A method and device for objectively and quantitatively evaluating a demura effect

Abstract

The application discloses a kind of objective quantitative evaluation method and device of Demura effect.First, the corresponding gray scale brightness under target brightness, the observation angle corresponding to observation screen and the spatial frequency corresponding to different size SandyMura are calculated respectively;Second, the contrast threshold corresponding to different size SandyMura is calculated using the above parameters and Barten model;Then, the combination ratio of each monochrome data is determined according to the mean value of the central region of monochrome shooting data;Then, the monochrome shooting data is combined into white shooting data using the combination ratio;The contrast of different size SandyMura in white shooting data is calculated, and SandyMura score is given to each point;Finally, the full-screen Sandy Mura evaluation result is calculated.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of image processing, and relates to a method and device for objectively and quantitatively evaluating Demura effect. BACKGROUND

[0002] AMOLED screen (full name: Active Matrix Organic Light Emitting Diode) is a screen structure without backlight, using an organic material layer and a glass substrate, and has considerable advantages in screen power consumption, brightness and contrast ratio due to its self-luminous characteristics. In various aspects of panel manufacturing process, film formation, crystallization, etching unevenness, alignment difference and the like can all cause unevenness in brightness and chroma of the display panel, that is, Mura phenomenon. Sandy Mura (gravel-like Mura) is a defect phenomenon occurring in the production process, mainly showing that the pixel electrode is broken due to excessive etching, thereby causing abnormal deflection of liquid crystal molecules, forming dark spots. The way to eliminate Mura can be mainly divided into three categories: process improvement, internal compensation and external compensation, wherein the external compensation is to compensate the screen in brightness and chroma by adding a DeMura compensation circuit.

[0003] The compensation effect of the Demura compensation circuit needs to be observed by experienced quality personnel with naked eyes for grade determination, and the evaluation results between different quality personnel will have certain subjective differences. In order to avoid such subjective differences, an evaluation method is needed, which can not rely on quality personnel, but can directly analyze and determine the Demura effect through optical instrument shooting data and algorithm, and at the same time, the determination result of the method needs to be consistent with the observation and determination result of the human eye as much as possible.

[0004] Therefore, the present application provides a method and device for objectively and quantitatively evaluating Demura effect. SUMMARY

[0005] The present application provides a method for objectively and quantitatively evaluating Demura effect, comprising:

[0006] Step one, respectively calculating corresponding gray scale brightness under target brightness, observation angle corresponding to the observation screen and spatial frequency corresponding to SandyMura of different sizes;

[0007] Step two, using the parameters in step one and Barten1990 model to calculate the contrast threshold value corresponding to SandyMura of different sizes;

[0008] Step three, determining the combination ratio of each monochromatic data according to the central region mean value of monochromatic shooting data;

[0009] Step 4: Combine the monochrome shooting data into white shooting data using the combination ratio in step 3;

[0010] Step 5: Calculate the contrast of SandyMura of different sizes in the white shooting data and perform SandyMura scoring for each point

[0011] Step 6. Calculate the full-screen SandyMura score.

[0012] In step 1, assuming that the grayscale corresponding to the current shooting data is gray, the corresponding brightness L of the current grayscale is calculated using the following formula: ,in, is the target brightness of 255 grayscale, ; In order to obtain the observation angle of the AMOLED screen in human vision , calculated as , where S is the horizontal width of the screen in cm, D is the distance from the human eye to the screen in cm, assuming that the space occupied by Sandy Mura is When the Mura is dense enough, it looks like a checkerboard pattern. The spatial frequencies corresponding to Sandy Mura of different sizes are calculated: , where W is the number of horizontal pixels on the screen, For the observation angle.

[0013] In step 2, the contrast sensitivity function of the Barten 1990 model is calculated as follows , where a, b, and c are adjustment parameters. , , , is the viewing angle, L is the corresponding brightness of the current grayscale, and the contrast threshold is the reciprocal of contrast sensitivity, calculated separately Corresponding spatial frequency , and the contrast threshold .

