Display driving method and device of display device, and display device

Abstract

The present application discloses a display driving method and device for a display device, and a display device. The display driving method of the present application obtains the Gaussian probability of a preset color of a preset scene in the picture to be displayed, compares the Gaussian probability with a set threshold, and sets the polarity of a sub-pixel according to the comparison result, thereby reducing the risk of crosstalk caused by the inability to offset the voltage drops of coupling capacitors on adjacent data lines.

Description

Technical Field

[0001] The present application relates to the field of display technology, and in particular to a display driving method and device for a display device, and a display device. Background Art

[0002] With the development of science and technology, the resolution of display panels has gradually improved. At present, the resolution of display panels has reached 8K (resolution of 7680×4320) or above. Under the condition that the size of the display panel remains unchanged, the impact of the improvement in resolution is that the aperture ratio is reduced, thereby reducing the transmittance of the display panel. Therefore, the display panel with an 8-domain pixel electrode structure that uses a viewing angle improvement solution cannot be used in higher resolution products due to the loss of transmittance. Instead, a display panel with a 4-domain pixel electrode structure is used. However, the display panel with a 4-domain pixel electrode structure will also have a deteriorated viewing angle characteristic. Therefore, the display panel with a 4-domain pixel electrode structure also needs to improve the viewing angle characteristic through viewing angle compensation.

[0003] In viewing angle compensation, a plurality of sub-pixels are generally used to form a grayscale pixel group, and the grayscale pixel group includes high grayscale sub-pixels and low grayscale sub-pixels. The side view display effect can be improved by displaying the grayscale pixel group. In the existing sub-pixel array structure of viewing angle compensation, a data line is provided on each column of sub-pixels, and the sub-pixels on each column of sub-pixels are connected to the same data line. In some viewing angle compensation methods, in order to reduce the flickering of the screen, there is a setting method of using the same polarity of adjacent data lines. Therefore, the polarity of adjacent data lines will have two repeated polarities of "positive, positive" and "negative, negative". In the above two cases, the voltage drops of the coupling capacitors on the adjacent data lines cannot offset each other, resulting in a higher risk of crosstalk in the column direction. Summary of the invention

[0004] The present application provides a display driving method and device for a display device, and a display device, so as to improve the problem of crosstalk risk caused by the inability to offset the voltage drops of coupling capacitors on adjacent data lines.

[0005] The present application provides a display driving method for a display device, the display device comprising:

[0006] A plurality of sub-pixels arranged in an array;

[0007] A plurality of data lines, each column of sub-pixels corresponds to and is connected to one of the data lines, and a column of sub-pixels is arranged between adjacent data lines;

[0008] A plurality of grayscale pixel groups, each of the grayscale pixel groups includes a 2N×3M matrix of sub-pixels, where N and M are positive integers;

[0009] The display driving method comprises the following steps:

[0010] Acquire a first to-be-processed chromaticity data set and a second to-be-processed chromaticity data set about preset colors of a preset scene in a picture to be displayed;

[0011] Acquire, according to the first chromaticity data set to be processed and the second chromaticity data set to be processed, a Gaussian probability of a preset color of a preset scene in the picture to be displayed;

[0012] When the Gaussian probability is greater than or equal to a set threshold, the polarities of the sub-pixels in adjacent columns of each grayscale pixel group are set oppositely, and the polarities of the sub-pixels in adjacent grayscale pixel groups in a row direction are set symmetrically, and then the image to be displayed is displayed;

[0013] When the Gaussian probability is less than a set threshold, the polarities of the sub-pixels in adjacent columns are set oppositely, and then the image to be displayed is displayed.

[0014] Optionally, in some embodiments of the present application, before acquiring the Gaussian probability of a preset color of a preset scene in the picture to be displayed according to the first chromaticity data set to be processed and the second chromaticity data set to be processed, the display driving method further includes:

[0015] Acquire a first initial chromaticity data set and a second initial chromaticity data set of preset colors in a preset scene of the preprocessed image;

[0016] Establishing a Gaussian model about the preset color according to the first initial chromaticity data set and the second initial chromaticity data set;

[0017] The obtaining, according to the first chromaticity data set to be processed and the second chromaticity data set to be processed, the Gaussian probability of the preset color of the preset scene in the picture to be displayed includes:

[0018] According to the first chromaticity data set to be processed and the second chromaticity data set to be processed, the Gaussian probability of a preset color of a preset scene in the picture to be displayed is obtained by the Gaussian model.

[0019] Optionally, in some embodiments of the present application, obtaining a first initial chromaticity data set and a second initial chromaticity data set of preset colors in a preset scene of a preprocessed image includes:

[0020] Acquire a plurality of pre-processed images containing the preset scene;

[0021] Color data of a preset color in the preset scene is extracted from any of the preprocessed images to obtain the first initial chromaticity data set and the second initial chromaticity data set.

[0022] Optionally, in some embodiments of the present application, establishing a Gaussian model about the preset color according to the first initial chromaticity data set and the second initial chromaticity data set includes:

[0023] respectively obtaining means of the first initial chromaticity data set and the second initial chromaticity data set;

[0024] Obtaining a covariance matrix, an inverse of the covariance matrix, and a rank of the covariance matrix for the first initial chrominance data set and the second initial chrominance data set;

[0025] The Gaussian model is established according to the covariance matrix, the inverse of the covariance matrix and the rank of the covariance matrix.

[0026] Optionally, in some embodiments of the present application, obtaining, by the Gaussian model, a Gaussian probability of a preset color of a preset scene in the picture to be displayed according to the first chromaticity data set to be processed and the second chromaticity data set to be processed, includes:

[0027] Determining whether the picture to be displayed contains the preset scene;

[0028] According to the result of determining that the picture to be displayed contains the preset scene, assigning a correlation coefficient of the picture to be displayed with respect to the preset scene;

[0029] For any of the preset colors of the preset scene in the to-be-displayed picture, obtaining an initial probability of the preset color by the Gaussian model according to the first to-be-processed chromaticity data set and the second to-be-processed chromaticity data set;

[0030] The Gaussian probability of a preset color of a preset scene in the picture to be displayed is obtained according to the initial probability and the correlation coefficient.

