OLED display screen Demura method based on iteration

Abstract

The invention provides an OLED (Organic Light Emitting Diode) display screen Demura method based on iteration. In order to obtain the Mura characteristics of a screen, brightness information of some gray scales of R, G and B channels is generally collected through an industrial camera, and data is subjected to some processing, so that characteristic parameters of each pixel point are obtained. Parameters are burnt into an IC, and Demura compensation can be completed through a certain algorithm. However, limited by the precision of an industrial camera and some other factors, the characteristic parameters generated through one-time data acquisition cannot achieve a very good compensation effect sometimes. By collecting data for multiple times and iterating the characteristic parameters, the Demura compensation effect can be effectively improved and approximates to an ideal effect.

Description

Technical Field

[0001] The invention discloses an iteration-based OLED display screen Demura method, belonging to the field of image processing. Background Art

[0002] OLED (Organic Light Emitting Diode) has been widely used in high-performance displays due to its many advantages such as bright colors, low power consumption, and thinness. When produced on a large-area glass substrate, due to the limitations of the crystallization process, TFT has non-uniformity in electrical parameters such as threshold voltage and mobility, which will lead to current differences and brightness differences in the display device, resulting in Mura. If the screen is not Demura-treated, the entire screen will have uneven brightness and color cast when displayed.

[0003] Different manufacturers have different Demura algorithms. Generally, compensation parameters or grayscale compensation tables are obtained through the brightness information captured to achieve compensation for the display screen Mura. Currently, industrial cameras are used in production lines to capture the brightness information of the display screen in the three channels of R, G, and B at several gray levels (such as 32, 64, 128, 192, and 255). However, due to the accuracy of the shooting equipment, the characteristic parameters generated by collecting data once sometimes cannot achieve very good compensation effects.

[0004] The present invention designs a method for improving the final demura compensation effect by collecting parameters multiple times by an industrial camera and iterating the parameters. By collecting data multiple times and iterating the characteristic parameters, the demura compensation effect can be effectively improved and approach the ideal effect. Summary of the invention

[0005] The present invention provides an iteration-based OLED display screen Demura method, comprising the following steps: Step 1: The industrial camera collects brightness data for the first time and builds a model of target brightness; Step 2: Generate simulation diagram or burn data into IC; Step 3: The industrial camera collects brightness data; Step 4: Generate and save the parameters of the current step; Step 5: Determine whether to terminate the iteration. If yes, combine the parameters and burn them into the IC. If no, choose to continue the iteration and re-execute step 2.

[0006] Furthermore, the data is collected by an industrial camera, and the brightness information of several grayscales of the three channels R, G, and B is collected to extract the characteristic parameters of each sub-pixel point, and at the same time, the ideal characteristic parameters of the target brightness model are determined; The relationship between OLED screen brightness and grayscale: (1) , is the coefficient, is brightness, is grayscale; Three target brightness models are established corresponding to the three channels of R, G, and B. The processing of the three channels is the same. The relationship between the channels is: (2) , For ideal brightness output, is the actual grayscale input of the IC, , is the corresponding coefficient; However, due to the existence of the Mura phenomenon, for each pixel there are: (3) is the actual brightness output, is the gray value after compensation, , is the corresponding coefficient. The actual brightness is different from the ideal brightness. By processing the data collected by the industrial camera, the actual brightness of each sub-pixel is obtained. and If the brightness of the sub-pixel is to be the same as the ideal brightness, the following mapping relationship is established between the actual input grayscale value and the compensated grayscale value of each pixel through equations (2) and (3): {g}_{r}=\sqrt[{{b}_{r}}] {\frac {{a}_{i}} {{a}_{r}}*{{g}_{i}}^{{b}_{i}}}=\left ( {\frac {{a}_{i}} {{a}_{r}}} \right )^{\frac {1} {{b}_{r}}}*{{g}_{i}}^{\frac {{b}_{i}} {{b}_{r}}} (4) remember: (5) (6) Formula (4) is simplified as: (7) Through the IC's built-in algorithm, the actual input grayscale is output as , for any grayscale input of a single pixel on the screen The output will be converted into compensated output through the formula ; Perform the above operations on each pixel to achieve full-screen Demura.