[0014] In step 3, the color analyzer is used to measure the absolute brightness ratio of the central area of ​​each monochrome image at the corresponding grayscale, and the ratio is used to correct the monochrome shooting data. Assume that the monochrome shooting data of the i-th row and j-th column is , and take the central area to calculate the average value , assuming that the brightness of the RGB image measured by the color analyzer is , then use the following coefficients Correction of monochrome shooting data .

[0015] The calculation formula in step four is,

[0016] .

[0017] The white shooting data in step five is subjected to mean filtering of , the regional background brightness is calculated, M is an odd number, , wherein m is a natural number, ;

[0018] The white shooting data is subjected to mean filtering of , the average brightness of different block sizes is calculated, if N is an odd number, , wherein n is a natural number, , if N is an even number, , , wherein .

[0019] The contrast of Sandy Mura of different sizes is represented as , The Sandy Mura score of each point is calculated as follows: , wherein, represents taking the maximum value, is the maximum value of the ratio of the contrast of each point to the contrast threshold value among different sizes, is the Sandy Mura score of each point, ranging from 0 to 100.

[0020] In step six, it is assumed that the settable score threshold is , the number of rows of screen pixels is H, and the number of columns of pixels is W, and the score threshold is used for binarization of each pixel, then the full-screen Sandy Mura score is represented as , wherein, , ranging from 0 to 100.

[0021] The application also provides an objective quantitative evaluation device of Demura effect, comprising:

[0022] A shooting data preprocessing module calculates the input monochrome brightness shooting data according to a specific ratio to obtain the brightness shooting data of a white picture and transmits the brightness shooting data to a filtering and contrast calculation module;

[0023] The filtering and contrast calculation module performs filtering processing of different degrees on the brightness shooting data of the white picture and calculates the contrast of Sandy Mura of different sizes;

[0024] A contrast threshold calculation module uses the input predicted model parameters to calculate the contrast threshold of Sandy Mura of different sizes;

[0025] The single-point Sandy Mura degree determination module determines the degree of Sandy Mura of a single point in combination with the contrast of Sandy Mura of different sizes and the proportional relationship of the contrast threshold, and transmits the single-point determination result to the full-screen Sandy Mura degree determination module.

[0026] The full-screen Sandy Mura degree determination module calculates the Sandy Mura degree determination result of the full screen in combination with the single-point Sandy Mura performance.

[0027] The model is based on experimental observation and data fitting to obtain an observation curve close to the characteristics of human eyes, and fully considers the influence of factors such as brightness, observation distance and angle, and spatial frequency on the visual characteristics of human eyes. The objective and quantitative evaluation method of Demura effect provided by the application can better replace quality personnel to scientifically evaluate the Demura effect. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 The flowchart of the application is shown;

[0029] Figure 2 The screen observation angle diagram of the application is shown;

[0030] Figure 3 Different sizes of chessboards of the application are shown;

[0031] Figure 4 The CSF function and Mt function curve of the application are shown;

[0032] Figure 5 The device diagram of the application is shown. DETAILED DESCRIPTION

[0033] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the application.

[0034] Please refer to Figures 1-5 The application provides an objective and quantitative evaluation method of Demura effect, which comprises:

[0035] Step one, respectively calculate the corresponding gray scale brightness under the target brightness, the observation angle corresponding to the observation screen and the spatial frequency corresponding to the Sandy Mura of different sizes;

[0036] Step two, use the parameters in step one and Barten1990 model to calculate the contrast threshold corresponding to different size Sandy Mura;

[0037] Step three, determine the combination ratio of each monochrome data according to the average of the center area of the monochrome shooting data;

[0038] Step four, combine the monochrome shooting data into white shooting data using the combination ratio in step three;

[0039] Step five, calculate the contrast of different size Sandy Mura in white shooting data, and Sandy Mura score for each point

[0040] Step six, calculate the full screen Sandy Mura score.