[0031] Optionally, in some embodiments of the present application, there are multiple preset scenes and multiple preset colors;

[0032] For any of the preset scenes, the initial probability of the preset color of the preset scene is modified using the correlation coefficient;

[0033] The sum of the initial probabilities of the preset colors of the plurality of preset scenes in the picture to be displayed corrected by the correlation coefficient is calculated to obtain the Gaussian probabilities of the preset colors of the plurality of preset scenes in the picture to be displayed.

[0034] Optionally, in some embodiments of the present application, the sub-pixels in adjacent rows of each grayscale pixel group include high-grayscale sub-pixels and low-grayscale sub-pixels.

[0035] Optionally, in some embodiments of the present application, the grayscales of the sub-pixels of each grayscale pixel group in the row direction are arranged in an alternating manner of high grayscale and low grayscale.

[0036] Optionally, in some embodiments of the present application, adjacent rows of sub-pixels in each grayscale pixel group include a first row of sub-pixels and a second row of sub-pixels, the first row of sub-pixels are low grayscale sub-pixels, and the second row of sub-pixels are high grayscale sub-pixels.

[0037] Accordingly, the present application provides a display driving device of a display device, the display device comprising:

[0038] A plurality of sub-pixels arranged in an array;

[0039] A plurality of data lines, each column of sub-pixels corresponds to and is connected to one of the data lines, and a column of sub-pixels is arranged between adjacent data lines;

[0040] A plurality of grayscale pixel groups, each of the grayscale pixel groups includes a 2N×3M matrix of sub-pixels, where N and M are positive integers;

[0041] The display driving device comprises:

[0042] A data acquisition module, the data acquisition module is used to acquire a first chromaticity data set to be processed and a second chromaticity data set to be processed about a preset color of a preset scene in a picture to be displayed;

[0043] A data processing module, the data processing module being used to obtain a Gaussian probability of a preset color of a preset scene in the picture to be displayed according to the first chromaticity data set to be processed and the second chromaticity data set to be processed;

[0044] A comparison driving module, the comparison driving module is used to compare the Gaussian probability with a set threshold value, and when the Gaussian probability is greater than or equal to the set threshold value, the polarities of the sub-pixels in adjacent columns of each grayscale pixel group are set oppositely, and the polarities of the sub-pixels in adjacent grayscale pixel groups in the row direction are set symmetrically, and then the image to be displayed is displayed;

[0045] When the Gaussian probability is less than a set threshold, the polarities of the sub-pixels in adjacent columns are set oppositely, and then the image to be displayed is displayed.

[0046] Correspondingly, the present application also provides a display device, which includes a processor, a memory, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the steps in the above-mentioned display driving method.

[0047] The present application provides a display driving method and device for a display device, and a display device, wherein the display driving method comprises the following steps: obtaining a first chromaticity data set to be processed and a second chromaticity data set to be processed about a preset color of a preset scene in a picture to be displayed; obtaining the Gaussian probability of the preset color of the preset scene in the picture to be displayed according to the first chromaticity data set to be processed and the second chromaticity data set to be processed; when the Gaussian probability is greater than or equal to a set threshold, the polarities of the sub-pixels of the adjacent columns of each grayscale pixel group are set oppositely, and the polarities of the sub-pixels of the adjacent grayscale pixel groups in the row direction are set symmetrically, and then the picture to be displayed is displayed; when the Gaussian probability is less than the set threshold, the polarities of the sub-pixels of the adjacent columns are set oppositely, and then the picture to be displayed is displayed. The present application obtains the Gaussian probability of the preset color of the preset scene in the picture to be displayed, and then compares the Gaussian probability with the set threshold, and sets the polarity of the sub-pixel according to the comparison result, thereby reducing the problem of crosstalk risk caused by the inability to offset the voltage drops of coupling capacitors on adjacent data lines. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 A flow chart of a first embodiment of a display driving method for a display device provided by the present application;

[0050] Figure 2 is a schematic diagram of a first structure of the display device of the present application when the Gaussian probability is greater than or equal to a set threshold;

[0051] Figure 3 is a schematic diagram of a first structure of the display device of the present application when the Gaussian probability is less than a set threshold;

[0052] Figure 4 is a schematic diagram of a second structure of a display device of the present application;

[0053] Figure 5 A flow chart of a second embodiment of a display driving method for a display device provided by the present application;

[0054] Figure 6 A flowchart of step S40 of the second embodiment of the display driving method for a display device provided by the present application;

[0055] Figure 7A flowchart of step S50 of the second embodiment of the display driving method of the display device provided by the present application;

[0056] Figure 8 A flowchart of step S20 of the second embodiment of the display driving method for a display device provided in the present application;

[0057] Fig. 9 This is a Gaussian model simulation effect diagram of the display driving method of the display device provided in the present application;

[0058] Fig.10 A schematic diagram of a display driving device of a display device provided in the present application. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0060] In the description of the present application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, which are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first" and "second" are used only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more features. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0061] In this application, the word "exemplary" is used to mean "used as an example, illustration or description". Any embodiment described as "exemplary" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. In order to enable any technician in the field to implement and use the present application, the following description is given. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present application can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid unnecessary details that make the description of the present application obscure. Therefore, the present application is not intended to be limited to the embodiments shown, but is consistent with the widest range of principles and features disclosed in the present application. Unless otherwise specified, the parallel or perpendicular in orientation involved in the present application is not parallel or perpendicular in the strict sense, as long as the corresponding structure can achieve the corresponding purpose.

[0062] The present application provides a display driving method and device for a display device, and a display device, which are described in detail below. It should be noted that the description order of the following embodiments is not intended to limit the preferred order of the embodiments of the present application.

