various dimming levels to obtain a training measurement current value set corresponding to each of the training light-emitting units at the various dimming levels; each of the training light-emitting units is calibrated based on the training measurement current value set to generate a preliminary training calibration current setting value set for each of the training light-emitting units; a real data matrix is ​​established based on the various dimming levels, the training measurement current value set, and the preliminary training calibration current setting value set. A portion of the real data matrix is ​​sampled as a training input matrix. This training input matrix is ​​then input into the generator to produce a training output matrix. The number of data points in the training input matrix is ​​less than the number of data points in the training output matrix, and the number of data points in the training output matrix is ​​equal to the number of data points in the real data matrix. The real data matrix and the training output matrix are then input into a discriminator. The discriminator compares the training output matrix with the real data matrix to generate a discrimination result. The generator completes training based on the discrimination result to obtain the trained model.

10. The method of establishment as described in claim 9, wherein, During the training phase, it is determined whether the discrimination result generated by the discriminator falls within a qualified range. If it does not fall within the qualified range, the process is repeated, sampling a portion of the real data matrix as the input data matrix for training and inputting it into the generator to generate the output data matrix for training, until the discrimination result falls within the qualified range. The generator then completes the training based on the discrimination result to obtain the trained model.

11. The method of establishment as described in claim 9, wherein, During the training phase, a portion of the real data matrix is ​​sampled as the input data matrix for training and input into the generator to produce the output data matrix for training. The real data matrix and the output data matrix for training are then input into the discriminator, which compares the output data matrix for training with the real data matrix to produce a discrimination result. The above steps are performed multiple times to produce multiple discrimination results. When each of these discrimination results does not fall within a qualified range, the generator completes the training based on the smallest value among the discrimination results to obtain the trained model.

12. The method of establishing as described in claim 10 or 11 further includes: A loss function is obtained based on the discrimination result of the discriminator. And the generator and the discriminator are trained based on the loss function.

13. The method of establishing as described in claim 12, wherein, During this training phase, the loss function that optimizes the generator and the discriminator is less than a loss threshold.

14. The method of establishing as claimed in claim 8, wherein the specific light-emitting units are selected or specified from at least two of the light-emitting units, and wherein the shape of the arrangement of the light-emitting units is any polygon, any shape, any arc, or a combination thereof.

15. The method of establishing as described in claim 14 further includes: If the shape in which these light-emitting units are arranged at least includes the arbitrary arc shape, then the curvature of the arbitrary arc shape is calculated; And when the curvature at a position among the light-emitting units is greater than a curvature threshold, at least one of the light-emitting units within a range of that position is defined as one of the specific light-emitting units.

16. The method of establishing as described in claim 14 further includes: If the shape in which the light-emitting units are arranged includes at least the arbitrary shape, then at least one of the light-emitting units within the endpoint range of the arbitrary shape is defined as one of the specific light-emitting units; and if the shape in which the light-emitting units are arranged includes at least the any polygon, then at least one of the light-emitting units within the endpoint range or the included angle range of the any polygon is defined as one of the specific light-emitting units.

17. The method of establishment as described in claim 9, wherein, The training light-emitting units are arranged in any shape, any shape, any arc, or a combination thereof. These training light-emitting units are divided into multiple training-specific light-emitting units and multiple training-non-specific light-emitting units. The training-specific light-emitting units are arranged around the training-non-specific light-emitting units. During the training phase, the proportion of the training-specific light-emitting units in the training input data matrix is ​​greater than the proportion of the training-non-specific light-emitting units.

18. The method of establishing as described in claim 17, wherein, These light-emitting units also include multiple non-specific light-emitting units. The input data matrix is ​​established based on the multiple dimming levels of these non-specific light-emitting units and these specific light-emitting units, the measurement current value group and the initial setting value group of the correction current. When the input data matrix is ​​input into the trained model, the proportion of these specific light-emitting units in the input data matrix is ​​less than the proportion of these non-specific light-emitting units.

19. An electronic terminal, wherein the electronic terminal is used to obtain the optimal set of correction current settings for driving the light-emitting units of the backlight module according to the method for establishing correction current settings according to any one of claims 8 to 18.

20. A training method for calibration current setpoints, used to train a generator to obtain a trained model, which is then used to generate multiple calibration current setpoints for multiple light-emitting units of a backlight module, the training method comprising: Multiple training backlight modules are acquired, each of which includes multiple training light-emitting units; the training light-emitting units are driven, and the current of each training light-emitting unit is measured at multiple dimming levels to obtain a training measurement current value set corresponding to each of the training light-emitting units at each of the multiple dimming levels; each of the training light-emitting units is calibrated based on the training measurement current value set to generate a preliminary training calibration current setting value set for each of the training light-emitting units; a real data matrix is ​​established based on the multiple dimming levels, the training measurement current value set, and the preliminary training calibration current setting value set. A portion of the real data matrix is ​​sampled as a training input matrix. This training input matrix is ​​then input into the generator to produce a training output matrix. The number of data points in the training input matrix is ​​less than the number of data points in the training output matrix, and the number of data points in the training output matrix is ​​equal to the number of data points in the real data matrix. The real data matrix and the training output matrix are then input into a discriminator. The discriminator compares the training output matrix with the real data matrix to generate a discrimination result. The generator completes training based on the discrimination result to obtain the trained model.

21. The training method as described in claim 20 further includes: The discriminator determines whether the discrimination result generated by the discriminator falls within a qualified range. If it does not fall within the qualified range, the process is repeated, sampling a portion of the real data matrix as the input data matrix for training and inputting it into the generator to generate the output data matrix for training, until the discrimination result falls within the qualified range. The generator then completes training based on the discrimination result to obtain the trained model.

22. The training method as described in claim 20 further includes: A portion of the real data matrix is ​​sampled and used as the input data matrix for training. This sample is then input into the generator to produce the output data matrix for training. The real data matrix and the output data matrix are then input into the discriminator, which compares the output data matrix with the real data matrix to generate a discrimination result. This process is repeated multiple times to produce multiple discrimination results. When any of these discrimination results does not fall within a valid range, the generator uses the smallest value among the discrimination results to complete the training and obtain the trained model.

23. The training method as described in claim 21 or 22 further includes: A loss function is obtained based on the discrimination result of the discriminator. And the generator and the discriminator are trained based on the loss function.

24. The training method as described in claim 23, wherein, The loss function of the generator and the discriminator is optimized to be less than a loss threshold.

25. The training method as described in claim 20, wherein, The training light-emitting units are arranged in any shape, any shape, any arc, or a combination thereof. These training light-emitting units are divided into multiple training-specific light-emitting units and multiple training-non-specific light-emitting units. The training-specific light-emitting units are arranged around the training-non-specific light-emitting units. In the training input data matrix, the proportion of the training-specific light-emitting units is greater than the proportion of the training-non-specific light-emitting units.

26. An electronic terminal, wherein the electronic terminal is used to train the generator to complete training and obtain the trained model according to the training method for the correction current setting value of any one of claims 20 to 25, so as to apply it to generate the correction current setting values ​​of the light-emitting units of the backlight module.

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