[0041] In step one, assuming that the current shooting data corresponds to gray level, the corresponding luminance L of the current gray level is calculated using the following formula , is the target luminance of 255 gray levels, ; in order to obtain the observation angle of AMOLED screen in human vision , the calculation method is , wherein S is the horizontal width of the screen, unit: cm, D is the distance from the human eye to the screen, unit: cm, assuming that the Sandy Mura occupies space The size of a pixel point, when the Mura is dense enough, it looks like a chessboard pattern, respectively calculate the spatial frequency corresponding to different size Sandy Mura: , is the observation angle.

[0042] In step two, the calculation method of Barten1990 model contrast sensitivity function is as follows , , , , is the observation angle, L is the corresponding luminance of the current gray level, contrast threshold is the inverse of contrast sensitivity, respectively calculate the spatial frequency corresponding to , and the contrast threshold .

[0043] In step three, since the monochrome shooting data is relative luminance value, it cannot be directly added to combine into white shooting data. If the luminance distribution under white picture is wanted, the absolute luminance proportion of the center area under each monochrome picture corresponding to gray scale needs to be measured by using color analyzer first, and the monochrome shooting data is corrected by using the proportion. Assuming that the monochrome shooting data of the i-th row and the j-th column is , the average value of the center area is calculated respectively as , assuming that the luminance under RGB picture measured by the color analyzer is , the monochrome shooting data is corrected by using the following coefficient .

[0044] In step four, the calculation formula is

[0045] .

[0046] In step five, the mean filter of is performed on the white shooting data to calculate the area background luminance, M is an odd number, , ; wherein M is usually large, such as M = 31;

[0047] The mean filter of is performed on the white shooting data respectively to calculate the average luminance of different block sizes. If N is an odd number, , , if N is an even number, , , wherein .

[0048] The contrast of SandyMura of different sizes is represented as , The SandyMura score of each point is calculated as follows: , wherein, represents taking the maximum value, is the maximum value of the ratio of the contrast of each point to the contrast threshold value among different sizes, is the SandyMura score of each point, ranging from 0 to 100.

[0049] In step six, assuming that the settable score threshold value is , and the number of rows of screen pixels is H and the number of columns of screen pixels is W, the score threshold value is used for binarization of each pixel , then the full-screen SandyMura score is represented as , wherein, , ranging from 0 to 100.

[0050] ​The application further provides an objective quantitative evaluation device of Demura effect, comprising:

[0051] The shooting data preprocessing module calculates the input monochrome brightness shooting data according to a specific ratio to obtain the brightness shooting data of a white picture and transmits the data to the filtering and contrast calculation module;

[0052] The filtering and contrast calculation module performs filtering processing of different degrees on the brightness shooting data of the white picture and calculates the contrast of Sandy Mura of different sizes;

[0053] The contrast threshold calculation module uses the input prediction model parameter to calculate the contrast threshold of Sandy Mura of different sizes;

[0054] The single-point Sandy Mura degree determination module combines the contrast of Sandy Mura of different sizes and the contrast threshold ratio relationship to perform single-point Sandy Mura degree determination and transmits the single-point determination result to the full-screen Sandy Mura degree determination module;

[0055] The full-screen Sandy Mura degree determination module combines the single-point Sandy Mura performance to calculate the full-screen Sandy Mura degree determination result.

[0056] The method provided by the application is mainly used for evaluating Sandy Mura, and the determination methods of other types of Mura (such as brightness uniformity) are relatively simple.

[0057] Although the embodiments of the application have been shown and described, it is to be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the application, and the scope of the application is limited by the appended claims and their equivalents.