[0063] See also Figures 1 to 3 , Figure 1 This is a flow chart of a first embodiment of a display driving method of a display device 100 provided in the present application. Figure 2 is a schematic diagram of the first structure of the display device 100 of the present application when the Gaussian probability is greater than or equal to the set threshold, Figure 3 Schematic diagram of a first structure of a display device 100 of the present application when the Gaussian probability is less than a set threshold. The present application provides a display driving method of a display device 100, the display device comprising:

[0064] A plurality of sub-pixels 10 arranged in an array;

[0065] A plurality of data lines 20, each column of sub-pixels 10 corresponds to and is connected to one data line 20, and a column of sub-pixels 10 is disposed between adjacent data lines 20;

[0066] A plurality of grayscale pixel groups 30, each of the grayscale pixel groups 30 includes a 2N×3M matrix of sub-pixels 10;

[0067] The display driving method comprises the following steps:

[0068] S10, obtaining a first to-be-processed chromaticity data set and a second to-be-processed chromaticity data set about preset colors for a preset scene in a picture to be displayed;

[0069] S20, acquiring a Gaussian probability of a preset color of a preset scene in the picture to be displayed according to the first chromaticity data set to be processed and the second chromaticity data set to be processed;

[0070] S30, when the Gaussian probability is greater than or equal to a set threshold, the polarities of the sub-pixels 10 in adjacent columns of each grayscale pixel group 30 are set oppositely, and the polarities of the sub-pixels 10 in adjacent grayscale pixel groups 30 in the row direction are set symmetrically, and then the image to be displayed is displayed;

[0071] When the Gaussian probability is less than a set threshold, the polarities of the sub-pixels 10 in adjacent columns are set oppositely, and then the image to be displayed is displayed.

[0072] Please refer to Figure 2 , Figure 2 Schematic diagram of the first structure of the display device of the present application when the Gaussian probability is greater than or equal to the set threshold. Specifically, when the Gaussian probability is greater than or equal to the set threshold, the polarities of the sub-pixels 10 in the adjacent columns of each grayscale pixel group 30 are set oppositely, and the polarities of the sub-pixels 10 in the adjacent grayscale pixel groups 30 in the row direction are set symmetrically, and then the image to be displayed is displayed; since the polarities of the sub-pixels 10 in the adjacent grayscale pixel groups 30 in the row direction are set symmetrically, the flickering of the picture can be reduced, but at the same time, there is also a situation where the polarities of the adjacent data lines 20 will be repeated in two polarities of "positive, positive" and "negative, negative".

[0073] Please refer to Figure 3 , Figure 3 Schematic diagram of the first structure of the display device of the present application when the Gaussian probability is less than the set threshold. When the Gaussian probability is less than the set threshold, the polarities of the sub-pixels 10 in adjacent columns are set oppositely, and then the image to be displayed is displayed; that is, at this time, there is no need to perform viewing angle compensation on the image to be displayed, and the polarities of adjacent data lines will not have the repetition of the two polarities of "positive, positive" and "negative, negative". However, since when the Gaussian probability is less than the set threshold, even if the viewing angle compensation method is not used to display the image to be displayed, the displayed image has a good viewing angle effect, and the viewing angle characteristics will not be deteriorated.

[0074] Among them, the threshold is set according to the image quality requirements of the actual display device. Taking 8K resolution (resolution of 7680*4320) as an example, the threshold is set in the range of 4727808 to 525472. Specifically, the threshold can be set to 4976640.

[0075] Therefore, the present application obtains the Gaussian probability of the preset color of the preset scene in the picture to be displayed, and then uses the Gaussian probability to compare with the set threshold, and displays the picture to be displayed based on the comparison result whether to use the viewing angle compensation method, thereby reducing the risk of crosstalk caused by the inability to offset each other due to the voltage drops of the coupling capacitors on adjacent data lines 20.

[0076] Please refer to Figure 2 or Figure 3 In some embodiments, the sub-pixels 10 in adjacent rows of each grayscale pixel group 30 include high grayscale sub-pixels and low grayscale sub-pixels, and the grayscale arrangement of the sub-pixels 10 in the column direction of each grayscale pixel group 30 can be: high grayscale and low grayscale, or low grayscale and high grayscale. That is, the viewing angle compensation method of each grayscale pixel group 30 is: the sub-pixels 10 in adjacent rows include high grayscale sub-pixels and low grayscale sub-pixels. Take the viewing angle compensation of 128 grayscale as an example: find a pair of high grayscale and low grayscale on the gamma curve, and the brightness of the pair of high grayscale and low grayscale is equal to the brightness of 128 grayscale. Assuming that the high grayscale finally found is 180 and the low grayscale is 50, the problem of side view color drift can be improved.

[0077] Further, each of the grayscale pixel groups 30 includes a pixel unit, and the pixel unit includes a first sub-pixel 11, a second sub-pixel 12, and a third sub-pixel 13. Each of the grayscale pixel groups 30 includes a plurality of pixel units arranged sequentially in the sub-pixels 10 in the row direction. The first sub-pixel 11 is a red sub-pixel, the second sub-pixel 12 is a green sub-pixel, and the third sub-pixel 13 is a blue sub-pixel.

[0078] Specifically, in some embodiments, the grayscales of the sub-pixels 10 in the row direction of each grayscale pixel group 30 are arranged in a high grayscale and a low grayscale alternating manner. For example, each grayscale pixel group 30 includes a 2×6 matrix of sub-pixels 10. Then the grayscale arrangement of the sub-pixels 10 in the row direction of each grayscale pixel group 30 can be: high grayscale, low grayscale, high grayscale, low grayscale, high grayscale and low grayscale, or low grayscale, high grayscale, low grayscale, high grayscale, low grayscale and high grayscale.