[0007] Further, choose to use the acquisition parameters to generate grayscale simulation images or burn the data into the IC to collect the next round of data, process the collected data, extract parameters and save them; The formula (7) is: (8) When there is no iteration, the input to output can be directly obtained by the IC internal algorithm through equation (7). If n iterations are performed, the input of each round in the iteration process is recorded as: (9) The coefficients of each round are processed into the form of equations (5) and (6), and the coefficients corresponding to each round are: (10) (11) In formulas (5) and (6), , are the model parameters set initially. , The brightness data is collected and processed by an industrial camera in each round of iteration; The method of collecting brightness is as described in step 1, but the grayscale of the collected brightness has changed from the beginning. When it comes to the kth iteration among n iterations, the grayscale of the brightness that needs to be collected for each pixel becomes ; When n=0, it is the initial input grayscale, and the result obtained at this time is the result without iteration; when n≥1, there is the following iterative process: (12) ... (13).

[0008] Furthermore, after one iteration, observe the display effects of different grayscales. If the effect meets expectations, stop the iteration. If not, perform another iteration. After n rounds of iteration, n ≥ 1. When n = 0, no iteration is performed. The parameters of multiple rounds of iterations are combined through the following relationship so that the final output still satisfies the operation relationship corresponding to formula (7): (14) (15) If the screen display effect meets expectations, the combined parameters are burned into the IC according to equations (14) and (15). and These are the characteristic parameters required in the end. At this point, the iteration is completed and the display effect is optimized.

[0009] The present invention can effectively improve the Demura compensation effect and approach the ideal effect by collecting data multiple times and iterating the characteristic parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 Shown is a flow chart of the present invention. DETAILED DESCRIPTION

[0011] 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 only 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.

[0012] See also Figure 1 The present invention provides a method for improving the final Demura compensation effect by collecting parameters multiple times by an industrial camera and performing parameter iteration. The method comprises the following steps: Step 1: Establish a target brightness model and use an industrial camera to collect data for the first time to generate parameters and save them; Step 2: Prepare for the next round of data collection by generating a simulation diagram or burning the data into the IC, and generate and save the current characteristic parameters based on the collected data; Step 3: Determine whether to terminate the iteration. If it is terminated, combine all parameters saved during the iteration according to a certain mathematical relationship. If you choose to continue the iteration, repeat step 2.

[0013] Step 1: Collect data through industrial cameras. It is necessary to collect brightness information of several grayscales (such as 32, 64, 128, 192, 255) of the three channels R, G, and B to extract the characteristic parameters of each sub-pixel point and determine the ideal characteristic parameters of the target brightness model. Generally, we believe that there is the following relationship between the brightness and grayscale of OLED screens: (1) , is the coefficient, is brightness, is grayscale; Three target brightness models are established corresponding to the three channels of R, G, and B. The processing of the three channels is the same. Take one channel i as an example: (2) , For ideal brightness output, is the actual grayscale input of the IC, , is the corresponding coefficient; However, due to the existence of the Mura phenomenon, for each pixel there are: (3) is the actual brightness output, If it is also the actual grayscale input of IC, , is the corresponding coefficient, indicating that the actual brightness is different from the ideal brightness, that is, for equal hour, Not equal to By processing the data collected by the industrial camera, the actual value of each sub-pixel can be obtained. and If the brightness of the sub-pixel is to be the same as the ideal brightness, the following mapping relationship is established between the actual input grayscale value and the compensated grayscale value of each pixel through equations (2) and (3): {g}_{r}=\sqrt[{{b}_{r}}] {\frac {{a}_{i}} {{a}_{r}}*{{g}_{i}}^{{b}_{i}}}=\left ( {\frac {{a}_{i}} {{a}_{r}}} \right )^{\frac {1} {{b}_{r}}}*{{g}_{i}}^{\frac {{b}_{i}} {{b}_{r}}} (4) remember: (5) (6) Formula (4) can be simplified as: (7) Through the IC's built-in algorithm, the actual input grayscale will be output as , for any grayscale input of a single pixel on the screen The output will be converted into compensated output through the formula By performing the above operations on each pixel, full-screen demura can be achieved.

[0014] Step 2: You can choose to use the acquisition parameters to generate a grayscale simulation image or burn the data into the IC to collect the next round of data, process the collected data, extract the parameters and save them; The formula (7) is: (8) When there is no iteration, the input to output can be directly obtained by the IC internal algorithm through equation (7). If n iterations are performed, the input of each round in the iteration process is recorded as: (9) The coefficients of each round are processed into the form of equations (5) and (6), and the coefficients corresponding to each round are: (10) (11) In formulas (5) and (6), , are the model parameters set initially. , The brightness data is collected and processed by an industrial camera in each round of iteration; The method of collecting brightness is as described in step 1, but the grayscale of the collected brightness has changed from the beginning. When it comes to the kth iteration among n iterations, the grayscale of the brightness that needs to be collected for each pixel becomes ; When n=0, it is the initial input grayscale, and the result obtained at this time is the result without iteration; when n≥1, there is the following iterative process: (12) (13).