Claims

1. A method for objectively and quantitatively evaluating Demura effect, characterized by Comprising: Step one, respectively calculate the corresponding gray scale brightness under the target brightness, the corresponding observation angle of the observation screen and the spatial frequency corresponding to the different size SandyMura; Step two, using the parameters in step one and Barten1990 model to calculate the contrast threshold corresponding to different size SandyMura; Step three, according to the mean value of the central region of monochrome shooting data to determine the combination ratio of each monochrome data; Step four, using the combination ratio in step three to combine monochrome shooting data into white shooting data; Step five, calculate the contrast of different size SandyMura in white shooting data, and SandyMura score for each point; Step six, calculate the full screen SandyMura score; In step one, assuming the current shooting data corresponds to gray level gray, the corresponding brightness L of the current gray level is calculated using the following formula , wherein is the target brightness of 255 gray levels, ; in order to obtain the observation angle of the AMOLED screen in human eye vision , the calculation method is , wherein S is the horizontal width of the screen, in cm, D is the distance from the human eye to the screen, in cm, and it is assumed that the Sandy Mura occupies a space of pixel points, when the Mura is dense enough, it seems to be in the form of a chessboard, and the spatial frequency corresponding to Sandy Mura of different sizes is calculated respectively: , wherein W is the number of pixel points in the horizontal direction of the screen, is the observation angle.

2. The method for objective quantitative evaluation of Demura effect according to claim 1, characterized in that: In step two, the contrast sensitivity function of Barten 1990 model is calculated as follows where a, b, c are adjustment parameters, , , , is the observation angle, L is the corresponding luminance of the current gray level, and the contrast threshold is the inverse of the contrast sensitivity, respectively calculated corresponding spatial frequency , and the contrast threshold .

3. The method for objective quantitative evaluation of Demura effect according to claim 2, characterized in that: In step three, the absolute luminance proportion of the center region under each monochrome picture corresponding to the gray scale is measured by using the color analyzer, and the monochrome shooting data is corrected using the proportion. Assuming that the monochrome shooting data of the i-th row and the j-th column is , the average value of the center region is calculated as , and assuming that the luminance of the RGB picture measured by the color analyzer is , the monochrome shooting data is corrected using the following coefficient . .

4. The method for objective quantitative evaluation of Demura effect according to claim 3, characterized in that: The calculation formula in step four is, 。 5. The method for objective quantitative evaluation of Demura effect according to claim 4, characterized in that: The white shooting data in step five is subjected to mean filtering , the average value filtering of the region background brightness, M is an odd number, , ; wherein m is a natural number, The white shooting data is subjected to mean filtering respectively , the average brightness of different block sizes is calculated, if N is odd, , wherein n is a natural number, , if N is even, , , wherein .

6. The method for objective quantitative evaluation of Demura effect according to claim 5, characterized in that: The contrast of different sizes of SandyMura is represented as , The SandyMura score of each point is calculated as follows: wherein, represents taking the maximum value, is the maximum value of the ratio of the contrast of each point to the contrast threshold value between different sizes, is the SandyMura score of each point, ranging from 0 to 100.

7. The method for objective quantitative evaluation of Demura effect according to claim 6, characterized in that: The settable score threshold value in step six is assumed to be , the number of rows of screen pixels is H, the number of columns of pixels is W, and the score threshold value is used for binarization of each pixel , the full-screen SandyMura score is represented as , wherein , , and the range is 0~100.

8. An apparatus for objectively and quantitatively evaluating Demura effect, which employs any one of the methods for objectively and quantitatively evaluating Demura effect according to claims 1 to 7, characterized by Comprising: The shooting data preprocessing module calculates the input monochrome brightness shooting data according to a certain proportion to obtain the brightness shooting data of the white picture, and transmits it to the filtering and contrast calculation module; The filtering and contrast calculation module performs different degree of filtering processing on the brightness shooting data of the white picture, and calculates the contrast of different size Sandy Mura; The contrast threshold calculation module uses the input prediction model parameters to calculate the contrast threshold of different size Sandy Mura; The single point Sandy Mura degree judgment module combines the contrast of different size Sandy Mura and the contrast threshold ratio relationship to determine the single point Sandy Mura degree, and transmits the single point judgment result to the full screen Sandy Mura degree judgment module; The full screen Sandy Mura degree judgment module combines the single point Sandy Mura performance to calculate the full screen Sandy Mura degree judgment result.

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