[0079] Please refer to Figure 4 , Figure 4 This is a schematic diagram of the second structure of the display device 100 of the present application. The sub-pixels 10 in adjacent rows of each grayscale pixel group 30 include a first row of sub-pixels 10 and a second row of sub-pixels 10. The first row of sub-pixels 10 are low grayscale sub-pixels, and the second row of sub-pixels 10 are high grayscale sub-pixels. In this way, it can also be achieved that the sub-pixels 10 in adjacent rows of each grayscale pixel group 30 include high grayscale sub-pixels and low grayscale sub-pixels.

[0080] Please refer to Figure 5 , Figure 5 This is a flow chart of a second embodiment of a display driving method for a display device provided in the present application. Before step S20, the display driving method further includes:

[0081] S40, obtaining a first initial chromaticity data set and a second initial chromaticity data set of preset colors in a preset scene of the preprocessed image;

[0082] S50, establishing a Gaussian model about the preset color according to the first initial chromaticity data set and the second initial chromaticity data set;

[0083] The step S20 includes:

[0084] According to the first chromaticity data set to be processed and the second chromaticity data set to be processed, the Gaussian probability of a preset color of a preset scene in the picture to be displayed is obtained by the Gaussian model.

[0085] Please refer to Figure 6 , Figure 6 This is a flow chart of step S40 of the second embodiment of the display driving method for a display device provided in the present application. Further, in some embodiments, step S40 includes:

[0086] S41, obtaining a plurality of pre-processed images containing the preset scene;

[0087] S42: Extract color data of a preset color in the preset scene from any of the preprocessed images, and obtain the first initial chromaticity data set and the second initial chromaticity data set.

[0088] Please refer to Figure 7 , Figure 7 This is a flow chart of step S50 of the second embodiment of the display driving method for a display device provided in the present application. Further, in some embodiments, step S50 includes:

[0089] S51, respectively obtaining means of the first initial chromaticity data set and the second initial chromaticity data set;

[0090] S52, obtaining a covariance matrix, an inverse of the covariance matrix, and a rank of the covariance matrix of the first initial chromaticity data set and the second initial chromaticity data set;

[0091] S53. Establish the Gaussian model according to the covariance matrix, the inverse of the covariance matrix and the rank of the covariance matrix.

[0092] Specifically, in the embodiment of the present application, the constructed Gaussian model processes the relevant data in the picture to be displayed to obtain the Gaussian probability of the preset color of the preset scene in the picture to be displayed. When constructing the Gaussian model, multiple pre-processed pictures can be selected from the relevant database, and these pre-processed pictures respectively contain corresponding preset scenes. The type and number of preset scenes can be specifically set according to actual needs. In the picture processing method in the embodiment of the present application, when constructing the Gaussian model, the preset scene can be a variety of scenes such as portraits, blue sky, grass, food, animals, other natural scenery, buildings, etc., and the corresponding preset colors are the corresponding colors in each scene. For example, in the portrait scene, the preset color can be skin color; in the blue sky scene, the preset color can be blue; in the grass scene, the preset color can be green.

[0093] In the embodiment of the present application, when constructing the Gaussian model, the human eye sensitive colors and the corresponding scenes are selected as examples for explanation. In the embodiment of the present application, three preset scenes of portrait, blue sky and grass are selected, and the corresponding preset colors are skin color, blue and green respectively. The following description is based on the above three preset scenes and the corresponding preset colors as examples.

[0094] A plurality of first preprocessed images containing a portrait preset scene, a plurality of second preprocessed images containing a blue sky preset scene, and a plurality of third preprocessed images containing a grass preset scene are respectively selected from the database. The number of preprocessed images containing each preset scene is specifically set according to actual conditions.

[0095] For any first preprocessed image, the skin color data of the first preprocessed image is extracted, and the specific extraction method can adopt the current conventional extraction method. After obtaining the skin color data of the first preprocessed image, the skin color data can be decomposed and processed in the Ycbcr space to obtain brightness data about the skin color data and the first initial chromaticity data and the second initial chromaticity data.

[0096] Then, for the skin color data of the plurality of first pre-processed images, a brightness data set, a first initial chromaticity data set, and a second initial chromaticity data set about the skin color data can be obtained. The decomposition processing of the skin color data can be processed in the Ycbcr space or in the HSB color space, etc.; similarly, the following decomposition processing of the preset colors of other preset scenes can be processed in the Ycbcr space or in other color spaces, and the following description is made by taking the processing in the Ycbcr space as an example.

[0097] Specifically, the skin color data of the first pre-processed image can be processed using the following formula:

[0098] y skin(i) =(R*0.2567+G*0.5041+B*0.0979)+16 (1)

[0099] cb skin(i) =(R*0.1482+G*0.2909+B*0.4391)+128 (2)

[0100] cr skin(i) =(R*0.4392+G*0.3678+B*0.0714)+128 (3)

[0101] Among them, R, G, and B in the above formula are the red component value, green component value, and blue component value of the skin color data respectively, and y skin(i) is the brightness data of the skin color data, cb skin(i) is the first initial chromaticity data of the skin color data, cr skin(i) The second initial chromaticity data is the skin color data.

[0102] The same processing is performed on multiple first pre-processed images to obtain multiple brightness data y skin , to form a brightness data set; obtain a plurality of first initial chromaticity data cb skin , get the first initial chromaticity data set cb skin(1) 、cb skin(2) ......cb skin(i) .......; obtain a plurality of second initial chromaticity data cr skin , to form the second initial chromaticity data set cr skin(1) 、cr skin(2) .......cr skin(i) ........

[0103] Calculate the mean of the first initial chromaticity data set to obtain the first chromaticity mean μ of the skin color data skin1 ; and obtain the first initial chromaticity data in the first initial chromaticity data set and the first chromaticity mean μ skin1 The variance between skin . Calculate the mean of the second initial chromaticity data set to obtain the second chromaticity mean μ for the skin color data skin2 ; and obtain the second initial chromaticity data in the second initial chromaticity data set and the second chromaticity mean μ skin2 The variance between skin .

