An Iteration-Based Demura Method for OLED Displays

Abstract

The present invention proposes an iterative-based Demura method for OLED displays. To obtain the Mura characteristics of the screen, generally, the brightness information of some gray levels in the R, G, and B channels is collected by an industrial camera and the data is processed to obtain the characteristic parameters of each pixel. Burning the parameters into the IC and through a certain algorithm, the Demura compensation can be completed. However, limited by the accuracy of the industrial camera and some other factors, the characteristic parameters generated by collecting data once sometimes cannot achieve a very good compensation effect. By collecting data multiple times and iterating on the characteristic parameters, the Demura compensation effect can be effectively improved and approximated to the ideal effect.

Description

Technical Field

[0001] The present invention discloses an iterative-based Demura method for OLED displays, 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 fabricated on a large-area glass substrate, due to the limitations of the crystallization process, TFTs have non-uniformity in electrical parameters such as threshold voltage and mobility, which can lead to current differences and brightness differences in display devices, resulting in Mura phenomena. When the screen is not Demura processed, uneven brightness and color deviation across the entire screen may occur during display.

[0003] Different manufacturers have different Demura algorithms, which generally obtain compensation parameters or grayscale compensation tables through the captured brightness information to compensate for the Mura of the display screen. Currently, industrial cameras are used in production lines to separately capture the brightness information of the display screen at several gray levels (such as 32, 64, 128, 192, 255) in the R, G, and B channels. However, limited by the accuracy of the capture device, the characteristic parameters generated by a single data acquisition sometimes cannot achieve a very good compensation effect.

[0004] The present invention designs a method to improve the final Demura compensation effect by repeatedly acquiring parameters using an industrial camera and performing parameter iteration. By repeatedly acquiring data and iterating on the characteristic parameters, the Demura compensation effect can be effectively improved and approximated to the ideal effect. Summary of the Invention

[0005] The present invention provides an iterative-based Demura method for OLED displays, including the following steps:

[0006] Step 1: An industrial camera acquires brightness data for the first time and establishes a model of the target brightness.

[0007] Step 2: Generate a simulation diagram or burn the data into the IC.

[0008] Step 3: The industrial camera acquires brightness data.

[0009] Step 4: Generate the parameters for the current step and save them.

[0010] Step 5: Determine whether to terminate the iteration. If so, combine the parameters and burn them into the IC. If not, select to continue the iteration and re-execute Step 2.

[0011] Further, data is collected by an industrial camera, and the luminance information of several gray levels in the three channels of R, G, and B is collected to extract the characteristic parameters of each sub-pixel, and at the same time, the ideal characteristic parameters of the target luminance model are determined;

[0012] Relationship between the luminance and gray level of the OLED screen:

[0013] (1)

[0014] , is a coefficient, is the luminance, is the gray level;

[0015] Three models of target luminance are established corresponding to the three channels of R, G, and B. The processing conditions of the three channels are the same. Denote The relationship of the channel is:

[0016] (2)

[0017] , is the ideal luminance output, is the actual gray-level input of the IC, , are the corresponding coefficients;

[0018] However, due to the existence of the Mura phenomenon, for each pixel point, there is:

[0019] (3)

[0020] is the actual luminance output, is the compensated gray-level value, , are the corresponding coefficients. There is a difference between the actual luminance and the ideal luminance; by processing the data collected by the industrial camera, the actual and of each sub-pixel are obtained. If the luminance of the sub-pixel is to be the same as the ideal luminance, then the following mapping relationship is established for the actual input gray-level value and the compensated gray-level value of each pixel through equations (2) and (3):

[0021] {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)

[0022] Denote: (5)

[0023] (6)

[0024] Equation (4) is briefly denoted as:

[0025] (7)

[0026] Through the algorithm built into the IC, the actually input gray scale is output as , for any gray scale input of a single pixel point on the screen will be converted into the compensated output through the formula ; By performing the above operations on each pixel point, the Demura of the entire screen can be achieved.

[0027] Furthermore, select to generate a gray scale simulation diagram using the acquisition parameters or burn the data into the IC to collect the next round of data, process the collected data, extract the parameters and save them;

[0028] Denote Equation (7) as:

[0029] (8)

[0030] When there is no iteration, the input to the output can be directly obtained by the internal algorithm of the IC through Equation (7). If n iterations are performed, denote the input in each round during the iteration as:

[0031] (9)

[0032] Process the coefficients in each round into the forms of Equations (5) and (6), then the corresponding coefficients in each round are:

[0033] (10)

[0034] (11)

[0035] In Equations (5) and (6), , is the model parameter set at the beginning, , are obtained by processing the brightness data collected by the industrial camera in each iteration process respectively;

[0036] The way of collecting brightness is as described in Step 1, but the gray scale of the collected brightness has changed compared with the beginning. When it comes to the k-th time in the n-th iteration, the gray scale of the brightness to be collected for each pixel point becomes the in Formula (9);

[0037] When n = 0, it is the initial input gray scale, and the result obtained at this time is the result without iteration; when n ≥ 1, there is the following iteration process:

[0038] (12) ...