[0015] Step 3: After one iteration, observe the display effects of different grayscales. If the effect meets expectations, stop the iteration. If not, perform another iteration. After n rounds of iteration, n ≥ 1. When n = 0, no iteration is performed. The parameters of multiple rounds of iterations can be combined through the following relationship so that the final output still satisfies the operation relationship corresponding to formula (7): (14) (15) If the screen display effect meets expectations, the combined parameters are burned into the IC according to equations (14) and (15). and These are the characteristic parameters required in the end. At this point, the iteration is completed and the display effect is optimized.

[0016] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is limited by the appended claims and their equivalents.

Claims

1. An iterative OLED display screen Demura method, characterized in that: The following steps are involved: Step 1: The industrial camera collects brightness data for the first time and builds a model of target brightness; Step 2: Generate simulation diagram or burn data into IC; Step 3: The industrial camera collects brightness data; Step 4: Generate and save the parameters of the current step; Step 5: Determine whether to terminate the iteration. If yes, combine the parameters and burn them into the IC. If no, choose to continue the iteration and re-execute step 2.

2. The iterative OLED display screen Demura method according to claim 1, characterized in that: Collect data through industrial cameras, collect brightness information of several grayscales in the three channels of R, G, and B to extract the characteristic parameters of each sub-pixel, and determine the ideal characteristic parameters of the target brightness model; The relationship between OLED screen brightness and grayscale: (1) , is the coefficient, is brightness, is grayscale; Three target brightness models are established corresponding to the three channels of R, G, and B. The processing of the three channels is the same. The relationship between the channels is: (2) , For ideal brightness output, is the actual grayscale input of the IC, , is the corresponding coefficient; However, due to the existence of the Mura phenomenon, for each pixel there are: (3) is the actual brightness output, is the gray value after compensation, , is the corresponding coefficient. The actual brightness is different from the ideal brightness. By processing the data collected by the industrial camera, the actual brightness of each sub-pixel is obtained. and If the brightness of the sub-pixel is to be the same as the ideal brightness, the following mapping relationship is established between the actual input grayscale value and the compensated grayscale value of each pixel through equations (2) and (3): {g}_{r}=\sqrt[{{b}_{r}}] {\frac {{a}_{i}} {{a}_{r}}*{{g}_{i}}^{{b}_{i}}}=\left ( {\frac {{a}_{i}} {{a}_{r}}} \right )^{\frac {1} {{b}_{r}}}*{{g}_{i}}^{\frac {{b}_{i}} {{b}_{r}}} (4) remember: (5) (6) Formula (4) can be simplified as: (7) Through the IC's built-in algorithm, the actual input grayscale is output as , for any grayscale input of a single pixel on the screen The output will be converted into compensated output through the formula ; Perform the above operations on each pixel to achieve full-screen Demura.

3. The iterative OLED display screen Demura method according to claim 2, characterized in that: Choose to use the acquisition parameters to generate grayscale simulation images or burn the data into the IC to collect the next round of data, process the collected data, extract the parameters and save them; The formula (7) is: (8) When there is no iteration, the input to output can be directly obtained by the IC internal algorithm through equation (7). If n iterations are performed, the input of each round in the iteration process is recorded as: (9) The coefficients of each round are processed into the form of equations (5) and (6), and the coefficients corresponding to each round are: (10) (11) In formulas (5) and (6), , are the model parameters set initially. , The brightness data is collected and processed by an industrial camera in each round of iteration; The method of collecting brightness is as described in step 1, but the grayscale of the collected brightness has changed from the beginning. When it comes to the kth iteration among n iterations, the brightness grayscale that needs to be collected for each pixel becomes ; When n=0, it is the initial input grayscale, and the result obtained at this time is the result without iteration; when n≥1, there is the following iterative process: (12) ... (13)。 4. The iterative OLED display screen Demura method according to claim 3, characterized in that: After one iteration, observe the display effects of different grayscales. If the effect meets expectations, stop the iteration. If not, perform another iteration. After n rounds of iteration, n ≥ 1. When n = 0, no iteration is performed. The parameters of multiple rounds of iterations are combined through the following relationship so that the final output still satisfies the operation relationship corresponding to formula (7): (14) (15) If the screen display effect meets expectations, the combined parameters are burned into the IC according to equations (14) and (15). and These are the characteristic parameters required in the end. At this point, the iteration is completed and the display effect is optimized.

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