[0104] By variance a skin ,d skin , the first initial chromaticity data set cb skin(1) 、cb skin(2) ......cb skin(i) ......., the second initial chromaticity data set cr skin(1) 、cr skin(2).......cr skin(i) ......., obtain the covariance matrix cov(cb skin ,cr skin ), which is specifically described as follows:

[0105]

[0106] Among them, cb skin(i) is the first initial chrominance data of any first pre-processed picture, cr skin(i) is the second initial chrominance data of any first pre-processed picture, μ skin1 is the first chromaticity mean of the skin color data of the first preprocessed images, μ skin2 is the second chromaticity mean of the skin color data of the plurality of first pre-processed images, a skin is the first initial chromaticity data of the skin color data of the first preprocessed image and the first chromaticity mean μ skin1 The variance matrix between skin is the second initial chromaticity data of the skin color data of the first preprocessed image and the second chromaticity mean μ skin2 The variance matrix between skin 、c skin is a correlation between the first initial chromaticity data set and the second initial chromaticity data set regarding the skin color of the first preset picture.

[0107] From the above formula (4), we can get the cov(cb skin ,cr skin )'s inverse matrix cov -1 (cb skin ,cr skin ) or Σ skin -1 , and cov(cb skin ,cr skin )'s rank |Σ skin |. The above parameters can be used to construct a Gaussian model of skin color in portrait scenes, which can be specifically described as follows:

[0108]

[0109] Among them, A is the amplitude of the Gaussian model, and its value range is [0, 1]. skin (cb i ,cr i ) is the initial probability of skin color obtained by the Gaussian model in the portrait scene, a skin is the first initial chromaticity data of the skin color data of the first preprocessed image and the first chromaticity mean μ skin1The variance matrix between skin is the second initial chromaticity data of the skin color data of the first preprocessed image and the second chromaticity mean μ skin2 The variance matrix between i is the first chromaticity variable about skin color, cr i is the second chromaticity variable about skin color, Σ skin -1 is cov(cb skin ,cr skin ), |Σ skin | for cov(cb skin ,cr skin )’s rank, is the average value of the first initial chromaticity data set and the second initial chromaticity data set regarding the skin color of the first preset picture.

[0110] Similarly, for any second preprocessed image, extract the blue data of the second preprocessed image. After obtaining the blue data of the second preprocessed image, the blue data can be decomposed in the Ycbcr space to obtain brightness data, first initial chromaticity data, and second initial chromaticity data about the blue data. Then, for the blue data of multiple second preprocessed images, a brightness data set, a first initial chromaticity data set, and a second initial chromaticity data set about the blue data can be obtained.

[0111] Specifically, the blue data of the second pre-processed image can be processed using the following formula:

[0112] y sky(i) =(R*0.2567+G*0.5041+B*0.0979)+16 (6)

[0113] cb sky(i) =(R*0.1482+G*0.2909+B*0.4391)+128 (7)

[0114] cr sky(i) =(R*0.4392+G*0.3678+B*0.0714)+128 (8)

[0115] Among them, R, G, and B in the above formula are the red component value, green component value, and blue component value of the blue data respectively, and y sky(i) is the brightness data of blue data, cb sky(i) is the first initial chromaticity data of blue data, cr sky(i) The second initial chromaticity data is the blue data.

[0116] The covariance matrix cov(cb) of the first initial chromaticity data and the second initial chromaticity data of the blue color in the blue sky scene can be obtained from the above data of the second pre-processed image. sky ,cr sky ), which is specifically described as follows:

[0117]

[0118]

[0119] In the above formulas (9) and (10), cb sky(i) is the first initial chrominance data of any second pre-processed picture, cr sky(i) is the second initial chrominance data of any second pre-processed picture, μ sky1 is the first chromaticity mean of the blue data of multiple second pre-processed images, μ sky2 is the second chromaticity mean of the blue data of the plurality of second preprocessed images, asky is the variance matrix between the first initial chromaticity data of the blue data of the second preprocessed image and the first chromaticity mean μsky1, dsky is the variance matrix between the second initial chromaticity data of the blue data of the second preprocessed image and the second chromaticity mean μsky2, bsky and csky are the correlations between the first initial chromaticity data set and the second initial chromaticity data set; A is the amplitude of the Gaussian model, ranging from [0, 1], and gausssky(cbi,cri) is the Gaussian model under the blue sky scene. The initial probability of the blue color obtained by the model, asky is the variance matrix between the first initial chromaticity data of the blue data of the second preprocessed image and the first chromaticity mean μsky1, dsky is the variance matrix between the second initial chromaticity data of the blue data of the second preprocessed image and the second chromaticity mean μsky2, cbi is the first chromaticity variable about the blue color, cri is the second chromaticity variable about the blue color, Σsky-1 is the inverse matrix of cov(cbsky,crsky), |Σsky| is the rank of cov(cbsky,crsky), is an average value of the first initial chromaticity data set and the second initial chromaticity data set regarding the blue color of the second preset picture.

[0120] For any third preprocessed picture, the green data of the third preprocessed picture is extracted. After the green data of the third preprocessed picture is obtained, the green data can be decomposed in the Ycbcr space to obtain the brightness data, the first initial chromaticity data and the second initial chromaticity data of the green data. Then, for the green data of multiple third preprocessed pictures, the brightness data set, the first initial chromaticity data set and the second initial chromaticity data set of the green data can be obtained.

[0121] Specifically, the green data of the third pre-processed image can be processed using the following formula:

[0122] y grass(i) =(R*0.2567+G*0.5041+B*0.0979)+16 (11)

[0123] cb grass(i) =(R*0.1482+G*0.2909+B*0.4391)+128 (12)

[0124] cr grass(i) =(R*0.4392+G*0.3678+B*0.0714)+128 (13)

[0125] Among them, R, G, and B in the above formula are the red component value, green component value, and blue component value of the green data respectively, and y grass(i) is the brightness data of green data, cb grass(i) is the first initial chromaticity data of green data, cr grass(i) The second initial chromaticity data is green data.