[0039] (13).

[0040] Furthermore, after one iteration, observe the display effects of different gray scales. If the effect meets the expectation, stop the iteration; if it does not meet the expectation, iterate again; Denote that after n rounds of iteration, n ≥ 1, when n = 0, that is, no iteration is performed. The parameters of multiple rounds of iteration are combined through the following relationship so that the final output still satisfies the operation relationship corresponding to Formula (7):

[0041] (14)

[0042] (15)

[0043] If the display effect of the screen meets the expectation, burn the combined parameters into the IC according to Formulas (14) and (15), where the and are the characteristic parameters required finally. Thus, the iteration is completed and the optimization of the display effect is realized.

[0044] The present invention can effectively improve the Demura compensation effect and approach the ideal effect by collecting data multiple times and iterating on the characteristic parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 The figure shown is the flowchart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0047] Please refer to Figure 1 , the present invention provides a method for improving the final Demura compensation effect by collecting parameters multiple times through an industrial camera and performing parameter iteration. The method includes the following steps:

[0048] Step 1: Establish a target brightness model and use the industrial camera to collect data for the first time to generate parameters and save them;

[0049] Step 2: Prepare for the next round of data collection by generating a simulation diagram or burning the data into the IC, generate the current characteristic parameters based on the collected data and save them;

[0050] Step 3: Determine whether to terminate the iteration. If terminated, combine all the parameters saved during the iteration according to a certain mathematical relationship. If you choose to continue the iteration, repeat Step 2.

[0051] In Step 1, data is collected through an industrial camera. It is necessary to collect the brightness information of several gray levels (such as 32, 64, 128, 192, 255) in the R, G, and B channels to extract the characteristic parameters of each sub-pixel point, and at the same time determine the ideal characteristic parameters of the target brightness model;

[0052] Generally, we believe that there is the following relationship between the brightness of the OLED screen and the gray level: (1)

[0053] , is a coefficient, is the brightness, is the gray level;

[0054] Three target brightness models are established corresponding to the R, G, and B channels. The processing situations of the three channels are the same. Taking a certain channel i as an example:

[0055] (2)

[0056] , is the ideal brightness output, is the actual gray level input of the IC, 、 are the corresponding coefficients;

[0057] However, due to the existence of the Mura phenomenon, for each pixel, there is:

[0058] (3)

[0059] is the actual brightness output, if it is also the actual grayscale input of the IC, 、 are the corresponding coefficients, which indicates that there is a difference between the actual brightness and the ideal brightness, that is, for equal to when, is not equal to . By processing the data collected by the industrial camera, the actual and of each sub-pixel can be obtained. If the brightness of the sub-pixel is to be the same as the ideal brightness, the following mapping relationship is established for the actual input grayscale value and the compensated grayscale value of each pixel through equations (2) and (3):

[0060] {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)

[0061] Denote: (5)

[0062] (6)

[0063] Equation (4) can be briefly denoted as:

[0064] (7)

[0065] Through the algorithm built into the IC, the actual input grayscale will be output as , and for any grayscale input of a single pixel on the screen, it will be converted into the compensated output through the formula. By performing the above operations on each pixel, the full-screen Demura can be achieved.

[0066] In Step 2, it is possible to choose to generate a grayscale simulation image using the acquisition parameters or burn the data into the IC to collect the next round of data, process the collected data, extract the parameters and save them;

[0067] Denote Equation (7) as:

[0068] (8)

[0069] When there is no iteration, the input to the output can be directly obtained by the internal algorithm of the IC through Equation (7). If n iterations are performed, denote the input in each round during the iteration as:

[0070] (9)

[0071] Process the coefficients in each round into the forms of Equations (5) and (6), then the corresponding coefficients in each round are:

[0072] (10)

[0073] (11)

[0074] In Equations (5) and (6), 、 are the model parameters set at the beginning, 、 are respectively obtained by processing the brightness data collected by the industrial camera in each round of the iteration process;

[0075] The method of collecting brightness is as described in Step 1, but the gray scale of the collected brightness has changed compared to the beginning. When it comes to the kth time in the nth iteration, the gray scale of the brightness that needs to be collected for each pixel point becomes in Equation (9);

[0076] When n = 0, it is the initial input gray scale, and the result obtained at this time is the result without iteration; when n ≥ 1, there is the following iteration process:

[0077] (12) ...

[0078] (13).

[0079] In Step 3, after one iteration, observe the display effects of different gray scales. If the effects meet the expectations, stop the iteration; if not, perform the iteration again. Denote that after n rounds of iteration, n ≥ 1, when n = 0, that is, no iteration is performed. The parameters of multiple rounds of iteration can be combined through the following relationship so that the final output still satisfies the operation relationship corresponding to Equation (7):

[0080] (14)

[0081] (15)

[0082] 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.

[0083] 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. 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): (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 ; By performing the above operations on each pixel, full-screen Demura can be achieved; 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)； 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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