[0126] The covariance matrix cov(cb) of the first initial chromaticity data and the second initial chromaticity data of the blue color in the blue sky scene can be obtained from the above data of the second pre-processed image. sky ,cr sky ), which is specifically described as follows:

[0127]

[0128]

[0129] In the above formulas (14) and (15), cb grass(i) is the first initial chrominance data of any third pre-processed picture, cr grass(i) is the second initial chrominance data of any third pre-processed picture, μ grass1 is the first chromaticity mean of the green data of multiple third pre-processed images, μ grass2 is the second chromaticity mean of the green data of the plurality of third pre-processed images, a grass is the first initial chromaticity data of the green data of the third preprocessed picture and the first chromaticity mean μ grass1 The variance matrix between grass is the second initial chromaticity data of the green data of the third preprocessed image and the second chromaticity mean μ grass2 The variance matrix between grass 、c grassis the correlation between the first initial chromaticity data set and the second initial chromaticity data set; A is the amplitude of the Gaussian model, and its value range is [0, 1]. grass (cb i ,cr i ) is the initial probability of green color obtained by Gaussian model in the grass scene, a grass is the first initial chromaticity data of the green data of the third preprocessed picture and the first chromaticity mean μ grass1 The variance matrix between grass is the second initial chromaticity data of the green data of the third preprocessed image and the second chromaticity mean μ grass2 The variance matrix between i is the first chromaticity variable for green color, cr i is the second chromaticity variable for green color, Σ grass -1 is cov(cb grass ,cr grass ), |Σ grass | for cov(cb grass ,cr grass )’s rank, is an average value of the first initial chromaticity data set and the second initial chromaticity data set regarding the green color of the third preset picture.

[0130] By using the above method to construct a Gaussian model, the type and number of preset scenes can be set according to needs, and the type and number of preset colors can be set accordingly, and Gaussian models of each preset color under each preset scene can be established respectively, so as to flexibly adjust the specific composition of the Gaussian model according to different application scenarios, different customer needs or picture quality requirements, etc. In addition, the amplitude in the Gaussian model, the mean of the preset color in the pre-processed picture, the related covariance matrix and other parameters can be adjusted according to needs, and can also be adjusted according to accuracy or other considerations, and it has strong practicality and versatility.

[0131] Specifically, when processing the image to be displayed, the data of the preset color of the image to be displayed is first extracted. For example, when processing the portrait, blue sky and grass scene in the image to be displayed, the color data of the skin color, blue and grass in the image to be displayed are respectively extracted, and decomposed in the Ycbcr space respectively to obtain the first chromaticity data set to be processed and the second chromaticity data set to be processed for the skin color, the first chromaticity data set to be processed and the second chromaticity data set to be processed for the blue color, and the first chromaticity data set to be processed and the second chromaticity data set to be processed for the green color.

[0132] After obtaining the first chromaticity data set to be processed and the second chromaticity data set to be processed of each preset color, each chromaticity data in the first chromaticity data set to be processed and the second chromaticity data set to be processed of each preset color can be substituted into the Gaussian model of the corresponding preset color to obtain an initial probability map for the preset color.

[0133] For example, by correspondingly substituting each chromaticity data in the first chromaticity data set to be processed and the second chromaticity data set to be processed of skin color into the above formula (5), an initial probability map of skin color in the image to be displayed can be obtained. Similarly, by correspondingly substituting each chromaticity data in the first chromaticity data set to be processed and the second chromaticity data set to be processed of blue color into the above formula (10), an initial probability map of blue color in the image to be displayed can be obtained. By correspondingly substituting each chromaticity data in the first chromaticity data set to be processed and the second chromaticity data set to be processed of green color into the above formula (15), an initial probability map of green color in the image to be displayed can be obtained.

[0134] Please refer to Figure 8 , Figure 8 This is a flow chart of step S20 of the second embodiment of the display driving method of the display device provided in the present application. In some embodiments, step S20 includes:

[0135] S21, determining whether the picture to be displayed contains the preset scene;

[0136] S22, assigning a value to a correlation coefficient of the preset scene in the picture to be displayed according to a judgment result that the picture to be displayed contains the preset scene;

[0137] S23, for any preset color of a preset scene in the to-be-displayed picture, obtaining an initial probability of the preset color by the Gaussian model according to the first to-be-processed chromaticity data set and the second to-be-processed chromaticity data set;

[0138] S24. Obtaining the Gaussian probability of a preset color of a preset scene in the picture to be displayed according to the initial probability and the correlation coefficient.

[0139] Furthermore, in some embodiments, there are multiple preset scenes and multiple preset colors;

[0140] For any of the preset scenes, the initial probability of the preset color of the preset scene is modified using the correlation coefficient;

[0141] The sum of the initial probabilities of the preset colors of the plurality of preset scenes in the picture to be displayed corrected by the correlation coefficient is calculated to obtain the Gaussian probabilities of the preset colors of the plurality of preset scenes in the picture to be displayed.

[0142] Specifically, when processing the picture to be displayed, it is possible to judge whether the picture to be displayed contains a preset scene, and assign a correlation coefficient of the preset scene according to the judgment result. According to whether the picture to be displayed contains a preset scene, the correlation coefficient of the corresponding preset scene is corrected, and the Gaussian probability of the preset color of the preset scene obtained according to the Gaussian model is adjusted, which can not only improve the processing efficiency of the picture to be displayed, but also improve the accuracy of color detection, so as to avoid the false detection of colors similar to the preset colors of the preset scene in other scenes. At the same time, since only the preset scene preset colors in the picture to be displayed are processed, when the picture is output, the sense of grid can be effectively reduced and the picture quality can be improved. Among them, the method for judging whether the picture to be displayed contains a preset scene can be processed in the current conventional way.

[0143] Specifically, when multiple preset scenes and corresponding preset colors are set, the comprehensive Gaussian probability value of the preset colors of the multiple preset scenes can be obtained by the sum of the initial probabilities corrected by the correlation coefficients of the preset scenes, which can be specifically obtained by the following formula:

[0144] gauss(cb,cr)=α*gauss skin (cb i ,cr i )+β*gauss sky (cb i ,cr i )+γ*gauss grass (cb i ,cr i ) (16)

[0145] Where, gauss(cb, cr) is the Gaussian probability of the preset color of the preset scene in the picture to be displayed, α is the correlation coefficient of the portrait scene in the picture to be displayed, and gauss skin (cb i ,cr i ) is the initial probability of skin color obtained by Gaussian model, β is the correlation coefficient of the blue sky scene in the picture to be displayed, gauss sky (cb i ,cr i ) is the initial probability of blue color obtained by Gauss model, γ is the correlation coefficient of the grass scene in the picture to be displayed, gauss grass (cb i ,cr i ) is the initial probability of green color obtained by Gaussian model.

[0146] When there is no corresponding preset scene in the picture to be displayed, the corresponding correlation coefficient can be assigned to 0, and its product with the initial probability of the preset color of the preset scene obtained by fitting the Gaussian model is 0 to avoid misdetection of similar colors in the picture to be displayed.

[0147] For example, when the picture to be displayed contains a portrait scene, the correlation coefficient α about the portrait scene is assigned a value of 1; when there is no portrait scene in the picture to be displayed, the correlation coefficient α about the portrait scene is assigned a value of 0. Similarly, when the picture to be displayed contains a blue sky scene, the correlation coefficient β about the blue sky scene is assigned a value of 1; when there is no blue sky scene in the picture to be displayed, the correlation coefficient β about the blue sky scene is assigned a value of 0. When the picture to be displayed contains a grass scene, the correlation coefficient γ about the grass scene is assigned a value of 1; when there is no grass scene in the picture to be displayed, the correlation coefficient γ about the grass scene is assigned a value of 0.

[0148] For example, when the image to be displayed contains a portrait scene but no blue sky scene or grass scene, the color data of the image to be displayed is fitted by the Gaussian model and corrected by the corresponding preset scene correlation coefficient. The resulting Gaussian probability is gauss(cb,cr)=gauss skin (cb i ,cr i ), the Gaussian fitting probability for the blue sky scene and the grass scene is 0. When the image to be displayed contains a portrait scene and a blue sky scene, but no grass scene, the color data of the image to be displayed is fitted by the Gaussian model and corrected by the corresponding preset scene correlation coefficient. The resulting Gaussian probability is gauss(cb,cr)=gauss skin (cb i ,cr i )+gauss sky (cb i ,cr i ). When the image to be displayed contains portrait scenes, blue sky scenes, and grass scenes at the same time, the color data of the image to be displayed is fitted by the Gaussian model and corrected by the corresponding preset scene correlation coefficient. The resulting Gaussian probability is gauss(cb,cr)=gauss skin (cb i ,cr i )+gauss sky (cb i ,cr i )+gauss grass (cb i ,cr i ), see Fig. 9 Figures (a) and (b) are the front view and top view of the Gaussian fitting model data simulation diagram, respectively.

[0149] Please refer to Fig.10 , Fig.10 Schematic diagram of a display driving device of a display device provided by the present application. The embodiment of the present application further provides a display driving device of a display device 100, wherein the display device comprises: a plurality of sub-pixels 10 arranged in an array;

[0150] A plurality of data lines 20, each column of sub-pixels 10 corresponds to and is connected to one data line 20, and a column of sub-pixels 10 is disposed between adjacent data lines 20;

[0151] A plurality of grayscale pixel groups 30, each of the grayscale pixel groups 30 includes a 2N×3M matrix of sub-pixels 10;

[0152] The display driving device comprises:

[0153] A data acquisition module 40, the data acquisition module 40 is used to acquire a first to-be-processed chromaticity data set and a second to-be-processed chromaticity data set about a preset color of a preset scene in a picture to be displayed;

[0154] A data processing module 50, the data processing module 50 is used to obtain the Gaussian probability of a preset color of a preset scene in the picture to be displayed according to the first chromaticity data set to be processed and the second chromaticity data set to be processed;

[0155] A comparison driving module 60, the comparison driving module 60 is used to compare the Gaussian probability with a set threshold value, and when the Gaussian probability is greater than or equal to the set threshold value, the polarities of the sub-pixels 10 in adjacent columns of each grayscale pixel group 30 are set oppositely, and the polarities of the sub-pixels 10 in adjacent grayscale pixel groups 30 in the row direction are set symmetrically, and then the image to be displayed is displayed;

[0156] When the Gaussian probability is less than a set threshold, the polarities of the sub-pixels 10 in adjacent columns are set oppositely, and then the image to be displayed is displayed.

[0157] The present application obtains the Gaussian probability of a preset color of a preset scene in the image to be displayed, compares the Gaussian probability with a set threshold, and displays the image to be displayed based on the comparison result whether a viewing angle compensation method is used, thereby reducing the risk of crosstalk caused by the inability to offset the voltage drops of coupling capacitors on adjacent data lines 20.

[0158] The embodiment of the present application further provides a display device 100, which includes a processor, a memory, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the steps in the above-mentioned display driving method.

[0159] Specifically, the processor in the embodiment of the present application may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0160] It should also be understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of random access memory (RAM) are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM) and direct memory bus random access memory (DRRAM).

[0161] The display driving method and device of a display device and the display device 100 provided in the embodiments of the present application are introduced in detail above. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A display driving method for a display device, characterized in that: The display device comprises: A plurality of sub-pixels arranged in an array; a plurality of data lines, each column of sub-pixels corresponds to and is connected to one data line, and a column of sub-pixels is arranged between adjacent data lines; A plurality of grayscale pixel groups, each of the grayscale pixel groups includes a 2N×3M matrix of sub-pixels, where N and M are positive integers; The display driving method comprises the following steps: Acquire a first to-be-processed chromaticity data set and a second to-be-processed chromaticity data set about preset colors of a preset scene in a picture to be displayed; Acquire, according to the first chromaticity data set to be processed and the second chromaticity data set to be processed, a Gaussian probability of a preset color of a preset scene in the picture to be displayed; When the Gaussian probability is greater than or equal to a set threshold, the polarities of the sub-pixels in adjacent columns of each grayscale pixel group are set oppositely, and the polarities of the sub-pixels in adjacent grayscale pixel groups in a row direction are set symmetrically, and then the image to be displayed is displayed; When the Gaussian probability is less than a set threshold, the polarities of the sub-pixels in adjacent columns are set oppositely, and then the image to be displayed is displayed.

2. The display driving method according to claim 1, characterized in that: Before acquiring the Gaussian probability of the preset color of the preset scene in the picture to be displayed according to the first chromaticity data set to be processed and the second chromaticity data set to be processed, the display driving method further includes: Acquire a first initial chromaticity data set and a second initial chromaticity data set of preset colors in a preset scene of the preprocessed image; Establishing a Gaussian model about the preset color according to the first initial chromaticity data set and the second initial chromaticity data set; The obtaining, according to the first chromaticity data set to be processed and the second chromaticity data set to be processed, the Gaussian probability of the preset color of the preset scene in the picture to be displayed includes: According to the first chromaticity data set to be processed and the second chromaticity data set to be processed, the Gaussian probability of a preset color of a preset scene in the picture to be displayed is obtained by the Gaussian model.

3. The display driving method according to claim 2, characterized in that: Acquiring a first initial chromaticity data set and a second initial chromaticity data set of preset colors in a preset scene of a preprocessed image, comprising: Acquire a plurality of pre-processed images containing the preset scene; Color data of a preset color in the preset scene is extracted from any of the preprocessed images to obtain the first initial chromaticity data set and the second initial chromaticity data set.

4. The display driving method according to claim 2, characterized in that: Establishing a Gaussian model about the preset color according to the first initial chromaticity data set and the second initial chromaticity data set includes: respectively obtaining means of the first initial chromaticity data set and the second initial chromaticity data set; Obtaining a covariance matrix, an inverse of the covariance matrix, and a rank of the covariance matrix for the first initial chrominance data set and the second initial chrominance data set; The Gaussian model is established according to the covariance matrix, the inverse of the covariance matrix and the rank of the covariance matrix.

5. The display driving method according to claim 2, characterized in that: According to the first chromaticity data set to be processed and the second chromaticity data set to be processed, obtaining, by the Gaussian model, a Gaussian probability of a preset color of a preset scene in the picture to be displayed, comprising: Determining whether the picture to be displayed contains the preset scene; According to the result of determining that the picture to be displayed contains the preset scene, assigning a correlation coefficient of the picture to be displayed with respect to the preset scene; For any of the preset colors of the preset scene in the to-be-displayed picture, obtaining an initial probability of the preset color by the Gaussian model according to the first to-be-processed chromaticity data set and the second to-be-processed chromaticity data set; The Gaussian probability of a preset color of a preset scene in the picture to be displayed is obtained according to the initial probability and the correlation coefficient.

6. The display driving method according to claim 5, characterized in that: There are multiple preset scenes and multiple preset colors; For any of the preset scenes, the initial probability of the preset color of the preset scene is modified using the correlation coefficient; The sum of the initial probabilities of the preset colors of the plurality of preset scenes in the picture to be displayed corrected by the correlation coefficient is calculated to obtain the Gaussian probabilities of the preset colors of the plurality of preset scenes in the picture to be displayed.

7. The display driving method according to claim 1, characterized in that: The sub-pixels in adjacent rows of each grayscale pixel group include high-grayscale sub-pixels and low-grayscale sub-pixels.

8. The display driving method according to claim 7, characterized in that: The grayscales of the sub-pixels of each grayscale pixel group in the row direction are arranged in an alternating manner of high grayscale and low grayscale.

9. The display driving method according to claim 7, characterized in that: The adjacent rows of sub-pixels of each grayscale pixel group include a first row of sub-pixels and a second row of sub-pixels. The first row of sub-pixels are low grayscale sub-pixels, and the second row of sub-pixels are high grayscale sub-pixels.

10. A display driving device of a display device, characterized in that: The display device comprises: A plurality of sub-pixels arranged in an array; A plurality of data lines, each column of sub-pixels corresponds to and is connected to one of the data lines, and a column of sub-pixels is arranged between adjacent data lines; A plurality of grayscale pixel groups, each of the grayscale pixel groups includes a 2N×3M matrix of sub-pixels, where N and M are positive integers; The display driving device comprises: A data acquisition module, the data acquisition module is used to acquire a first chromaticity data set to be processed and a second chromaticity data set to be processed about a preset color of a preset scene in a picture to be displayed; A data processing module, the data processing module being used to obtain a Gaussian probability of a preset color of a preset scene in the picture to be displayed according to the first chromaticity data set to be processed and the second chromaticity data set to be processed; A comparison driving module, the comparison driving module is used to compare the Gaussian probability with a set threshold value, and when the Gaussian probability is greater than or equal to the set threshold value, the polarities of the sub-pixels in adjacent columns of each grayscale pixel group are set oppositely, and the polarities of the sub-pixels in adjacent grayscale pixel groups in the row direction are set symmetrically, and then the image to be displayed is displayed; When the Gaussian probability is less than a set threshold, the polarities of the sub-pixels in adjacent columns are set oppositely, and then the image to be displayed is displayed.

11. A display device, characterized in that: The display device comprises a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps in the display driving method according to any one of claims 1 to 9.

Images